research article case

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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• Published: 10 November 2020

Case study research for better evaluations of complex interventions: rationale and challenges

• Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
• Judith Green 2 ,
• Chrysanthi Papoutsi 1 ,
• Jamie Murdoch 3 ,
• Mark Petticrew 4 ,
• Trish Greenhalgh 1 ,
• Benjamin Hanckel 5 &
• Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

Peer Review reports

The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
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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.

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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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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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Home » Case Study – Methods, Examples and Guide

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Types of journal articles

It is helpful to familiarise yourself with the different types of articles published by journals. Although it may appear there are a large number of types of articles published due to the wide variety of names they are published under, most articles published are one of the following types; Original Research, Review Articles, Short reports or Letters, Case Studies, Methodologies.

Original Research:

This is the most common type of journal manuscript used to publish full reports of data from research. It may be called an  Original Article, Research Article, Research, or just  Article, depending on the journal. The Original Research format is suitable for many different fields and different types of studies. It includes full Introduction, Methods, Results, and Discussion sections.

Short reports or Letters:

These papers communicate brief reports of data from original research that editors believe will be interesting to many researchers, and that will likely stimulate further research in the field. As they are relatively short the format is useful for scientists with results that are time sensitive (for example, those in highly competitive or quickly-changing disciplines). This format often has strict length limits, so some experimental details may not be published until the authors write a full Original Research manuscript. These papers are also sometimes called Brief communications .

Review Articles:

Review Articles provide a comprehensive summary of research on a certain topic, and a perspective on the state of the field and where it is heading. They are often written by leaders in a particular discipline after invitation from the editors of a journal. Reviews are often widely read (for example, by researchers looking for a full introduction to a field) and highly cited. Reviews commonly cite approximately 100 primary research articles.

TIP: If you would like to write a Review but have not been invited by a journal, be sure to check the journal website as some journals to not consider unsolicited Reviews. If the website does not mention whether Reviews are commissioned it is wise to send a pre-submission enquiry letter to the journal editor to propose your Review manuscript before you spend time writing it.  

Case Studies:

These articles report specific instances of interesting phenomena. A goal of Case Studies is to make other researchers aware of the possibility that a specific phenomenon might occur. This type of study is often used in medicine to report the occurrence of previously unknown or emerging pathologies.

Methodologies or Methods

These articles present a new experimental method, test or procedure. The method described may either be completely new, or may offer a better version of an existing method. The article should describe a demonstrable advance on what is currently available.

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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

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• Open access
• Published: 03 June 2024

The use of evidence to guide decision-making during the COVID-19 pandemic: divergent perspectives from a qualitative case study in British Columbia, Canada

• Laura Jane Brubacher   ORCID: orcid.org/0000-0003-2806-9539 1 , 2 ,
• Chris Y. Lovato 1 ,
• Veena Sriram 1 , 3 ,
• Michael Cheng 1 &
• Peter Berman 1  

Health Research Policy and Systems volume  22 , Article number:  66 ( 2024 ) Cite this article

Metrics details

The challenges of evidence-informed decision-making in a public health emergency have never been so notable as during the COVID-19 pandemic. Questions about the decision-making process, including what forms of evidence were used, and how evidence informed—or did not inform—policy have been debated.

We examined decision-makers' observations on evidence-use in early COVID-19 policy-making in British Columbia (BC), Canada through a qualitative case study. From July 2021- January 2022, we conducted 18 semi-structured key informant interviews with BC elected officials, provincial and regional-level health officials, and civil society actors involved in the public health response. The questions focused on: (1) the use of evidence in policy-making; (2) the interface between researchers and policy-makers; and (3) key challenges perceived by respondents as barriers to applying evidence to COVID-19 policy decisions. Data were analyzed thematically, using a constant comparative method. Framework analysis was also employed to generate analytic insights across stakeholder perspectives.

Overall, while many actors’ impressions were that BC's early COVID-19 policy response was evidence-informed, an overarching theme was a lack of clarity and uncertainty as to what evidence was used and how it flowed into decision-making processes. Perspectives diverged on the relationship between 'government' and public health expertise, and whether or not public health actors had an independent voice in articulating evidence to inform pandemic governance. Respondents perceived a lack of coordination and continuity across data sources, and a lack of explicit guidelines on evidence-use in the decision-making process, which resulted in a sense of fragmentation. The tension between the processes involved in research and the need for rapid decision-making was perceived as a barrier to using evidence to inform policy.

Conclusions

Areas to be considered in planning for future emergencies include: information flow between policy-makers and researchers, coordination of data collection and use, and transparency as to how decisions are made—all of which reflect a need to improve communication. Based on our findings, clear mechanisms and processes for channeling varied forms of evidence into decision-making need to be identified, and doing so will strengthen preparedness for future public health crises.

Peer Review reports

The challenges of evidence-informed decision-making Footnote 1 in a public health emergency have never been so salient as during the COVID-19 pandemic, given its unprecedented scale, rapidly evolving virology, and multitude of global information systems to gather, synthesize, and disseminate evidence on the SARS-CoV-2 virus and associated public health and social measures [ 1 , 2 , 3 ]. Early in the COVID-19 pandemic, rapid decision-making became central for governments globally as they grappled with crucial decisions for which there was limited evidence. Critical questions exist, in looking retrospectively at these decision-making processes and with an eye to strengthening future preparedness: Were decisions informed by 'evidence'? What forms of evidence were used, and how, by decision-makers? [ 4 , 5 , 6 ].

Scientific evidence, including primary research, epidemiologic research, and knowledge synthesis, is one among multiple competing influences that inform decision-making processes in an outbreak such as COVID-19 [ 7 ]. Indeed, the use of multiple forms of evidence has been particularly notable as it applies to COVID-19 policy-making. Emerging research has also documented the important influence of ‘non-scientific’ evidence such as specialized expertise and experience, contextual information, and level of available resources [ 8 , 9 , 10 ]. The COVID-19 pandemic has underscored the politics of evidence-use in policy-making [ 11 ]; what evidence is used and how can be unclear, and shaped by political bias [ 4 , 5 ]. Moreover, while many governments have established scientific advisory boards, the perspectives of these advisors were reportedly largely absent from COVID-19 policy processes [ 6 ]. How evidence and public health policy interface—and intersect—is a complex question, particularly in the dynamic context of a public health emergency.

Within Canada, a hallmark of the public health system and endorsed by government is evidence-informed decision-making [ 12 ]. In British Columbia (BC), Canada, during the early phases of COVID-19 (March—June 2020), provincial public health communication focused primarily on voluntary compliance with recommended public health and social measures, and on supporting those most affected by the pandemic. Later, the response shifted from voluntary compliance to mandatory enforceable government orders [ 13 ]. Like many other jurisdictions, the government’s public messaging in BC asserted that the province took an approach to managing the COVID-19 pandemic and developing related policy that was based on scientific evidence, specifically. For example, in March 2021, in announcing changes to vaccination plans, Dr. Bonnie Henry, the Provincial Health Officer, stated, " This is science in action " [ 14 ]. As a public health expert with scientific voice, the Provincial Health Officer has been empowered to speak on behalf of the BC government across the COVID-19 pandemic progression. While this suggests BC is a jurisdiction which has institutionalized scientifically-informed decision-making as a core tenet of effective public health crisis response, it remains unclear as to whether BC’s COVID-19 response could, in fact, be considered evidence-informed—particularly from the perspectives of those involved in pandemic decision-making and action. Moreover, if evidence-informed, what types of evidence were utilized and through what mechanisms, how did this evidence shape decision-making, and what challenges existed in moving evidence to policy and praxis in BC’s COVID-19 response?

The objectives of this study were: (1) to explore and characterize the perspectives of BC actors involved in the COVID-19 response with respect to evidence-use in COVID-19 decision-making; and (2) to identify opportunities for and barriers to evidence-informed decision-making in BC’s COVID-19 response, and more broadly. This inquiry may contribute to identifying opportunities for further strengthening the synthesis and application of evidence (considered broadly) to public health policy and decision-making, particularly in the context of future public health emergencies, both in British Columbia and other jurisdictions.

Study context

This qualitative study was conducted in the province of British Columbia (BC), Canada, a jurisdiction with a population of approximately five million people [ 15 ]. Within BC’s health sector, key actors involved in the policy response to COVID-19 included: elected officials, the BC Government’s Ministry of Health (MOH), the Provincial Health Services Authority (PHSA), Footnote 2 the Office of the Provincial Health Officer (PHO), Footnote 3 the BC Centre for Disease Control (BCCDC), Footnote 4 and Medical Health Officers (MHOs) and Chief MHOs at regional and local levels.

