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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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What is a Case Study in Research? Definition, Methods, and Examples

Case study methodology offers researchers an exciting opportunity to explore intricate phenomena within specific contexts using a wide range of data sources and collection methods. It is highly pertinent in health and social sciences, environmental studies, social work, education, and business studies. Its diverse applications, such as advancing theory, program evaluation, and intervention development, make it an invaluable tool for driving meaningful research and fostering positive change.[ 1]  

Table of Contents

What is a Case Study?  

A case study method involves a detailed examination of a single subject, such as an individual, group, organization, event, or community, to explore and understand complex issues in real-life contexts. By focusing on one specific case, researchers can gain a deep understanding of the factors and dynamics at play, understanding their complex relationships, which might be missed in broader, more quantitative studies.  

When to do a Case Study?  

A case study design is useful when you want to explore a phenomenon in-depth and in its natural context. Here are some examples of when to use a case study :[ 2]  

• Exploratory Research: When you want to explore a new topic or phenomenon, a case study can help you understand the subject deeply. For example , a researcher studying a newly discovered plant species might use a case study to document its characteristics and behavior.  
• Descriptive Research: If you want to describe a complex phenomenon or process, a case study can provide a detailed and comprehensive description. For instance, a case study design   could describe the experiences of a group of individuals living with a rare disease.  
• Explanatory Research: When you want to understand why a particular phenomenon occurs, a case study can help you identify causal relationships. A case study design could investigate the reasons behind the success or failure of a particular business strategy.  
• Theory Building: Case studies can also be used to develop or refine theories. By systematically analyzing a series of cases, researchers can identify patterns and relationships that can contribute to developing new theories or refining existing ones.  
• Critical Instance: Sometimes, a single case can be used to study a rare or unusual phenomenon, but it is important for theoretical or practical reasons. For example , the case of Phineas Gage, a man who survived a severe brain injury, has been widely studied to understand the relationship between the brain and behavior.  
• Comparative Analysis: Case studies can also compare different cases or contexts. A case study example involves comparing the implementation of a particular policy in different countries to understand its effectiveness and identifying best practices.  

How to Create a Case Study – Step by Step  

Step 1: select a case  .

Careful case selection ensures relevance, insight, and meaningful contribution to existing knowledge in your field. Here’s how you can choose a case study design :[ 3]  

• Define Your Objectives: Clarify the purpose of your case study and what you hope to achieve. Do you want to provide new insights, challenge existing theories, propose solutions to a problem, or explore new research directions?  
• Consider Unusual or Outlying Cases: Focus on unusual, neglected, or outlying cases that can provide unique insights.  
• Choose a Representative Case: Alternatively, select a common or representative case to exemplify a particular category, experience, or phenomenon.   
• Avoid Bias: Ensure your selection process is unbiased using random or criteria-based selection.  
• Be Clear and Specific: Clearly define the boundaries of your study design , including the scope, timeframe, and key stakeholders.   
• Ethical Considerations: Consider ethical issues, such as confidentiality and informed consent.  

Step 2: Build a Theoretical Framework  

To ensure your case study has a solid academic foundation, it’s important to build a theoretical framework:   

• Conduct a Literature Review: Identify key concepts and theories relevant to your case study .  
• Establish Connections with Theory: Connect your case study with existing theories in the field.  
• Guide Your Analysis and Interpretation: Use your theoretical framework to guide your analysis, ensuring your findings are grounded in established theories and concepts.   

Step 3: Collect Your Data  

To conduct a comprehensive case study , you can use various research methods. These include interviews, observations, primary and secondary sources analysis, surveys, and a mixed methods approach. The aim is to gather rich and diverse data to enable a detailed analysis of your case study .  

Step 4: Describe and Analyze the Case  

How you report your findings will depend on the type of research you’re conducting. Here are two approaches:   

• Structured Approach: Follows a scientific paper format, making it easier for readers to follow your argument.  
• Narrative Approach: A more exploratory style aiming to analyze meanings and implications.  

Regardless of the approach you choose, it’s important to include the following elements in your case study :   

• Contextual Details: Provide background information about the case, including relevant historical, cultural, and social factors that may have influenced the outcome.  
• Literature and Theory: Connect your case study to existing literature and theory in the field. Discuss how your findings contribute to or challenge existing knowledge.  
• Wider Patterns or Debates: Consider how your case study fits into wider patterns or debates within the field. Discuss any implications your findings may have for future research or practice.  

What Are the Benefits of a Case Study   

Case studies offer a range of benefits , making them a powerful tool in research.  

1. In-Depth Analysis  

• Comprehensive Understanding: Case studies allow researchers to thoroughly explore a subject, understanding the complexities and nuances involved.  
• Rich Data: They offer rich qualitative and sometimes quantitative data, capturing the intricacies of real-life contexts.  

2. Contextual Insight  

• Real-World Application: Case studies provide insights into real-world applications, making the findings highly relevant and practical.  
• Context-Specific: They highlight how various factors interact within a specific context, offering a detailed picture of the situation.  

3. Flexibility  

• Methodological Diversity: Case studies can use various data collection methods, including interviews, observations, document analysis, and surveys.  
• Adaptability: Researchers can adapt the case study approach to fit the specific needs and circumstances of the research.  

4. Practical Solutions  

• Actionable Insights: The detailed findings from case studies can inform practical solutions and recommendations for practitioners and policymakers.  
• Problem-Solving: They help understand the root causes of problems and devise effective strategies to address them.  

5. Unique Cases  

• Rare Phenomena: Case studies are particularly valuable for studying rare or unique cases that other research methods may not capture.  
• Detailed Documentation: They document and preserve detailed information about specific instances that might otherwise be overlooked.  

What Are the Limitations of a Case Study   

While case studies offer valuable insights and a detailed understanding of complex issues, they have several limitations .  

1. Limited Generalizability  

• Specific Context: Case studies often focus on a single case or a small number of cases, which may limit the generalization of findings to broader populations or different contexts.  
• Unique Situations: The unique characteristics of the case may not be representative of other situations, reducing the applicability of the results.  

2. Subjectivity  

• Researcher Bias: The researcher’s perspectives and interpretations can influence the analysis and conclusions, potentially introducing bias.  
• Participant Bias: Participants’ responses and behaviors may be influenced by their awareness of being studied, known as the Hawthorne effect.  

3. Time-Consuming  

• Data Collection and Analysis: Gathering detailed, in-depth data requires significant time and effort, making case studies more time-consuming than other research methods.  
• Longitudinal Studies: If the case study observes changes over time, it can become even more prolonged.  

4. Resource Intensive  

• Financial and Human Resources: Conducting comprehensive case studies may require significant financial investment and human resources, including trained researchers and participant access.  
• Access to Data: Accessing relevant and reliable data sources can be challenging, particularly in sensitive or proprietary contexts.  

5. Replication Difficulties  

• Unique Contexts: A case study’s specific and detailed context makes it difficult to replicate the study exactly, limiting the ability to validate findings through repetition.  
• Variability: Differences in contexts, researchers, and methodologies can lead to variations in findings, complicating efforts to achieve consistent results.  

By acknowledging and addressing these limitations , researchers can enhance the rigor and reliability of their case study findings.  

Key Takeaways  

Case studies are valuable in research because they provide an in-depth, contextual analysis of a single subject, event, or organization. They allow researchers to explore complex issues in real-world settings, capturing detailed qualitative and quantitative data. This method is useful for generating insights, developing theories, and offering practical solutions to problems. They are versatile, applicable in diverse fields such as business, education, and health, and can complement other research methods by providing rich, contextual evidence. However, their findings may have limited generalizability due to the focus on a specific case.  

Frequently Asked Questions  

Q: What is a case study in research?  

A case study in research is an impactful tool for gaining a deep understanding of complex issues within their real-life context. It combines various data collection methods and provides rich, detailed insights that can inform theory development and practical applications.  

Q: What are the advantages of using case studies in research?  

Case studies are a powerful research method, offering advantages such as in-depth analysis, contextual insights, flexibility, rich data, and the ability to handle complex issues. They are particularly valuable for exploring new areas, generating hypotheses, and providing detailed, illustrative examples that can inform theory and practice.  

Q: Can case studies be used in quantitative research?  

While case studies are predominantly associated with qualitative research, they can effectively incorporate quantitative methods to provide a more comprehensive analysis. A mixed-methods approach leverages qualitative and quantitative research strengths, offering a powerful tool for exploring complex issues in a real-world context. For example , a new medical treatment case study can incorporate quantitative clinical outcomes (e.g., patient recovery rates and dosage levels) along with qualitative patient interviews.  

Q: What are the key components of a case study?  

A case study typically includes several key components:   

• Introductio n, which provides an overview and sets the context by presenting the problem statement and research objectives;  
• Literature review , which connects the study to existing theories and prior research;  
• Methodology , which details the case study design , data collection methods, and analysis techniques;   
• Findings , which present the data and results, including descriptions, patterns, and themes;   
• Discussion and conclusion , which interpret the findings, discuss their implications, and offer conclusions, practical applications, limitations, and suggestions for future research.  

Together, these components ensure a comprehensive, systematic, and insightful exploration of the case.  

References  

• de Vries, K. (2020). Case study methodology. In  Critical qualitative health research  (pp. 41-52). Routledge.  
• Fidel, R. (1984). The case study method: A case study.  Library and Information Science Research ,  6 (3), 273-288.  
• Thomas, G. (2021). How to do your case study.  How to do your case study , 1-320.  

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Peer Review reports

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

• Ask a Librarian
• Case Study Research
• What is a Case Study?
• Finding a Case Study

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What is a case study?

A case study is a type of research method. In case studies, the unit of analysis is a case . The case typically provides a detailed account of a situation that usually focuses on a conflict or complexity that one might encounter in the workplace.

• Case studies help explain the process by which a unit (a person, department, business, organization, industry, country, etc.) deals with the issue or problem confronting it, and offers possible solutions that can be applied to other units facing similar situations.
• The information presented in case studies is usually qualitative in nature - gathered through methods such as interview, observation, and document collection.
• There are different types of case study, including  intrinsic, instrumental, naturalistic,  and  pragmatic.

This research guide will assist you in finding individual case studies, as well as providing information on designing case studies. If you need assistance locating information, please Ask a Librarian .

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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

Scapens, R.W. (2011), "The Case Study as Research Method: A Practical Handbook", Qualitative Research in Accounting & Management , Vol. 8 No. 2, pp. 201-204. https://doi.org/10.1108/11766091111137582

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

This book aims to provide case‐study researchers with a step‐by‐step practical guide to “help them conduct the study with the required degree of rigour” (p. xi).

It seeks to “demonstrate that the case study is indeed a scientific method” (p. 104) and to show “the usefulness of the case method as one tool in the researcher's methodological arsenal” (p. 105). The individual chapters cover the various stages in conducting case‐study research, and each chapter sets out a number of practical steps which have to be taken by the researcher. The following are the eight stages/chapters and, in brackets, the number of steps in each stages:

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

It is particularly noticeable that ensuring accuracy of results has by far the largest number of number of steps – 21 steps compared to seven or fewer steps in the other stages. This reflects Gagnon's concern to demonstrate the scientific rigour of case‐study research. In the forward, he explains that the book draws on his experience in conducting his own PhD research, which was closely supervised by three professors, one of whom was inclined towards quantitative research. Consequently, his research was underpinned by the principles and philosophy of quantitative research. This is clearly reflected in the approach taken in this book, which seeks to show that case‐study research is just as rigorous and scientific as quantitative research, and it can produce an objective and accurate representation of the observed reality.

There is no discussion of the methodological issues relating to the use of case‐study research methods. This is acknowledged in the forward, although Gagnon refers to them as philosophical or epistemological issues (p. xii), as he tends to use the terms methodology and method interchangeably – as is common in quantitative research. Although he starts (step 1.1) by trying to distance case and other qualitative research from the work of positivists, arguing that society is socially constructed, he nevertheless sees social reality as objective and independent of the researcher. So for Gagnon, the aim of case research is to accurately reflect that reality. At various points in the book the notion of interpretation is used – evidence is interpreted and the (objective) case findings have to be interpreted.

So although there is a distancing from positivist research (p. 1), the approach taken in this book retains an objective view of the social reality which is being researched; a view which is rather different to the subjective view of reality taken by many interpretive case researchers. This distinction between an objective and a subjective view of the social reality being researched – and especially its use in contrasting positivist and interpretive research – has its origins the taxonomy of Burrell and Morgan (1979) . Although there have been various developments in the so‐called “objective‐subjective debate”, and recently some discussion in relation to management accounting research ( Kakkuri‐Knuuttila et al. , 2008 ; Ahrens, 2008 ), this debate is not mentioned in the book. Nevertheless, it is clear that Gagnon is firmly in the objective camp. In a recent paper, Johnson et al. (2006, p. 138) provide a more contemporary classification of the different types of qualitative research. In their terms, the approach taken in this book could be described as neo‐empiricist – an approach which they characterise as “qualitative positivists”.

The approach taken in this handbook leaves case studies open to the criticisms that they are a small sample, and consequently difficult to generalise, and to arguments that case studies are most appropriate for exploratory research which can subsequently be generalised though quantitative research. Gagnon explains that this was the approach he used after completing his thesis (p. xi). The handbook only seems to recognise two types of case studies, namely exploratory and raw empirical case studies – the latter being used where “the researcher is interested in a subject without having formed any preconceived ideas about it” (p. 15) – which has echoes of Glaser and Strauss (1967) . However, limiting case studies to these two types ignores other potential types; in particular, explanatory case studies which are where interpretive case‐study research can make important contributions ( Ryan et al. , 2002 ).

This limited approach to case studies comes through in the practical steps which are recommended in the handbook, and especially in the discussion of reliability and validity. The suggested steps seem to be designed to keep very close to the notions of reliability and validity used in quantitative research. There is no mention of the recent discussion of “validity” in interpretive accounting research, which emphasises the importance of authenticity and credibility and their implications for writing up qualitative and case‐study research ( Lukka and Modell, 2010 ). Although the final stage of Gagnon's handbook makes some very general comments about reporting the results, it does not mention, for example, Baxter and Chua's (2008) paper in QRAM which discusses the importance of demonstrating authenticity, credibility and transferability in writing qualitative research.

Despite Gagnon's emphasis on traditional notions of reliability and validity the handbook provides some useful practical advice for all case‐study researchers. For example, case‐study research needs a very good research design; case‐study researchers must work hard to gain access to and acceptance in the research settings; a clear strategy is needed for data collection; the case researcher should create field notes (in a field notebook, or otherwise) to record all the thoughts, ideas, observations, etc. that would not otherwise be collected; and the vast amount of data that case‐study research can generate needs to be carefully managed. Furthermore, because of what Gagnon calls the “risk of mortality” (p. 54) (i.e. the risk that access to a research site may be lost – for instance, if the organisation goes bankrupt) it is crucial for some additional site(s) to be selected at the outset to ensure that the planned research can be completed. This is what I call “insurance cases” when talking to my own PhD students. Interestingly, Gagnon recognises the ethical issues involved in doing case studies – something which is not always mentioned by the more objectivist type of case‐study researchers. He emphasises that it is crucial to honour confidentiality agreements, to ensure data are stored securely and that commitments are met and promises kept.

There is an interesting discussion of the advantages and disadvantages of using computer methods in analysing data (in stage 6). However, the discussion of coding appears to be heavily influenced by grounded theory, and is clearly concerned with producing an accurate reflection of an objective reality. In addition, Gagnon's depiction of case analysis is overly focussed on content analysis – possibly because it is a quantitative type of technique. There is no reference to the other approaches available to qualitative researchers. For example, there is no mention of the various visualisation techniques set out in Miles and Huberman (1994) .

To summarise, Gagnon's book is particularly useful for case‐study researchers who see the reality they are researching as objective and researcher independent. However, this is a sub‐set of case‐study researchers. Although some of the practical guidance offered is relevant for other types of case‐study researchers, those who see multiple realities in the social actors and/or recognise the subjectivity of the research process might have difficulty with some of the steps in this handbook. Gagnon's aim to show that the case study is a scientific method, gives the handbook a focus on traditional (quantitatively inspired) notions rigour and validity, and a tendency to ignore (or at least marginalise) other types of case study research. For example, the focus on exploratory cases, which need to be supplemented by broad based quantitative research, overlooks the real potential of case study research which lies in explanatory cases. Furthermore, Gagnon is rather worried about participant research, as the researcher may play a role which is “not consistent with scientific method” (p. 42), and which may introduce researcher bias and thereby damage “the impartiality of the study” (p. 53). Leaving aside the philosophical question about whether any social science research, including quantitative research, can be impartial, this stance could severely limit the potential of case‐study research and it would rule out both the early work on the sociology of mass production and the recent calls for interventionist research. Clearly, there could be a problem where a researcher is trying to sell consulting services, but there is a long tradition of social researchers working within organisations that they are studying. Furthermore, if interpretive research is to be relevant for practice, researchers may have to work with organisations to introduce new ideas and new ways of analysing problems. Gagnon would seem to want to avoid all such research – as it would not be “impartial”.

Consequently, although there is some good practical advice for case study researchers in this handbook, some of the recommendations have to be treated cautiously, as it is a book which sees case‐study research in a very specific way. As mentioned earlier, in the Forward Gagnon explicitly recognises that the book does not take a position on the methodological debates surrounding the use of case studies as a research method, and he says that “The reader should therefore use and judge this handbook with these considerations in mind” (p. xii). This is very good advice – caveat emptor .

Ahrens , T. ( 2008 ), “ A comment on Marja‐Liisa Kakkuri‐Knuuttila ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 291 ‐ 7 , Kari Lukka and Jaakko Kuorikoski.

Baxter , J. and Chua , W.F. ( 2008 ), “ The field researcher as author‐writer ”, Qualitative Research in Accounting & Management , Vol. 5 No. 2 , pp. 101 ‐ 21 .

Burrell , G. and Morgan , G. ( 1979 ), Sociological Paradigms and Organizational Analysis , Heinneman , London .

Glaser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , New York, NY .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2006 ), “ Evaluating qualitative management research: towards a contingent critieriology ”, International Journal of Management Reviews , Vol. 8 No. 3 , pp. 131 ‐ 56 .

Kakkuri‐Knuuttila , M.‐L. , Lukka , K. and Kuorikoski , J. ( 2008 ), “ Straddling between paradigms: a naturalistic philosophical case study on interpretive research in management accounting ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 267 ‐ 91 .

Lukka , K. and Modell , S. ( 2010 ), “ Validation in interpretive management accounting research ”, Accounting, Organizations and Society , Vol. 35 , pp. 462 ‐ 77 .

Miles , M.B. and Huberman , A.M. ( 1994 ), Qualitative Data Analysis: A Source Book of New Methods , 2nd ed. , Sage , London .

Ryan , R.J. , Scapens , R.W. and Theobald , M. ( 2002 ), Research Methods and Methodology in Finance and Accounting , 2nd ed. , Thomson Learning , London .

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Blog Beginner Guides 6 Types of Case Studies to Inspire Your Research and Analysis

6 Types of Case Studies to Inspire Your Research and Analysis

Written by: Ronita Mohan Sep 20, 2021

Case studies have become powerful business tools. But what is a case study? What are the benefits of creating one? Are there limitations to the format?

If you’ve asked yourself these questions, our helpful guide will clear things up. Learn how to use a case study for business. Find out how cases analysis works in psychology and research.

We’ve also got examples of case studies to inspire you.

Haven’t made a case study before? You can easily  create a case study  with Venngage’s customizable case study templates .

Click to jump ahead:

What is a case study?

6 types of case studies, what is a business case study, what is a case study in research, what is a case study in psychology, what is the case study method, benefits of case studies, limitations of case studies, faqs about case studies.

A case study is a research process aimed at learning about a subject, an event or an organization. Case studies are use in business, the social sciences and healthcare.

A case study may focus on one observation or many. It can also examine a series of events or a single case. An effective case study tells a story and provides a conclusion.

Healthcare industries write reports on patients and diagnoses. Marketing case study examples , like the one below, highlight the benefits of a business product.

Now that you know what a case study is, let’s look at the six different types of case studies next.

There are six common types of case reports. Depending on your industry, you might use one of these types.

Descriptive case studies

Explanatory case studies, exploratory case reports, intrinsic case studies, instrumental case studies, collective case reports.

We go into more detail about each type of study in the guide below.

Related:  15+ Professional Case Study Examples [Design Tips + Templates]

When you have an existing hypothesis, you can design a descriptive study. This type of report starts with a description. The aim is to find connections between the subject being studied and a theory.

Once these connections are found, the study can conclude. The results of this type of study will usually suggest how to develop a theory further.

A study like the one below has concrete results. A descriptive report would use the quantitative data as a suggestion for researching the subject deeply.

When an incident occurs in a field, an explanation is required. An explanatory report investigates the cause of the event. It will include explanations for that cause.

The study will also share details about the impact of the event. In most cases, this report will use evidence to predict future occurrences. The results of explanatory reports are definitive.

Note that there is no room for interpretation here. The results are absolute.

The study below is a good example. It explains how one brand used the services of another. It concludes by showing definitive proof that the collaboration was successful.

Another example of this study would be in the automotive industry. If a vehicle fails a test, an explanatory study will examine why. The results could show that the failure was because of a particular part.

Related: How to Write a Case Study [+ Design Tips]

An explanatory report is a self-contained document. An exploratory one is only the beginning of an investigation.

Exploratory cases act as the starting point of studies. This is usually conducted as a precursor to large-scale investigations. The research is used to suggest why further investigations are needed.

An exploratory study can also be used to suggest methods for further examination.

For example, the below analysis could have found inconclusive results. In that situation, it would be the basis for an in-depth study.

Intrinsic studies are more common in the field of psychology. These reports can also be conducted in healthcare or social work.

These types of studies focus on a unique subject, such as a patient. They can sometimes study groups close to the researcher.

