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• Published: 27 June 2011

The case study approach

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

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

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

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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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Acknowledgements

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

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Sarah Crowe & Anthony Avery

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

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Continuing to enhance the quality of case study methodology in health services research

Shannon l. sibbald.

1 Faculty of Health Sciences, Western University, London, Ontario, Canada.

2 Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

3 The Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

Stefan Paciocco

Meghan fournie, rachelle van asseldonk, tiffany scurr.

Case study methodology has grown in popularity within Health Services Research (HSR). However, its use and merit as a methodology are frequently criticized due to its flexible approach and inconsistent application. Nevertheless, case study methodology is well suited to HSR because it can track and examine complex relationships, contexts, and systems as they evolve. Applied appropriately, it can help generate information on how multiple forms of knowledge come together to inform decision-making within healthcare contexts. In this article, we aim to demystify case study methodology by outlining its philosophical underpinnings and three foundational approaches. We provide literature-based guidance to decision-makers, policy-makers, and health leaders on how to engage in and critically appraise case study design. We advocate that researchers work in collaboration with health leaders to detail their research process with an aim of strengthening the validity and integrity of case study for its continued and advanced use in HSR.

Introduction

The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the delivery and implementation of programs can increase the likelihood of success. 3 , 4 Case study methodology is particularly well suited for implementation research in health services because it can provide insight into the nuances of diverse contexts. 5 , 6 In 1999, Yin 7 published a paper on how to enhance the quality of case study in HSR, which was foundational for the emergence of case study in this field. Yin 7 maintains case study is an appropriate methodology in HSR because health systems are constantly evolving, and the multiple affiliations and diverse motivations are difficult to track and understand with traditional linear methodologies.

Despite its increased popularity, there is debate whether a case study is a methodology (ie, a principle or process that guides research) or a method (ie, a tool to answer research questions). Some criticize case study for its high level of flexibility, perceiving it as less rigorous, and maintain that it generates inadequate results. 8 Others have noted issues with quality and consistency in how case studies are conducted and reported. 9 Reporting is often varied and inconsistent, using a mix of approaches such as case reports, case findings, and/or case study. Authors sometimes use incongruent methods of data collection and analysis or use the case study as a default when other methodologies do not fit. 9 , 10 Despite these criticisms, case study methodology is becoming more common as a viable approach for HSR. 11 An abundance of articles and textbooks are available to guide researchers through case study research, including field-specific resources for business, 12 , 13 nursing, 14 and family medicine. 15 However, there remains confusion and a lack of clarity on the key tenets of case study methodology.

Several common philosophical underpinnings have contributed to the development of case study research 1 which has led to different approaches to planning, data collection, and analysis. This presents challenges in assessing quality and rigour for researchers conducting case studies and stakeholders reading results.

This article discusses the various approaches and philosophical underpinnings to case study methodology. Our goal is to explain it in a way that provides guidance for decision-makers, policy-makers, and health leaders on how to understand, critically appraise, and engage in case study research and design, as such guidance is largely absent in the literature. This article is by no means exhaustive or authoritative. Instead, we aim to provide guidance and encourage dialogue around case study methodology, facilitating critical thinking around the variety of approaches and ways quality and rigour can be bolstered for its use within HSR.

Purpose of case study methodology

Case study methodology is often used to develop an in-depth, holistic understanding of a specific phenomenon within a specified context. 11 It focuses on studying one or multiple cases over time and uses an in-depth analysis of multiple information sources. 16 , 17 It is ideal for situations including, but not limited to, exploring under-researched and real-life phenomena, 18 especially when the contexts are complex and the researcher has little control over the phenomena. 19 , 20 Case studies can be useful when researchers want to understand how interventions are implemented in different contexts, and how context shapes the phenomenon of interest.