Health research infrastructure within the province includes Michael Smith Health Research BC [ 16 ] and multiple post-secondary research and education institutions (e.g., The University of British Columbia). Unlike other provincial (e.g., Ontario) and international (e.g., UK) jurisdictions, BC did not establish an independent, formal scientific advisory panel or separate organizational structure for public health intelligence in COVID-19. That said, a Strategic Research Advisory Council was established, reporting to the MOH and PHO, to identify COVID-19 research gaps and commission needed research for use within the COVID-19 response [ 17 ].

This research was part of a multidisciplinary UBC case study investigating the upstream determinants of the COVID-19 response in British Columbia, particularly related to institutions, politics, and organizations and how these interfaced with, and affected, pandemic governance [ 18 ]. Ethics approval for this study was provided by the University of British Columbia (UBC)’s Institutional Research Ethics Board (Certificate #: H20-02136).

Data collection

From July 2021 to January 2022, 18 semi-structured key informant interviews were conducted with BC elected officials, provincial and regional-level health officials, and civil society actors (e.g., within non-profit research organizations, unions) (Table 1 ). Initially, respondents were purposively sampled, based on their involvement in the COVID-19 response and their positioning within the health system organizational structure. Snowball sampling was used to identify additional respondents, with the intent of representing a range of organizational roles and actor perspectives. Participants were recruited via email invitation and provided written informed consent to participate.

Interviews were conducted virtually using Zoom® videoconferencing, with the exception of one hybrid in-person/Zoom® interview. Each interview was approximately one hour in duration. One to two research team members led each interview. The full interview protocol focused on actors’ descriptions of decision-making processes across the COVID-19 pandemic progression, from January 2020 to the date of the interviews, and they were asked to identify key decision points (e.g., emergency declaration, business closures) [see Additional File 1 for the full semi-structured interview guide]. For this study, we used a subset of interview questions focused on evidence-use in the decision-making process, and the organizational structures or actors involved, in BC's early COVID-19 pandemic response (March–August 2020). Questions were adapted to be relevant to a respondent’s expertise and particular involvement in the response. ‘Evidence’ was left undefined and considered broadly by the research team (i.e., both ‘scientific’/research-based and ‘non-scientific’ inputs) within interview questions, and therefore at the discretion of the participant as to what inputs they perceived and described as ‘evidence’ that informed or did not inform pandemic decision-making. Interviews were audio-recorded over Zoom® with permission and transcribed using NVivo Release 1.5© software. Each transcript was then manually verified for accuracy by 1–2 members of the research team.

Data analysis

An inductive thematic analysis was conducted, using a constant comparative method, to explore points of divergence and convergence across interviews and stakeholder perspectives [ 19 ]. Transcripts were inductively coded in NVivo Release 1.5© software, which was used to further organize and consolidate codes, generate a parsimonious codebook to fit the data, and retrieve interview excerpts [ 20 ]. Framework analysis was also employed as an additional method for generating analytic insights across stakeholder perspectives and contributed to refining the overall coding [ 21 ]. Triangulation across respondents and analytic methods, as well as team collaboration in reviewing and refining the codebook, contributed to validity of the analysis [ 22 ].

How did evidence inform early COVID-19 policy-making in BC?

Decision-makers described their perceptions on the use of evidence in policy-making; the interface between researchers and policy-makers; and specific barriers to evidence-use in policy-making within BC’s COVID-19 response. In discussing the use of evidence, respondents focused on ‘scientific’ evidence; however, they noted a lack of clarity as to how and what evidence flowed into decision-making. They also acknowledged that ‘scientific’ evidence was one of multiple factors influencing decisions. The themes described below reflect the narrative underlying their perspectives.

Perceptions of evidence-use

Multiple provincial actors generally expressed confidence or had an overall impression that decisions were evidence-based (IDI5,9), stating definitively that, "I don’t think there was a decision we made that wasn’t evidence-informed" (IDI9) and that "the science became a driver of decisions that were made" (IDI5). However, at the regional health authority level, one actor voiced skepticism that policy decisions were consistently informed by scientific evidence specifically, stating, "a lot of decisions [the PHO] made were in contrast to science and then shifted to be by the science" ( IDI6). The evolving nature of the available evidence and scientific understanding of the virus throughout the pandemic was acknowledged. For instance, one actor stated that, "I’ll say the response has been driven by the science; the science has been changing…from what I’ve seen, [it] has been a very science-based response" (IDI3).

Some actors narrowed in on certain policy decisions they believed were or were not evidence-informed. Policy decisions in 2020 that actors believed were directly informed by scientific data included the early decision to restrict informal, household gatherings; to keep schools open for in-person learning; to implement a business safety plan requirement across the province; and to delay the second vaccine dose for maximum efficacy. One provincial public health actor noted that an early 2020 decision made, within local jurisdictions, to close playgrounds was not based on scientific evidence. Further, the decision prompted public health decision-makers to centralize some decision-making to the provincial level, to address decisions being made 'on the ground' that were not based on scientific evidence (IDI16). Similarly, they added that the policy decision to require masking in schools was not based on scientific evidence; rather, "it's policy informed by the noise of your community." As parents and other groups within the community pushed for masking, this was "a policy decision to help schools stay open."

Early in the pandemic response, case data in local jurisdictions were reportedly used for monitoring and planning. These "numerator data" (IDI1), for instance case or hospitalization counts, were identified as being the primary mode of evidence used to inform decisions related to the implementation or easing of public health and social measures. The ability to generate epidemiological count data early in the pandemic due to efficient scaling up of PCR testing for COVID-19 was noted as a key advantage (IDI16). As the pandemic evolved in 2020, however, perspectives diverged in relation to the type of data that decision-makers relied on. For example, it was noted that BCCDC administered an online, voluntary survey to monitor unintended consequences of public health and social measures and inform targeted interventions. Opinions varied on whether this evidence was successfully applied in decision-making. One respondent emphasized this lack of application of evidence and perceived that public health orders were not informed by the level and type of evidence available, beyond case counts: "[In] a communicable disease crisis like a pandemic, the collateral impact slash damage is important and if you're going to be a public health institute, you actually have to bring those to the front, not just count cases" (IDI1).

There also existed some uncertainty and a perceived lack of transparency or clarity as to how or whether data analytic ‘entities’, such as BCCDC or research institutions, fed directly into decision-making. As a research actor shared, "I’m not sure that I know quite what all those channels really look like…I’m sure that there’s a lot of improvement that could be driven in terms of how we bring strong evidence to actual policy and practice" (IDI14). Another actor explicitly named the way information flowed into decision-making in the province as "organic" (IDI7). They also noted the lack of a formal, independent science advisory panel for BC’s COVID-19 response, which existed in other provincial and international jurisdictions. Relatedly, one regional health authority actor perceived that the committee that was convened to advise the province on research, and established for the purpose of applying research to the COVID-19 response, "should have focused more on knowledge translation, but too much time was spent commissioning research and asking what kinds of questions we needed to ask rather than looking at what was happening in other jurisdictions" (IDI6). Overall, multiple actors noted a lack of clarity around application of evidence and who is responsible for ensuring evidence is applied. As a BCCDC actor expressed, in relation to how to prevent transmission of COVID-19:

We probably knew most of the things that we needed to know about May of last year [2020]. So, to me, it’s not even what evidence you need to know about, but who’s responsible for making sure that you actually apply the evidence to the intervention? Because so many of our interventions have been driven by peer pressure and public expectation rather than what we know to be the case [scientifically] (IDI1).

Some described the significance of predictive disease modelling to understand the COVID-19 trajectory and inform decisions, as well as to demonstrate to the public the effectiveness of particular measures, which "help[ed] sustain our response" (IDI2). Others, however, perceived that "mathematical models were vastly overused [and] overvalued in decision-making around this pandemic" (IDI1) and that modellers stepped outside their realm of expertise in providing models and policy recommendations through the public media.

Overall, while many actors’ impressions were that the response was evidence-informed, an overarching theme was a lack of clarity and uncertainty with respect to how evidence actually flowed into decision-making processes, as well as what specific evidence was used and how. Participants noted various mechanisms created or already in place prior to COVID-19 that fed data into, and facilitated, decision-making. There was an acknowledgement that multiple forms of evidence—including scientific data, data on public perceptions, as well as public pressure—appeared to have influenced decision-making.