The aim of such studies is to understand the subject better. This requires learning their history. The researcher will also examine how they interact with their environment.

For instance, if the case study below was about a unique brand, it could be an intrinsic study.

Once the study is complete, the researcher will have developed a better understanding of a phenomenon. This phenomenon will likely not have been studied or theorized about before.

Examples of intrinsic case analysis can be found across psychology. For example, Jean Piaget’s theories on cognitive development. He established the theory from intrinsic studies into his own children.

Related: What Disney Villains Can Tell Us About Color Psychology [Infographic]

This is another type of study seen in medical and psychology fields. Instrumental reports are created to examine more than just the primary subject.

When research is conducted for an instrumental study, it is to provide the basis for a larger phenomenon. The subject matter is usually the best example of the phenomenon. This is why it is being studied.

Take the example of the fictional brand below.

Assume it’s examining lead generation strategies. It may want to show that visual marketing is the definitive lead generation tool. The brand can conduct an instrumental case study to examine this phenomenon.

Collective studies are based on instrumental case reports. These types of studies examine multiple reports.

There are a number of reasons why collective reports are created:

• To provide evidence for starting a new study
• To find pattens between multiple instrumental reports
• To find differences in similar types of cases
• Gain a deeper understanding of a complex phenomenon
• Understand a phenomenon from diverse contexts

A researcher could use multiple reports, like the one below, to build a collective case report.

Related: 10+ Case Study Infographic Templates That Convert

A business or marketing case study aims at showcasing a successful partnership. This can be between a brand and a client. Or the case study can examine a brand’s project.

There is a perception that case studies are used to advertise a brand. But effective reports, like the one below, can show clients how a brand can support them.

Hubspot created a case study on a customer that successfully scaled its business. The report outlines the various Hubspot tools used to achieve these results.

Hubspot also added a video with testimonials from the client company’s employees.

So, what is the purpose of a case study for businesses? There is a lot of competition in the corporate world. Companies are run by people. They can be on the fence about which brand to work with.

Business reports  stand out aesthetically, as well. They use  brand colors  and brand fonts . Usually, a combination of the client’s and the brand’s.

With the Venngage  My Brand Kit  feature, businesses can automatically apply their brand to designs.

A business case study, like the one below, acts as social proof. This helps customers decide between your brand and your competitors.

Don’t know how to design a report? You can learn  how to write a case study  with Venngage’s guide. We also share design tips and examples that will help you convert.

Related: 55+ Annual Report Design Templates, Inspirational Examples & Tips [Updated]

Research is a necessary part of every case study. But specific research fields are required to create studies. These fields include user research, healthcare, education, or social work.

For example, this UX Design  report examined the public perception of a client. The brand researched and implemented new visuals to improve it. The study breaks down this research through lessons learned.

Clinical reports are a necessity in the medical field. These documents are used to share knowledge with other professionals. They also help examine new or unusual diseases or symptoms.

The pandemic has led to a significant increase in research. For example,  Spectrum Health  studied the value of health systems in the pandemic. They created the study by examining community outreach.

The pandemic has significantly impacted the field of education. This has led to numerous examinations on remote studying. There have also been studies on how students react to decreased peer communication.

Social work case reports often have a community focus. They can also examine public health responses. In certain regions, social workers study disaster responses.

You now know what case studies in various fields are. In the next step of our guide, we explain the case study method.

In the field of psychology, case studies focus on a particular subject. Psychology case histories also examine human behaviors.

Case reports search for commonalities between humans. They are also used to prescribe further research. Or these studies can elaborate on a solution for a behavioral ailment.

The American Psychology Association  has a number of case studies on real-life clients. Note how the reports are more text-heavy than a business case study.

Famous psychologists such as Sigmund Freud and Anna O popularised the use of case studies in the field. They did so by regularly interviewing subjects. Their detailed observations build the field of psychology.

It is important to note that psychological studies must be conducted by professionals. Psychologists, psychiatrists and therapists should be the researchers in these cases.

Related: What Netflix’s Top 50 Shows Can Teach Us About Font Psychology [Infographic]

The case study method, or case method, is a learning technique where you’re presented with a real-world business challenge and asked how you’d solve it.

After working through it independently and with peers, you learn how the actual scenario unfolded. This approach helps develop problem-solving skills and practical knowledge.

This method often uses various data sources like interviews, observations, and documents to provide comprehensive insights. The below example would have been created after numerous interviews.

Case studies are largely qualitative. They analyze and describe phenomena. While some data is included, a case analysis is not quantitative.

There are a few steps in the case method. You have to start by identifying the subject of your study. Then determine what kind of research is required.

In natural sciences, case studies can take years to complete. Business reports, like this one, don’t take that long. A few weeks of interviews should be enough.

The case method will vary depending on the industry. Reports will also look different once produced.

As you will have seen, business reports are more colorful. The design is also more accessible . Healthcare and psychology reports are more text-heavy.

Designing case reports takes time and energy. So, is it worth taking the time to write them? Here are the benefits of creating case studies.

• Collects large amounts of information
• Helps formulate hypotheses
• Builds the case for further research
• Discovers new insights into a subject
• Builds brand trust and loyalty
• Engages customers through stories

For example, the business study below creates a story around a brand partnership. It makes for engaging reading. The study also shows evidence backing up the information.

We’ve shared the benefits of why studies are needed. We will also look at the limitations of creating them.

Related: How to Present a Case Study like a Pro (With Examples)

There are a few disadvantages to conducting a case analysis. The limitations will vary according to the industry.

• Responses from interviews are subjective
• Subjects may tailor responses to the researcher
• Studies can’t always be replicated
• In certain industries, analyses can take time and be expensive
• Risk of generalizing the results among a larger population

These are some of the common weaknesses of creating case reports. If you’re on the fence, look at the competition in your industry.

Other brands or professionals are building reports, like this example. In that case, you may want to do the same.

What makes a case study a case study?

A case study has a very particular research methodology. They are an in-depth study of a person or a group of individuals. They can also study a community or an organization. Case reports examine real-world phenomena within a set context.

How long should a case study be?

The length of studies depends on the industry. It also depends on the story you’re telling. Most case studies should be at least 500-1500 words long. But you can increase the length if you have more details to share.

What should you ask in a case study?

The one thing you shouldn’t ask is ‘yes’ or ‘no’ questions. Case studies are qualitative. These questions won’t give you the information you need.

Ask your client about the problems they faced. Ask them about solutions they found. Or what they think is the ideal solution. Leave room to ask them follow-up questions. This will help build out the study.

How to present a case study?

When you’re ready to present a case study, begin by providing a summary of the problem or challenge you were addressing. Follow this with an outline of the solution you implemented, and support this with the results you achieved, backed by relevant data. Incorporate visual aids like slides, graphs, and images to make your case study presentation more engaging and impactful.

Now you know what a case study means, you can begin creating one. These reports are a great tool for analyzing brands. They are also useful in a variety of other fields.

Use a visual communication platform like Venngage to design case studies. With Venngage’s templates, you can design easily. Create branded, engaging reports, all without design experience.

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JSmol Viewer

A patient-centered conceptual model of aya cancer survivorship care informed by a qualitative interview study.

Simple Summary

1. introduction, 2.1. recruitment, 2.2. interview approach, 2.3. analysis, 3.1. overall themes, 3.2. care coordination and healthcare system navigation support.

“So there really wasn’t much time. Or was there? I didn’t know to ask that question. Okay, I know this is growing—is there enough time for me to get a consultation? I don’t know if maybe I could have waited a few days. I just don’t know, because I didn’t know that question to ask... But I just went ahead and signed away because I felt like I was—I hate to say the word bullied, but I felt like I was in a corner. I was like oh my god—this cancer’s bigger than me, just get it out, kill it! Do what you need to do.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“I, I mostly blamed myself for my inexperience in hospitals, I guess. But yeah, I felt like people weren’t necessarily completely clear, well, telling me exactly what I had to do. What I should do. Like when I should ask for help or when I didn’t need to, that sort of thing.”— Participant 2, female, renal cell carcinoma, 30–39 years old at diagnosis .

“I felt like I had to be the care coordinator. I had to make sure everybody knew what the other was doing. Proactively ask for appointments—like okay, I’m going to have to get radiation next. And they’re like oh, you can wait for that until the week before, and I was like, but what if I don’t like [the provider]? You’re going to put me in a box. So I had to just be proactive to get the kind of care that I wanted to get. And I felt like my care coordinator, which is exhausting.”— Participant 4, female, breast cancer, 30–39 years old at diagnosis .

“I was first getting treatment somewhere and I didn’t feel completely taken care of there. As a nurse practitioner, I felt like I was asking—I was supposed to be a patient then, I wasn’t supposed to be a health care provider. So I felt like I was directing my care and I was reminding them of things. It didn’t feel like the right fit for me with my oncologist and the care team, so I ended up after getting a second opinion switching to another hospital.”— Participant 3, female, Hodgkin’s lymphoma, 20–29 years old at diagnosis .

“Gosh, that’s really why I became an advocate—I just couldn’t believe the lack of treating me as a holistic person. I understand that I guess to be an oncologist you’re going to meet patients who ultimately die from it, and I get that they’re trying to make sure that you don’t die, and that is of course great, you kind of need that. But what about a nurse navigator or even like the nurse? There was no follow up... there needs to be a middle person. Whether it be that nurse or that social worker, and it should be mandatory that every AYA... have an initial conversation [with them] and then determine if you want to work with them...The follow ups just go through the cracks.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“I felt like my oncologist was very good at giving me medications to deal with nausea and other side effects when I needed them...But I had to research online what are things that I could use and then go and ask for it, as opposed to someone presenting me with “these are all the resources” or “these are things you should consider, let us know what you need”. I felt like the latter would have been much more helpful. I went to [other specialty cancer centers, and] both of those hospitals did provide that. Like “here’s your coordinator, here’s a whole pamphlet, here’s all the resources we have. Here’s how you use each one”. So I thought that was really cool.”— Participant 4, female, breast cancer, 30–39 years old at diagnosis .

3.3. Mental Health Support

“Definitely anxiety, depression for sure. I think those would be the biggest two that I’ve had to deal with. It’s an everyday struggle … Anxious about my cancer getting worse or also having cancer in my family or friends, since I already know what it feels like, having cancer. I wouldn’t want any of my loved ones to go through the same thing.”— Participant 6, female, breast cancer, 30–39 years old at diagnosis .

“Cancer is trauma, and even though a lot may not equate it with that term, because they just don’t know, a lot of us have PTSD. And that’s not talked about enough… every experience in the AYA community matters. So that might be why someone would not [talk to a researcher about their cancer experience], because they might feel like you could talk to someone better. It’s really about insecurity, but also too how they’ve been treated throughout their treatment. It can be hard to discuss and be traumatic. I can now verbally talk about it without bursting into tears, but not everyone can.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“Obviously having cancer kind of like fucks you up mentally. But I’ve been going to therapy, I actually take an antianxiety med now.”— Participant 8, female, Hodgkin’s lymphoma, 15–19 years old at diagnosis .

“Like I thought, I thought I was alone for like five years … Post treatment I actually had a really bad depressive episode, because I was just in such despair because I thought I was alone and no one else was like me. And I did hours of searching and finally found a couple of organizations that led me to other things. But I would have liked to have those resources [earlier], I wouldn’t have felt so alone.”— Participant 8, female, Hodgkin’s lymphoma, 15–19 years old at diagnosis .

“I actually learned about the support groups from Instagram … just as a young Black woman, [it was important] to see other women of color that were young and that looked like me, because I was not seeing that at my cancer center. So that was a huge support for me. Also, just by sharing my story, it allowed me to pay it forward to other young adults and also inspired me to get involved in advocacy work.”— Participant 9, female, breast cancer, 30–39 years old at diagnosis .

3.4. Peer Support and Making “Cancer Friends”

“It’s bad enough I’m an AYA, it’s bad enough I’m Black, it’s bad enough I’m a woman, it’s bad enough that I am an only child. I feel like all of these things were hitting me—and I have cancer, and now I literally have no one? It’s been hard.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“So, I think at the time the quintessential experience of being the youngest person at the cancer center in the waiting room, you know, not seeing anybody else my age unless they were in a caregiver capacity... And just feeling like I was the only person my age that had cancer and was getting treatment. And so the experience was very different when you are under 40. I didn’t know other people that had gone through that at the time.”— Participant 10, male, testicular cancer, 30–39 years old at diagnosis .

“As I was nearing the end of chemotherapy, I was feeling like I couldn’t really talk to my friends the same, and I didn’t really have people to relate to, and I felt like an astronaut. My brain was foggy, I really wanted to talk to someone about [my side effects and stuff] without worrying people. I remember Stupid Cancer was the big [AYA organization] at the time, and I saw that they had in-person Meetups. I decided to go … and then I instantly was like oh, maybe this [is] a window into a community I didn’t even know existed. I didn’t picture people in their 20s and 30s with cancer hanging out before this. That was the beginning of making cancer friends, [we have fun but] also if someone does need to vent about their situation, treatment, insurance, or relationships going away because of cancer, you’re the perfect [person] to talk to about it.”— Participant 11, male, testicular cancer, 30–39 years old at diagnosis .

“I went through a lot of side effects. I literally had the motherlode of side effects and what was very hurtful was when my oncologist would be like yeah, you know, a lot of patients get that. Well, it’s my first time seeing my tongue turn black, so you might want to have some sort of—I don’t know, like compassion for how freaked out I would be. Even my throat would swell and I had difficulty swallowing. ‘Oh, I’ve seen it before, I’ve seen worse.’ Well, I’ve never seen worse.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“I wish that that there was an AYA program at the hospital to tell me about these resources. To tell me like, hey, there’s a Gilda’s Club, it’s 10 to 15 min from here. There’s a meeting once a month. You can go and meet people your own age. It’s safe. People are really cool. Check it out. And now you can join these virtually. Just having somebody to say to me that is totally normal to feel that way. There are other people your age that get treatment here and you can meet them. That would have been really awesome.”— Participant 10, male, testicular cancer, 30–39 years old at diagnosis .

“I think just introducing for patients, that adolescent young adult oncology exists, and there is support out there for AYA’s. I didn’t really dive into the AYA support community until after treatment and got connected to a lot of resources and a lot of friends that way. But I think if I had known that resources like that existed while I was going through treatment, it would have been helpful just to know that I wasn’t alone and all these amazing organizations exist.”— Participant 12, female, osteosarcoma, 15–19 years old at diagnosis .

3.5. Empathic Communication about Fertility Preservation

“When I got diagnosed in the hospital … they had brought in a blood specialist and he described leukemia to me … After he left one of the interns immediately asked me, like so do you have any kids? And I was like no. And he was like, have you thought about freezing your eggs? And I’m like, dude, this dude just told me about cancer, like I haven’t, I can’t talk about kids right now like. You know?”— Participant 13, female, leukemia, 20–29 years old at diagnosis .

“The timing was rushed because it was overwhelming. I feel like if you sit down with anybody, man, woman, whatever, and tell them you might not be able to have kids, that’s pretty heavy and something you want to sit with. And … it’s not like it was free to go get the sperm banking done and have it stored. But I was like well, if I don’t do this, that might be it, I might never have kids. Even if I don’t want them at the moment, taking the option off just seemed scary. So yeah, I would have liked to have had more time.”— Participant 11, male, testicular cancer, 30–39 years old at diagnosis .

“Everything for me happened within like three days, so there was no, no ability to like, I don’t even know what it’s called. But to … freeze my eggs, I didn’t have that option because of the type of cancer I had everything had to be done so quickly. The only thing I was told in regards to fertility is you may not be able to have kids. There’s a high likelihood with the chemotherapy you are receiving that you may not be able to have children after this. There was no offering of like any type of resources. I only found that out afterwards, [about] all like the different type of programs for patients.”— Participant 15, female, leukemia, 20–29 years old at diagnosis .

“We talked about [fertility preservation] in [my support] group before and I guess, well, I mean for guys it’s easy, so they’re super on top of it as far as when we spoke about it. But a lot of [women] who were in similar positions to me where it was all just really sad. From my experience [the doctors] were like, okay, you’re here now, here’s your doctor, here’s your treatment. Oh, by the way there’s this [fertility preservation option], we kind of want to get started right now, so could you just not [have kids] … It wasn’t a huge deal, but I was a little sad.”— Participant 14, female, leukemia, 20–29 years old at diagnosis .

“There should have been a follow up call [after my diagnosis]. Because that was a really intense moment. My first time as the patient … Why wasn’t there a follow up? Like hey, I know you just heard a lot of information, let’s talk about this. I feel like I should have at least been required to get a consultation with an infertility specialist, even though it wouldn’t have been covered under my insurance. I feel that conversation should at least have been had so they could make sure I was really making the best decision for myself at that time. Sorry, I get really passionate and very angered about it.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“I lost my fertility. No one prepared me for that. I didn’t receive initial counseling going into that surgery or coming out of it. I didn’t expect to experience that kind of grief, because I was single all this time, and childless, and now I am chronically single and barren forever. None of my doctors cared to see how that would affect me.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

“I don’t really have trouble communicating with [doctors]. I’m a lawyer and I did a lot of research, so I generally got the comments that ‘oh, you’re so knowledgeable, you’re an easy patient.’ [But] I don’t think they necessarily answered all my questions, or gave me all the resources that were available, or were upfront about side effects, which I found frustrating…[the doctors failed] to mention fertility resources [so] I found my own stuff … I certainly wouldn’t say I got most of my information from my oncologist, but I found it in other places.”— Participant 4, female, breast cancer, 30–39 years old at diagnosis .

“My oncologist is very respectful of my wishes in terms of wanting to have another baby … but then [she] also wasn’t afraid to tell me, you know, we can only do one round of harvesting your eggs, because it’s not safe to do more. She did a really good job acknowledging my dream and weighing that accordingly, [so] I’m not risking life … but I’m still able to try to, you know, preserve my fertility before having this definitive surgery.”— Participant 5, female, ovarian cancer, 30–39 years old at diagnosis .

“Before I started chemo, my social worker came to talk to me in the hospital room and she just wanted me to know like hey, your doctors want you to do chemo, but you don’t have to do it right now, you can work on the fertility thing, if it’s important to you. So she made me feel comfortable that it was okay to delay the treatment.”— Participant 7, female, leukemia, 20–29 years old at diagnosis .

3.6. Financial Burden and Need for Support

“We needed help, we had help from family and friends, but again, the financial burden … is just a nightmare. You got the financial burden, you got the paperwork. You’re supposed to be focusing on your health.”— Participant 5, female, ovarian cancer, 30–39 years old at diagnosis .

“I worked in fine dining and didn’t have any insurance … And then the diagnosis alone racked up I think tens of thousands of [dollars in] debt and I was just through biopsies and scans and you know. I was going to, which is laughable, but it was called free clinic. It took a long time before I was diagnosed; go get bloodwork, come back in two weeks, schedule another appointment for two weeks later. And debt was mounting.”— Participant 16, male, Hodgkin’s lymphoma, 20–29 years old at diagnosis .

“I probably know more about the American health services than I ever wanted to know … it’s just not the way I would have liked to have learned it.”— Participant 8, female, Hodgkin’s lymphoma, 15–19 years old at diagnosis .

“With my age I am able to be on my dad’s insurance and it is a really good insurance plan. So it hasn’t been like insanely expensive or anything … But as I approach my 26th birthday, the cutoff [of staying on my parents’ insurance], I have lots of concerns with finding good health care on my own.”— Participant 14, female, leukemia, 20–29 years old at diagnosis .

3.7. Quality of Life

“When I was first diagnosed I was studying for a board license for civil engineering. I was still thinking I’m going to be in chemo for eight hours, I’ll have a lot of time to study at the hospital. It wasn’t like that at all. That’s when I was in denial, and I think after that, that’s when depression hit me. I was like you know what? It’s over, I’m just going to keep my job now. There’s no way I can study for the exam … Sometimes in my back of my mind I’m still thinking I want to be a licensed engineer and all I have to do is pass that exam. I start dreaming that when I pass the exam, I’m going to get my promotion and travel more, which I used to do before diagnosis … I guess career-wise I still think about getting my license, even if I don’t keep working in the engineering field, I want to feel accomplished. I want to be able to say even through or despite cancer, I was still able to accomplish that.”— Participant 6, female, breast cancer, 30–39 years old at diagnosis .

“So because I got sick, at least with my internship hours, I could have been done last December. But I was going through treatment. And my friend and I were collecting hours and going to school at the same time. She already finished herself, got certified, she’s my boss right now. She’s my supervisor. We were like at the same level, she’s already above me. So and she doesn’t treat me any lower, but I’m still a little upset sometimes because I could have been there by now if I hadn’t gotten sick.”— Participant 13, female, leukemia, 20–29 years old at diagnosis .

“I’ve been a dog groomer on and off for about 10 years. And I when I was finally able to get back into work [right after my surgery], I felt like they didn’t understand what I was going through. Like I was very anxious, and there’s a lot of sounds in a grooming salon. And it was really putting me on edge. And I started to wear earplugs to deal with that. And then I started getting like looks from my coworkers and like I just started to feel less and less welcome there. And I just gave up on it and I ended up quitting that job. I just didn’t feel very good there anymore.”— Participant 2, female, renal cell carcinoma, 30–39 years old at diagnosis .

“I did officially go back up to my regular hours, but there are some days that I take time off for appointments. I try to schedule for example my scans in one day, for example, so I only have to take one day off whenever I can…It’s not just cancer that we deal with, we still have to deal with what other people go through as well, for example taking time out for dental and eye doctor appointments. I still have to take time off for that.”— Participant 6, female, breast cancer, 30–39 years old at diagnosis .

“I had never been to the hospital before. And so I had to go through getting my diagnosis. Going through all these different procedures. And every one alone. They transferred me because they didn’t have the resources where I live to treat me. They transferred me to Houston, so my life got uprooted. My job put on hold. I had to move about five hours away so I could get treatment.”— Participant 13, female, leukemia, 20–29 years old at diagnosis .