In addition to demonstrating coherency with the type of questions case study is suited to answer, there are four key tenets to case study methodologies: (1) be transparent in the paradigmatic and theoretical perspectives influencing study design; (2) clearly define the case and phenomenon of interest; (3) clearly define and justify the type of case study design; and (4) use multiple data collection sources and analysis methods to present the findings in ways that are consistent with the methodology and the study’s paradigmatic base. 9 , 16 The goal is to appropriately match the methods to empirical questions and issues and not to universally advocate any single approach for all problems. 21

Approaches to case study methodology

Three authors propose distinct foundational approaches to case study methodology positioned within different paradigms: Yin, 19 , 22 Stake, 5 , 23 and Merriam 24 , 25 ( Table 1 ). Yin is strongly post-positivist whereas Stake and Merriam are grounded in a constructivist paradigm. Researchers should locate their research within a paradigm that explains the philosophies guiding their research 26 and adhere to the underlying paradigmatic assumptions and key tenets of the appropriate author’s methodology. This will enhance the consistency and coherency of the methods and findings. However, researchers often do not report their paradigmatic position, nor do they adhere to one approach. 9 Although deliberately blending methodologies may be defensible and methodologically appropriate, more often it is done in an ad hoc and haphazard way, without consideration for limitations.

Cross-analysis of three case study approaches, adapted from Yazan 2015

The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.

Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.

Defining a case

A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6

Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.

Designing the case study approach

Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.

Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.

Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).

Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36

Data collection and analysis

Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5 , 37 By seeking patterns within and across data sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39

Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.

Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.

Practical applications of case study research

Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.

An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46

New directions in case study

Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.

Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55

Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37

Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.

The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

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

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

Table of contents

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

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

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

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

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

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

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

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

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

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

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

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

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

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

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

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

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

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

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

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

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

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Impacts of distribution data on accurate species modeling: a case study of litsea auriculata (lauraceae).

1. Introduction

2.1. current distribution pattern of litsea auriculata, 2.2. spatial pattern and driving factors of potential distribution areas of various data, 2.3. pca analyses of different datasets of litsea auriculata under different climatic conditions, 2.4. distribution and conservation status of litsea auriculata under various climatic conditions using different datasets, 3. discussion, 3.1. importance of accurate and complete species distribution records for species distribution modeling, 3.2. potential distribution and conservation assessment based on accurate identification and complete distribution dataset of litsea auriculata, 4. materials and methods, 4.1. data collection and processing, 4.1.1. distribution data of litsea auriculata, 4.1.2. environment variable data, 4.2. potential distribution prediction based on maxent model, 4.3. calculating hotspots in protected areas, 5. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Tan, C.; Ferguson, D.K.; Yang, Y. Impacts of Distribution Data on Accurate Species Modeling: A Case Study of Litsea auriculata (Lauraceae). Plants 2024 , 13 , 2581. https://doi.org/10.3390/plants13182581

Tan C, Ferguson DK, Yang Y. Impacts of Distribution Data on Accurate Species Modeling: A Case Study of Litsea auriculata (Lauraceae). Plants . 2024; 13(18):2581. https://doi.org/10.3390/plants13182581

Tan, Chao, David Kay Ferguson, and Yong Yang. 2024. "Impacts of Distribution Data on Accurate Species Modeling: A Case Study of Litsea auriculata (Lauraceae)" Plants 13, no. 18: 2581. https://doi.org/10.3390/plants13182581

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Relative risks are calculated compared with mean annual PM 2.5 exposure of 10.6 μg/m 3 . Tick marks indicate distribution of exposure in the total sample; shading, 95% CI.

eTable. Frequency of Tremor-Predominant and Akinetic Rigid PD Subtypes by Average PM 2.5 Exposure Prior to PD Symptom Onset

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Krzyzanowski B , Mullan AF , Turcano P , Camerucci E , Bower JH , Savica R. Air Pollution and Parkinson Disease in a Population-Based Study. JAMA Netw Open. 2024;7(9):e2433602. doi:10.1001/jamanetworkopen.2024.33602

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Air Pollution and Parkinson Disease in a Population-Based Study

• 1 Barrow Neurological Institute, Phoenix, Arizona
• 2 Department of Neurology, Mayo Clinic, Rochester, Minnesota
• 3 Department of Neurology, University of Kansas Medical Center, Kansas City
• 4 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota

Question   Is air pollution in the form of particulate matter with a diameter of 2.5 µm or less (PM 2.5 ) and nitrogen dioxide (NO 2 ) associated with the risk and clinical characteristics of Parkinson disease (PD)?