Interface between researchers and policy-makers

There was a general sense that the Ministry supported the use of scientific and research-based evidence specifically. Some actors identified particular Ministry personnel as being especially amenable to research and focused on data to inform decisions and implementation. More broadly, the government-research interface was characterized by one actor as an amicable one, a "research-friendly government", and that the Ministry of Health (MOH), specifically, has a research strategy whereby, "it’s literally within their bureaucracy to become a more evidence-informed organization" (IDI11). The MOH was noted to have funded a research network intended to channel evidence into health policy and practice, and which reported to the research side of the MOH.

Other actors perceived relatively limited engagement with the broader scientific community. Some perceived an overreliance on 'in-house expertise' or a "we can do that [ourselves] mentality" within government that precluded academic researchers’ involvement, as well as a sense of "not really always wanting to engage with academics to answer policy questions because they don’t necessarily see the value that comes" (IDI14). With respect to the role of research, an actor stated:

There needs to be a provincial dialogue around what evidence is and how it gets situated, because there’s been some tension around evidence being produced and not used or at least not used in the way that researchers think that it should be (IDI11).

Those involved in data analytics within the MOH acknowledged a challenge in making epidemiological data available to academic researchers, because "at the time, you’re just trying to get decisions made" (IDI7). Relatedly, a research actor described the rapid instigation of COVID-19 research and pivoting of academic research programs to respond to the pandemic, but perceived a slow uptake of these research efforts from the MOH and PHSA for decision-making and action. Nevertheless, they too acknowledged the challenge of using research evidence, specifically, in an evolving and dynamic pandemic:

I think we’ve got to be realistic about what research in a pandemic situation can realistically contribute within very short timelines. I mean, some of these decisions have to be made very quickly...they were intuitive decisions, I think some of them, rather than necessarily evidence-based decisions (IDI14).

Relatedly, perspectives diverged on the relationship between 'government' and public health expertise, and whether or not public health actors had an independent voice in articulating evidence to inform governance during the pandemic. Largely from Ministry stakeholders, and those within the PHSA, the impressions were that Ministry actors were relying on public health advice and scientific expertise. As one actor articulated, "[the] government actually respected and acknowledged and supported public health expertise" (IDI9). Others emphasized a "trust of the people who understood the problem" (IDI3)—namely, those within public health—and perceived that public health experts were enabled "to take a lead role in the health system, over politics" (IDI12). This perspective was not as widely held by those in the public health sector, as one public health actor expressed, "politicians and bureaucrats waded into public health practice in a way that I don't think was appropriate" and that, "in the context of a pandemic, it’s actually relatively challenging to bring true expert advice because there’s too many right now. Suddenly, everybody’s a public health expert, but especially bureaucrats and politicians." They went on to share that the independence of public health to speak and act—and for politicians to accept independent public health advice—needs to be protected and institutionalized as "core to good governance" (IDI1). Relatedly, an elected official linked this to the absence of a formal, independent science table to advise government and stated that, "I think we should have one established permanently. I think we need to recognize that politicians aren't always the best at discerning scientific evidence and how that should play into decision-making" (IDI15).

These results highlight the divergent perspectives participants had as to the interface between research and policy-making and a lack of understanding regarding process and roles.

Challenges in applying evidence to policy decisions

Perspectives converged with respect to the existence of numerous challenges with and barriers to applying evidence to health policy and decision-making. These related to the quality and breadth of available data, both in terms of absence and abundance. For instance, as one public health actor noted in relation to health policy-making, "you never have enough information. You always have an information shortage, so you're trying to make the best decisions you can in the absence of usually really clear information" (IDI8). On the other hand, as evidence emerged en masse across jurisdictions in the pandemic, there were challenges with synthesizing evidence in a timely fashion for 'real-time' decision-making. A regional health authority actor highlighted this challenge early in the COVID-19 pandemic and perceived that there was not a provincial group bringing new synthesized information to decision-makers on a daily basis (IDI6). Other challenges related to the complexity of the political-public health interface with respect to data and scientific expertise, which "gets debated and needs to be digested by the political process. And then decisions are made" (IDI5). This actor further expressed that debate among experts needs to be balanced with efficient crisis response, that one has to "cut the debate short. For the sake of expediency, you need to react."

It was observed that, in BC’s COVID-19 response, data was gathered from multiple sources with differing data collection procedures, and sometimes with conflicting results—for instance, 'health system data' analyzed by the PHSA and 'public health data' analyzed by the BCCDC. This was observed to present challenges from a political perspective in discerning "who’s actually getting the 'right' answers" (IDI7). An added layer of complexity was reportedly rooted in how to communicate such evidence to the public and "public trust in the numbers" (IDI7), particularly as public understanding of what evidence is, how it is developed, and why it changes, can influence public perceptions of governance.

Finally, as one actor from within the research sector noted, organizationally and governance-wise, the system was "not very well set up to actually use research evidence…if we need to do better at using evidence in practice, we need to fix some of those things. And we actually know what a lot of those things are." For example , "there’s no science framework for how organizations work within that" and " governments shy away from setting science policy " (IDI11). This challenge was framed as having a macro-level dimension, as higher-level leadership structures were observed to not incentivize the development and effective use of research among constituent organizations, and also micro-level implications. From their perspective, researchers will struggle without such policy frameworks to obtain necessary data-sharing agreements with health authorities, nor will they be able to successfully navigate other barriers to conducting action-oriented research that informs policy and practice.

Similarly, a research actor perceived that the COVID-19 pandemic highlighted pre-existing fragmentation, "a pretty disjointed sort of enterprise" in how research is organized in the province:

I think pandemics need strong leadership and I think pandemic research response needed probably stronger leadership than it had. And I think that’s to do with [how] no one really knew who was in charge because no one really was given the role of being truly in charge of the research response (IDI14).

This individual underscored that, at the time of the interview, there were nearly 600 separate research projects being conducted in BC that focused on COVID-19. From their perspective, this reflected the need for more centralized direction to provide leadership, coordinate research efforts, and catalyze collaborations.

Overall, respondents perceived a lack of coordination and continuity across data sources, and a lack of explicit guidelines on evidence-use in the decision-making process, which resulted in a sense of fragmentation. The tension between the processes involved in research and the need for rapid decision-making was perceived as a barrier to using evidence to inform policy.

This study explored the use of evidence to inform early COVID-19 decision-making within British Columbia, Canada, from the perspectives of decision-makers themselves. Findings underscore the complexity of synthesizing and applying evidence (i.e., ‘scientific’ or research-based evidence most commonly discussed) to support public health policy in 'real-time', particularly in the context of public health crisis response. Despite a substantial and long-established literature on evidence-based clinical decision-making [ 23 , 24 ], understanding is more limited as to how public health crisis decision-making can be evidence-informed or evidence-based. By contributing to a growing global scholarship of retrospective examinations of COVID-19 decision-making processes [ 25 , 26 , 27 , 28 ], our study aimed to broaden this understanding and, thus, support the strengthening of public health emergency preparedness in Canada, and globally.

Specifically, based on our findings on evidence-based public health practice, we found that decision-makers clearly emphasized ‘evidence-based’ or ‘evidence-informed’ as meaning ‘scientific’ evidence. They acknowledged other forms of evidence such as professional expertise and contextual information as influencing factors. We identified four key points related to the process of evidence-use in BC's COVID-19 decision-making, with broader implications as well:

Role Differences: The tensions we observed primarily related to a lack of clarity among the various agencies involved as to their respective roles and responsibilities in a public health emergency, a finding that aligns with research on evidence-use in prior pandemics in Canada [ 29 ]. Relatedly, scientists and policy-makers experienced challenges with communication and information-flow between one another and the public, which may reflect their different values and standards, framing of issues and goals, and language [ 30 ].

Barriers to Evidence-Use: Coordination and consistency in how data are collected across jurisdictions reportedly impeded efficiency and timeliness of decision-making. Lancaster and Rhodes (2020) suggest that evidence itself should be treated as a process, rather than a commodity, in evidence-based practice [ 31 ]. Thus, shifting the dialogue from 'barriers to evidence use' to an approach that fosters dialogue across different forms of evidence and different actors in the process may be beneficial.

Use of Evidence in Public Health versus Medicine: Evidence-based public health can be conflated with the concept of evidence-based medicine, though these are distinct in the type of information that needs to be considered. While ‘research evidence’ was the primary type of evidence used, other important types of evidence informed policy decisions in the COVID-19 public health emergency—for example, previous experience, public values, and preferences. This concurs with Brownson’s (2009) framework of factors driving decision-making in evidence-based public health [ 32 ]. Namely, that a balance between multiple factors, situated in particular environmental and organizational context, shapes decision-making: 1) best available research evidence; 2) clients'/population characteristics, state, needs, values, and preferences; and 3) resources, including a practitioner’s expertise. Thus, any evaluation of evidence-use in public health policy must take into consideration this multiplicity of factors at play, and draw on frameworks specific to public health [ 33 ]. Moreover, public health decision-making requires much more attention to behavioural factors and non-clinical impacts, which is distinct from the largely biology-focused lens of evidence-based medicine.