3.8. Information about and Support Mitigating Side Effects and Late Effects

“The important elements for young adult cancer care compared to the typical cancer patient that you think of, like 50, 60, 70, they’re worried more about the here and now, and they don’t necessarily have to worry about side effects 20, 30 years down the road, because life expectancy, they won’t be there. I was diagnosed at 25. God willing, I’ll be alive for 50 more years beyond that. I don’t want to be dealing with side effects for years on end, so if there’s an option that’s a little bit more conservative treatment, which will possibly result in less side effects but maybe instead of saying it’s 100% certain, it’s 80% certain. That’s a 20% difference, so I think addressing that in terms that are easily understood by young adults, and also not in a talk down to manner, is super important.”— Participant 17, male, testicular cancer, 20–29 years old at diagnosis .

“Oh, and then the thing I always forget are the other secondary effects of treatment. I had to have both shoulders and both hips replaced, and I had no idea that was going to be in my future whatsoever, at the time of treatment.”— Participant 18, female, leukemia, 20–29 years old at diagnosis .

“I have osteoporosis and I’m not even 25 yet, so that’s kind of concerning for the future.”— Participant 14, female, leukemia, 20–29 years old at diagnosis .

“The one thing I do deal with is, because of all the surgery I’ve had, I have chronic nerve pain, nerve damage, so that’s not fun to deal with. I wish I would have known that it was a possibility, because I was not told that it was a possibility that this could happen.”— Participant 19, female, sarcoma, 15–19 years old at diagnosis .

“I’ve got major issues with the majority of my organs. I have liver damage. I have heart failure. I was in a wheelchair for a while. I was on bedrest for a very long time right after everything. I am disabled. I am on disability. And I do not have the energy I once did. Napping and every couple days just being totally exhausted is kind of part of my life.”— Participant 20, female, leukemia, 30–39 years old at diagnosis .

“I have permanent damage—I don’t feel my feet, my toes from the upper balls to my toes. Sometimes the numbness goes up my legs… and I’ve fallen, actually almost fractured my ankle in January because I didn’t feel my foot. It was so sudden and severe, and … no one seemed to take it as seriously as I did, which is frustrating.”— Participant 1, female, breast cancer, 30–39 years old at diagnosis .

3.9. Attention to the Unique Needs of Young Adults

“[My center had] an AYA program. Granted, they have so much volume because they have a special unit, so I think volume begets resources. But they have providers who are knowledgeable and not just oncologists, but lots of different providers who are knowledgeable about issues that AYA’s face, especially fertility. Sometimes we respond differently to drugs. If every center could have somebody who has a special research focus, to keep up to date on AYA’s. Or a pamphlet, a website, that even would have been helpful. I feel like there’s many ways to skin the cat, but it’s just providing age-appropriate information.”— Participant 4, female, breast cancer, 30–39 years old at diagnosis .

“But I definitely wanted more [young adult] support specifically. And not just in general cancer support, I went through this huge ordeal; it’s completely life changing. And I just, to me the more support I’m getting I feel more in control and I have more power.”— Participant 5, female, ovarian cancer, 30–39 years old at diagnosis .

4. Discussion

4.1. care coordination and healthcare system navigation, 4.2. mental health support, 4.3. aya peer support, 4.4. empathic communication about fertility preservation, 4.5. financial burden, 4.6. quality of life, 4.7. education and support regarding side effects and late effects, 4.8. attention to the unique needs of young adults, 4.9. limitations, 4.10. implications for cancer survivors, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Figueroa Gray, M.S.; Shapiro, L.; Dorsey, C.N.; Randall, S.; Casperson, M.; Chawla, N.; Zebrack, B.; Fujii, M.M.; Hahn, E.E.; Keegan, T.H.M.; et al. A Patient-Centered Conceptual Model of AYA Cancer Survivorship Care Informed by a Qualitative Interview Study. Cancers 2024 , 16 , 3073. https://doi.org/10.3390/cancers16173073

Figueroa Gray MS, Shapiro L, Dorsey CN, Randall S, Casperson M, Chawla N, Zebrack B, Fujii MM, Hahn EE, Keegan THM, et al. A Patient-Centered Conceptual Model of AYA Cancer Survivorship Care Informed by a Qualitative Interview Study. Cancers . 2024; 16(17):3073. https://doi.org/10.3390/cancers16173073

Figueroa Gray, Marlaine S., Lily Shapiro, Caitlin N. Dorsey, Sarah Randall, Mallory Casperson, Neetu Chawla, Brad Zebrack, Monica M. Fujii, Erin E. Hahn, Theresa H. M. Keegan, and et al. 2024. "A Patient-Centered Conceptual Model of AYA Cancer Survivorship Care Informed by a Qualitative Interview Study" Cancers 16, no. 17: 3073. https://doi.org/10.3390/cancers16173073

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• Open access
• Published: 05 September 2024

Household economic burden of type-2 diabetes and hypertension comorbidity care in urban-poor Ghana: a mixed methods study

• Samuel Amon 1 , 2 ,
• Moses Aikins 2 ,
• Hassan Haghparast-Bidgoli 3 ,
• Irene Akwo Kretchy 4 ,
• Daniel Kojo Arhinful 1 ,
• Leonard Baatiema 2 , 5 ,
• Raphael Baffour Awuah 6 ,
• Vida Asah-Ayeh 1 ,
• Olutobi Adekunle Sanuade 7 ,
• Sandra Boatemaa Kushitor 8 , 9 ,
• Sedzro Kojo Mensah 1 ,
• Mawuli Komla Kushitor 3 , 10 , 11 ,
• Carlos Grijalva-Eternod 3 , 11 ,
• Ann Blandford 12 ,
• Hannah Jennings 13 , 14 ,
• Kwadwo Koram 1 ,
• Publa Antwi 13 ,
• Ethan Gray 3 , 12 &
• Edward Fottrell 3  

BMC Health Services Research volume  24 , Article number:  1028 ( 2024 ) Cite this article

Metrics details

Non-communicable diseases (NCDs) predispose households to exorbitant healthcare expenditures in health systems where there is no access to effective financial protection for healthcare. This study assessed the economic burden associated with the rising burden of type-2 diabetes (T2D) and hypertension comorbidity management, and its implications for healthcare seeking in urban Accra.

A convergent parallel mixed-methods study design was used. Quantitative sociodemographic and cost data were collected through survey from a random community-based sample of 120 adults aged 25 years and older and living with comorbid T2D and hypertension in Ga Mashie, Accra, Ghana in November and December 2022. The monthly economic cost of T2D and hypertension comorbidity care was estimated using a descriptive cost-of-illness analysis technique from the perspective of patients. Thirteen focus group discussions (FGDs) were conducted among community members with and without comorbid T2D and hypertension. The FGDs were analysed using deductive and inductive thematic approaches. Findings from the survey and qualitative study were integrated in the discussion.

Out of a total of 120 respondents who self-reported comorbid T2D and hypertension, 23 (19.2%) provided complete healthcare cost data. The direct cost of managing T2D and hypertension comorbidity constituted almost 94% of the monthly economic cost of care, and the median direct cost of care was US$19.30 (IQR:10.55–118.88). Almost a quarter of the respondents pay for their healthcare through co-payment and insurance jointly, and 42.9% pay out-of-pocket (OOP). Patients with lower socioeconomic status incurred a higher direct cost burden compared to those in the higher socioeconomic bracket. The implications of the high economic burden resulting from self-funding of healthcare were found from the qualitative study to be: 1) poor access to quality healthcare; (2) poor medication adherence; (3) aggravated direct non-medical and indirect cost; and (4) psychosocial support to help cope with the cost burden.

The economic burden associated with healthcare in instances of comorbid T2D and hypertension can significantly impact household budget and cause financial difficulty or impoverishment. Policies targeted at effectively managing NCDs should focus on strengthening a comprehensive and reliable National Health Insurance Scheme coverage for care of chronic conditions.

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Introduction

Globally, non-communicable diseases (NCDs) lead to about 15 million premature deaths annually [ 1 , 2 ], and about eight in every ten deaths occur in low-and-middle-income countries (LMICs) [ 3 ]. World Health Organization (WHO) has projected that by 2025, NCDs will account for over 70% of all deaths globally, with more than 80% of the death occurring in developing countries [ 4 ]. Developing countries will incur NCDs related economic losses of US$21.3 trillion over the next two decades [ 5 ]. Existing literature indicates that diabetes, cancer, chronic lung diseases and cardiovascular diseases (CVD), alongside mental health, will cumulatively pose a global economic loss of 47 trillion US$ by 2030. This estimate is about 75% of the global gross domestic product (GDP) [ 6 ], which is projected to have disproportionate impacts on LMICs due to their fragile health systems. Approximately 10% of households globally are faced with high healthcare spending, of which the situation is projected to be worse in African countries [ 7 ]. In addition to Africa battling the attainment of universal health coverage (UHC) and financial risk protection schemes, over 2 billion people lack efficient, equitable and adequately funded healthcare systems [ 8 ]. Compared to high-income countries (HIC), the household financial burden of NCDs care in LMICs is much higher [ 9 , 10 ].

Evidence suggests that NCDs predispose households to a higher risk of health expenditure [ 11 ]. For instance, the mean household total costs per year in LMICs of CVD, cancers and diabetes were US$6055.99, US$3303.81 and US$1017.05 respectively [ 9 ]. The mean annual financial cost of managing one diabetic case at the outpatient clinic in Ghana was estimated at US$194.09 [ 12 ] and the mean healthcare management cost was US$38.68 [ 13 ]. Also, uncontrolled hypertension was found to be independent predictor of a higher cost of treatment in patients who died compared to those who survive in urban Ghana [ 14 ]. Excessive out-of-pocket (OOP) spending on healthcare services weakens households financially by wiping out savings and other durable resources, thereby plunging families into poverty [ 15 ]. Poor and vulnerable groups are least likely to obtain treatment for NCDs due to the high impact of OOP spending [ 16 , 17 ]. Meanwhile, there is growing evidence that governments’ expenditures on healthcare in SSA rarely focus on NCDs, suggesting that the costs of healthcare are passed on to patients [ 18 , 19 ]. Also, available evidence suggests there is poor coverage of NCD care by National Health Insurance Schemes [ 20 ], including Ghana. These phenomena hamper progress towards the attainment of UHC [ 11 ].

Comorbidity (co-existence of two or more conditions within an individual) is a growing public health challenge globally [ 21 ], substantially effecting individuals, carers and society [ 22 ]. Meanwhile, healthcare models in many LMICs have been designed to manage single health conditions rather than multiple conditions. Comparatively, individuals with comorbid chronic conditions often suffer higher rates of unplanned hospitalizations and frequent use of emergency services than those with single conditions [ 23 ]. In healthcare systems similar to Ghana where health insurance is ineffective and out of pocket payment as well as co-payments for healthcare is high, comorbidity exert more catastrophic healthcare expenditure on households [ 23 , 24 ]. Although the Ghana National Health Insurance Scheme (NHIS) benefit package is supposed to cover essential services like lab diagnosis and medicines, these often are not accessible to patients. The benefit routinely ends at catering for consultation fee. Consequently, most individuals with multiple chronic conditions become economically dependent on their relatives and support networks [ 23 , 24 ]. Also, the high healthcare cost drive people with NCDs to seek relatively more affordable alternative means of treatment (i.e., herbal and spiritual) to complement or completely replace orthodox medication [ 25 , 26 ].

There is a dearth of research on the effects of the healthcare-related economic burden of NCDs comorbidity on patients in Africa [ 27 , 28 ]. Although NCDs multimorbidity cause high financial burdens on households [ 29 , 30 ], the full extent of the economic burden that patients endure while seeking and receiving care is seldom reported. Costs incurred at each stage of the cascade of care (i.e., screening and diagnosis, treatment, management, and palliative care) include direct medical and non-medical costs, as well as indirect costs. These costs have implications for healthcare for people with NCDs, including comorbid T2D and hypertension [ 31 ]. Another major limitation in the literature is that, despite increasing scholarship on the economic burden caused by NCDs globally, most of the existing literature is from high-income countries and is disease specific [ 32 , 33 , 34 ].

As part of the ‘Contextual Awareness, Response and Evaluation: Diabetes in Ghana’ (CARE-Diabetes) project [ 35 ] (a mixed-methods study to generate a contextual understanding of T2D in an urban poor population), this study estimated the economic burden associated with T2D and hypertension multimorbidity in urban Ghana and discussed implications for interventions targeted at improving financial risk protection in vulnerable population in Ghana and other similar LMICs.

Study design

A convergent parallel mixed-methods study design was used. Quantitative and qualitative data were concurrently collected independently and analysed to assess the burden imposed by T2D and hypertension comorbidity, and its implications for healthcare. A descriptive cost-of-illness (COI) approach was used to estimate the economic burden of managing comorbid T2D and hypertension. The COI is a study method used to evaluate the economic burden imposed by an illness on individuals, institutions and/or society as a whole [ 36 ]. We further conducted focus group discussions (FGDs) to explore the cost burden implications for healthcare. Given that the CARE-Diabetes study focused on T2D, only the participants that self-reported an earlier diagnosis of T2D (index case) and co-occurrence of hypertension were used in this study.

Study setting

The study was carried out in Ga Mashie, a densely populated impecunious urban setting comprising two indigenous communities, namely James Town and Ussher Town, located in the Greater Accra Region of Ghana. The mean monthly household income in the study setting is USD78.83, and about three-quarters of the population have attained up to Junior High School (or middle school) education and above [ 37 ]. The twin towns, i.e., James Town and Ussher Town, are indigenous communities with fishing, petty trading and other fishing-related activities being the main economic activities and primary sources of livelihood for community members. Health services are provided mainly by government hospitals including Ussher Town Polyclinic and the Korle-Bu Teaching Hospital, a tertiary-level healthcare facility located close by. Also, there are few private hospitals offering healthcare services to the residents. More details of the study settings can be found elsewhere [ 35 ].

Sample size and sampling

Quantitative study.

This study was part of the CARE-Diabetes project[ 35 ], which had a target sample size of 1,242 adults aged ≥ 25 years within 959 households across 80 enumeration areas (EAs) of Ga Mashie. The sample size was determined on the ability to estimate the prevalence of T2D, and the sample was randomly selected from the 2021 population census [ 38 ]. The study excluded pregnant women or those who had given birth within the past six months as well as individuals who were unable to provide informed consent or had difficulty completing the survey, including those who were mentally incapacitated. All participants (n = 120) who self-reported T2D and hypertension were included in the present analysis.

Qualitative study

Likewise, the qualitative study used data from the CARE-Diabetes project. This study used 13 focus group discussions (FGDs) with community members. The participants included men and women with T2D and hypertension comorbidity, and people caring for relatives with the comorbid conditions. The respondents were enlisted using three sampling techniques. Firstly, relying on T2D patients scheduled for appointment on NCD clinic day at the Ussher Hospital (the main public health facility serving the people of Ga Mashie), we identified people with T2D and recruited them for FGD on the first day of data collection. Secondly, using the people with T2D identified from the hospital as index, a snowball technique was used to identify and recruit community members with comorbid T2D and hypertension. The snowball process continued until the required number of participants for the 5 FGDs was reached. Thirdly, participant (caregivers) without comorbid T2D and hypertension (n = 8) were recruited using convenient sampling technique, whereby a community liaison guided the research team to select potential participants from across the community.

Data collection

Quantitative.

Forty enumerators were recruited and trained to gather survey data on Open Data Kit (ODK) using mobile tablets in November and December 2022 [ 35 ]. Prior to data collection, the survey questionnaire was pretested in a different community outside Ga Mashie. Overall, 854 individuals completed the survey for the CARE-Diabetes project. Of this number, 120 (14%) self-reported co-morbid hypertension and T2D, all of whom were included in the present analysis.

Qualitative

Using pretested FGD guides, a total of 13 FGDs among community members with and without T2D and hypertension comorbidity were conducted from November to December, 2022 in the two predominant local dialects (Ga and Twi). The participants were different from those who participated in the survey. The topic guides were developed based on a literature review, and used to gather information on social norms, experiences, and attitudes regarding prevention, control, and care-seeking for T2D and hypertension comorbidity. Prior to the data collection, the topic guide was pretested in a different community. Copies of the FGD topic guides are attached to this manuscript as Supplementary files . The FGDs were led by trained research assistants. The training focused on the study guides and standard operating procedures (SOPs) for qualitative interviews. The total number of FGDs was considered sufficient for thematic saturation (i.e., no new information could be harnessed from interviews) [ 39 ]. The FGDs lasted for approximately one hour and were recorded digitally and detailed notes of the interactions were taken.

Data analyses

Quantitative analysis.

We generate a household wealth index using Principal Components Analysis (PCA) [ 40 ]. For the PCA, we selected and inputted into the model 15 out of the 23 assets, because they were reported to be owned by ≥ 5% but ≤ 95% of households. We also inputted into the PCA model whether the household had access to improved sources of drinking water, toilet facilities, gas or electricity as cooking fuels, and a separate room for the kitchen and the number of rooms in the household. We categorised the generated household wealth index into tertiles, specifically as ‘most poor’, ‘poor,’ and ‘least poor’.

Direct and indirect cost analyses were conducted using Microsoft Excel and STATA version 17. We adjusted for cluster and unequal probability survey design in the analysis by weighting. Direct medical cost was estimated by summing total cost incurred by people with comorbid T2D and hypertension on consultation, diagnostics and medication. Non-medical was estimated by summing the total cost of travel to and from hospital for comorbid T2D and hypertension medical care during the past one month. Total direct cost was estimated by summing the total direct medical and non-medical costs. The median and interquartile range were estimated. Indirect cost was estimated using the human capital approach (HCA). The HCA is a method commonly used to estimate lost productivity that results from disease, disability or premature death—which is an important component of the economic burden of chronic conditions [ 41 ]. Indirect cost was estimated by multiplying total productive hours lost (i.e., seeking comorbid T2D and hypertension care by patient and their caregiver). The national minimum wage per day of GHS13.53 for Ghana (US$1.00 equivalent to GHS8.58 (Bank of Ghana mean monthly interbank exchange rate, December 2022) was used to estimate value lost to productivity (Ministry of Finance, December 2022). The ratio of direct cost to income, by sex and socioeconomic status, was analysed. The mean economic cost of managing comorbid T2D and hypertension was estimated by dividing the sum of direct and indirect costs by the total participants. The robustness of cost estimates was tested through one-way and multi-way sensitivity analyses. This was done by varying critical cost components of the data which lacked certainty (i.e., medications and wages) by 3%, 8%, and 10% [ 42 ].

Qualitative analysis

All FGDs were transcribed and translated into English by trained fieldworkers who also conducted/facilitated the interviews. Transcripts were analysed thematically using the framework approach [ 43 ]. By this, a deductive coding framework was developed jointly by three of the authors based on existing literature on the consequences of the direct cost of managing comorbid T2D and hypertension for healthcare [ 44 ]. The framework was expanded when new codes or themes emerged through joint deliberation and review of the transcripts by the three authors (inductive approach). All transcripts were loaded into QSR NVIVO Version 11 to facilitate data coding and analysis. The thematic coding was done by the first author (who was part of the joint review and has extensive experience in qualitative thematic analysis). One person did the coding because the involvement of three authors in the development of the coding framework allowed for consensus building on all the codes relative to its alignments with the respective themes . After coding, the three authors jointly reviewed the output, and resolved any discordance between codes and themes. The coding exclusively focused on the consequences of direct OOP cost in the management of T2D and hypertension comorbidity on patients’ healthcare. Data are reported following the Consolidated Criteria for Reporting Qualitative Research (COREQ) [ 45 ].

The findings from the qualitative and quantitative works were synthesized by categorizing the findings to identify complementary themes that correspond with the research questions about the economic cost burden (direct and indirect cost) and its consequences for healthcare for people with T2D and hypertension co-morbidity [ 46 ].

Findings from the quantitative study

Survey data were gathered from 854 individuals in 629 households (household response rate of 66%; individual response rate of 69%). Of the 854 individuals who completed the survey, 120 (14%) self-reported comorbid T2D and hypertension, all of whom were included in the present analysis. However, the cost analysis included 23/120 (19.2%) comorbid T2D and hypertension individuals that provided completed healthcare cost data. Individuals who could not provide complete set of direct and indirect cost data were excluded in the economic burden analysis. As shown in Table  1 , many of the survey respondents were women (81.7%). More than half were ≥ 60 years, and most were unemployed (51.7%). Almost a quarter of the respondents reported that their healthcare was funded by co-payment and insurance jointly. A third reported funding their healthcare by insurance, whereas 42.9% reported funding solely out-of-pocket (OOP). Of the 94 participants of the FGDs, most were females (52.1%), almost two-third were widowed/single, and more than 56% were aged 25–49.

As presented in Table  2 , over 80% of the survey participants who provided complete direct and indirect costs information and were actually included in the economic cost analysis were females. The majority of the participants (60.9%) were employed, and most paid directly out-of-pocket for health care (42.9%).

As shown in Table  3. , the direct cost of managing T2D and hypertension comorbidity constituted almost 94% of the total economic cost of care, and the median monthly direct household cost of care was US$19.30 (IQR:10.55–118.88).

Further analysis of the proportion of direct cost to income, by patients’ socioeconomic status and sex, are presented in Table  4 . The absolute value of the mean direct cost for the poorest tertile was higher than the absolute value of the mean direct costs for the other wealth tertiles, although our sample size was too small to assess for statistical differences among groups. Also, men reported spending 122% of their income on healthcare compared to women (76.5%), although our sample size was too small to assess for statistical differences among groups. Furthermore, patients that paid for healthcare directly out of pocket spent over 100% of their income on care.

Findings from the qualitative study

The findings presented above on the proportion of the income expended on the direct cost of healthcare demonstrate the huge cost burden posed on people with comorbid T2D and hypertension. The remaining results sections focus on the implications of this cost burden on healthcare seeking, from the perspectives of patients and their caregivers (those without T2D and hypertension).