Findings   This case-control study including 346 patients with PD matched with 4813 controls found that PM 2.5 and NO 2 exposure was associated with statistically significant increases in PD risk and risk of developing dyskinesia. Higher exposure to PM 2.5 was associated with a statistically significant increase in risk of the akinetic rigid PD subtype in subcohort analysis among patients with PD.

Meaning   These findings suggest that a reduction in air pollution may help reduce PD risk, modifying the PD phenotype and the risk of dyskinesia in patients with PD.

Importance   The role of air pollution in risk and progression of Parkinson disease (PD) is unclear.

Objective   To assess whether air pollution is associated with increased risk of PD and clinical characteristics of PD.

Design, Setting, and Participants   This population-based case-control study included patients with PD and matched controls from the Rochester Epidemiology Project from 1998 to 2015. Data were analyzed from January to June 2024.

Exposures   Mean annual exposure to particulate matter with a diameter of 2.5 µm or less (PM 2.5 ) from 1998 to 2015 and mean annual exposure to nitrogen dioxide (NO 2 ) from 2000 to 2014.

Main Outcomes and Measures   Outcomes of interest were PD risk, all-cause mortality, presence of tremor-predominant vs akinetic rigid PD, and development of dyskinesia. Models were adjusted for age, sex, race and ethnicity, year of index, and urban vs rural residence.

Results   A total of 346 patients with PD (median [IQR] age 72 [65-80] years; 216 [62.4%] male) were identified and matched on age and sex with 4813 controls (median [IQR] age, 72 [65-79] years, 2946 [61.2%] male). Greater PM 2.5 exposure was associated with increased PD risk, and this risk was greatest after restricting to populations within metropolitan cores (odds ratio [OR], 1.23; 95% CI, 1.11-1.35) for the top quintile of PM 2.5 exposure compared with the bottom quintile. Greater NO 2 exposure was also associated with increased PD risk when comparing the top quintile with the bottom quintile (OR, 1.13; 95% CI, 1.07-1.19). Air pollution was associated with a 36% increased risk of akinetic rigid presentation (OR per each 1-μg/m 3 increase in PM 2.5 , 1.36; 95% CI, 1.02-1.80). In analyses among patients with PD only, higher PM 2.5 exposure was associated with greater risk for developing dyskinesia (HR per 1-μg/m 3 increase in PM 2.5 , 1.42; 95% CI, 1.17-1.73), as was increased NO 2 exposure (HR per 1 μg/m 3 increase in NO 2 , 1.13; 95% CI, 1.06-1.19). There was no association between PM 2.5 and all-cause mortality among patients with PD.

Conclusions and Relevance   In this case-control study of air pollution and PD, higher levels of PM 2.5 and NO 2 exposure were associated with increased risk of PD; also, higher levels of PM 2.5 exposure were associated with increased risk of developing akinetic rigid PD and dyskinesia compared with patients with PD exposed to lower levels. These findings suggest that reducing air pollution may reduce risk of PD, modify the PD phenotype, and reduce risk of dyskinesia.