Transparency: Many participants emphasized a lack of explanation about why certain decisions were made and a lack of understanding about who was involved in decisions and how those decisions were made. This point was confirmed by a recent report on lessons learned in BC during the COVID-19 pandemic in which the authors describe " the desire to know more about the reasons why decisions were taken " as a " recurring theme " (13:66). These findings point to a need for clear and transparent mechanisms for channeling evidence, irrespective of the form used, into public health crisis decision-making.

Our findings also pointed to challenges associated with the infrastructure for utilizing research evidence in BC policy-making, specifically a need for more centralized authority on the research side of the public health emergency response to avoid duplication of efforts and more effectively synthesize findings for efficient use. Yet, as a participant questioned, what is the realistic role of research in a public health crisis response? Generally, most evidence used to inform crisis response measures is local epidemiological data or modelling data [ 7 ]. As corroborated by our findings, challenges exist in coordinating data collection and synthesis of these local data across jurisdictions to inform 'real-time' decision-making, let alone to feed into primary research studies [ 34 ].

On the other hand, as was the case in the COVID-19 pandemic, a 'high noise' research environment soon became another challenge as data became available to researchers. Various mechanisms have been established to try and address these challenges amid the COVID-19 pandemic, both to synthesize scientific evidence globally and to create channels for research evidence to support timely decision-making. For instance: 1) research networks and collaborations are working to coordinate research efforts (e.g., COVID-END network [ 35 ]); 2) independent research panels or committees within jurisdictions provide scientific advice to inform decision-making; and 3) research foundations, funding agencies, and platforms for knowledge mobilization (e.g., academic journals) continue to streamline funding through targeted calls for COVID-19 research grant proposals, or for publication of COVID-19 research articles. While our findings describe the varied forms of evidence used in COVID-19 policy-making—beyond scientific evidence—they also point to the opportunity for further investments in infrastructure that coordinates, streamlines, and strengthens collaborations between health researchers and decision-makers that results in timely uptake of results into policy decisions.

Finally, in considering these findings, it is important to note the study's scope and limitations: We focused on evidence use in a single public health emergency, in a single province. Future research could expand this inquiry to a multi-site analysis of evidence-use in pandemic policy-making, with an eye to synthesizing lessons learned and best practices. Additionally, our sample of participants included only one elected official, so perspectives were limited from this type of role. The majority of participants were health officials who primarily referred to and discussed evidence as ‘scientific’ or research-based evidence. Further work could explore the facilitators and barriers to evidence-use from the perspectives of elected officials and Ministry personnel, particularly with respect to the forms of evidence—considered broadly—and other varied inputs, that shape decision-making in the public sphere. This could include a more in-depth examination of policy implementation and how the potential societal consequences of implementation factor into public health decision-making.

We found that the policy decisions made during the initial stages of the COVID-19 pandemic were perceived by actors in BC's response as informed by—not always based on—scientific evidence, specifically; however, decision-makers also considered other contextual factors and drew on prior pandemic-related experience to inform decision-making, as is common in evidence-based public health practice [ 32 ]. The respondents' experiences point to specific areas that need to be considered in planning for future public health emergencies, including information flow between policy-makers and researchers, coordination in how data are collected, and transparency in how decisions are made—all of which reflect a need to improve communication. Furthermore, shifting the discourse from evidence as a commodity to evidence-use as a process will be helpful in addressing barriers to evidence-use, as well as increasing understanding about the public health decision-making process as distinct from clinical medicine. Finally, there is a critical need for clear mechanisms that channel evidence (whether ‘scientific’, research-based, or otherwise) into health crisis decision-making, including identifying and communicating the decision-making process to those producing and synthesizing evidence. The COVID-19 pandemic experience is an opportunity to reflect on what needs to be done to guild our public health systems for the future [ 36 , 37 ]. Understanding and responding to the complexities of decision-making as we move forward, particularly with respect to the synthesis and use of evidence, can contribute to strengthening preparedness for future public health emergencies.

Availability of data and materials

The data that support the findings of this study are not publicly available to maintain the confidentiality of research participants.

The terms 'evidence-informed' and 'evidence-based' decision-making are used throughout this paper, though are distinct. The term 'evidence-informed' suggests that evidence is used and considered, though not necessarily solely determinative in decision-making [ 38 ].

The Provincial Health Services Authority (PHSA) works with the Ministry of Health (MOH) and regional health authorities to oversee the coordination and delivery of programs.

The Office of the Provincial Health Officer (PHO) has binding legal authority in the case of an emergency, and responsibility to monitor the health of BC’s population and provide independent advice to Ministers and public offices on public health issues.

The British Columbia Centre for Disease Control (BCCDC) is a program of the PHSA and provides provincial and national disease surveillance, detection, treatment, prevention, and consultation.

Abbreviations

British Columbia

British Columbia Centre for Disease Control

Coronavirus Disease 2019

Medical Health Officer

Ministry of Health

Provincial Health Officer

Provincial Health Services Authority

Severe Acute Respiratory Syndrome Coronavirus—2

University of British Columbia

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Acknowledgements

We would like to extend our gratitude to current and former members of the University of British Columbia Working Group on Health Systems Response to COVID-19 who contributed to various aspects of this study, including Shelly Keidar, Kristina Jenei, Sydney Whiteford, Dr. Md Zabir Hasan, Dr. David M. Patrick, Dr. Maxwell Cameron, Mahrukh Zahid, Dr. Yoel Kornreich, Dr. Tammi Whelan, Austin Wu, Shivangi Khanna, and Candice Ruck.

Financial support for this work was generously provided by the University of British Columbia's Faculty of Medicine (Grant No. GR004683) and Peter Wall Institute for Advanced Studies (Grant No. GR016648), as well as a Canadian Institutes of Health Research Operating Grant (Grant No. GR019157). These funding bodies were not involved in the design of the study, the collection, analysis or interpretation of data, or in the writing of this manuscript.

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CYL, PB, and VS obtained funding for and designed the study. LJB, MC, and PB conducted data collection. LJB and VS analyzed the qualitative data. CYL and LJB collaboratively wrote the manuscript. All authors read and approved the final manuscript.

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Semi-structured interview guide [* = questions used for this specific study]

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Brubacher, L.J., Lovato, C.Y., Sriram, V. et al. The use of evidence to guide decision-making during the COVID-19 pandemic: divergent perspectives from a qualitative case study in British Columbia, Canada. Health Res Policy Sys 22 , 66 (2024). https://doi.org/10.1186/s12961-024-01146-2

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The case for experiential research

In work described as "social research and people analytics for the built environment," Plastarc founder and executive director Melissa Marsh, Assoc. AIA,  focuses on the study of “activity-based working & wellness” for organizations. 

AIA's editor for the Practice Management Knowledge Community Rebecca W. E. Edmunds, AIA, talks to Marsh, an expert in workplace strategy and leader in change management, about what her research means for architects and their clients.

What special areas of research do you conduct? 

We are focused on the human experience of space and leveraging research methodologies from both a scale larger than architecture and a scale smaller than architecture. I would argue urban design has a practice of social research within their approach, product and material design have a practice of research, but the building scale of architecture tends not to.  So, we use the urban and product scales as starting points for understanding how we can think differently and help our clients think differently about human experience in architecture. The urban scale is about everything from participative design to using big data resources like consumer data and social media data to inform and understand an environment’s experience. 

We advocate for using existing buildings and spending time to understand the experience of different environments to inform the next generation of buildings. We also look down a scale to product design to understand ideas of prototyping and digital prototyping, align with user experience design in technology.   These methods may be used to build a mockup, a pilot, or add a digital layer, either in situ like with VR goggles, rendering, or other means that combine physical resources with digital to test the design proposition inspired by that smaller scale. So, the foundation of our viewpoint in relation to architecture is to bring the sensibility of both qualitative and quantitative design information to the architectural scale.   

Can you share the types of methods you mentioned, such as gathering data from social media and prototyping?   