Implications of economic burden of managing T2D and hypertension comorbidity on healthcare seeking

The possible implications of the economic burden imposed by comorbid T2D and hypertension are classified into four broad themes and further elucidated in the subsequent sections of the results. These were: 1) poor access to quality healthcare; (2) poor medication adherence; (3) direct non-medical and indirect treatment cost aggravating burden; and (4) psychosocial support helps to cope with economic burden.

High treatment cost impacts access to quality healthcare

The high cost of managing T2D and hypertension comorbidity posed a huge burden for people living with these conditions. Most of the study respondents emphasized that availability and quality of healthcare were not a problem; however, affordability was a major hindrance to access. Thus, obtaining quality treatment was tied to the patient’s ability to pay for health services. Meanwhile, the extent of healthcare services offered depended on the patient’s ability to pay OOP at the point of seeking care. Even with the National Health Insurance Scheme (NHIS), patients were denied medication when they could not afford to pay OOP. The cost of healthcare services including labs, diagnostic tests, and certain medications often deter healthcare utilisation. Scheduled appointments were not adhered to due to the cost of health services.

“The healthcare provision is good, but it all depends on money. Treatment is not free, even though the health insurance covers part of the treatments, it does not cover most of the labs done by people living with T2D and hypertension.” (Man with comorbid T2D and hypertension )

“The main obstacle to accessing the services is the cost…The cost of the services, including lab, diagnostic tests, and medications, can be prohibitive. It prevents people from getting the care they need, even when they have an appointment scheduled.” (Woman with comorbid T2D and hypertension)

The inability to afford quality biomedical care led to plurality of healthcare, further complications and deteriorated conditions of patients. Some respondents shared experiences of the devastating consequences of their inability to meet the financial strains posed by direct and indirect costs of care. Due to the cost barrier to approved biomedical care, comorbid patients resorted to inferior treatment from multiple sources, which often worsen cost burden and health outcomes. That said, some patients noted that the use of complementary alternative medicines was also not cheap.

“They gave me the excuses that the health insurance does not cover the bills of the lab test. I resorted to using herbal medicine and going for prayers at different churches. After two years, I went to checkup on the same issue again at the hospital, and they realized the illness has worsened.” ( Woman with comorbid T2D and hypertension )

“Using Korle Bu hospital as an example, if you or any member of your family is admitted and you do not have the financial means to cater for the bills, I am sorry you will die. I have had a personal experience with them when my wife was admitted... Meanwhile herbal medicine is also not cheap” ( Man with comorbid T2D and hypertension )

Furthermore, the limited and unreliable NHIS coverage contributes to the direct cost burden. This is mainly because of a lack of knowledge on NHIS coverage by people with T2D and hypertension. Whereas some respondents believed that T2D and hypertension services were supposed to be free under the NHIS, others believed just a portion was covered. There was a widely held view among respondents that treatments for NCDs, particularly T2D and hypertension are supposed to be free under the NHIS. However, most medicines and services such as laboratory investigations were paid OOP.

“We were told that T2D and hypertension medicine is supposed to be free. All the health facilities in this community charge us for the service they render to us, none is free.” (Woman with comorbid T2D and hypertension)

“…we are told that insurance doesn’t cover the labs we do, and so we must pay. But it is through the lab result that diagnosis can be made, so they must review that aspect for us.” (Woman with comorbid T2D and hypertension

“The health insurance covers some of the diabetic’s drugs such as metformin, and some hypertensive drugs. But if the doctor prescribes specific one for you, you would be told it’s not available unless you pay out of pocket.” (Woman with comorbid T2D and hypertension)

According to some of the respondents with comorbid T2D and hypertension, the NHIS helped cover part of their hospital bills. However, patients bemoaned the limited and unreliable operations of the NHIS. They observed that medicines which were supposed to be free under the insurance were routinely sold to NHIS subscribers. The consequences were often devastating for those unable to co-pay. About three-quarters of the respondents (both those with and without comorbidity) accentuated the limited coverage of the NHIS and wondered what the relevance of subscribing to the NHIS was if their health needs could not freely or significantly be catered for.

“I heard the medication for T2D and hypertension was not to be sold, but right now if you don’t have money and you go to the hospital, you will die.” ( Man with comorbid T2D and hypertension )

“…We need a lot of medications, and they are expensive. If I don’t have money, I wouldn’t go to the hospital even though I have insurance… Last week I heard someone also confirm that the national health insurance is not working. (Woman with comorbid T2D and hypertension)

Cost affects adherence to medication

Even with the NHIS, patients with comorbid T2D and hypertension could not always get prescribed medications, even if they are supposedly entitled to them. People with T2D and hypertension comorbidity were compelled to pay a portion of the cost (i.e., co-payment) before being served with medication. Inability to afford healthcare results in patients not being attended to, affecting medication adherence. Thus, the cost of medication affects adherence to treatment regimens, as most patients manage their condition by heavily relying on financial support. The erratic financial support system for people with T2D and hypertension comorbidity led to non-adherence to treatment schedules. All respondents acknowledged that non-adherence to medication due to cost often led to dire complications like foot ulcers and cardiovascular diseases.

“…if you don’t have money, they will not sell the medicine to you, but in the health insurance it is supposed to be free, but they tell us it is not free, you must pay something. If you are not able to do so, your prescription will be given back to you.” ( Woman with comorbid T2D and hypertension )

“…My brother for instance takes injections twice a day; these drugs are very expensive…If he doesn’t get financial help, he skips the appointment. When he goes later after the default, he is sacked.” (Female without comorbid T2D and hypertension)

“Financial issues worry us a lot... When I run out of insulin, my legs will get swollen within four to five days and I will become very lean, which means the condition is becoming serious. Then my blood pressure will rise” (Man with comorbid T2D and hypertension)

Direct non-medical and indirect care cost adds to the burden

Some caregivers highlighted the additional burden imposed by the indirect cost of managing T2D and hypertension on their relatives. This mainly relates to the special diets recommended by healthcare specialists. Furthermore, the devastating nature of comorbid T2D and hypertension rendered most patients incapacitated for productive ventures. A respondent with T2D and hypertension comorbidity observed that the negative effects of the conditions on work and productivity plunged most people living with the conditions into impoverishment, thereby affecting their livelihood as well as their dependents.

“I also think money is the only solution to their problem because they need to eat certain meals which are different from what everyone else in the family eats. So, they need money to be able to afford that kind of life.” ( Woman without comorbid T2D and hypertension)

“This disease causes one to spend a lot of money. Lacking financial means when one develops this disease renders the victim’s life miserable. Say you are the breadwinner of the family; developing this illness hinders you from working hence bring about hunger in your home.” ( Man with comorbid T2D and hypertension)

Psychosocial support helps to cope with economic burden

All study respondents emphasized the importance of social support in the management of their T2D and hypertension comorbidity. Specifically, the inability of family and friends to financially and emotionally support healthcare for people with comorbid T2D and hypertension resulted in non-adherence to the treatment regimen, thereby causing significant emotional and psychosocial burden, for example depression, anxiety, frustration, and confusion. The study respondents reiterated that there was no way they could have solely managed their comorbid condition without psychosocial and physical support from family and friends.

“If maybe I need money and family and friends do not have money to help, it makes me overthink, depressed, anxious, worried, unhappy, frustrated and confusion . I am told not to overthink, but it is something that has been disturbing me.” (Man with comorbid T2D and hypertension)

“…in fact, if you don’t have a strong family support, you would be humiliated because everything about diabetes and hypertension involve money…if you don’t have anyone in the family to support and always be close to you, you will deteriorate. Because at a point, if you don’t get support financially and physically, you will die from stress and depression.” (Man with comorbid T2D and hypertension)

“Sometimes my siblings help me, sometimes too they don’t help, so there are times I am not able to afford my medication. The Country’s economy is in bad state, so you cannot burden people with your financial challenges because they also have responsibilities.” (Woman with comorbid T2D and hypertension)

This study sought to understand and add to the limited literature available on the economic cost associated with the rising burden of T2D and hypertension comorbidity in the economically disadvantaged urban setting of Ga Mashie Accra and its implications for seeking healthcare. The study found a significant economic cost burden associated with management of T2D and hypertension comorbidity. Patients spent excessively more than their income on healthcare. Our findings are consistent with those of previous studies conducted in SSA that have reported high direct costs of managing chronic diseases [ 10 , 47 ], most specifically, T2D [ 48 , 49 , 50 , 51 ], hypertension [ 52 ], and comorbid T2D and hypertension [ 53 ].

Like other studies conducted in Ghana [ 13 , 54 ], evidence from this study emphasizes that the cost of managing T2D and hypertension comorbidity is high. Other studies in Ghana have reported that the cost of managing T2D can lead to catastrophic healthcare spending [ 49 , 55 ]. Although the estimated mean economic cost of managing comorbid T2D and hypertension [US$63.08 (95% CI:0.00- 145.35)] was analysed from a patient perspective, the cost is comparable to that reported in urban Kenya (US$38) which was analysed from a societal perspective [ 53 ]. This implies a higher burden of managing the comorbid condition in Ga Mashie compared to Kenya since the societal perspective estimates economic cost from a broader perspective comprising both patient and institutional costs. Overall, individuals with the comorbid condition spent almost 81% of their income on healthcare. This can be attributed to the poor healthcare seeking behaviour of people with NCDs in poverty-stricken urban communities of Ghana, whereby individuals seek healthcare in a worsened state and thus incur high cost of care [ 56 ].

The burden is aggravated by the fact that most comorbid T2D and hypertension patients are unemployed and rely heavily on financial and social support systems within the already impoverished community where income levels are generally low [ 37 ]. Hence, the economic cost burden imposed by the condition transcends the individual suffering from the disease. As shown by this study, the economic burden has far-reaching effects on healthcare. From the qualitative study, we found four main possible implications of the high economic burden on individual’s healthcare. Firstly, the cost burden affected access to care and treatment quality; secondly, the high cost affected medication adherence; thirdly, direct non-medical and indirect treatment cost add to the economic burden; and finally, lack of psychosocial support aggravates the economic burden. These themes are discussed below.

High economic burden impacts access to care and treatment quality

Firstly, the high healthcare cost impacts access to T2D and hypertension care and treatment quality among the poor urban community of Ga Mashie. In this study, the high-cost burden imposed by approved sources of care (health facilities) coupled with low socioeconomic status are barriers to access to quality comorbid T2D and hypertension care. Other studies conducted in Africa have reported the association between low socioeconomic status and limited access to treatment due to high cost [ 50 , 57 ], likewise other regions of the world [ 58 , 59 ].

Similar to available evidence on NCDs care and management across Africa [ 60 ], there are three main means through which people with T2D and hypertension in Ga Mashie seek healthcare and manage their condition. These are biomedical, ethnomedical (herbal) and faith/spiritual treatments. Often, biomedical treatment sources like government and private health facilities serve as the first point of call to persons with T2D and hypertension for diagnosis and medical education by health professionals. However, many comorbid T2D and hypertension patients in Ga Mashie consider biomedical treatment very expensive. The expenses incurred include consultation, diagnosis, medication, and other hospital bills. Meanwhile, evidence on biomedical therapy for NCDs globally indicates that most patients must take medication for the rest of their lives and on a regular basis [ 61 , 62 ]. Hence, borne out of desperation to lessen the economic burden through cheaper sources that promise rapid and permanent cure, patients resort to pluralistic means of combining biomedical, ethnomedical (herbal) and/or spiritual care, thereby compromising treatment quality.

A further possible implication of the high economic cost of biomedical treatment is that, not only does it serve as a barrier to accessing quality care but also to accessing biomedically approved medications, as people seek alternative means (i.e., herbal and spiritual) of treatment to complement or completely replace orthodox medication. Herbal drugs are perceived to be relatively more affordable than pharmaceutical drugs. This confirms the findings of other studies conducted in the African region [ 25 , 26 ]. Also, it is common in SSA that due to the high economic burden associated with managing T2D and hypertension, some people with T2D in poverty-stricken urban communities like Ga Mashie typically combine biomedical therapy with spiritual therapy, whereas others solely depend on spiritual/faith healing therapy as a cost-effective rapid measure to manage their T2D [ 63 , 64 ].

The economic burden of managing T2D in Ga Mashie is untenable for most of the patients in need of care. Bekele et al. reported that having health insurance is a strong predictor of access to screening of T2D and effective biomedical care [ 65 ]. In Ghana, the NHIS is the main strategy for delivering social protection. The NHIS Act (Act 850, 2012) exempts children under 18 years, lactating mothers, and the elderly over 70 years from premium payments. The exemptions aim to support the management of various ill-health conditions including NCDs. Although the NHIS targets everybody, principally the vulnerable, there is a plethora of evidence to show that due to the inability to afford premiums because of low socioeconomic status, segments of the population are not covered [ 66 , 67 ]. Our findings show low confidence in the NHIS due to its erratic and unreliable operations as well as inconsistent information on the insurance coverage. This pushes patients to seek healthcare outside the approved biomedical care system. The consequence of the cost barrier to reliable access to approved biomedical care is the inferiority of treatment sought from multiple sources often leading to an exacerbated cost burden and poor health outcomes.

Cost affects medication adherence

Our findings are consistent with those of other studies that have found that non-adherence to treatment schedule and medication is endemic among people with T2D [ 68 ] and hypertension [ 69 ] in Ghana. They also corroborate other studies on diabetes in SSA that highlighted the high cost of biomedical medication, the absence of reliable health insurance cover for diabetes care [ 70 ], and the inability of patients to afford consultation fees and laboratory services [ 71 ] creating health system barriers for medical adherence among T2D patients. The cost barrier is fundamental to the non-adherence to prescribed medications among study participants. Thus, this study found that non-adherence to T2D medication occurs mainly because of patients' inability to afford direct medical and/or non-medical costs of treatment. Affordability is a real problem partly because most comorbid T2D and hypertension patients were found to be elderly and, thus, were not productively engaged for financial income. Hence, the majority of T2D patients rely heavily on social support for their healthcare needs.

Adherence to medication and treatment plans for patients in Ga Mashie critically depends on financial and social support from relatives and friends [ 72 , 73 ]. Our findings show that comorbid T2D and hypertension patients rely heavily on relatives to pay for direct medical and non-medical costs associated with care. Relatives support direct medical cost expenses like consultation, laboratory diagnosis, medication and other healthcare costs. Likewise, relatives and friends assist with non-medical expenses like transportation to and from the healthcare facilities as well as other subsistence costs. Consequently, erratic financial support from relatives and friends has implications for adherence to the systematic plan for their treatment therapy and, ultimately, health outcomes. Furthermore, adherence to biomedical treatment among T2D and hypertension comorbidity patients in poor urban communities like Ga Mashie depends on the type of treatment and cost [ 74 , 75 ]. By this, care providers routinely compromise healthcare quality to meet the financial strength of patients. Patients cannot afford the right dosage of medication required for effective management of their condition, hence the need to modify the treatment regimen.

Non-medical and indirect treatment cost adds to the burden

Besides the direct medical cost of comorbid T2D and hypertension treatment, there are other costs which are often not extensively considered in the economic burden of NCD dialogues. These are direct non-medical (e.g., transportation costs to and from healthcare facilities and cost of dietary and nutritional therapy) and indirect costs (i.e., productive workdays lost due to health-seeking or health condition) of care. Akin to a study in south-eastern Tanzania that reported lived experiences of diabetes management among adults [ 75 ], this study found that the cost of transportation to and from health facilities imposes an additional cost burden on patients.

Similar to some studies in SSA [ 65 , 76 ], we found changes in the pattern of diet and nutritional arrangements for persons with T2D and hypertension comorbidity recommended by dieticians. It was widely observed among this study's respondents that adherence to dietary changes is an integral factor in the management of T2D and hypertension comorbidity due to its vital contribution to blood pressure and glycaemic control. However, the cost of purchasing suitable foods regularly is problematic, thereby preventing strict adherence to the recommended dietary patterns. Literature in Africa supports the observation made by this study that comorbid T2D and hypertension patients of low socioeconomic status find it challenging to adhere to recommended dietary plans because of the associated cost burden [ 77 ].

Furthermore, although the findings of this study show a minimal contribution of indirect cost to the cost profile, the far-reaching impact on patients’ livelihoods is devastating. The health condition of most people with comorbid T2D and hypertension prevented them from engaging in any meaningful productive work, thereby indirectly worsening the cost burden. Consequently, patients mostly rely on the benevolence of family and friends for the management of their illness and general subsistence. Given the low socioeconomic status of the people of Ga Mashie coupled with the catastrophic direct medical cost of treatment, these direct non-medical and indirect costs exacerbate the burden on patients.

Psychosocial support helps to cope with the economic burden

The significant psychosocial burden imposed on people with NCDs cannot be underestimated [ 78 , 79 ]. Patients' inability to independently or substantially cater for themselves often poses psychological stress on them and their caregivers [ 60 ]. Like findings of a systematic review of experiences of people living with NCDs in Africa [ 60 ], the psychological changes T2D and hypertension comorbidity patients in Ga Mashie go through include depression, stress, guilt, anxiety, anger, confusion frustration, and fear of death. These adverse psychosocial experiences intangibly contribute to the cost burden and physical deterioration in underprivileged communities like Ga Mashie. This happens partly because the psychosocial burden imposed by the disease is often overlooked by health professionals notwithstanding its overwhelming consequences [ 80 ]. Social support is therefore the most viable option available for people living with the disease in Ga Mashie.

Consistent with prior literature on the experiences of people living with NCDs in Africa, the findings of this study show that primary caregivers and other family members as well as friends play significant roles in the healthcare and management of comorbid T2D and hypertension [ 65 , 81 , 82 ]. Particularly among the aged, there is always active support from partners, children, caregivers, and other family members in the management of the disease. The main psychosocial support provided includes financial, biological, emotional, spiritual, cultural, social, and mental. The support includes accompanying patients to health facilities and ensuring medical and dietary adherence. Respondents have attributed any semblance of good quality of life among people with T2D and hypertension comorbidity in Ga Mashie to the unwavering financial support from their families [ 83 ]. However, in the long run, the huge healthcare cost burden, loss of caregivers' productive hours, and disruption in family members’ routine socioeconomic activities lead to neglect of patients in a poor urban setting like Ga Mashie [ 60 ].

Policy and practice implications

Although the NHIS coverage has greatly expanded in Ghana over the years, the current modalities still offer limited protection against high healthcare expenditure for patients with comorbid T2D and hypertension. To address the high-cost burden of managing T2D and hypertension comorbidity, population-based interventions aimed at eliminating the catastrophic healthcare expenditure and strengthening health systems for the provision of effective biomedical care for those affected are essential. Policies should crucially consider reform of the NHIS benefits packages for NCDs to improve its potency for financial risk protection and reliability of biomedical care, particularly for people with T2D and hypertension comorbidity. These should consider subsidies/exemptions on medication and sensitization on the consequences of medical pluralism and NHIS coverage.

Study strengths and limitations

The major strength of this study is the triangulation of quantitative and qualitative data source that promoted a richer understanding of the findings. However, the small sample of respondents who provided complete cost data for the quantitative analysis is a limitation which may have reduced the precision of our cost estimates, and hinders generalizability of the findings. Future studies intent on measuring the economic cost of comorbid NCDs should consider larger sample sizes. Also, although the CARE-Diabetes project’s survey participants were selected using rigorous multi-stage sampling approach, females constituted over 80% of the subset data used for this analysis, suggesting likelihood of a highly biased sampling method. However, this may also be ascribed to women being more conscious of their health status – as cases of comorbid T2D and hypertension were self-reported. For the qualitative study, the thematic coding was done by one person—an approach which may have compromised the analysis. However, we made cautious efforts to maintain the internal validity of the data by having three of the authors check the transcripts to resolve any discordance between codes and global/organizing themes. Furthermore, there may not necessarily be a direct relationship between the qualitative and quantitative results presented due to the different populations (of living with T2D and hypertension) used, and thus possible variations in the degree of disease burden across the two groups.

The economic burden of managing T2D and hypertension comorbidity is significant in deprived urban Ghana. The burden weighs heavily on household budgets, thereby negatively affecting health and healthcare seeking patterns of patients. To alleviate the economic burden of medical care and promote appropriate therapy, the NHIS should prioritize free/affordable medical care for patients with NCDs to facilitate the effective management of T2D and hypertension comorbidity. Future research should consider using a larger sample size for the cost analysis and consider assessing the catastrophic health expenditure associated with healthcare (proportion of healthcare expenditure to household monthly food and non-food spending).

Availability of data and materials

The data and materials that support the findings of this study are available from the authors upon reasonable request.

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Acknowledgements

The work was supported by the Medical Research Council (MRC) through the United Kingdom Research and Innovation (UKRI), grant number MR/T029919/1. We are grateful to members of the CARE-Diabetes project team who helped execute this research work.

This research was funded by the United Kingdom Research and Innovation (UKRI)—Medical Research Council (MRC) through a Grant [reference MR/T029919/1]. The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of this manuscript.

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S,A., MA and H.H-B conceived the study. S.A., M.A., H.H-B and E.F. contributed to the methodology of the study. S.A., L.B., R.B.A., I.A.K., K.K., V.A-A, S.B.K., H.J., P.A., E.G. and D.K.A. contributed to the implementation of the study. SA and MA led the analyses with support from HHB, SKM and CGE. SA drafted the original manuscript with significant revisions from M.A., H.H-B, L.O., I.A.K., R.B.A., O.A.S., E.F., S.B.K., A.B., C.G-E, D.K.A., S.K.M., H.J., P.A., E.G. and K.K. All authors reviewed the final draft of the manuscript.