Parkinson disease (PD) is a degenerative disease that affects 2% of the population aged 70 years and older. 1 The number of individuals with PD within the population is estimated to triple in the next 20 years. 2 Several theories have been formulated to explain the progressive increase in the incidence of PD. Complex interactions among environmental factors, genetic predisposition, and known risk factors have been reported through the years as possible causes. 3 , 4 Among environmental exposures, studies have suggested air pollution, in the form of aerosolized particulate matter with a diameter 2.5 µm or less (PM 2.5 ), is associated with increased risk of PD. 5 - 14 The ultrafine particles (≤0.1 µm) contained within PM 2.5 may cross the blood brain barrier in humans, 15 leading to inflammation, oxidative stress, and microglia activation, which are potential pathogenic mechanisms for the development of PD. 15 - 19 At this time, there are no available national datasets on ultrafine particles contained in traffic pollution; however, ultrafine particles are traffic-related pollutants, 20 along with nitrogen dioxide (NO 2 ), for which nationwide data exist. 21 Additionally, previous studies have implicated NO 2 exposure as a PD risk factor. 22 Thus, assessing the association of PM 2.5 and NO 2 with PD may help provide insight into the roles of different sources of air pollution in PD risk. In addition to potentially increasing the risk of developing PD, we hypothesize that air pollution exposure may also be associated with phenotypical manifestations and treatment outcomes. To our knowledge, no studies have explored the association between PM 2.5 exposure and clinical phenotypes of PD. For this reason, we conducted a population-based study using data from the Rochester Epidemiology Project (REP) medical records linkage system to explore the association between PD and air pollution exposure. We also studied the association of air pollution exposure with patient mortality, different clinical characteristics, and presence of dyskinesia.

This case-control study was granted an exemption from review and informed consent by the Mayo Clinic institutional reviewer board. All patients and controls had Minnesota research authorization for use of medical records. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We identified patients with PD in Olmstead County, Minnesota, from 1991 to 2015 using International Classification of Diseases, Ninth Revision ( ICD-9 ) (332.0, 333.0, 331.82) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision ( ICD-10 ) (G20, G21, G23.1, G23.2, G31.83) codes within the Rochester Epidemiology Project (REP) medical records linkage system. 23 The records of all patients identified by ICD-9 and ICD-10 codes were reviewed by a movement disorder specialist (R.S.) to confirm the diagnosis of PD and determine the date of motor symptom onset. Cognitive symptoms were also reviewed for the diagnosis of PD. Details regarding the methods have been reported elsewhere. 23 Although patients with PD were required to be living in Olmsted County at diagnosis date, they were not required to have lived in Olmsted County before that date. Therefore, our analysis includes patients with PD who lived outside of Olmsted County during the exposure window of interest (10 years prior to the date of PD symptom onset), and exposures were linked based on their prior addresses.

Controls were identified from the 27-county REP region in Minnesota, Iowa, and Wisconsin. 24 Controls were screened for the same ICD-9 and ICD-10 codes for PD as were used to identify patients with PD in the case cohort. Controls were matched (using a randomly sorted greedy algorithm) to patients with PD 20:1 on sex and age within an index date that was 3 years prior to motor symptom onset for the matched patient with PD. All controls were required to not have any ICD-9 or ICD-10 codes for PD prior to the index date or up to 5 years after to ensure that no control developed PD motor symptoms. Patients with PD were divided into 2 subgroups (akinetic rigid and tremor-predominant PD subtypes) according to their most prominent feature on examination. 23 Due to a low number of patients with tremor-predominant PD in the cohort, patients presenting with rest tremor and either bradykinesia or rigidity were considered tremor predominant in the analysis.

Mean annual PM 2.5 exposure data were collected from 1998 to 2019 from the Washington University in St Louis Atmospheric Composition Analysis Group. 25 In addition, mean annual nitrogen dioxide (NO 2 ) exposure data were collected from 2000 to 2014 from the Socioeconomic Data and Application Center. 26 The PM 2.5 and NO 2 values for each patient and control were identified each year up to 10 years prior to the index date based on the 1-km 2 area containing their home address of residency each year. Patients with PD with missing data for all 10 years before the index date were excluded, along with their corresponding matched controls.

As a sensitivity analysis, we restricted our patient population to metropolitan cores. In doing so, we ensure that our cases and controls were more comparable in terms of the spectrum of pollution they might have been exposed to. Metropolitan populations were defined as those living in a Rural Urban Commuting Area (RUCA) classification of metropolitan area core (RUCA = 1).

Our primary outcome was risk of incident PD. Secondary outcomes were assessed only among patients with PD and included all-cause mortality following PD symptom onset, presence of tremor-predominant vs akinetic rigid PD, and development of dyskinesia.