We refer to broad categories, the qualitative ones being workshops, focus groups, interviews, and so on that are typical of architectural pre-design and programming. To that we add a variety of data collection methods, which are all evolving due to the increasing proportion of buildings that are smart in some capacity or another.  I try to simplify how we speak about research, which is more like the science we learned in middle school, such as a science fair project that uses a scientific process to observe a phenomenon happening in the world and ask, why is that happening? With background research to see if someone has the answer, and if not, one can formulate a hypothesis.  Referring to people’s earliest research experiences helps establish the foundation of any type of study.  For tools or methods, we use workshops, observation and occupancy metrics, surveys, interviews, and analysis. But the secret sauce isn’t the methods; it's a combination of curiosity and then a rigorous exploration.  Do architects or their clients or both come to you? And what draws them to your practice? 

While I’d love architects to come to us, it is invariably their clients that do. We are often engaged in what I call the Scooby-Doo “ruh-roh” moment; something has gone wrong, not necessarily from a building forensic perspective, but from a human experience/occupancy perspective. Maybe things are underutilized, or there's negative feedback on the building or the environment. Metaphorically, it’s a rejected transplant organ, where a design solution has been imposed on people without sufficient communication in relation to a solution that isn’t performing from a people perspective. We’re engaged in, essentially, the social forensics of building design. 

At the other end—the more positive aspect of what we do—clients come to us because they are focused on the employee or customer experience and how it can be executed through architecture. To what extent can we describe or measure those factors in a way that is temporarily ahead of the architecture? We may work at a portfolio strategy level, or an organization is thinking of many buildings simultaneously, or it's augmenting an architectural design sequence. For example, we may work parallel to an architecture firm’s process. The client may ask for spaces for serendipitous interaction; we add science to inform the characteristics and qualities of that environment on behalf of the client but serving the architectural endeavor.  Do you have case studies of how that research can in turn be used by clients or by architects to alter, amend, or add to what they're doing? 

Client projects are included on our website, and we actively publish the results of our work. We've presented at ANFA (Academy of Neuroscience for Architecture) or EDRA (Environmental Design Research Association) over the last ten years to share our discoveries, including recent work around personality factors and employee expectations in return to office models and early work around occupancy and occupancy sensors to help determine what environmental characteristics people liked or didn't like.  What markets are interested in the type of research you do? 

Our approach is market agnostic. It's about the human experience of space, whether teachers teach, coders code, nurses nurse; we work in any environment where people are doing their most important work. The physical environment has an impact, positive or negative, on its occupants, even more so if the person is in a building on a regular basis. We tend to be in the world of workplace and office environments, though more as a matter of social network and business than of our approach or belief on how people experience space and how it can and should be studied.  Can you share an example of outcomes based on the research you do and what it means for users or whoever has hired you? 

In the corporate world, it's productivity. Every client, every building developer, owner, occupier, has their definition of a desired future condition. An academic environment involves the intersection between learning, culture, and experience but invariably involves the connection between a university and its alumni to create a donor network as an academic driver. In office and work environments, it’s the intersection of individual, team, and corporate performance, which predominantly and historically are financial metrics.    In the future, every building typology and every organization using that typology has performance metrics. That's probably what's most exciting about different clients in different spaces. A single building can hold 10 different clients, each with a different objective or view of what high performance space should deliver. 

Back to our sensibility that human health, personal experience, and community are at the center of desirable spaces...much of our work focuses on an awareness that humans are sensors; we seek out what works for people, and we avoid what doesn't work. We use this idea as a means of thinking about spaces that are truly occupied and that people enjoy being and working in to understand the characteristics of those spaces as opposed to the world of the workplace, where, essentially, have to pay people to occupy. This idea is being tested today in the corporate sector around returning to the office.  But even in a single environment within a building—a hospitality or hospital space, academic or a corporate environment—you see the places people choose to hang out in and those they don't. Why do we build so many spaces that we know have undesirable characteristics? Why not build more spaces with features we know people like? In large part, we don't go back and look at how our buildings are occupied and the experience within them.  Do architects misuse the word “research”?

It's multiply misused, or firms believe research is only done from a technical perspective, or furniture and material suppliers should do it. The simplest form of research is pre- and post-occupancy evaluations (POE) to understand the experience of an environment to either inform a future environment or diagnose a recently completed environment, and so on. Very little has changed around POEs in the over two decades since I finished grad school—the frequency of doing them, the methodology used, the levels of incorporation of POE results into design services.  This lack of evolution would've surprised the me of 20 years ago, who thought POEs would become the go-to approach to free architecture from the Middle Ages. Prior generations of architects seemed uninterested in seeking feedback. I jokingly say it's the equivalent of never saying, 'Was it good for you?'  Architectural education teaches that lack of user feedback to students of every gender, every culture. Instead, it teaches us to fight for, advocate for, or advance a set of solutions without seeking anecdotal research or actual research from a scientific perspective. 

Imagine if medicine were still being practiced the same way it was a hundred years ago? Somehow, we've been able to infuse the industry and education with that sensibility from a materials perspective, but not from a social sciences perspective. It's a similar characterization to saying only nurses need a bedside manner and only doctors need credentials. Generally, when architects repeatedly do work for a single client, there is a logic of, let's go see what happened last time. What has more systematically injured the field is that the architectural industry has had to go after projects rather than nurture, enrich, and continue developmental relationships with clients.  Think of the gender roles of “hunter” and “gatherer”—typically assigned respectively as male and female. The absence of women in architecture firm leadership has meant that many firms are in that cycle of hunting for projects rather than nurturing clients. It has driven the entire profession of project management, which wouldn't need to exist if architects were trained to be good project managers. It has driven this lack of reciprocal and constantly improving relationships with clients, because those individuals who are naturally good at relationships weren't given the opportunity, so it didn't become the basis of practice.

Learn more about research in practice. 

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• 31 May 2024

Biomedical paper retractions have quadrupled in 20 years — why?

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Retraction rates in European biomedical science papers have quadrupled since 2000. Credit: bagi1998/Getty

The retraction rate for European biomedical-science papers increased fourfold between 2000 and 2021, a study of thousands of retractions has found.

Two-thirds of these papers were withdrawn for reasons relating to research misconduct, such as data and image manipulation or authorship fraud . These factors accounted for an increasing proportion of retractions over the roughly 20-year period, the analysis suggests.

“Our findings indicate that research misconduct has become more prevalent in Europe over the last two decades,” write the authors, led by Alberto Ruano‐Ravina, a public-health researcher at the University of Santiago de Compostela in Spain.

Other research-integrity specialists point out that retractions could be on the rise because researchers and publishers are getting better at investigating and identifying potential misconduct. There are more people working to spot errors and new digital tools to screen publications for suspicious text or data.

Rising retractions

Scholarly publishers have faced increased pressure to clear up the literature in recent years as sleuths have exposed cases of research fraud , identified when peer review has been compromised and uncovered the buying and selling of research articles . Last year saw a record 10,000 papers retracted . Although misconduct is a leading cause of retractions, it is not always responsible: some papers are retracted when authors discover honest errors in their work.

More than 10,000 research papers were retracted in 2023 — a new record

The latest research, published on 4 May in Scientometrics 1 , looked at more than 2,000 biomedical papers that had a corresponding author based at a European institution and were retracted between 2000 and mid-2021. The data included original articles, reviews, case reports and letters published in English, Spanish or Portuguese. They were listed in a database collated by the media organization Retraction Watch, which records why papers are retracted.

The authors found that overall retraction rates quadrupled during the study period — from around 11 retractions per 100,000 papers in 2000 to almost 45 per 100,000 in 2020. Of all the retracted papers, nearly 67% were withdrawn due to misconduct and around 16% for honest errors. The remaining retractions did not give a reason.

Looking at the papers retracted for misconduct specifically, Ruano‐Ravina and his colleagues found that the major causes have changed over time. In 2000, the highest proportions of retractions were attributed to ethical and legal problems, authorship issues — including dubious or false authorships, objections to authorship by institutions and lack of author approval — and duplication of images , data or large passages of text. By 2020, duplication was still one of the top reasons for retraction, but a similar proportion of papers was retracted owing to ‘unreliable data’ (see ‘Misconduct retractions’).

Source: Ref 1

‘Unreliable data’ refers to studies that cannot be trusted for reasons including original data not being provided and problems with bias or lack of balance. The authors suggest that the rise in retractions attributable to this cause could be related to an increase in the number of papers suspected to be produced by paper mills , businesses that generate fake or poor-quality papers to order.

Authorship problems fell to the joint fifth reason for retractions in 2020. This is “possibly due to the implementation of authorship control systems and increased researcher awareness”, write Ruano‐Ravina and colleagues.