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Amon, S., Aikins, M., Haghparast-Bidgoli, H. et al. Household economic burden of type-2 diabetes and hypertension comorbidity care in urban-poor Ghana: a mixed methods study. BMC Health Serv Res 24 , 1028 (2024). https://doi.org/10.1186/s12913-024-11516-9

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Environmental impacts of extensive beef production in Colombia by life cycle assessment: a case study

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• Published: 07 September 2024

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• Sara Arcila 1 ,
• Natalia Correa 1 ,
• Sebastián Pachón 2 ,
• Luis Giraldo A. Valderrama 2 &
• Natalia A. Cano-Londoño   ORCID: orcid.org/0000-0003-4828-6442 3 , 4  

The increase in the negative effects of global change promotes the search for alternatives to supply the demand for food worldwide aligned with the Sustainable Development Goals (SDGs) to ensure food security. Animal protein, which is a main source of nutrients in the diet of today’s society, especially beef, which is one of the most demanded products nowadays, has been criticized not only for its high water consumption and land occupation for production but also for the emission of greenhouse gases (GHG) from enteric methane generated in the fermentation process within the bovine rumen and deforestation for the adaptation of pastures. This study is mainly motivated by the lack of quantifiable scientific information in Colombia on the environmental impacts of beef production. Therefore, it is intended to estimate some of the impacts of beef production in extensive systems using the life cycle assessment (LCA) method under a particular scenario considering all the production phases (from raw material to fattening, where the cattle are ready to be slaughtered). The study was conducted with data supplied by a farm in Antioquia, Colombia, and the functional unit (FU) was defined as 1 kg of live weight (LW). The scope of this study was gate-to-gate. “The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories” (IPCC  2006 ; IPCC 2019 ) was used to calculate methane and nitrous oxide emissions. LCA modeling was developed with Ecoinvent database v3.8 and the Umberto LCA + software. It was found that the most affected category of damage was ecosystem quality, which represents 77% of the total, followed by human health at 17% and resources at 6%. The category impact of agricultural land occupation is the one that represents the most significant contribution to the ecosystem quality endpoint, with a percentage of 87%, due to the soil’s compaction and the loss of the soil’s properties. Additionally, the obtained carbon footprint for the system was 28.9 kg of CO 2 -eq/kg LW.

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Acknowledgements

To the coauthors for their expertise and assistance throughout all aspects of our study and for their help in writing the manuscript.

To research group BIORUM of the Science Faculty of the National University of Colombia.

To research group Fenómenos de Superficie-Michael Polanyi of the Mining Faculty of the National University of Colombia.

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Sara Arcila & Natalia Correa

Grupo de Investigación BIORUM, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia−Sede Medellín, 050041, Medellín, Colombia

Sebastián Pachón & Luis Giraldo A. Valderrama

Grupo de Investigación en Fenómenos de Superficie-Michael Polanyi, Facultad de Minas, Universidad Nacional de Colombia−Sede Medellín, 050041, Medellín, Colombia

Natalia A. Cano-Londoño

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All authors contributed to the study’s conception and design. Sara Arcila, Natalia Correa, and Sebastián Pachón prepared materials, collected data, and analyzed them. Natalia A. Cano-Londoño and Luis Giraldo-Valderrama supervised them. Natalia Correa and Sebastián Pachón wrote the first draft of the manuscript, and all authors commented on previous versions. All authors read and approved the final manuscript.

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• Enteric methane emissions from manure management and direct/indirect nitrous oxide emissions from manure and soil management were calculated for suckling calves, growing, breeding stock, and fattening phases.

• LCA allows analyzing the environmental impacts of livestock farming in Colombia, which may not have been previously considered.

• This LCA supports environmental decision-making and formulating sustainable livestock policies and projects of best agricultural practices.

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Arcila, S., Correa, N., Pachón, S. et al. Environmental impacts of extensive beef production in Colombia by life cycle assessment: a case study. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-34463-8

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DOI : https://doi.org/10.1007/s11356-024-34463-8

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• Vishnu Prasad Pandey 3 &
• Pawan Kumar Bhattarai 4  

Environmental Systems Research volume  13 , Article number:  37 ( 2024 ) Cite this article

Metrics details

Studies on concrete dam breach are limited compared to earthen and other types of dams. With an increase in the construction of concrete dams, particularly in the developing world, it is imperative to have a better understanding of the dam breach phenomena and the identification of the most influential breach parameters. This study aims to contribute to this gap by taking the case of the concrete arch dam proposed for the 1200 MW Budhigandaki Hydropower Project located in central Nepal. This study carries special significance for Nepal, primarily because of the increasing number of under construction and proposed large dams for water resources development in the country. We carry out dam breach analysis of the Budhigandaki dam using HEC-RAS 2D model to calculate the flood discharge peaks, time to peak, water surface elevation and the extent of inundation for two scenarios (with and without probable maximum flood) to estimate the damage on four downstream settlements. We carry out sensitivity analysis of the breach parameters on the flood magnitudes and severity. Results show that all the study locations lie in the high flood hazard zone. Flood peaks can reach as high as 286,000 m 3 s − 1 to 511,000 m 3 s − 1 in the considered settlements. The time to peak ranges from 11.3 to 17 h after the breach at these locations. We estimate that if a breach should happen, it would most likely inundate around 150,000 buildings, impact nearly 672,000 lives and flood 3,500 km of road downstream. Furthermore, dam breach elevation is found to be the most sensitive parameter to downstream floods. Hence, rather than structural measures, it is recommended that non-structural measures are implemented for minimizing the impacts of flood disasters at the study locations. The findings could be a useful reference for future dam projects in Nepal and other areas with similar hydrological and topographical conditions.

Introduction

Dams are storage structures providing beneficial functions such as flood control and water supply for different types of users (for example, domestic water supply, hydropower, irrigation, recreation and water transport). The construction of large dams along with generation of electricity started during the industrial revolution in Europe and America. The early 1900s ushered in an era of “big dam” building in America mostly for hydropower generation as demands for electricity increased, the Hoover Dam being regarded as an engineering marvel. The Asian region includes some of the largest dams in the world today such as Tarbela Dam and Mangla Dam in Pakistan, Nurek Dam in Tajikistan, San Rogue Dam in Phillipines and Three Gorges Dam in China, mostly for hydropower generation.

Despite the benefits, failure of dams can cause tremendous losses by generation of unforeseen flood magnitudes in downstream areas. Unfortunately, the history of dams has been studded with disasters of various types, sometimes of great magnitude, with loss of human lives and destruction of property and infrastructure (Aureli et al. 2021 ). USACE ( 2018 ) lists causes of dam breach as earthquakes, landslides, extreme storms, piping, equipment malfunction, structural damage, foundation failure, and sabotage. Regardless of the reason, almost all failures begin with a breach formation.

Basically, breach is defined as the opening formed in the dam body that leads the dam to fail and this phenomenon causes the stored water behind the dam to propagate rapidly downstream (Dincergok 2007 ). Despite piping or overtopping being the main modes of dam failure, the actual mechanics are still not completely understood for either earthen or concrete dams (USACE 2018 ). Past dam-failure disasters have shown that the majority of dams that have failed are earthen (74 dam breaks out of 7812 earthen dams) and the highest percentage of failure of rockfill dams (17 dam breaks out of 200 rockfill dams) (Fang et al. 2017 ). The world’s worst dam disaster happened in China in 1975 when the Banqiao and Shimantan dams failed killing about 171,000 people while 11 million lost their homes (Vincent et al. 2020 ). In 1979, the 25 m high Machu Dam in India, which stored 100 million m 3 , failed after several hours of over-topping causing about 10,000 deaths, 150,000 people were displaced, and 10,000 habitations were destroyed (Lempérière 2017 ). A recent case of the failure of the Rishiganga dam in Uttarakhand (India) in 2021 due to glacier avalanche caused more than 200 deaths and severely damaged infrastructure (Shugar et al. 2021 ). Similarly, failure of the Edenville dam followed by the Sanford dam downstream on the same day in 2020 due to heavy rain in Michigan USA ( Independent Forensic Team 2022 ), and failure of the Spencer Dam in Nebraska USA in 2019 due to ice run (Ettema et al. 2021 ), demonstrate the devastation that dam breaches can lead to. Thus, identification of the vulnerable areas and being aware of the likely damages are key for minimization of the adverse impacts of dam breach.

Dam breach analysis involves three key sequential steps: predicting the reservoir outflow hydrograph, determining dam breach parameters, and routing the hydrograph downstream. Essentially, the breach flood hydrograph depends on the prediction of breach geometry and breach formation time (Basheer et al. 2017 ). There have been many studies on dam breach analysis around the world from the 1980’s (Leng et al. 2023 ; Singh and Snorrason 1984 ; USACE 2024 ). Dam breach analysis is generally carried out by either numerical/computer models or scaled-down physical models. The United States Department of Interior ( 1988 ), recommends estimating a reasonable maximum breach discharge using four principal methods:

Physically Based Methods: Using erosion models based on principles of hydraulics, sediment transport and soil mechanics, development of breach and resulting breach outflow are estimated;

Parametric Models: Time to failure and ultimate breach geometry are assessed utilizing case studies; breach growth is simulated as a time-dependent linear process and breach outflows are computed using principles of hydraulics;

Predictor Equations: Using data of case studies, peak discharge is estimated from empirical equations and a reasonable shape of outflow hydrograph is assumed; and.

Comparative Analysis: Breach parameters are determined by comparison of dam under consideration and a dam that failed.

There are far fewer studies on the failures of concrete dams compared to earthen dams, especially due to breaches which leads to difficulty in determining the concrete dam breach parameters (Fang et al. 2017 ). Moreover, a study of well documented dam-failure cases showed that empirical formulas provide results closer to reality (Fang et al. 2017 ). For instance, Froehlich( 1995 ) developed a prediction equation for the average breach width based on 63 cases of embankment-dam failures and an equation for the breach-formation time based on 21 cases. Focusing on earthen dams has been driven by their historical prevalence, cost-effectiveness, and adaptability. However, studying concrete arch dams is crucial for advancing engineering practices, improving safety and efficiency in dam construction, supporting hydroelectric power generation, addressing environmental impacts, and preserving significant cultural landmarks. Many federal agencies such as FERC ( 1993 ),Office of the State Engineer( 2020 ) and USACE ( 2014 ) have published guidelines recommending possible ranges of values for breach width, side slopes, and development time for different types of dams. This study aims to investigate the breach characteristics of concrete arch dams, an area with limited existing literature. Several dam breach analysis studies have been carried out in Nepal such as in Kulekhani dam using HEC-RAS (Pandey et al. 2023 ), Kaligandaki landslide dam using BREACH (Bricker et al. 2017 ), Koshi high dam using HEC-RAS (Gyawali, D.R. and Devkota, 2015 ), among others. However, no sensitivity analysis of dam breach parameters has been carried out for the afore-mentioned studies.

The proposed Budhigandaki dam located in the transboundary Budhigandaki Basin, spread over southern China and central Nepal, is taken as a case. The Government of Nepal (GoN) has prioritized hydropower generation as the backbone of economic development to attain the goals to raise the country’s status to middle income country level by 2030 (Government of Nepal 2020 ). As a result, there are currently more than 9 planned and proposed large hydropower dam projects by the state (Nepal Electricity Authority 2022 ). The Budhigandaki Hydropower Project (BGHPP) could be the largest storage project of Nepal, if constructed, which could lead to catastrophic damages downstream in the event of a breach.

Hence, the overarching objective of this study is to assess the flood impacts of the Budhigandaki Dam on the downstream settlements due to possible dam breach scenarios. Specifically, this study intends to quantify the peak discharge, time to peak, and the water surface elevation at the downstream locations due to a dam-breach flood. Further, sensitivity analysis of five different dam breach parameters is conducted to acquire information about extent of influence of each parameter on the dam breach. The analysis is carried out in the widely-used hydraulic model Hydrologic Engineering Center’s - River Analysis System (HEC-RAS) developed by the United States Army Corps of Engineers (USACE). Furthermore, zoning of the downstream settlement areas in Geographic Information System (GIS) based on flood severity provides meaningful information to the project developers as well as planners in the impacted areas.

Materials and methods

The Budhigandaki Hydropower Project (BGHPP) is a 1200 MW storage type proposed project of Nepal located approximately 2 km upstream of the confluence of Budhigandaki River with Trishuli River as shown in Fig.  1 . The Budhigandaki Dam is a 263 m high double curvature concrete arch dam with a reservoir volume of 4.5 billion cubic meters (BCM), out of which the active storage is 2.2 BCM. The dam crest length is 737.4 m and the reservoir Full Supply Level (FSL) is at 540 m above sea level (masl) (Budhigandaki Development Committee, 14a). There are some major settlement areas nearly 110 km downstream which are susceptible to danger in case of dam breach. For this study, four major towns namely, Narayangarh, Baraghare, Divyanagar and Meghauli, have been assessed. Moreover, future risk of impact from the dam failure can be expected to increase as increased in population growth due to improved job opportunities and other economic activities in the area because of the construction of the dam. Therefore, the Budhigandaki Dam has been taken as a case in this study to assess the flooding impacts of the dam on the downstream areas through simulation of a hypothetical dam failure.

Location of Budhigandaki dam and downstream settlement areas

Methodology

Dam breach analysis of the Budhigandaki dam has been carried out in HEC-RAS using unsteady flow simulation with terrain and land cover as the geometric input data. The upstream boundary condition is the probable maximum flood (PMF) hydrograph which has been generated using an empirical method while the downstream boundary condition is normal depth. Two dam failure scenarios, namely, dam breach at reservoir full condition with PMF (Scenario I: base case) and dam breach at reservoir full condition without PMF (Scenario II), have been modelled in the study. Outputs of the simulation are used for creating flood inundation maps, flood hazard vulnerability maps and flood arrival time maps corresponding to the different scenarios. Sensitivity analysis of the dam breach parameters is also carried out to assess their impacts on the flood conditions downstream of the dam. Figure  2 summarizes the overall research methodology.

Overall research methodology of this study. DEM: Digital Elevation Model, PMP: Probable Maximum Precipitation, PMF: Probable Maximum Flood, SA: Storage Area, 2D: Two Dimensional, FSL: Full Supply Level

The spatial inputs required to model the dam breach are digital elevation model (DEM), land cover and Manning’s roughness coefficient. Rainfall and discharge are needed for generation of inflow hydrograph as upstream boundary condition to the model. In addition, infrastructure data of the downstream area is required for estimating the impacts of floods. Details of the required data and their sources are presented in Table  1 .

PMP and PMF

The probable maximum precipitation (PMP) is the theoretical maximum precipitation for a given duration under current meteorological conditions (World Meteorological Organization 2009 ). Daily maximum rainfall data of 13 surrounding stations from 1972 to 2014 has been used for the calculation of PMP. The 1-day PMP for all the stations was calculated using Hershfield formula (Hershfield 1965 ) given in Eq. ( 1 ) :

Where, PMP  = Probable maximum precipitation.

M  = mean of maximum daily rainfall sample S  = Standard deviation.

K  = Frequency factor = 15 (Hershfield 1965 ).

The calculated 1-day PMP of the point stations was further interpolated using Thiessen Polygon, Kriging, Spline and Inverse Distance Weighing (IDW) methods in GIS to compute the 1-day PMP for the Budhigandaki Basin. In order to model a worst-case scenario, the maximum value of the PMP among these methods was chosen for generating the PMF hydrograph.

Probable Maximum Flood (PMF) is theoretically the flood resulting from a combination of the most severe meteorological and hydrologic conditions that could conceivably occur in a given area (FERC 2001 ). HEC-RAS requires a flood hydrograph to be provided as input for the unsteady flow analysis in the dam breach model. Therefore, a synthetic unit hydrograph was developed using Snyder’s Method (American Geophysical Union 1938 ) using the following equations (Eq. ( 2 ) to Eq. ( 7 ) which was then transposed to generate a direct runoff hydrograph of PMF.

Mathematically,

Dam breach analysis

Dam breach analysis of the Budhigandaki dam has been carried out in HEC-RAS model under two-dimensional dynamic (unsteady-flow) mode. Hypothetical breach of the dam and its propagation downstream has been modelled using 2D Diffusion wave equations (Eq. ( 8 ) to Eq. ( 10 )).

Where, h is the water depth (m), p and q are the specific flow in the x and y directions (m 2 s − 1 ), ζ is the surface elevation (m), g is the acceleration due to gravity (9.8 m s − 2 ), n is the Manning’s coefficient, ρ is the water density (1000 kg m − 3 ), τ xx , τ yy , and τ xy are the components of the effective shear stress along x and y directions (N m − 2 ), and f is the Coriolis (s − 1 ).

Two-dimensional (2D) mesh of size 100 m x 100 m was chosen to represent the downstream land. Comparison of different mesh sizes (100 m and 200 m) indicated no significant difference in model performance. The storage areas and downstream areas are connected using an inline structure (Budhigandaki dam) as shown in Fig.  3 . “Storage Area” refers to upstream reservoir of the dam axis while “Downstream Study Area” represents the four towns (Narayangarh, Baraghare, Divyanagar, and Meghauli) located downstream which are likely to be inundated in case of dam breach (BGHP, 2015). Boundary conditions are required at the upstream and downstream ends of the model for flood routing. The upstream boundary was fixed at the reservoir extent (storage area) and the boundary condition was provided in the form of flood hydrograph generated from PMF. Outlet is the downstream boundary past the settlement areas as shown in Fig.  3 while the boundary condition of normal depth is maintained by providing the river bed-slope obtained from the DEM.

HEC-RAS 2D flow area and model schematic for the flood simulation of Budhigandaki dam breach

Scenarios and sensitivity analysis

In order to quantify the downstream effects of the Budhigandaki dam breach, the following two scenarios have been simulated:

Scenario 1: Dam breach when reservoir is at FSL with PMF. Scenario 2: Dam breach when reservoir is at FSL.

Only overtopping breach mode was analyzed as the dam is made up of concrete and there are less chances of other failure modes (Zhang et al. 2016 ). Moreover, for better understanding the Budhigandaki dam breach mechanism and impacts, sensitivity analysis of the following five important breach parameters as breach bottom elevation, breach bottom width, breach weir coefficient, breach formation time and breach side slope was carried out by varying their values over a reasonable range obtained from literature.

Scenario I have been considered as the base case. Sensitivity of the above-mentioned breach parameters on flood peak discharge, water surface elevation and flood arrival time at the four downstream locations along with inundation area are analyzed considering the base case.

The inputs for the dam break analysis adopted for the base case i.e., Scenario I is listed in the Table  2 . The values of breach parameters have been derived from FERC ( 1993 ), Office of the State Engineer ( 2020 ) and USACE ( 2014 ) specific for concrete dams.

Flood characteristics from 2D simulations

Using RAS Mapper, a series of flood maps were generated based on the outputs of the 2D simulation of the Scenario I dam breach. These maps were helpful in identifying the potentially risky and safe areas. The outputs of the HEC-RAS model were exported to GIS for further analysis and mapping.

Maximum Flood depth map

Using the simulation results, flood inundation maps were prepared illustrating the maximum flood depths across the study area for the different scenarios.

Flood Hazard Vulnerability Map : A flood hazard vulnerability map based on the product of depth and velocity was prepared using the Australian Rainfall-Runoff Guidelines (Australian Rainfall and Runoff 2019 ) which categorize the flood in six zones as: H1 ( D*V ≤  0.3, D max = 0.3 m, V max = 2.0 m/s, safe for people, vehicles and buildings); H2 ( D*V ≤  0.6, D max = 0.5 m, V max = 2.0 m/s, unsafe for small vehicles); H3 ( D*V  ≤ 0.6, D max = 1.2 m, V max = 2.0 m/s, unsafe for vehicles, children and elderly); H4 ( D*V  ≤ 1.0, D max = 2.0 m, V max = 2.0 m/s, unsafe for people and vehicles); H5 ( D*V  ≤ 4.0, D max = 4.0 m, V max = 4.0 m/s, unsafe for people and vehicles, buildings vulnerable to structural damage) ; H6 ( D*V >  4.0, unsafe for people and vehicles, all buildings vulnerable to failure) where D and V refer to the flood depth and velocity, respectively while D max and V max refers to the maximum depth and maximum velocity, respectively.

Flood arrival Time Map

Flood arrival time maps represent the computed time (in hours or days) from a specified time in the simulation when the water depth reaches a specified inundation depth. For the case of Budhigandaki dam breach, flood arrival times at the four settlement areas were calculated and mapped.

Estimated values of PMP and PMF

The 1-day PMP value using the 13 precipitation stations was calculated to be 518 mm, 530 mm, 556 mm and 485 mm using Thiessen polygon, Kriging, inverse distance weighted (IDW), and Spline interpolation methods, respectively. As a worst-case scenario, we chose the IDW method, which gave the maximum value of PMP among the four methods, for generating the PMF hydrograph. Using the input data listed in the Appendix 1, ordinates of the synthetic unit hydrograph was computed using Snyder’s method as shown in Fig.  4 .

Synthetic Unit Hydrograph and Probable Maximum Flood Hydrograph for the Budhigandaki dam

From the synthetic unit hydrograph and rainfall intensity duration curve, Direct Runoff Hydrograph was generated. The flood values are generated for a 60-minute interval by linear interpolation between the ordinates of the unit hydrograph. August is the month with the highest flows at the Budhigandaki dam site. Therefore, base flow of 441 m 3 s − 1 which is the mean August flow (during 1964–2012) was added to obtain the final hydrographs (BGHPP Development Committee 2014b ). The final results are plotted in Fig.  4 . I t can be seen that the peak discharge of 11,669 m 3 s − 1 occurs at 33.9 h after the start of rainfall for PMF + base flow.

Flood depth and flood hazard vulnerability

The river valley of 110 km length from Budhigandaki dam to Meghauli was considered for the analysis. The maximum flood depth Fig.  5 shows that the flood depth is as high as 212 m in the upstream area as the river channel is narrow whereas the depth becomes lesser in the downstream river sections where the area is relatively wide and plain. The maximum water depths at Narayangarh is estimated to be 90 m followed by 50.3 m at Baraghare.