We included 2 study designs: a case-control study design to assess the association of PM 2.5 exposure with incidence of PD and a cohort study design focusing on PD subtypes and outcomes (dyskinesia and mortality) within our case group. All statistical analyses were performed during the January to June 2024. P values were 2-sided, and statistical significance was set at P  ≤ .05. All analyses were conducted using R software version 4.2.2 (R Project for Statistical Computing).

In our case-control study, we modeled exposure in quintiles and using 2 linear splines, similar to prior studies of PM 2.5 . 5 The placement of the knot was determined using bootstrap sampling to maximize the area under the receiver operating characteristics curve. Logistic regression was used with PD as the outcome and PM 2.5 (or NO 2 ) as the risk factor, adjusting for age, sex, race, ethnicity, year of index, and residency RUCA. All race and ethnicity information was taken directly from categories used in medical records. The other race category was reported directly in the medical record and not otherwise defined. We adjust for demographics that are well-established risk factors of PD. We adjust for year of index to diminish the potential impact of historical cohort effects. We adjust for RUCA to diminish the impact of differences that exist between urban and rural air pollution composition profiles. We further expect that our RUCA adjustment also diminishes the impact of differences that exist between urban and rural populations regarding other toxic exposures, including prior occupational exposures. RUCA designation was categorized as metropolitan area cores (RUCA = 1) and not metropolitan area cores (RUCA = 2-10). Results were reported as odds ratios (ORs) with 95% CIs.

For our PD-specific cohort study of secondary outcomes, the risk of akinetic rigid subtype was assessed using logistic regression and the risk of all-cause mortality, and risk of dyskinesia was assessed using Cox proportional hazards regression. All models were adjusted for age, sex, race, ethnicity, and residency RUCA. Patient follow-up was censored at last available medical encounter or death, and PM 2.5 was considered as a linear risk factor per 1 μg/m 3 . Model results were reported with ORs or hazard ratios (HRs) with 95% CIs. Differences in outcome based on PM 2.5 exposure were highlighted using Kaplan-Meier cumulative incidence curves with PM 2.5 divided into tertiles for patients with PD.

Of the 450 incident cases of PD identified from Olmsted County, 9 patients (2.0%) were excluded for missing address information and 95 patients (21.1%) were excluded for missing PM 2.5 exposure data, resulting in 346 PD cases (76.9%; median [IQR] age 72 [65-80] years; 216 [62.4%] male) included for analysis, with 1 American Indian or Alaskan Native patient (0.3%), 6 Asian patients (1.7%), 5 Black or African American patients (1.4%), 1 Hawaiian or Pacific Islander patient (0.3%), 330 White patients (95.4%), and 3 patients identifying as other race (0.9%); 7 patients identified as Hispanic or Latino (2.0%) and 339 patients identified as not Hispanic or Latino (98.0%). Among 6920 controls matched to these included PD cases, 1875 (27.1%) were excluded for missing address information and 232 (3.4%) were excluded for missing PM 2.5 exposure data, for a total of 4183 controls (69.6%; median [IQR] age, 72 [65-79] years, 2946 [61.2%] male), including 9 American Indian or Alaskan Native individuals (0.2%), 49 Asian individuals (1.0%), 33 Black or African American individuals (0.7%), 1 Hawaiian or Pacific Islander individual (<0.1%), 4164 White individuals (86.5%), 69 individuals identifying as other race (1.4%), and 488 individuals with unknown or undisclosed race (10.1%); 50 individuals identified as Hispanic or Latino (1.0%) and 4278 individuals identified as not Hispanic or Latino (88.9%). The median (IQR) time lived at these the current address was 15.9 (5.0-39.8) years. Most patients with PD lived inside metropolitan area cores (79.5%) compared with approximately one-third of controls (32.7%), which is why we include our metropolitan-restricted sensitivity analysis ( Table 1 ; eTable in Supplement 1 ).