International variation

The study also identified the four European countries that had the highest number of retracted biomedical science papers: Germany, the United Kingdom, Italy and Spain. Each had distinct ‘profiles’ of misconduct-related retractions. In the United Kingdom, for example, falsification was the top reason given for retractions in most years, but the proportion of papers withdrawn because of duplication fell between 2000 and 2020. Meanwhile, Spain and Italy both saw huge rises in the proportion of papers retracted because of duplication.

Arturo Casadevall, a microbiologist at Johns Hopkins University in Baltimore, Maryland, contributed to work that in 2012 found similar rates of paper withdrawal for misconduct 2 . “To me, this argues that the underlying problems in science have not changed appreciably in the past 12 years,” he says.

But the overall increase in retraction rates could reflect the fact that authors, institutions and journals are increasingly using retractions to correct the literature, he adds.

Science’s fake-paper problem: high-profile effort will tackle paper mills

Sholto David, a biologist and research-integrity specialist based in Wales, UK, points out that methods for detecting errors in research improved during the 20-year study period. An increasing number of people now scan the literature and point out flaws, which could help to explain increasing retraction rates, he says. In particular, the launch of the post-publication peer-review website PubPeer in 2012 has offered sleuths the opportunity to scrutinize papers en masse, he adds, and it has become much more common for researchers to send whistle-blowing e-mails to journals.

Ivan Oransky, Retraction Watch’s co-founder who is based in New York City, suggests that the routine use of plagiarism-detection software by publishers during the past decade might have contributed to the rising rates of retraction because of plagiarism and duplication. It remains to be seen how more recent digital tools, such as those that detect image manipulation, could affect paper withdrawal rates in the coming years, he adds.

doi: https://doi.org/10.1038/d41586-024-01609-0

Freijedo-Farinas, F., Ruano-Ravina, A., Pérez-Ríos, M., Ross, J. & Candal-Pedreira, C. Scientometrics https://doi.org/10.1007/s11192-024-04992-7 (2024).

Article   Google Scholar  

Fang, F. C., Steen, R. G. & Casadevall, A. Proc. Natl Acad. Sci. USA109 , 17028–17033 (2012).

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Why the Pandemic Probably Started in a Lab, in 5 Key Points

By Alina Chan

Dr. Chan is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “Viral: The Search for the Origin of Covid-19.”

On Monday, Dr. Anthony Fauci will return to the halls of Congress to testify before the House subcommittee investigating the Covid-19 pandemic. He will most likely be questioned about how the National Institute of Allergy and Infectious Diseases, which he directed until retiring in 2022, supported risky virus work at a Chinese institute whose research may have caused the pandemic.

For more than four years, reflexive partisan politics have derailed the search for the truth about a catastrophe that has touched us all. It has been estimated that at least 25 million people around the world have died because of Covid-19, with over a million of those deaths in the United States.

Although how the pandemic started has been hotly debated, a growing volume of evidence — gleaned from public records released under the Freedom of Information Act, digital sleuthing through online databases, scientific papers analyzing the virus and its spread, and leaks from within the U.S. government — suggests that the pandemic most likely occurred because a virus escaped from a research lab in Wuhan, China. If so, it would be the most costly accident in the history of science.

Here’s what we now know:

1 The SARS-like virus that caused the pandemic emerged in Wuhan, the city where the world’s foremost research lab for SARS-like viruses is located.

• At the Wuhan Institute of Virology, a team of scientists had been hunting for SARS-like viruses for over a decade, led by Shi Zhengli.
• Their research showed that the viruses most similar to SARS‑CoV‑2, the virus that caused the pandemic, circulate in bats that live r oughly 1,000 miles away from Wuhan. Scientists from Dr. Shi’s team traveled repeatedly to Yunnan province to collect these viruses and had expanded their search to Southeast Asia. Bats in other parts of China have not been found to carry viruses that are as closely related to SARS-CoV-2.

The closest known relatives to SARS-CoV-2 were found in southwestern China and in Laos.

Large cities

Mine in Yunnan province

Cave in Laos

South China Sea

The closest known relatives to SARS-CoV-2

were found in southwestern China and in Laos.

philippines

The closest known relatives to SARS-CoV-2 were found

in southwestern China and Laos.

Sources: Sarah Temmam et al., Nature; SimpleMaps

Note: Cities shown have a population of at least 200,000.

There are hundreds of large cities in China and Southeast Asia.

There are hundreds of large cities in China

and Southeast Asia.

The pandemic started roughly 1,000 miles away, in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away,

in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away, in Wuhan,

home to the world’s foremost SARS-like virus research lab.

• Even at hot spots where these viruses exist naturally near the cave bats of southwestern China and Southeast Asia, the scientists argued, as recently as 2019 , that bat coronavirus spillover into humans is rare .
• When the Covid-19 outbreak was detected, Dr. Shi initially wondered if the novel coronavirus had come from her laboratory , saying she had never expected such an outbreak to occur in Wuhan.
• The SARS‑CoV‑2 virus is exceptionally contagious and can jump from species to species like wildfire . Yet it left no known trace of infection at its source or anywhere along what would have been a thousand-mile journey before emerging in Wuhan.

2 The year before the outbreak, the Wuhan institute, working with U.S. partners, had proposed creating viruses with SARS‑CoV‑2’s defining feature.

• Dr. Shi’s group was fascinated by how coronaviruses jump from species to species. To find viruses, they took samples from bats and other animals , as well as from sick people living near animals carrying these viruses or associated with the wildlife trade. Much of this work was conducted in partnership with the EcoHealth Alliance, a U.S.-based scientific organization that, since 2002, has been awarded over $80 million in federal funding to research the risks of emerging infectious diseases.
• The laboratory pursued risky research that resulted in viruses becoming more infectious : Coronaviruses were grown from samples from infected animals and genetically reconstructed and recombined to create new viruses unknown in nature. These new viruses were passed through cells from bats, pigs, primates and humans and were used to infect civets and humanized mice (mice modified with human genes). In essence, this process forced these viruses to adapt to new host species, and the viruses with mutations that allowed them to thrive emerged as victors.
• By 2019, Dr. Shi’s group had published a database describing more than 22,000 collected wildlife samples. But external access was shut off in the fall of 2019, and the database was not shared with American collaborators even after the pandemic started , when such a rich virus collection would have been most useful in tracking the origin of SARS‑CoV‑2. It remains unclear whether the Wuhan institute possessed a precursor of the pandemic virus.
• In 2021, The Intercept published a leaked 2018 grant proposal for a research project named Defuse , which had been written as a collaboration between EcoHealth, the Wuhan institute and Ralph Baric at the University of North Carolina, who had been on the cutting edge of coronavirus research for years. The proposal described plans to create viruses strikingly similar to SARS‑CoV‑2.
• Coronaviruses bear their name because their surface is studded with protein spikes, like a spiky crown, which they use to enter animal cells. Although never funded by the United States , the Defuse project proposed to search for and create SARS-like viruses carrying spikes with a unique feature: a furin cleavage site — the same feature that enhances SARS‑CoV‑2’s infectiousness in humans, making it capable of causing a pandemic.

The Wuhan lab ran risky experiments to learn about how SARS-like viruses might infect humans.

1. Collect SARS-like viruses from bats and other wild animals, as well as from people exposed to them.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of human cells.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of

human cells.

In Defuse, the scientists proposed to add a furin cleavage site to the spike protein.

3. Create new coronaviruses by inserting spike proteins or other features that could make the viruses more infectious in humans.

4. Infect human cells, civets and humanized mice with the new coronaviruses, to determine how dangerous they might be.

• While it’s possible that the furin cleavage site could have evolved naturally (as seen in some distantly related coronaviruses), out of the hundreds of SARS-like viruses cataloged by scientists, SARS‑CoV‑2 is the only one known to possess a furin cleavage site in its spike. And the genetic data suggest that the virus had only recently gained the furin cleavage site before it started the pandemic.
• Ultimately, a never-before-seen SARS-like virus with a newly introduced furin cleavage site, matching the description in the Wuhan institute’s Defuse proposal, caused an outbreak in Wuhan less than two years after the proposal was drafted.
• When the Wuhan scientists published their seminal paper about Covid-19 as the pandemic roared to life in 2020, they did not mention the virus’s furin cleavage site — a feature they should have been on the lookout for, according to their own grant proposal, and a feature quickly recognized by other scientists.
• Worse still, as the pandemic raged, their American collaborators failed to publicly reveal the existence of the Defuse proposal. The president of EcoHealth, Peter Daszak, recently admitted to Congress that he doesn’t know about virus samples collected by the Wuhan institute after 2015 and never asked the lab’s scientists if they had started the work described in Defuse. In May, citing failures in EcoHealth’s monitoring of risky experiments conducted at the Wuhan lab, the Biden administration suspended all federal funding for the organization and Dr. Daszak and initiated proceedings to bar them from receiving future grants.
• Separately, Dr. Baric described the competitive dynamic between his research group and the institute when he told Congress that the Wuhan scientists would probably not have shared their most interesting newly discovered viruses with him . Documents and email correspondence between the institute and Dr. Baric are still being withheld from the public while their release is fiercely contested in litigation.
• In the end, American partners very likely knew of only a fraction of the research done in Wuhan. According to U.S. intelligence sources, some of the institute’s virus research was classified or conducted with or on behalf of the Chinese military .