Flood Inundation Map Based on Maximum Depths

Similarly, Flood Hazard Vulnerability Map based on the depth and velocity was prepared as shown in Fig.  6 . It can be identified from the map that all the downstream area lies in H6 zone i.e., unsafe for people and vehicles and all buildings are vulnerable to failure.

Flood Hazard Vulnerability Mapping Based on Depth and Velocity

Flood arrival time

Simulated flood peak arrival times calculated at the four downstream settlement areas are shown in Fig.  7 . It is useful in designing of early warning systems at these locations. It can be seen that the travel times range from 11.3 h (Narayangarh) to 17 h (Meghauli) immediately after the dam breach depending on the proximity from the dam.

Flood arrival time for the major downstream settlement locations; D/S is downstream

Flood inundation across different land covers

As an impact of dam breach on land cover, it is seen that the inundated type to be most likely inundated is agricultural area (538 km 2 ). Similarly, 239 km 2 of forest is likely to be inundated second in rank. Grassland, water body, barren area, built-up area and shrub land are expected to be inundated with areas of 43 km 2 , 38 km 2 , 25 km 2 , 22 km 2 and 1.5 km 2 respectively as shown in Fig.  8 .

Inundation extent due to dam breach by land cover

Flood Impact on Water Surface Elevation (WSE) and peak discharge

Water surface elevations along the modelled river reach corresponding to the two scenarios are shown in Fig.  9 . It is seen that the water surface is nearly 110 m above the bed level at immediate downstream of the dam site while it is as low as 30 m in the downstream study areas. There is an enormous volume of water flowing down in a very short time because of the breach resulting in such high values of water depths along the river reach. There is very less change in the water surface elevation between Scenario-1 and 2. Also, at the settlement areas, the flow width is large i.e., flat plain area and hence lesser change is seen on the water surface elevation at downstream areas.

Profile of water surface elevation and river bed for Scenario I and Scenario II. Scenario I: Dam Breach at FSL with PMF and Scenario II: Dam Breach at FSL without PMF

For the two scenarios (Scenario-1 and Scenario-2), the flow hydrographs have been compared at immediate downstream of the dam and at the four major settlement locations as shown in Fig.  10 . It is to be noted that the peak discharge occurs nearly at the same time for both scenarios at all locations. At Narayangarh, peak discharges for Scenarios-1 and 2 are 511,587 m 3 s − 1 and 501,479 m 3 s − 1 respectively i.e., around 2% of difference in the value. Similarly, at Baraghare, the peak discharge for Scenario-1 is 454,267 m 3 s − 1 whereas 441,862 m 3 s − 1 for Scenario-2 and for Divyanagar, the peak discharge for Scenario-1 is 364,697 m 3 s − 1 whereas 357,294 m 3 s − 1 for Scenario II respectively. Lastly for Meghauli, the peak discharge for Scenario-1 is 294,928 m 3 s − 1 whereas 286,813 m 3 s − 1 for Scenario-2. It is obvious that the peak discharge for Scenario-1 is greater than that of Scenario-2, however, the differences in the peak values between the two scenarios are quite small (in the range of 2–3%). This implies that the storage volume of the dam is the major contributor to the flood discharge rather than the PMF.

Comparison of flood hydrographs at major study locations for Scenario I and Scenario II. Scenario I: Dam Breach at FSL with PMF and Scenario II: Dam Breach at FSL without PMF

Flood impact on infrastructure

The possible impact of inundation due to dam breach on buildings and roads was assessed. The total road length includes several types of roads such as highways, feeder roads, district roads and local roads. The inundated highway road length has been computed separately and all other types of roads has been kept as other roads (Table  3 ). It can be seen that Chitwan is the most impacted district with 58.5% of buildings and 2,541 km of road likely to be inundated. Meanwhile, Gorkha is expected to be the least affected district with 2.6% buildings and 132.4 km road inundated. Also, 149,311 numbers of buildings are inundated in total. If the total number of persons on average per household is taken as 4.5 (Cental Bureau of Statisitics 2016 ), a total of about 0.7 million people are likely to be affected by inundation in the case of dam breach. This is about 2.3% of the total population of Nepal.

• Sensitivity analysis

Sensitivity analysis was performed in order to estimate the impact of the breach parameters on the simulated floods in the downstream impacted areas. The values of the input breach parameters were changed within a reasonable range, one at a time, in the dam breach model and the corresponding values of the peak discharge, water surface elevation, flood arrival time and land inundation area were recorded. Breach bottom elevation was varied from 450 masl to 525 masl. Similarly, breach width was varied from 55 m to 150 m and breach weir coefficient was varied from 0.9 to 1.7. Also, breach formation time was varied from 0.05 h to 0.3 h and breach side slope was varied from 0.7:1 to 2.5:1 ( H : V ). Results of the sensitivity analysis have been presented in Table  4 .

Breach bottom elevation

It is seen from Table  4 that as the breach bottom elevation is increased from 450 masl to 525 masl, the value of peak discharge and WSE are significantly decreased at the different downstream locations. It is observed that a 30% increase in breach bottom elevation (450 masl to 475 masl) led to 20–35% decrease in peak discharge, 20–25% decrease in WSE at different downstream locations and nearly 30% decrease in inundation area (893 km 2 to 735 km 2 ). However, the flood peak arrival time is not much altered due to change in breach bottom elevation.

Breach bottom Width

It is seen from Table  4 that an increase in breach width from 55 m to 150 m corresponds to an increase in discharge, WSE and inundation area but the change is not as significant as compared to that of change in breach bottom elevation. A 30% increase in breach width (80 m to 105 m) led to nearly 3% increase in peak discharge at all downstream locations. However, not much change is seen on the WSE, flood arrival time and inundation area due to change in breach bottom width.

Breach weir coefficient

An increase in the breach weir coefficient from 0.9 to 1.7 led to increase in discharge, WSE and inundation area but with a smaller magnitude compared to that of change in breach bottom elevation (Table  4 ). A 20% increase in breach weir coefficient (1.44 to 1.7) led to nearly 3% increase in peak discharge at all downstream locations. Also, no significant change is seen on the WSE, flood arrival time and inundation area due to change in breach weir coefficient.

Breach formation time

Interestingly, there is very insignificant change in peak discharge, WSE, flood arrival time and inundation area due to varying breach formation time (Table  4 ). The values of peak discharge, WSE, flood arrival time and inundation area remain almost unchanged despite the breach formation time is increased up to 200% (0.1 h to 0.3 h).

Breach side slope

A 50% increase in the side slope (1.3:1 to 2:1) led to nearly 2–3% increase in peak discharge as shown in Table  4 . Also, no significant change is seen on the WSE, flood arrival time and inundation area due to change in breach side slope.

Thus, results of the sensitivity analysis varying the values of the breach parameters, namely, dam breach bottom elevation, breach bottom width, breach weir coefficient, breach formation time and breach side slope on the peak discharge, WSE, flood arrival time and downstream inundation area has been summarized in Table  5 . It can be seen that dam breach bottom elevation is the most sensitive parameter with respect to output values such as peak discharge, WSE and downstream inundation area while breach formation time is the least sensitive parameter with respect to all the output parameters.

We have estimated the PMP followed by PMF which is the upstream boundary condition required for the dam breach model in HEC-RAS. The PMP value was chosen as 556 mm from the IDW method. Also, the PMP value as per the detail design report (BGHPP Development Committee 2014b ) is 594 mm. Both the values of PMP are generated using Hershfield formula. However, this slight variation in the PMP values is due to the difference in the values of frequency factor. The value of frequency factor in this study is taken as 15 (Hershfield 1965 ). Subsequently, the PMF value for this study is generated using Snyder’s Unit Hydrograph Method with peak discharge as 11,669 m 3 s − 1 . Besides, by using regional method the PMF was calculated to be 11,479 m 3 s − 1 and regional regression flood analysis method 11,957 m 3 s − 1 (Department of Electricity Development 2006 ). Hence, the PMF values considered in this study are assumed to be reliable.

Impacts of dam breach and sensitivity analysis of dam breach parameters

Simulation results of Scenario I and Scenario II showed that there is a huge peak discharge immediately downstream of the dam breach (Fig.  10 and the difference in discharge values for both scenarios is low. The reason for this is due to the large storage volume of the dam leading to minimum effect of PMF being observed. Also, the downstream tributaries are much smaller compared to the Budhigandaki mainstream river. Hence, their additional impacts on the dam breach flood magnitudes can be considered to be marginal. Additionally, the outputs such as peak discharge, WSE, flood arrival time and inundation area from the dam breach has been estimated as a standalone event. The impact of addition of inflows from the other tributaries (for example, due to localized cloudburst events) to the mainstream river in the downstream settlement area could be areas of further study.

Previous dam breach analysis on Budhigandaki dam has been carried out by Tractebel and jade consult as JV using TELEMAC software (BGHPP Development Committee 2014a ). The output results of the previous study appeared to be quite different from the study carried out using HEC-RAS. There could be various reasons for such discrepancies. The TELEMAC model has considered full dam breach whereas our study does not consider full dam breach. Also, the earlier model has considered high accuracy resolution LiDAR data and other input data (mesh size 30 m*50 m) whereas our study considers 30 m*30 m DEM data and 100 m*100 m mesh size due to model stability issues. However, the pattern of change in peak discharge and WSE at the different study locations are quite similar for both models.

Dam breach analysis has been carried out in different parts of the world using HEC-RAS adopting a methodology similar to ours. For example, simulations of the breach of Batutegi earthen Dam, Indonesia (Wahyudi 2004 ), Mosul earthen Dam, Iraq (Basheer et al. 2017 ) and the results of sensitivity analysis are found out to be quite similar to this study. All these studies showed that dam breach bottom elevation is the most sensitive parameter. Further, the trends in WSE and peak discharge with time and distance from the dam obtained in these studies are also comparable to those of our study. The WSE and peak discharge increased with the increase in the breach parameters as breach bottom elevation, breach bottom width, breach weir coefficient and breach side slope. The peak discharge decreased with increase in breach formation time and negligible change was seen on WSE. Hence, through sensitivity analysis, it is seen that dam breach bottom elevation is the most sensitive parameter while breach formation time is the least sensitive parameter with regards to the floods.

Challenges to flood management

This analysis of a hypothetical dam breach provides insight to the level of possible damage should such a breach occur. Also, it can be deduced from this study that construction of embankments along the river is not a practical mitigation measure because of the extremely high-water depths (nearly 90 m) that these structures need to retain within them. Hence, other non-structural preventive measures such as creating awareness regarding flood risks, community-based flood early warning system (CBFEWS), training and deployment of efficient disaster response teams, zoning of high-risk areas, avoiding construction/settlements in such areas, identification of evacuation centers etc. are recommended. The Yokohama Strategy and Plan of Action (World Conference on Natural Disaster Reduction 1994 ), Hyogo Framework for Action 2005–2015 (International Strategy for Disaster Reduction 2005 ), and the current Sendai Framework for Action 2015–2030 (United Nations 2015 ) highlight the importance of early warning in reducing disaster risk and enhancing the resilience of vulnerable communities. CBFEWS generates and disseminates meaningful and timely flood warnings to vulnerable communities threatened by flood, so they can prepare and act correctly in sufficient time to minimize the possibility of harm. Owing to non-structural measures, the response and adaptation to floods of the vulnerable communities vary widely and are impacted upon by various factors, such as community resilience and susceptibility to flood. Also, the effectiveness of the non-structural measures appears sensitive to the socio-economic changes and governance arrangements (Dawson et al. 2011 ). Nonetheless, non-structural measures provide flexible flood management options for adapting to the ever-changing river basins, socio-economic and climate scenarios, and are in line with the spirit of environment friendly and sustainable development (Shah et al. 2018 ). Also, research on identification of shelter areas and evacuation plan can be an extension of this study using network analysis, buffers and proximity analysis in GIS. Moreover, the sensitivity analysis depicts the most sensitive breach parameters which need to be considered with extreme importance during planning, design, construction and operation of the dam.

Conclusions

This paper simulated the dam breach scenarios of the proposed Budhigandaki dam in central Nepal using HEC-RAS and assessed the impacts on the downstream settlements. Flood peaks, water surface elevations and flood arrival times were calculated for the two scenarios with and without PMF. In addition, sensitivity analysis was carried out to examine the influence of the breach parameters on the flood characteristics.

Results show that the entire downstream area lies in high hazard zone with flood arrival times at Narayangarh, Baraghare, Divyanagar and Meghauli ranges from 11.3 h to 17 h. Moreover, a total of 1,49,311 number of buildings are prone to inundation in the case of dam breach along with 671,900 lives at risk and around 3,500 km stretch of road most likely to be severely damaged. The dam-break flood peak exceeds 650,000 m 3 s − 1 in the immediate downstream of the dam while it attenuates to 511,000 and 286,000 m 3 s − 1 at Narayangarh and Meghauli, respectively. The maximum depth of water ranges from 30 m (in the downstream flat areas) to 212 m (in the upstream steep gorges) clearly discarding the physical and economic feasibility of structural measures for flood management in this case. In addition, 538 km 2 of agricultural land and 25 km 2 of built-up land is at risk of flood inundation. Therefore, it is imperative to implement preventive and non-structural measures such as creating awareness regarding flood risks, developing community-based flood early warning system (CBFEWS), training and deployment of efficient disaster response teams, zoning of high-risk areas, avoiding construction/settlements in such areas, identification of evacuation centers, monitoring and constant auscultation of the structure and developing robust and efficient emergency and alert plans.

Furthermore, the differences in the peak discharges and water surface elevations between the two scenarios are very less at the study locations. This implies that the impact of the huge storage volume of the reservoir on the breach flood characteristics is considerably larger in comparison to the PMF. In addition, change in dam breach bottom elevation was found to be the most sensitive to floods compared to other dam breach parameters.

Additionally, the methodology applied in this study is conveniently replicable of other dams, large or small. However, the simulation run-times may vary depending upon the size of the dam, mesh size, simulation time step and other model complexities. It is to be noted that the case may change for snow fed rivers and glacier lakes. Also, while applying this method to other projects, one should always be careful about the boundary conditions and the initial values of dam breach parameters as they vary depending upon the dam under consideration.

Nepal has currently only one storage dam hydropower project (Kulekhani) in operation. With a greater number of storage projects being planned and under construction, this study could be a useful reference for such future projects. Moreover, this study provides interesting results particularly related to the sensitivity of the breach parameters of concrete arch dams, which could be applicable in study of similar dams in other regions of the world.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Catchment Area (km2)

Peak flow coefficient (-)

Lag Coefficient (-)

Coriolis (s − 1 )

acceleration due to gravity (m s − 2 )

water depth (m)

Frequency Factor (-)

main channel length from basin outlet to upstream watershed boundary (km)

main channel length from outlet to a point nearest to centroid of watershed (km)

Mean of Maximum daily rainfall (mm)

Manning’s Coefficient (-)

Specific flow in x-direction (m 2 s − 1 )

Probable maximum precipitation (mm)

Discharge (m 3 s − 1 )

Specific flow in y-direction (m 2 s − 1 )

Unit peak discharge (m 3 s − 1 )

Standard Deviation (mm)

Base time (hours)

Rainfall excess duration time (hours)

Basin Lag time (hours)

Width of unit hydrograph at discharge value exceeded 50% of the peak discharge (hours)

Width of unit hydrograph at discharge value exceeded 75% of the peak discharge (hours)

Surface Elevation (m)

Water Density (kg m − 3 )

Effective Shear Stress (N m − 2 )

Effective Shear Stress along x direction (N m − 2 )

Effective Shear Stress along x and y direction (N m − 2 )

Effective Shear Stress along y direction (N m − 2 )

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Acknowledgements

We wish to express my very deepest thanks and gratitude to Mr. Shreeram Shrestha, Civil Engineer, Chilime Hydropower Company Limited, Nepal for his continuous guidance, inspiration and encouragement during the initial preparation of building HEC-RAS model to result interpretation and completion of this study.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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A.A. and P.K.B. devised the project, the main conceptual ideas, and the proof outline. A.A. worked out almost all of the technical details, prepared figures, and performed the model analysis for the suggested topics. A.A., P.K.B, and U.B. verified the numerical results. A.A. and V.P.P. interpreted the Results. A.A. with the help of U.B., P.K.B., and V.P.P. wrote the manuscript. U.B., P.K.B., and V.P.P. worked on the discussion of results and commented on the manuscript. A.A. finalizes the manuscript after all the edits.

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Awal, A., Bhattarai, U., Pandey, V.P. et al. Downstream impacts of dam breach using HEC-RAS: a case of Budhigandaki concrete arch dam in central Nepal. Environ Syst Res 13 , 37 (2024). https://doi.org/10.1186/s40068-024-00358-3

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Green spaces provide substantial but unequal urban cooling globally

• Yuxiang Li 1 ,
• Jens-Christian Svenning   ORCID: orcid.org/0000-0002-3415-0862 2 ,
• Weiqi Zhou   ORCID: orcid.org/0000-0001-7323-4906 3 , 4 , 5 ,
• Kai Zhu   ORCID: orcid.org/0000-0003-1587-3317 6 ,
• Jesse F. Abrams   ORCID: orcid.org/0000-0003-0411-8519 7 ,
• Timothy M. Lenton   ORCID: orcid.org/0000-0002-6725-7498 7 ,
• William J. Ripple 8 ,
• Zhaowu Yu   ORCID: orcid.org/0000-0003-4576-4541 9 ,
• Shuqing N. Teng 1 ,
• Robert R. Dunn 10 &
• Chi Xu   ORCID: orcid.org/0000-0002-1841-9032 1  

Nature Communications volume  15 , Article number:  7108 ( 2024 ) Cite this article

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• Climate-change mitigation
• Urban ecology

Climate warming disproportionately impacts countries in the Global South by increasing extreme heat exposure. However, geographic disparities in adaptation capacity are unclear. Here, we assess global inequality in green spaces, which urban residents critically rely on to mitigate outdoor heat stress. We use remote sensing data to quantify daytime cooling by urban greenery in the warm seasons across the ~500 largest cities globally. We show a striking contrast, with Global South cities having ~70% of the cooling capacity of cities in the Global North (2.5 ± 1.0 °C vs. 3.6 ± 1.7 °C). A similar gap occurs for the cooling adaptation benefits received by an average resident in these cities (2.2 ± 0.9 °C vs. 3.4 ± 1.7 °C). This cooling adaptation inequality is due to discrepancies in green space quantity and quality between cities in the Global North and South, shaped by socioeconomic and natural factors. Our analyses further suggest a vast potential for enhancing cooling adaptation while reducing global inequality.

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Introduction.

Heat extremes are projected to be substantially intensified by global warming 1 , 2 , imposing a major threat to human mortality and morbidity in the coming decades 3 , 4 , 5 , 6 . This threat is particularly concerning as a majority of people now live in cities 7 , including those cities suffering some of the hottest climate extremes. Cities face two forms of warming: warming due to climate change and warming due to the urban heat island effect 8 , 9 , 10 . These two forms of warming have the potential to be additive, or even multiplicative. Climate change in itself is projected to result in rising maximum temperatures above 50 °C for a considerable fraction of the world if 2 °C global warming is exceeded 2 ; the urban heat island effect will cause up to >10 °C additional (surface) warming 11 . Exposures to temperatures above 35 °C with high humidity or above 40 °C with low humidity can lead to lethal heat stress for humans 12 . Even before such lethal temperatures are reached, worker productivity 13 and general health and well-being 14 can suffer. Heat extremes are especially risky for people living in the Global South 15 , 16 due to warmer climates at low latitudes. Climate models project that the lethal temperature thresholds will be exceeded with increasing frequencies and durations, and such extreme conditions will be concentrated in low-latitude regions 17 , 18 , 19 . These low-latitude regions overlap with the major parts of the Global South where population densities are already high and where population growth rates are also high. Consequently, the number of people exposed to extreme heat will likely increase even further, all things being equal 16 , 20 . That population growth will be accompanied by expanded urbanization and intensified urban heat island effects 21 , 22 , potentially exacerbating future Global North-Global South heat stress exposure inequalities.

Fortunately, we know that heat stress can be buffered, in part, by urban vegetation 23 . Urban green spaces, and especially urban forests, have proven an effective means through which to ameliorate heat stress through shading 24 , 25 and transpirational cooling 26 , 27 . The buffering effect of urban green spaces is influenced by their area (relative to the area of the city) and their spatial configuration 28 . In this context, green spaces become a kind of infrastructure that can and should be actively managed. At broad spatial scales, the effect of this urban green infrastructure is also mediated by differences among regions, whether in their background climate 29 , composition of green spaces 30 , or other factors 31 , 32 , 33 , 34 . The geographic patterns of the buffering effects of green spaces, whether due to geographic patterns in their areal extent or region-specific effects, have so far been poorly characterized.

On their own, the effects of climate change and urban heat islands on human health are likely to become severe. However, these effects will become even worse if they fall disproportionately in cities or countries with less economic ability to invest in green space 35 or in other forms of cooling 36 , 37 . A number of studies have now documented the so-called ‘luxury effect,’ wherein lower-income parts of cities tend to have less green space and, as a result, reduced biodiversity 38 , 39 . Where the luxury effect exists, green space and its benefits become, in essence, a luxury good 40 . If the luxury effect holds among cities, and lower-income cities also have smaller green spaces, the Global South may have the least potential to mitigate the combined effects of climate warming and urban heat islands, leading to exacerbated and rising inequalities in heat exposure 41 .