Median (IQR) PM 2.5 exposure prior to the index date was 10.07 (9.35-10.69) μg/m 3 among patients with PD and 9.44 (8.69-10.22) μg/m 3 among controls (Wilcoxon rank-sum P  < .001). There was a positive association between PM 2.5 and risk of PD: compared with the lowest quintile of PM 2.5 exposure, the increased risk of PD associated with PM 2.5 exposure ranged from 4% in the second quintile (OR, 1.04; 95% CI, 1.02-1.06) to 14% in the top quintile (OR, 1.14; 95% CI, 1.11-1.18) ( Table 2 ). The median (IQR) NO 2 exposure prior to the index date was 17.47 (15.46-19.99) μg/m 3 for patients with PD and 17.17 (14.31-19.46) μg/m 3 for controls (Wilcoxon rank-sum P  = .27). There was a positive association between NO 2 and risk of PD, but only for the top 2 quintiles of NO 2 exposure. Compared with the lowest quintile of NO 2 exposure, the odds of PD were increased by 5% in the fourth quintile (OR, 1.05; 95% CI, 1.01-1.10) and by 13% in the top quintile (OR, 1.13; 95% CI, 1.07-1.19) ( Table 2 ).

The trend in odds ratios across PM 2.5 exposure was positive and linear with some tapering at the higher levels ( Figure 1 ). This was observed in linear splines with a regression knot optimized at 10.6 μg/m 3 , with a 4.9% increase per 1-μg/m 3 increase in PM 2.5 exposure (OR per 1-μg/m 3 increase, 1.05; 95% CI, 1.04-1.06) up to the knot at 10.6 μg/m 3 and then a 1.7% increase per 1 μg/m 3 above the knot (OR, 1.02; 95% CI, 1.00-1.03). A likelihood ratio test comparing the spline model to a linear model favored the nonlinear spline for modeling risk of PD ( P  < .001).

Overall, there was a significant association between PM 2.5 exposure and the development of akinetic rigid PD ( Table 3 ). After accounting for patient age, sex, and residency RUCA classification, increased PM 2.5 exposure was associated with a 36% increased risk of akinetic rigid presentation (OR per 1-μg/m 3 increase, 1.36; 95% CI, 1.02-1.80, P  = .03). The median (IQR) annual PM 2.5 exposure for patients with tremor-predominant PD was 9.98 (9.31-10.65) μg/m 3 , compared with 10.51 (9.90-10.83) μg/m 3 for patients with akinetic rigid PD.

Among 346 patients with PD included in the study, 259 (74.9%) were deceased at the time of data abstraction, with a median (IQR) of 9.0 (6.0-11.8) years from PD symptom onset to death. After accounting for patient demographics (age, sex, race, and ethnicity) and RUCA, there was no significant association between level of PM 2.5 exposure and mortality risk (HR per 1-μg/m 3 increase, 0.93; 95% CI, 0.82-1.05; P  = .23).

A total of 54 patients with PD (15.6%) developed dyskinesia at any time during the disease course. The median (IQR) time from PD symptom onset to dyskinesia was 5.6 (4.4-7.9) years. The Kaplan-Meier cumulative incidence for dyskinesia is shown in Figure 2 , with PM 2.5 classified by tertiles (high, medium, low). After accounting for patient demographics and RUCA, each 1-μg/m 3 increase in PM 2.5 was associated with 42% greater risk for developing dyskinesia (HR, 1.42; 95% CI, 1.17-1.73; P  < .001).

Our analysis restricted to metropolitan core populations provided larger estimates compared with our primary analysis that included both metropolitan and nonmetropolitan populations ( Table 1 ). In metropolitan populations, there was a positive association between PM 2.5 exposure and PD risk. Compared with the lowest quintile of PM 2.5 exposure, metropolitan populations had 10% to 23% increased odds of PD (second quintile: OR, 1.10; 95% CI, 1.00-1.21; fifth quintile: OR, 1.23; 95% CI, 1.11-1.35) ( Table 2 ). However, our metropolitan-restricted analysis of dyskinesia (274 patients with PD; 37 dyskinesia events) found a lower risk compared with the analysis that included both metropolitan and nonmetropolitan populations. Specifically, we found that each 1-μg/m 3 increase in PM 2.5 was associated with 35% greater risk for dyskinesia (HR, 1.35; 95% CI, 1.06-1.72; P  = .01) after accounting for patient demographics.