3 The Wuhan lab pursued this type of work under low biosafety conditions that could not have contained an airborne virus as infectious as SARS‑CoV‑2.

• Labs working with live viruses generally operate at one of four biosafety levels (known in ascending order of stringency as BSL-1, 2, 3 and 4) that describe the work practices that are considered sufficiently safe depending on the characteristics of each pathogen. The Wuhan institute’s scientists worked with SARS-like viruses under inappropriately low biosafety conditions .

In the United States, virologists generally use stricter Biosafety Level 3 protocols when working with SARS-like viruses.

Biosafety cabinets prevent

viral particles from escaping.

Viral particles

Personal respirators provide

a second layer of defense against breathing in the virus.

DIRECT CONTACT

Gloves prevent skin contact.

Disposable wraparound

gowns cover much of the rest of the body.

Personal respirators provide a second layer of defense against breathing in the virus.

Disposable wraparound gowns

cover much of the rest of the body.

Note: ​​Biosafety levels are not internationally standardized, and some countries use more permissive protocols than others.

The Wuhan lab had been regularly working with SARS-like viruses under Biosafety Level 2 conditions, which could not prevent a highly infectious virus like SARS-CoV-2 from escaping.

Some work is done in the open air, and masks are not required.

Less protective equipment provides more opportunities

for contamination.

Some work is done in the open air,

and masks are not required.

Less protective equipment provides more opportunities for contamination.

• In one experiment, Dr. Shi’s group genetically engineered an unexpectedly deadly SARS-like virus (not closely related to SARS‑CoV‑2) that exhibited a 10,000-fold increase in the quantity of virus in the lungs and brains of humanized mice . Wuhan institute scientists handled these live viruses at low biosafet y levels , including BSL-2.
• Even the much more stringent containment at BSL-3 cannot fully prevent SARS‑CoV‑2 from escaping . Two years into the pandemic, the virus infected a scientist in a BSL-3 laboratory in Taiwan, which was, at the time, a zero-Covid country. The scientist had been vaccinated and was tested only after losing the sense of smell. By then, more than 100 close contacts had been exposed. Human error is a source of exposure even at the highest biosafety levels , and the risks are much greater for scientists working with infectious pathogens at low biosafety.
• An early draft of the Defuse proposal stated that the Wuhan lab would do their virus work at BSL-2 to make it “highly cost-effective.” Dr. Baric added a note to the draft highlighting the importance of using BSL-3 to contain SARS-like viruses that could infect human cells, writing that “U.S. researchers will likely freak out.” Years later, after SARS‑CoV‑2 had killed millions, Dr. Baric wrote to Dr. Daszak : “I have no doubt that they followed state determined rules and did the work under BSL-2. Yes China has the right to set their own policy. You believe this was appropriate containment if you want but don’t expect me to believe it. Moreover, don’t insult my intelligence by trying to feed me this load of BS.”
• SARS‑CoV‑2 is a stealthy virus that transmits effectively through the air, causes a range of symptoms similar to those of other common respiratory diseases and can be spread by infected people before symptoms even appear. If the virus had escaped from a BSL-2 laboratory in 2019, the leak most likely would have gone undetected until too late.
• One alarming detail — leaked to The Wall Street Journal and confirmed by current and former U.S. government officials — is that scientists on Dr. Shi’s team fell ill with Covid-like symptoms in the fall of 2019 . One of the scientists had been named in the Defuse proposal as the person in charge of virus discovery work. The scientists denied having been sick .

4 The hypothesis that Covid-19 came from an animal at the Huanan Seafood Market in Wuhan is not supported by strong evidence.

• In December 2019, Chinese investigators assumed the outbreak had started at a centrally located market frequented by thousands of visitors daily. This bias in their search for early cases meant that cases unlinked to or located far away from the market would very likely have been missed. To make things worse, the Chinese authorities blocked the reporting of early cases not linked to the market and, claiming biosafety precautions, ordered the destruction of patient samples on January 3, 2020, making it nearly impossible to see the complete picture of the earliest Covid-19 cases. Information about dozens of early cases from November and December 2019 remains inaccessible.
• A pair of papers published in Science in 2022 made the best case for SARS‑CoV‑2 having emerged naturally from human-animal contact at the Wuhan market by focusing on a map of the early cases and asserting that the virus had jumped from animals into humans twice at the market in 2019. More recently, the two papers have been countered by other virologists and scientists who convincingly demonstrate that the available market evidence does not distinguish between a human superspreader event and a natural spillover at the market.
• Furthermore, the existing genetic and early case data show that all known Covid-19 cases probably stem from a single introduction of SARS‑CoV‑2 into people, and the outbreak at the Wuhan market probably happened after the virus had already been circulating in humans.

An analysis of SARS-CoV-2’s evolutionary tree shows how the virus evolved as it started to spread through humans.

SARS-COV-2 Viruses closest

to bat coronaviruses

more mutations

Source: Lv et al., Virus Evolution (2024) , as reproduced by Jesse Bloom

The viruses that infected people linked to the market were most likely not the earliest form of the virus that seeded the pandemic.

• Not a single infected animal has ever been confirmed at the market or in its supply chain. Without good evidence that the pandemic started at the Huanan Seafood Market, the fact that the virus emerged in Wuhan points squarely at its unique SARS-like virus laboratory.

5 Key evidence that would be expected if the virus had emerged from the wildlife trade is still missing.

In previous outbreaks of coronaviruses, scientists were able to demonstrate natural origin by collecting multiple pieces of evidence linking infected humans to infected animals.

Infected animals

Earliest known

cases exposed to

live animals

Antibody evidence

of animals and

animal traders having

been infected

Ancestral variants

of the virus found in

Documented trade

of host animals

between the area

where bats carry

closely related viruses

and the outbreak site

Infected animals found

Earliest known cases exposed to live animals

Antibody evidence of animals and animal

traders having been infected

Ancestral variants of the virus found in animals

Documented trade of host animals

between the area where bats carry closely

related viruses and the outbreak site

For SARS-CoV-2, these same key pieces of evidence are still missing , more than four years after the virus emerged.

For SARS-CoV-2, these same key pieces of evidence are still missing ,

more than four years after the virus emerged.

• Despite the intense search trained on the animal trade and people linked to the market, investigators have not reported finding any animals infected with SARS‑CoV‑2 that had not been infected by humans. Yet, infected animal sources and other connective pieces of evidence were found for the earlier SARS and MERS outbreaks as quickly as within a few days, despite the less advanced viral forensic technologies of two decades ago.
• Even though Wuhan is the home base of virus hunters with world-leading expertise in tracking novel SARS-like viruses, investigators have either failed to collect or report key evidence that would be expected if Covid-19 emerged from the wildlife trade . For example, investigators have not determined that the earliest known cases had exposure to intermediate host animals before falling ill. No antibody evidence shows that animal traders in Wuhan are regularly exposed to SARS-like viruses, as would be expected in such situations.
• With today’s technology, scientists can detect how respiratory viruses — including SARS, MERS and the flu — circulate in animals while making repeated attempts to jump across species . Thankfully, these variants usually fail to transmit well after crossing over to a new species and tend to die off after a small number of infections. In contrast, virologists and other scientists agree that SARS‑CoV‑2 required little to no adaptation to spread rapidly in humans and other animals . The virus appears to have succeeded in causing a pandemic upon its only detected jump into humans.

The pandemic could have been caused by any of hundreds of virus species, at any of tens of thousands of wildlife markets, in any of thousands of cities, and in any year. But it was a SARS-like coronavirus with a unique furin cleavage site that emerged in Wuhan, less than two years after scientists, sometimes working under inadequate biosafety conditions, proposed collecting and creating viruses of that same design.