Here, we assess the global inequalities in the cooling capability of existing urban green infrastructure across urban areas worldwide. To this end, we use remotely sensed data to quantify three key variables, i.e., (1) cooling efficiency, (2) cooling capacity, and (3) cooling benefit of existing urban green infrastructure for ~500 major cities across the world. Urban green infrastructure and temperature are generally negatively and relatively linearly correlated at landscape scales, i.e., higher quantities of urban green infrastructure yield lower temperatures 42 , 43 . Cooling efficiency is widely used as a measure of the extent to which a given proportional increase in the area of urban green infrastructure leads to a decrease in temperature, i.e., the slope of the urban green infrastructure-temperature relationship 42 , 44 , 45 (see Methods for details). This simple metric allows quantifying the quality of urban green infrastructure in terms of ameliorating the urban heat island effect. Meanwhile, the extent to which existing urban green infrastructure cools down an entire city’s surface temperatures (compared to the non-vegetated built-up areas) is referred to as cooling capacity. Hence, cooling capacity is a function of the total quantity of urban green infrastructure and its cooling efficiency (see Methods).

As a third step, we account for the spatial distributions of urban green infrastructure and populations to quantify the benefit of cooling mitigation received by an average urban inhabitant in each city given their location. This cooling benefit is a more direct measure of the cooling realized by people, after accounting for the within-city geography of urban green infrastructure and population density. We focus on cooling capacity and cooling benefit as the measures of the cooling capability of individual cities for assessing their global inequalities. We are particularly interested in linking cooling adaptation inequality with income inequality 40 , 46 . While this can be achieved using existing income metrics for country classifications 47 , here we use the traditional Global North/South classification due to its historical ties to geography which is influential in climate research.

Results and discussion

Our analyses indicate that existing green infrastructure of an average city has a capability of cooling down surface temperatures by ~3 °C during warm seasons. However, a concerning disparity is evident; on average Global South cities have only two-thirds the cooling capacity and cooling benefit compared to Global North cities. This inequality is attributable to the differences in both quantity and quality of existing urban green infrastructure among cities. Importantly, we find that there exists considerable potential for many cities to enhance the cooling capability of their green infrastructure; achieving this potential could dramatically reduce global inequalities in adaptation to outdoor heat stress.

Quantifying cooling inequality

Our analyses showed that both the quantity and quality of the existing urban green infrastructure vary greatly among the world’s ~500 most populated cities (see Methods for details, and Fig.  1 for examples). The quantity of urban green infrastructure measured based on remotely sensed indicators of spectral greenness (Normalized Difference Vegetation Index, NDVI, see Methods) had a coefficient of variation (CV) of 35%. Similarly, the quality of urban green infrastructure in terms of cooling efficiency (daytime land surface temperatures during peak summer) had a CV of 37% (Supplementary Figs.  1 , 2 ). The global mean value of cooling capacity is 2.9 °C; existing urban green infrastructure ameliorates warm-season heat stress by 2.9 °C of surface temperature in an average city. In truth, however, the variation in cooling capacity was great (global CV in cooling capacity as large as ~50%), such that few cities were average. This variation is strongly geographically structured. Cities closer to the equator - tropical and subtropical cities - tend to have relatively weak cooling capacities (Fig.  2a, b ). As Global South countries are predominantly located at low latitudes, this pattern leads to a situation in which Global South cities, which tend to be hotter and relatively lower-income, have, on average, approximately two-thirds the cooling capacity of the Global North cities (2.5 ± 1.0 vs. 3.6 ± 1.7°C, Wilcoxon test, p  = 2.7e-12; Fig.  2c ). The cities that most need to rely on green infrastructure are, at present, those that are least able to do so.

a , e , i , m , q Los Angeles, US. b , f , j , n , r Paris, France. c , g , k , o , s Shanghai, China. d , h , l , p , t Cairo, Egypt. Local cooling efficiency is calculated for different local climate zone types to account for within-city heterogeneity. In densely populated parts of cities, local cooling capacity tends to be lower due to reduced green space area, whereas local cooling benefit (local cooling capacity multiplied by a weight term of local population density relative to city mean) tends to be higher as more urban residents can receive cooling amelioration.

a Global distribution of cooling capacity for the 468 major urbanized areas. b Latitudinal pattern of cooling capacity. c Cooling capacity difference between the Global North and South cities. The cooling capacity offered by urban green infrastructure evinces a latitudinal pattern wherein lower-latitude cities have weaker cooling capacity ( b , cubic-spline fitting of cooling capacity with 95% confidence interval is shown), representing a significant inequality between Global North and South countries: city-level cooling capacity for Global North cities are about 1.5-fold higher than in Global South cities ( c ). Data are presented as box plots, where median values (center black lines), 25th percentiles (box lower bounds), 75th percentiles (box upper bounds), whiskers extending to 1.5-fold of the interquartile range (IQR), and outliers are shown. The tails of the cooling capacity distributions are truncated at zero as all cities have positive values of cooling capacity. Notice that no cities in the Global South have a cooling capacity greater than 5.5 °C ( c ). This is because no cities in the Global South have proportional green space areas as great as those seen in the Global North (see also Fig.  4b ). A similar pattern is found for cooling benefit (Supplementary Fig.  3 ). The two-sided non-parametric Wilcoxon test was used for statistical comparisons.

When we account for the locations of urban green infrastructure relative to humans within cities, the cooling benefit of urban green infrastructure realized by an average urban resident generally becomes slightly lower than suggested by cooling capacity (see Methods; Supplementary Fig.  3 ). Urban residents tend to be densest in the parts of cities with less green infrastructure. As a result, the average urban resident experiences less cooling amelioration than expected. However, this heterogeneity has only a minor effect on global-scale inequality. As a result, the geographic trends in cooling capacity and cooling benefit are similar: mean cooling benefit for an average urban resident also presents a 1.5-fold gap between Global South and North cities (2.2 ± 0.9 vs. 3.4 ± 1.7 °C, Wilcoxon test, p  = 3.2e-13; Supplementary Fig.  3c ). Urban green infrastructure is a public good that has the potential to help even the most marginalized populations stay cool; unfortunately, this public benefit is least available in the Global South. When walking outdoors, the average person in an average Global South city receives only two-thirds the cooling amelioration from urban green infrastructure experienced by a person in an average Global North city. The high cooling amelioration capacity and benefit of the Global North cities is heavily influenced by North America (specifically, Canada and the US), which have both the highest cooling efficiency and the largest area of green infrastructure, followed by Europe (Supplementary Fig.  4 ).

One way to illustrate the global inequality of cooling capacity or benefit is to separately look at the cities that are most and least effective in ameliorating outdoor heat stress. Our results showed that ~85% of the 50 most effective cities (with highest cooling capacity or cooling benefit) are located in the Global North, while ~80% of the 50 least effective are Global South cities (Fig.  3 , Supplementary Fig.  5 ). This is true without taking into account the differences in the background temperatures and climate warming of these cities, which will exacerbate the effects on human health; cities in the Global South are likely to be closer to the limits of human thermal comfort and even, increasingly, the limits of the temperatures and humidities (wet-bulb temperatures) at which humans can safely work or even walk, such that the ineffectiveness of green spaces in those cities in cooling will lead to greater negative effects on human health 48 , work 14 , and gross domestic product (GDP) 49 . In addition, Global South cities commonly have higher population densities (Fig.  3 , Supplementary Fig.  5 ) and are projected to have faster population growth 50 . This situation will plausibly intensify the urban heat island effect because of the need of those populations for housing (and hence tensions between the need for buildings and the need for green spaces). It will also increase the number of people exposed to extreme urban heat island effects. Therefore, it is critical to increase cooling benefit via expanding urban green spaces, so that more people can receive the cooling mitigation from a given new neighboring green space if they live closer to each other. Doing so will require policies that incentivize urban green spaces as well as architectural innovations that make innovations such as plant-covered buildings easier and cheaper to implement.

The axes on the right are an order of magnitude greater than those on the left, such that the cooling capacity of Charlotte in the United States is about 37-fold greater than that of Mogadishu (Somalia) and 29-fold greater than that of Sana’a (Yemen). The cities presenting lowest cooling capacities are most associated with Global South cities at higher population densities.

Of course, cities differ even within the Global North or within the Global South. For example, some Global South cities have high green space areas (or relatively high cooling efficiency in combination with moderate green space areas) and hence high cooling capacity. These cities, such as Pune (India), will be important to study in more detail, to shed light on the mechanistic details of their cooling abilities as well as the sociopolitical and other factors that facilitated their high green area coverage and cooling capabilities (Supplementary Figs.  6 , 7 ).

We conducted our primary analyses using a spatial grain of 100-m grid cells and Landsat NDVI data for quantifying spectral greenness. Our results, however, were robust at the coarser spatial grain of 1 km. We find a slightly larger global cooling inequality (~2-fold gap between Global South and North cities) at the 1-km grain using MODIS data (see Methods and Supplementary Fig.  17 ). MODIS data have been frequently used for quantifying urban heat island effects and cooling mitigation 44 , 45 , 51 . Our results reinforce its robustness for comparing urban thermal environments between cities across broad scales.

Influencing factors

The global inequality of cooling amelioration could have a number of proximate causes. To understand their relative influence, we first separately examined the effects of quality (cooling efficiency) and quantity (NDVI as a proxy indicator of urban green space area) of urban green infrastructure. The simplest null model is one in which cooling capacity (at the city scale) and cooling benefit (at the human scale) are driven primarily by the proportional area in a city dedicated to green spaces. Indeed, we found that both cooling capacity and cooling benefit were strongly correlated with urban green space area (Fig.  4 , Supplementary Fig.  8 ). This finding is useful with regards to practical interventions. In general, cities that invest in saving or restoring more green spaces will receive more cooling benefits from those green spaces. By contrast, differences among cities in cooling efficiency played a more minor role in determining the cooling capacity and benefit of cities (Fig.  4 , Supplementary Fig.  8 ).

a Relationship between cooling efficiency and cooling capacity. b Relationship between green space area (measured by mean Landsat NDVI in the hottest month of 2018) and cooling capacity. Note that the highest level of urban green space area in the Global South cities is much lower than that in the Global North (dashed line in b ). Gray bands indicate 95% confidence intervals. Two-sided t-tests were conducted. c A piecewise structural equation model based on assumed direct and indirect (through influencing cooling efficiency and urban green space area) effects of essential natural and socioeconomic factors on cooling capacity. Mean annual temperature and precipitation, and topographic variation (elevation range) are selected to represent basic background natural conditions; GDP per capita is selected to represent basic socioeconomic conditions. The spatial extent of built-up areas is included to correct for city size. A bi-directional relationship (correlation) is fitted between mean annual temperature and precipitation. Red and blue solid arrows indicate significantly negative and positive coefficients with p  ≤ 0.05, respectively. Gray dashed arrows indicate p  > 0.05. The arrow width illustrates the effect size. Similar relationships are found for cooling benefits realized by an average urban resident (see Supplementary Fig.  8 ).

A further question is what shapes the quality and quantity of urban green infrastructure (which in turn are driving cooling capacity)? Many inter-correlated factors are possibly operating at multiple scales, making it difficult to disentangle their effects, especially since experiment-based causal inference is usually not feasible for large-scale urban systems. From a macroscopic perspective, we test the simple hypothesis that the background natural and socioeconomic conditions of cities jointly affect their cooling capacity and benefit in both direct and indirect ways. To this end, we constructed a minimal structural equation model including only the most essential variables reflecting background climate (mean annual temperature and precipitation), topographic variation (elevation range), as well as gross domestic product (GDP) per capita and city area (see Methods; Fig.  4c ).

We found that the quantity of green spaces in a city (again, in proportion to its size) was positively correlated with GDP per capita and city area; wealthier cities have more green spaces. It is well known that wealth and green spaces are positively correlated within cities (the luxury effect) 40 , 46 ; our analysis shows that a similar luxury effect occurs among them at a global scale. In addition, larger cities often have proportionally more green spaces, an effect that may be due to the tendency for large cities (particularly in the US and Canada) to have lower population densities. Cities that were hotter and had more topographic variation tended to have fewer green spaces and those that were more humid tended to have more green spaces. Given that temperature and humidity are highly correlated with the geography of the Global South and Global North, it is difficult to know whether these effects are due to the direct effects of temperature and precipitation, for example, on the growth rate of vegetation and hence the transition of abandoned lots into green spaces, or are associated with historical, cultural and political differences that via various mechanisms correlate to climate. Our structural equation model explained only a small fraction of variation among cities in their cooling efficiency, which is to say the quality of their green space. Cooling efficiency was modestly influenced by background temperature and precipitation—the warmer a city, the greater the cooling efficiency in that city; conversely, the more humid a city the less the cooling efficiency of that city.

Our analyses suggested that the lower cooling adaptation capabilities of Global South cities can be explained by their lower quantity of green infrastructure and, to a much lesser extent, their weaker cooling efficiency (quality; Supplementary Fig.  2 ). These patterns appear to be in part structured by GDP, but are also associated with climatic conditions 39 , and other factors. A key question, unresolved by our work, is whether the climatic correlates of the size of green spaces in cities are due to the effects of climate per se or if they, instead, reflect correlates between contemporary climate and the social, cultural, and political histories of cities in the Global South 52 . Since urban planning has much inertia, especially in big cities, those choices might be correlated with climate because of the climatic correlates of political histories. It is also possible that these dynamics relate, in part, to the ways in which climate influences vegetation structure. However, this seems less likely given that under non-urban conditions vegetation cover (and hence cooling capacity) is normally positively correlated with mean annual temperature across the globe, opposite to our observed negative relationships for urban systems (Supplementary Fig.  9g ). Still, it is possible that increased temperatures in cities due to the urban heat island effects may lead to temperature-vegetation cover-cooling capacity relationships that differ from those in natural environments 53 , 54 . Indeed, a recent study found that climate warming will put urban forests at risk, and the risk is disproportionately higher in the Global South 55 .

Our model serves as a starting point for unraveling the mechanisms underlying global cooling inequality. We cannot rule out the possibility that other unconsidered factors correlated with the studied variables play important roles. We invite systematic studies incorporating detailed sociocultural and ecological variables to address this question across scales.

Potential of enhancing cooling and reducing inequality

Can we reduce the inequality in cooling capacity and benefits that we have discovered among the world’s largest cities? Nuanced assessments of the potential to improve cooling mitigation require comprehensive considerations of socioeconomic, cultural, and technological aspects of urban management and policy. It is likely that cities differ greatly in their capacity to implement cooling through green infrastructure, whether as a function of culture, governance, policy or some mix thereof. However, any practical attempts to achieve greater cooling will occur in the context of the realities of climate and existing land use. To understand these realities, we modeled the maximum additional cooling capacity that is possible in cities, given existing constraints. We assume that this capacity depends on the quality (cooling efficiency) and quantity of urban green infrastructure. Our approach provides a straightforward metric of the cooling that could be achieved if all parts of a city’s green infrastructure were to be enhanced systematically.

The positive outlook is that our analyses suggest a considerable potential of improving cooling capacity by optimizing urban green infrastructure. An obvious way is through increases in urban green infrastructure quantity. We employ an approach in which we consider each local climate zone 56 to have a maximum NDVI and cooling efficiency (see Methods). For a given local climate zone, the city with the largest NDVI values or cooling efficiency sets the regional upper bounds for urban green infrastructure quantities or quality that can be achieved. Notably, these maxima are below the maxima for forests or other non-urban spaces for the simple reason that, as currently imagined, cities must contain gray (non-green) spaces in the form of roads and buildings. In this context, we conduct a thought experiment. What if we could systematically increase NDVI of all grid cells in each city, per local climate zone type, to a level corresponding to the median NDVI of grid cells in that upper bound city while keeping cooling efficiency unchanged (see Methods). If we were able to achieve this goal, the cooling capacity of cities would increase by ~2.4 °C worldwide. The increase would be even greater, ~3.8°C, if the 90th percentile (within the reference maximum city) was reached (Fig.  5a ). The potential for cooling benefit to the average urban resident is similar to that of cooling capacity (Supplementary Fig.  10a ). There is also potential to reduce urban temperatures if we can enhance cooling efficiency. However, the benefits of increases in cooling efficiency are modest (~1.5 °C increases at the 90th percentile of regional upper bounds) when holding urban green infrastructure quantity constant. In theory, if we could maximize both quantity and cooling efficiency of urban green infrastructure (to 90th percentiles of their regional upper bounds respectively), we would yield increases in cooling capacity and benefit up to ~10 °C, much higher than enhancing green space area or cooling efficiency alone (Fig.  5a , Supplementary Fig.  10a ). Notably, such co-maximization of green space area and cooling efficiency would substantially reduce global inequality to Gini <0.1 (Fig.  5b , Supplementary Fig.  10b ). Our analyses thus provide an important suggestion that enhancing both green space quantity and quality can yield a synergistic effect leading to much larger gains than any single aspect alone.

a The potential of enhancing cooling capacity via either enhancing urban green infrastructure quality (i.e., cooling efficiency) while holding quantity (i.e., green space area) fixed (yellow), or enhancing quantity while holding quality fixed (blue) is much lower than that of enhancing both quantity and quality (green). The x-axis indicates the targets of enhancing urban green infrastructure quantity and/or quality relative to the 50–90th percentiles of NDVI or cooling efficiency, see Methods). The dashed horizontal lines indicate the median cooling capacity of current cities. Data are presented as median values with the colored bands corresponding to 25–75th percentiles. b The potential of reducing cooling capacity inequality is also higher when enhancing both urban green infrastructure quantity and quality. The Gini index weighted by population density is used to measure inequality. Similar results were found for cooling benefit (Supplementary Fig.  10 ).

Different estimates of cooling capacity potential may be reached based on varying estimates and assumptions regarding the maximum possible quantity and quality of urban green infrastructure. There is no single, simple way to make these estimates, especially considering the huge between-city differences in society, culture, and structure across the globe. Our example case (above) begins from the upper bound city’s median NDVI, taking into account different local climate zone types and background climate regions (regional upper bounds). This is based on the assumption that for cities within the same climate regions, their average green space quantity may serve as an attainable target. Still, urban planning is often made at the level of individual cities, often only implemented to a limited extent and made with limited consideration of cities in other regions and countries. A potentially more realistic reference may be taken from the existing green infrastructure (again, per local climate zone type) within each particular city itself (see Methods): if a city’s sparsely vegetated areas was systematically elevated to the levels of 50–90th percentiles of NDVI within their corresponding local climate zones within the city, cooling capacity would still increase, but only by 0.5–1.5 °C and with only slightly reduced inequalities among cities (Supplementary Fig.  11 ). This highlights that ambitious policies, inspired by the greener cities worldwide, are necessary to realize the large cooling potential in urban green infrastructure.

In summary, our results demonstrate clear inequality in the extent to which urban green infrastructure cools cities and their denizens between the Global North and South. Much attention has been paid to the global inequality of indoor heat adaptation arising from the inequality of resources (e.g., less affordable air conditioning and more frequent power shortages in the Global South) 36 , 57 , 58 , 59 . Our results suggest that the inequality in outdoor adaptation is particularly concerning, especially as urban populations in the Global South are growing rapidly and are likely to face the most severe future temperature extremes 60 .

Previous studies have been focusing on characterizing urban heat island effects, urban vegetation patterns, resident exposure, and cooling effects in particular cities 26 , 28 , 34 , 61 , regions 22 , 25 , 62 , or continents 32 , 44 , 63 . Recent studies start looking at global patterns with respect to cooling efficiency or green space exposure 35 , 45 , 64 , 65 . Our approach is one drawn from the fields of large-scale ecology and macroecology. This approach is complementary to and, indeed, can, in the future, be combined with (1) mechanism driven biophysical models 66 , 67 to predict the influence of the composition and climate of green spaces on their cooling efficiency, (2) social theory aimed at understanding the factors that govern the amount of green space in cities as well as the disparity among cities 68 , (3) economic models of the effects of policy changes on the amount of greenspace and even (4) artist-driven projects that seek to understand the ways in which we might reimagine future cities 69 . Our simple explanatory model is, ultimately, one lens on a complex, global phenomenon.

Our results convey some positive outlook in that there is considerable potential to strengthen the cooling capability of cities and to reduce inequalities in cooling capacities at the same time. Realizing this nature-based solution, however, will be challenging. First, enhancing urban green infrastructure requires massive investments, which are more difficult to achieve in Global South cities. Second, it also requires smart planning strategies and advanced urban design and greening technologies 37 , 70 , 71 , 72 . Spatial planning of urban green spaces needs to consider not only the cooling amelioration effect, but also their multifunctional aspects that involve multiple ecosystem services, mental health benefits, accessibility, and security 73 . In theory, a city can maximize its cooling while also maximizing density through the combination of high-density living, ground-level green spaces, and vertical and rooftop gardens (or even forests). In practice, the current cities with the most green spaces tend to be lower-density cities 74 (Supplementary Fig.  12 ). Still, innovation and implementation of new technologies that allow green spaces and high-density living to be combined have the potential to reduce or disconnect the negative relationship between green space area and population density 71 , 75 . However, this development has yet to be realized. Another dimension of green spaces that deserves more attention is the geography of green spaces relative to where people are concentrated within cities. A critical question is how best should we distribute green spaces within cities to maximize cooling efficiency 76 and minimize within-city cooling inequality towards social equity 77 ? Last but not least, it is crucial to design and manage urban green spaces to be as resilient as possible to future climate stress 78 . For many cities, green infrastructure is likely to remain the primary means people will have to rely on to mitigate the escalating urban outdoor heat stress in the coming decades 79 .