In this population-based case-control study, PM 2.5 exposure was associated with an increased risk of developing PD, particularly the akinetic-rigid phenotype, and risk was higher with increasing PM 2.5 levels. Exposure to NO 2 was also associated with an increased risk of developing PD. Additionally, higher levels of PM 2.5 and NO 2 were associated with an increased risk of developing dyskinesia following the onset of PD. Contrary to our hypothesis and prior research, 27 PD mortality was not associated with PM 2.5 exposure. We speculate that patients with PD in our study area may have better access to medical care compared to individuals with PD in the general population.

Several studies have reported an association between PM 2.5 exposure and adverse neurological outcomes. 5 , 28 , 29 The ultrafine particles (≤0.1 µm) contained within PM 2.5 cross the blood brain barrier, 15 and PM 2.5 in particular has been reported to be associated with inflammation, oxidative stress, and microglia activation, which are potential pathogenic mechanisms for the development of PD. 15 - 19 Moreover, studies have demonstrated that higher levels of PM 2.5 result in greater neurotoxic effects. 30 Similar to other studies, 5 , 6 , 11 , 31 , 32 we observed that the association between PM 2.5 and PD risk tapered off at the highest levels of PM 2.5 . The reason for this plateauing remains unclear; however, some researchers have suggested that differences in PM 2.5 composition in high-PM 2.5 and low-PM 2.5 regions may account for these findings . 5 Specifically, PM 2.5 composition may be more heterogeneous in regions with the relatively high PM 2.5 , making PM 2.5 alone a less reliable indicator of exposure to specific neurotoxic subcomponents in those regions. Nevertheless, we also acknowledge the possibility that the observed ceiling effect might be tied to a potential biological limit on the mechanisms of neuronal damage occurring in individuals chronically exposed to higher levels of PM 2.5 .

It is possible that PM 2.5 may have varied effects on the development and progression of neurodegenerative disease based on its composition. A multicountry study in Europe confirmed the importance of considering the subcomponents of PM 2.5 . 7 In our study, we were unable to explore broader ranges of PM 2.5 , since the range of PM 2.5 in our study area (parts of Minnesota, Wisconsin, and Iowa) was relatively small compared with the range of PM 2.5 observed nationwide. However, a 2022 study 33 identified notable geographical variation of PM 2.5 subcomponents in the Midwest, finding a north-south gradient in PM 2.5 , nitrite, and organic carbon composition, as well as an inverse gradient of sulfate composition. Additionally, the detected association with NO 2 and the larger effect size observed in metropolitan core populations suggest the possibility that the PM 2.5 association may be driven by traffic-related particulates. Unfortunately, without complete information of prior toxic exposures, we are limited in our ability to draw causal conclusions.

Importantly, in 2024, the US Environmental Protection Agency reduced the annual PM 2.5 standard from 12 μg/m 3 to 9 μg/m 3 due to growing evidence of negative health effects at levels below the previously set standard. 34 Our study not only supports the findings that led to this change, but suggests that the upper limit should be lowered to 8 μg/m 3 —a level previously advocated for by the American Lung Association and other health organizations. Notably, the World Health Organization recommends a more stringent limit than this, setting their standard to 5 μg/m 3 . 35