While several natural spillover scenarios remain plausible, and we still don’t know enough about the full extent of virus research conducted at the Wuhan institute by Dr. Shi’s team and other researchers, a laboratory accident is the most parsimonious explanation of how the pandemic began.

Given what we now know, investigators should follow their strongest leads and subpoena all exchanges between the Wuhan scientists and their international partners, including unpublished research proposals, manuscripts, data and commercial orders. In particular, exchanges from 2018 and 2019 — the critical two years before the emergence of Covid-19 — are very likely to be illuminating (and require no cooperation from the Chinese government to acquire), yet they remain beyond the public’s view more than four years after the pandemic began.

Whether the pandemic started on a lab bench or in a market stall, it is undeniable that U.S. federal funding helped to build an unprecedented collection of SARS-like viruses at the Wuhan institute, as well as contributing to research that enhanced them . Advocates and funders of the institute’s research, including Dr. Fauci, should cooperate with the investigation to help identify and close the loopholes that allowed such dangerous work to occur. The world must not continue to bear the intolerable risks of research with the potential to cause pandemics .

A successful investigation of the pandemic’s root cause would have the power to break a decades-long scientific impasse on pathogen research safety, determining how governments will spend billions of dollars to prevent future pandemics. A credible investigation would also deter future acts of negligence and deceit by demonstrating that it is indeed possible to be held accountable for causing a viral pandemic. Last but not least, people of all nations need to see their leaders — and especially, their scientists — heading the charge to find out what caused this world-shaking event. Restoring public trust in science and government leadership requires it.

A thorough investigation by the U.S. government could unearth more evidence while spurring whistleblowers to find their courage and seek their moment of opportunity. It would also show the world that U.S. leaders and scientists are not afraid of what the truth behind the pandemic may be.

More on how the pandemic may have started

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• Indian J Psychol Med
• v.44(3); 2022 May

Research Design: Case-Control Studies

Chittaranjan andrade.

1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India.

Case-control studies are observational studies in which cases are subjects who have a characteristic of interest, such as a clinical diagnosis, and controls are (usually) matched subjects who do not have that characteristic. After cases and controls are identified, researchers “look back” to determine what past events (exposures), if any, are significantly associated with caseness. For “looking back,” data may be obtained by clinical history-taking or from medical records such as case files or large electronic health care databases. The data are analyzed using logistic regression, which adjusts for confounding variables and yields an odds ratio and a probability value for the association between the exposure of interest (independent variable) and caseness (dependent variable). Because case-control studies are not randomized controlled studies, cause–effect relationships do not necessarily explain significant associations detected in the regressions; unexplored confounding may be responsible. These concepts are explained with the help of examples.

Earlier articles in this series described classifications in research design, 1 prospective and retrospective studies, cross-sectional and longitudinal studies, 2 and cohort studies. 3 This article considers a research design that is often used in present-day research in medicine and psychiatry: the case-control study.

Case-Control Study: General Description

A case-control study is one in which cases are compared with controls to identify historical exposures that are significantly associated with a current state or, stated in different words, variables that are significantly associated with caseness. In case-control studies, cases are subjects with a particular characteristic. The characteristic that defines caseness may be a clinical diagnosis (e.g., schizophrenia [Sz]), a treatment outcome (e.g., treatment-resistance), a side effect (e.g., tardive dyskinesia), or any other characteristic that is the subject of interest. Controls are subjects who do not have the characteristic that defines caseness. For Sz, controls may be healthy controls; for treatment-resistance, controls would be subjects with the same diagnosis and who are treatment-responsive; for tardive dyskinesia, controls would be subjects who received the same treatment but did not develop this adverse outcome. Controls are commonly selected based on matching with cases for variables such as age, sex, site of recruitment, and other variables. Matching may be 1:1, but when data are drawn from large electronic databases, it is often possible to match five or even 10 controls with each case. In such studies, there may be thousands of cases and tens or even hundreds of thousands of controls.

As an actual example of a case-control study, children with autism spectrum disorder (ASD) may be compared with normally developing children to determine whether a history of maternal antidepressant use during pregnancy is more frequent among cases than among controls; if it is, and if the association remains statistically significant after adjusting for confounding variables, one may speculate that gestational exposure to antidepressants predisposes to autism spectrum disorder. 4 Here, readers may note that there is only one exposure of interest: gestational exposure to antidepressant drugs.

As a hypothetical example of a case-control study, patients with Sz may be compared with healthy controls to determine whether a family history of Sz, viral infection during pregnancy, season of birth, obstetric complications during pregnancy, brain insults in early childhood, and other variables are associated with Sz in the sample. Here, readers may note that all the variables listed are exposures of interest and corrections are desirable to protect against the risk of Type 1 statistical error associated with multiple hypothesis testing. 5

In summary, in case-control studies, there are cases and there are controls that are matched with cases. Researchers then “look back” to ascertain what past events (exposures) are associated with caseness. The exposures of interest may be one or many.

Analysis of Case-Control Studies

Case-control studies are analyzed using logistic regression. The dependent variable is the (dichotomous) grouping variable: case vs. control. The independent variables are the exposure(s) of interest plus the confounding variables whose effects must be adjusted for in the regression to understand the unique effect of the exposure variable(s). The logistic regression yields an odds ratio and a statistical significance (P) value for each independent variable; this allows us to understand whether or not the independent variables are significantly associated with caseness, and, if they are, what the effect sizes are, as exemplified by the odds ratios. Readers may note that whether a significant association is a marker of risk or a cause of the risk cannot be determined from an observational study; this was explained in an earlier article. 3

As a special note, when cases and controls are well matched on many important variables, a procedure known as conditional logistic regression analysis may be employed. 6

Characteristics of Case-Control Studies

How do case-control studies fit into classifications of research design described in an earlier article? 1 Case-control studies are empirical studies that are based on samples, not individual cases or case series. They are cross-sectional because cases and controls are identified and evaluated for caseness, historical exposures, and confounding variables at a single point in time. They are observational; there is no intervention. They are prospective when cases and controls are identified and interviewed in real time, such as in an outpatient department, and retrospective when they are identified in and studied from medical records or electronic health care databases. Strengths and limitations of prospectively vs. retrospectively ascertained data were described in an earlier article. 3

The nested case-control study is a special situation in which cases and controls are both identified from within a cohort. So, instead of studying the entire cohort, which would be time- and labor- intensive, the researchers study only cases and matched controls within that cohort. 7 To explain with the help of an actual example, Gronich et al. 8 examined the electronic database of the largest health care provider in Israel and identified a cohort of 1,762,164 adults who did not have a diagnosis of Parkinson’s disease (PD). During follow-up, 11,314 patients were newly diagnosed with PD. Each patient (case) was matched with 10 randomly selected controls based on age, sex, ethnicity, and duration of follow-up. Thus, rather than extracting data for 11,314 cases and the rest of the 1,762,164 adults who did not develop PD and who were therefore noncases, the authors carved out a smaller sample of controls from within the cohort. Thus, the final sample of 11,314 cases and 113,140 controls was “nested” within the original cohort; studying this smaller sample took less time and was less labor-intensive than studying the entire cohort.

Parting Notes

There are two reasons why, in case-control studies, large samples are desirable, and why many controls may be matched to a single case. One reason is that patients are not randomized to be cases or controls. In such circumstances, as in quasi-controlled studies, 9 there is bound to be confounding. With larger samples, statistical power to adjust for confounding will improve. The other reason is that, in case-control studies, data are usually drawn from medical records or databases. Information extracted from such sources is very unlikely to have been collected and recorded with the expectation of use in future research. So, there are bound to be inaccuracies. When data are blurred (inaccurate), there is statistical noise. When the sample size is large, it becomes easier to see a signal through the noise.

Cohort and case-control study designs are not “opposites” as are prospective vs. retrospective, or cross-sectional vs. longitudinal, or controlled vs. uncontrolled research designs. Rather, like the randomized controlled and quasi-controlled designs, these designs are special kinds of research design in the controlled vs. uncontrolled classification. Note that whereas a case-control study is always a special kind of controlled study, a cohort study can be classified under controlled or uncontrolled, depending on whether or not there is a comparison group for the group of interest.

Case-control studies in India tend to be poor in quality because they are based on small sample sizes. Small samples do not have sufficient statistical power to adjust for the multitude of confounding variables that bedevil research in psychiatry. Large samples cannot be identified because India does not as yet have large electronic health care databases as a source of data.

Finally, case-control studies, like cohort studies, are observational in nature, and authors who conduct and report such studies should follow the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.