We used the world population data from the World’s Cities in 2018 Data Booklet 80 to select 502 major cities with population over 1 million people (see Supplementary Data  1 for the complete list of the studied cities). Cities are divided into the Global North and Global South based on the Human Development Index (HDI) from the Human Development Report 2019 81 . For each selected city, we used the 2018 Global Artificial Impervious Area (GAIA) data at 30 m resolution 82 to determine its geographic extent. The derived urban boundary polygons thus encompass a majority of the built-up areas and urban residents. In using this approach, rather than urban administrative boundaries, we can focus on the relatively densely populated areas where cooling mitigation is most needed, and exclude areas dominated by (semi) natural landscapes that may bias the subsequent quantifications of the cooling effect. Our analyses on the cooling effect were conducted at the 100 m spatial resolution using Landsat data and WorldPop Global Project Population Data of 2018 83 . In order to test for the robustness of the results to coarser spatial scales, we also repeated the analyses at 1 km resolution using MODIS data, which have been extensively used for quantifying urban heat island effects and cooling mitigation 44 , 45 , 51 . We discarded the five cities with sizes <30 km 2 as they were too small for us to estimate their cooling efficiency based on linear regression (see section below for details). We combined closely located cities that form contiguous urban areas or urban agglomerations, if their urban boundary polygons from GAIA merged (e.g., Phoenix and Mesa in the United States were combined). Our approach yielded 468 polygons, each representing a major urbanized area that were the basis for all subsequent analyses. Because large water bodies can exert substantial and confounding cooling effects, we excluded permanent water bodies including lakes, reservoirs, rivers, and oceans using the Copernicus Global Land Service (CGLS) Land Cover data for 2018 at 10 m resolution 84 .

Quantifying the cooling effect

As a first step, we calculated cooling efficiency for each studied city within the GAIA-derived urban boundary. Cooling efficiency quantifies the extent to which a given area of green spaces in a city can reduce temperatures. It is a measure of the effectiveness (quality) of urban green spaces in terms of heat amelioration. Cooling efficiency is typically measured by calculating the slope of the relationship between remotely-sensed land surface temperature (LST) and vegetation cover through ordinary least square regression 42 , 44 , 45 . It is known that cooling efficiency varies between cities. Influencing factors might include background climate 29 , species composition 30 , 85 , landscape configuration 28 , topography 86 , proximity to large water bodies 33 , 87 , urban morphology 88 , and city management practices 31 . However, the mechanism underlying the global pattern of cooling efficiency remains unclear.

We used Landsat satellite data provided by the United States Geological Survey (USGS) to calculate the cooling efficiency of each studied city. We used the cloud-free Landsat 8 Level 2 LST and NDVI data. For each city we calculated the mean LST in each month of 2018 to identify the hottest month, and then derived the hottest month LST; we used the cloud-free Landsat 8 data to calculate the mean NDVI for the hottest month correspondingly.

We quantified cooling efficiency for different local climate zones 56 separately for each city, to account for within-city variability of thermal environments. To this end, we used the Copernicus Global Land Service data (CGLS) 84 and Global Human Settlement Layers (GHSL) Built-up height data 89 of 2018 at the 100 m resolution to identify five types of local climate zones: non-tree vegetation (shrubs, herbaceous vegetation, and cultivated vegetation according to the CGLS classification system), low-rise buildings (built up and bare according to the CGLS classification system, with building heights ≤10 m according to the GHSL data), medium-high-rise buildings (built up and bare areas with building heights >10 m), open tree cover (open forest with tree cover 15–70% according to the CGLS system), and closed tree cover (closed forest with tree cover >70%).

For each local climate zone type in each city, we constructed a regression model with NDVI as the predictor variable and LST as the response variable (using the ordinary least square method). We took into account the potential confounding factors including topographic elevation (derived from MERIT DEM dataset 90 ), building height (derived from the GHSL dataset 89 ), and distance to water bodies (derived from the GSHHG dataset 91 ), the model thus became: LST ~ NDVI + topography + building height + distance to water. Cooling efficiency was calculated as the absolute value of the regression coefficient of NDVI, after correcting for those confounding factors. To account for the multi-collinearity issue, we conducted variable selection based on the variance inflation factor (VIF) to achieve VIF < 5. Before the analysis, we discarded low-quality Landsat pixels, and filtered out the pixels with NDVI < 0 (normally less than 1% in a single city). Cooling efficiency is known to be influenced by within-city heterogeneity 92 , 93 , and, as a result, might sometimes better fit non-linear relationships at local scales 65 , 76 . However, our central aim is to assess global cooling inequality based on generalized relationships that fit the majority of global cities. Previous studies have shown that linear relationships can do this job 42 , 44 , 45 , therefore, here we used linear models to assess cooling efficiency.

As a second step, we calculated the cooling capacity of each city. Cooling capacity is a positive function of the magnitude of cooling efficiency and the proportional area of green spaces in a city and is calculated based on NDVI and the derived cooling efficiency (Eq.  1 , Supplementary Fig.  13 ):

where CC lcz and CE lcz are the cooling capacity and cooling efficiency for a given local climate zone type in a city, respectively; NDVI i is the mean NDVI for 100-m grid cell i ; NDVI min is the minimum NDVI across the city; and n is the total number of grid cells within the local climate zone. Local cooling capacity for each grid cell i (Fig.  1 , Supplementary Fig.  7 ) can be derived in this way as well (Supplementary Fig.  13 ). For a particular city, cooling capacity may be dependent on the spatial configuration of its land use/cover 28 , 94 , but here we condensed cooling capacity to city average (Eq.  2 ), thus did not take into account these local-scale factors.

where CC is the average cooling capacity of a city; n lcz is the number of grid cells of the local climate zone; m is the total number of grid cells within the whole city.

As a third step, we calculated the cooling benefit realized by an average urban resident (cooling benefit in short) in each city. Cooling benefit depends not only on the cooling capacity of a city, but also on where people live within a city relative to greener or grayer areas of the city. For example, cooling benefits in a city might be low even if the cooling capacity is high if the green parts and the dense-population parts of a city are inversely correlated. Here, we are calculating these averages while aware that in any particular city the exposure of a particular person will depend on the distribution of green spaces in a city, and the occupation, movement trajectories of a person, etc. On the scale of a city, we calculated cooling benefit following a previous study 35 , that is, simply adding a weight term of population size per 100-m grid cell into cooling capacity in Eq. ( 1 ):

Where CB lcz is the cooling benefit of a given local climate zone type in a specific city, pop i is the number of people within grid cell i , \(\overline{{pop}}\) is the mean population of the city.

Where CB is the average cooling benefit of a city. The population data were obtained from the 100-m resolution WorldPop Global Project Population Data of 2018 83 . Local cooling benefit for a given grid cell i can be calculated in a similar way, i.e., local cooling capacity multiplied by a weight term of local population density relative to mean population density. Local cooling benefits were mapped for example cities for the purpose of illustrating the effect of population spatial distribution (Fig.  1 , Supplementary Fig.  7 ), but their patterns were not examined here.

Based on the aforementioned three key variables quantified at 100 m grid cells, we conducted multivariate analyses to examine if and to what extent cooling efficiency and cooling benefit are shaped by essential natural and socioeconomic factors, including background climate (mean annual temperature from ECMWF ERA5 dataset 95 and precipitation from TerraClimate dataset 96 ), topography (elevation range 90 ), and GDP per capita 97 , with city size (geographic extent) corrected for. We did not include humidity because it is strongly correlated with temperature and precipitation, causing serious multi-collinearity problems. We used piecewise structural equation modeling to test the direct effects of these factors and indirect effects via influencing cooling efficiency and vegetation cover (Fig.  4c , Supplementary Fig.  8c ). To account for the potential influence of spatial autocorrelation, we used spatially autoregressive models (SAR) to test for the robustness of the observed effects of natural and socioeconomic factors on cooling capacity and benefit (Supplementary Fig.  14 ).

Testing for robustness

We conducted the following additional analyses to test for robustness. We obtained consistent results from these robustness analyses.

(1) We looked at the mean hottest-month LST and NDVI within 3 years (2017-2019) to check the consistency between the results based on relatively short (1 year) vs. long (3-year average) time periods (Supplementary Fig.  15 ).

(2) We carried out the approach at a coarser spatial scale of 1 km, using MODIS-derived NDVI and LST, as well as the population data 83 in the hottest month of 2018. In line with our finer-scale analysis of Landsat data, we selected the hottest month and excluded low-quality grids affected by cloud cover and water bodies 98 (water cover > 20% in 1 × 1 km 2 grid cells) of MODIS LST, and calculated the mean NDVI for the hottest month. We ultimately obtained 441 cities (or urban agglomerations) for analysis. At the 1 km resolution, some local climate zone types would yield insufficient samples for constructing cooling efficiency models. Therefore, instead of identifying local climate zone explicitly, we took an indirect approach to account for local climate confounding factors, that is, we constructed a multiple regression model for a whole city incorporating the hottest-month local temperature 95 , precipitation 96 , and humidity (based on NASA FLDAS dataset 99 ), albedo (derived from the MODIS MCD43A3 product 100 ), aerosol loading (derived from the MODIS MCD19A2 product 101 ), wind speed (based on TerraClimate dataset 96 ), topography elevation 90 , distance to water 91 , urban morphology (building height 102 ), and human activity intensity (VIIRS nighttime light data as a proxy indicator 103 ). We used the absolute value of the linear regression coefficient of NDVI as the cooling efficiency of the whole city (model: LST ~ NDVI + temperature + precipitation + humidity + distance to water + topography + building height + albedo + aerosol + wind speed + nighttime light), and calculated cooling capacity and cooling benefit based on the same method. Variable selection was conducted using the criterion of VIF < 5.

Our results indicated that MODIS-based cooling capacity and cooling benefit are significantly correlated with the Landsat-based counterparts (Supplementary Fig.  16 ); importantly, the gap between the Global South and North cities is around two-fold, close to the result from the Landsat-based result (Supplementary Fig.  17 ).

(3) For the calculation of cooling benefit, we considered different spatial scales of human accessibility to green spaces: assuming the population in each 100 × 100 m 2 grid cell could access to green spaces within neighborhoods of certain extents, we calculated cooling benefit by replacing NDVI i in Eq. ( 3 ) with mean NDVI within the 300 × 300 m 2 and 500 × 500 m 2 extents centered at the focal grid cell (Supplementary Fig.  18 ).

(4) Considering cities may vary in minimum NDVI, we assessed if this variation could affect resulting cooling capacity patterns. To this end, we calculated the cooling capacity for each studied city using NDVI = 0 as the reference (i.e., using NDVI = 0 instead of minimum NDVI in Supplementary Fig.  13b ), and correlated it with that using minimum NDVI as the reference (Supplementary Fig.  19 ).

Quantifying between-city inequality

Inequalities in access to the benefits of green spaces in cities exist within cities, as is increasingly well-documented 104 . Here, we focus instead on the inequalities among cities. We used the Gini coefficient to measure the inequality in cooling capacity and cooling benefit between all studied cities across the globe as well as between Global North or South cities. We calculated Gini using the population-density weighted method (Fig.  5b ), as well as the unweighted and population-size weighted methods (Supplementary Fig.  20 ).

Estimating the potential for more effective and equal cooling amelioration

We estimated the potential of enhancing cooling amelioration based on the assumptions that urban green space quality (cooling efficiency) and quantity (NDVI) can be increased to different levels, and that relative spatial distributions of green spaces and population can be idealized (so that their spatial matches can maximize cooling benefit). We assumed that macro-climate conditions act as the constraints of vegetation cover and cooling efficiency. We calculated the 50th, 60th, 70th, 80th, and 90th percentiles of NDVI within each type of local climate zone of each city. For a given local climate zone type, we obtained the city with the highest NDVI per percentile value as the regional upper bounds of urban green infrastructure quantity. The regional upper bounds of cooling efficiency are derived in a similar way. For each local climate zone in a city, we generated a potential NDVI distribution where all grid cells reach the regional upper bound values for the 50th, 60th, 70th, 80th, or 90th percentile of urban green space quantity or quality, respectively. NDVI values below these percentiles were increased, whereas those above these percentiles remained unchanged. The potential estimates are essentially dependent on the references, i.e., the optimal cooling efficiency and NDVI that a given city can reach. However, such references are obviously difficult to determine, because complex natural and socioeconomic conditions could play important roles in determining those cooling optima, and the dominant factors are unknown at a global scale. We employed the simplifying assumption that background climate could act as an essential constraint according to our results. We therefore used the Köppen climate classification system 105 to determine the reference separately in each climate region (tropical, arid, temperate, and continental climate regions were involved for all studied cities).

We calculated potential cooling capacity and cooling benefit based on these potential NDVI maps (Fixed cooling efficiency in Fig.  5 ). We then calculated the potentials if cooling efficiency of each city can be enhanced to 50–90th percentile across all urban local climate zones within the corresponding biogeographic region (Fixed green space area in Fig.  5 ). We also calculated the potentials if both NDVI and cooling efficiency were enhanced (Enhancing both in Fig.  5) to a certain corresponding level (i.e., i th percentile NDVI +  i th percentile cooling efficiency). We examined if there are additional effects of idealizing relative spatial distributions of urban green spaces and humans on cooling benefits. To this end, the pixel values of NDVI or population amount remained unchanged, but their one-to-one correspondences were based on their ranking: the largest population corresponds to the highest NDVI, and so forth. Under each scenario, we calculated cooling capacity and cooling benefit for each city, and the between-city inequality was measured by the Gini coefficient.

We used the Google Earth Engine to process the spatial data. The statistical analyses were conducted using R v4.3.3 106 , with car v3.1-2 107 , piecewiseSEM v2.1.2 108 , and ineq v0.2-13 109 packages. The global maps of cooling were created using the ArcGIS v10.3 software.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

City population statistics data is collected from the Population Division of the Department of Economic and Social Affairs of the United Nations ( https://www.un.org/development/desa/pd/content/worlds-cities-2018-data-booklet ). Global North-South division is based on Human Development Report 2019 which from United Nations Development Programme ( https://hdr.undp.org/content/human-development-report-2019 ). Global urban boundaries from GAIA data are available from Star Cloud Data Service Platform ( https://data-starcloud.pcl.ac.cn/resource/14 ) . Global water data is derived from 2018 Copernicus Global Land Service (CGLS 100-m) data ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global ), European Space Agency (ESA) WorldCover 10 m 2020 product ( https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100 ), and GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database) at https://www.soest.hawaii.edu/pwessel/gshhg/ . Landsat 8 LST and NDVI data with 30 m resolution are available at  https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 . Land surface temperature (LST) data with 1 km from MODIS Aqua product (MYD11A1) is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 . NDVI (1 km) dataset from MYD13A2 is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2 . Population data (100 m) is derived from WorldPop ( https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop ). Local climate zones are also based on 2018 CGLS data ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global ), and built-up height data is available from Global Human Settlement Layers (GHSL, 100 m) ( https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H ). Temperature data is calculated from ERA5-Land Monthly Aggregated dataset ( https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR ). Precipitation and wind data are calculated from TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho) ( https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE ). Humidity data is calculated from Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System ( https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 ). Topography data from MERIT DEM (Multi-Error-Removed Improved-Terrain DEM) product is available at https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3 . GDP from Gross Domestic Product and Human Development Index dataset is available at https://doi.org/10.5061/dryad.dk1j0 . VIIRS nighttime light data is available at https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG . City building volume data from Global 3D Building Structure (1 km) is available at https://doi.org/10.34894/4QAGYL . Albedo data is derived from the MODIS MCD43A3 product ( https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 ), and aerosol data is derived from the MODIS MCD19A2 product ( https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES ). All data used for generating the results are publicly available at https://doi.org/10.6084/m9.figshare.26340592.v1 .

Code availability

The codes used for data collection and analyses are publicly available at https://doi.org/10.6084/m9.figshare.26340592.v1 .

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Acknowledgements

We thank all the data providers. We thank Marten Scheffer for valuable discussion. C.X. is supported by the National Natural Science Foundation of China (Grant No. 32061143014). J.-C.S. was supported by Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173), and his VILLUM Investigator project “Biodiversity Dynamics in a Changing World”, funded by VILLUM FONDEN (grant 16549). W.Z. was supported by the National Science Foundation of China through Grant No. 42225104. T.M.L. and J.F.A. are supported by the Open Society Foundations (OR2021-82956). W.J.R. is supported by the funding received from Roger Worthington.

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Y.L., S.N.T., R.R.D., and C.X. designed the study. Y.L. collected the data, generated the code, performed the analyses, and produced the figures with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., Z.Y., S.N.T., R.R.D. and C.X. Y.L., S.N.T., R.R.D. and C.X. wrote the first draft with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., and Z.Y. All coauthors interpreted the results and revised the manuscript.

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Li, Y., Svenning, JC., Zhou, W. et al. Green spaces provide substantial but unequal urban cooling globally. Nat Commun 15 , 7108 (2024). https://doi.org/10.1038/s41467-024-51355-0

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Keeping native bees buzzing requires rethinking pest control, key points:.

New research shows a strong correlation between pesticide use and declining sightings of wild bees, with pesticide use causing appearances of some species to drop as much as 56%.

The loss of wild bees could disrupt ecosystems, affecting plant survival and the wildlife dependent on those plants, while also posing a significant risk to agricultural productivity.

Researchers advocate for integrated pest management strategies and more long-term studies to better understand and mitigate the impact of pesticides on wild bees and other pollinators.

Native wild bees play a crucial ecological role, ensuring the survival and reproduction of countless plant species — including many agricultural crops — by spreading pollen as they forage for food. Unfortunately, their numbers seem to be declining, and despite experts suggesting multiple causes, the exact reason remains a mystery.

A new study published in Nature Sustainability sheds light on one potential cause: pesticide use. The research reveals a stark decline in the number of wild bee sightings, with appearances of some species dropping as much as 56% in areas of high pesticide use compared to areas with no pesticide use.

The study points to pesticides as a significant factor in wild bee decline and suggests that alternative pest control methods, such as those proposed by the U.S. Environmental Protection Agency, could reduce the damage.

Pesticide effects on wild bee populations scrutinized

Loss of wild bees could disrupt entire ecosystems, affecting not just plants but also the wildlife that depend on those plants for food and habitat. The multibillion-dollar agricultural industry could also suffer; wild bees, alongside honeybees, play a crucial role in pollinating three-quarters of food crops and nearly 90% of flowering plant species.

Recognizing the urgent threat posed by bee population declines, Laura Melissa Guzman of the USC Dornsife College of Letters, Arts and Sciences, along with an international team of researchers, set out to investigate the impact of pesticides on wild bees. They also examined the effects of agricultural practices and how the presence of honeybee colonies might influence wild bee populations.

Guzman, Gabilan Assistant Professor of Biological Sciences and Quantitative and Computational Biology , and the team inspected museum records, ecological surveys and community science data collected between 1996 and 2015 from across the contiguous United States.

We’re ignoring the unique responses of … wild bee species to pesticide exposure.

Using advanced computational methods, they sifted through more than 200,000 unique observations of over 1,000 species — representing one-third of all known bee species in the U.S. — to assess how frequently different species were observed in various locations.

In addition, they analyzed data from several government sources, such as the U.S. Geological Survey’s National Land Cover Database and Pesticide National Synthesis Project . The former tracks U.S. land cover types (crop, urban, forest, wetland, etc.) with snapshots taken every two to three years from 2001 to 2016, whereas the latter provides detailed data on pesticide use by county from 1992 to 2021.

By integrating these resources, the researchers correlated factors such as land use, pesticide application, honeybee colony presence, and types of agricultural crops with wild bee sightings over the past two to three decades.

Pesticides emerge as a top factor harming wild bees

The research provides compelling evidence that pesticide use is a major contributor to the declining numbers of wild bees. The study found a strong correlation between pesticide use and fewer wild bee sightings, suggesting a direct link between pesticide exposure and bee population declines.

Some scientists have speculated that certain crops might adversely affect wild bees. However, Guzman and the team uncovered evidence to the contrary. Among crops frequented by pollinators, they found just as many wild bees in counties with a lot of agriculture versus a little.

Interestingly, the study hinted that the presence of colonies of honeybees, an invasive species, had almost no effect on wild bee populations, despite some evidence to the contrary. The researchers caution, however, that they need more detailed data and further study to confirm this conclusion.

“While our calculations are sophisticated, much of the spatial and temporal data is coarse,” Guzman said. “We plan to refine our analysis and fill in the gaps as much as possible.”

Wild bees need alternative pest management methods

The researchers view their findings as compelling evidence that alternative pest control strategies, such as integrated pest management, are essential for conserving these crucial pollinators.

Integrated pest management involves controlling pests by using natural predators, modifying practices to reduce pest establishment, and using traps, barriers and other physical means, with pesticide use reserved as a last resort.

The team also emphasizes the need for more long-term studies that collect data on more localized bee populations over extended periods. “We need to combine these large-scale studies that span continents with field experiments that expose bees to chemicals over longer periods and under natural conditions to get a clearer picture of how these chemicals affect bees,” Guzman said.

Building a case for better pesticide risk assessment

The current study builds on work published earlier this year by Guzman and scientists from Washington State University and Canada’s Université Laval. That study found that ecological risk assessments (ERAs) underestimate pesticide threats to wild bees and other pollinators.

Currently, ERAs measure pesticide effects on honeybees, often in lab studies, then extrapolate those findings to native bee species. However, Guzman and her colleagues revealed that current ERAs vary wildly — as much as a million-fold — when estimating how lethal pesticides are to honeybees. And many wild bees are even more sensitive to pesticides, compounding the problem, the research showed.

“When we only focus on the western honeybee, we’re ignoring the unique responses of other wild bee species to pesticide exposure,” Guzman said, calling for regulatory agencies, scientists and policymakers to rethink ERA methods.

“More data and analysis on the long-term effects of pesticides will help guide these efforts to the benefit of all pollinators, including wild bees,” Guzman said.

About the study

In addition to corresponding author Guzman, study authors include Elizabeth Elle and Leithen M’Gonigle of Simon Fraser University; Lora Morandin of the Pollinator Partnership; Neil Cobb of Biodiversity Outreach Network (BON); Paige Chesshire of BON and Northern Arizona University; Lindsie McCabe of the USDA-ARS Pollinating Insects Research Unit; Alice Hughes of the University of Hong Kong; and Michael Orr of State Museum of Natural History Stuttgart.

Funding came from the National Science Foundation grant number DBI-2216927 (iDigBees); USC Dornsife; Simon Fraser University and the Natural Sciences and Engineering Research Council of Canada; and the Liber Ero Fellowship Program.

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