Individuals with PD who were exposed to higher levels of PM 2.5 were more likely to develop the akinetic rigid subtype of PD. Bradykinesia and rigidity are the predominant findings in these individuals, and this subtype has been linked to faster disease progression. Studies suggest that akinesia and resting tremor may result from different neurobiological processes, with the former resulting from both tonic (sustained) and phasic (intermittent) dopamine levels, and the latter from tonic release of dopamine and dopamine receptor responsiveness. 36 We speculate that these differences may result from differences in PM 2.5 subcomponents and subfractions. Interestingly, similar findings have been reported when using the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) as a model for PD. 37 Indeed, MPTP has been shown to produce both phasic and tonic dopamine dysregulation in the basal ganglia. 38 - 40 In humans, MPTP can produce all major Parkinsonian symptoms, including akinesia and rest tremor; however, in many primate models, MPTP produces akinesia and rigidity without low-frequency tremor. 41 Although MPTP is not found naturally in the environment, it is often referenced when exploring the role of environmental toxins that might cause neurodegeneration by a mechanism similar to MPTP. 42 Thus our finding that PM 2.5 exposure was associated with greater risk of the akinetic rigid PD subtype aligns with the possible evidence of a different clinical manifestations of the disease secondary to an external neurotoxin exposures (MPTP). 41 This work provides insight into the role of PM 2.5 exposure in the development of the different PD phenotypes. Furthermore, our study may offer a new explanation for the onset of dyskinesias that is not solely based on patient demographics, genetics, clinical characteristics, or drug response. 43 - 46 In fact, it possible that environmental factors may lead to an increased risk of developing dyskinesia.

Our study has several strengths. First, we used population-based incidence data, which allows us to better answer questions of PD etiology. Second, rather than relying on ICD-9 and ICD-10 codes alone, all identified cases were screened by a movement disorder specialist to confirm diagnosis of PD. Third, we used address-level data to assign exposure, which is a stronger proxy for patient-level PM 2.5 exposure compared with less precise geographies (eg, zip codes or census tracts). Fourth, our REP data also allowed us to assign PM 2.5 and NO 2 exposure based on multiple years of address information for each patient, meaning that we were able to follow our patients forward through time.

This study also has some limitations. Our population-based dataset had a limited geographical extent. However, the REP captures data from patients for all health systems within our study area, making it a comprehensive population-based dataset. 24 Our study was limited in that the REP population is predominantly White, given the demographics of the study region; however, our results reflect what other studies have found using diverse cohorts, including the nationwide Medicare population. 5 We acknowledge that in our subtype analysis of PD cases, the distribution of PM 2.5 among our PD cases was relatively small. Additionally, we were unable to adjust for all additional clinical characteristics associated with dyskinesia (eg, body weight, disease severity, and levodopa treatment). We did not have information on occupational history, work address, or activity space information; therefore, our results may be vulnerable to exposure misclassification errors (eg, for patients who spend more time at locations other than their home address). Long-term neurotoxic exposures are likely key in PD development. Due to the long prodromal period of PD, 47 we used PM 2.5 estimates for up to 10 years prior to symptom onset date. The relevant exposure window may extend back further, but PM 2.5 estimates prior to 1998 are unavailable. Additionally, a limitation of many epidemiological studies is the use of clinical criteria that do not necessarily correlate with pathology findings and, usually, do not consider the presence of copathology. Importantly, it is possible that the toxicant role of PM 2.5 may interfere with a change in the pathology cascade. On the other hand, we previously reported an clinicopathology concordance of 86.7% synucleoinopathies, supporting our case identification and classiffication. 48

This population-based case-control study provides evidence in support of an association of PM 2.5 and NO 2 exposure with the risk of developing PD. Higher levels of PM 2.5 exposure were associated with increased risk of developing akinetic rigid disease and dyskinesias compared with lower levels of exposure. These findings suggest that a reduction in PM 2.5 may help reduce the risk of PD and affect the clinical profile of PD and disease complications (modifying the PD phenotype and the risk of dyskinesia in patients with PD).

Accepted for Publication: July 19, 2024.

Published: September 16, 2024. doi:10.1001/jamanetworkopen.2024.33602

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Krzyzanowski B et al. JAMA Network Open .

Corresponding Author: Brittany Krzyzanowski, PhD, Barrow Neurological Institute, 240 W Thomas Rd, Phoenix, AZ 85013 ( [email protected] ); Rodolfo Savica MD, PhD, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 ( [email protected] ).

Author Contributions: Drs Krzyzanowski and Mullan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Krzyzanowski, Mullan, Savica.

Acquisition, analysis, or interpretation of data: Krzyzanowski, Mullan, Turcano, Camerucci, Bower.

Drafting of the manuscript: Krzyzanowski, Mullan, Savica.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Krzyzanowski, Mullan, Savica.

Administrative, technical, or material support: Mullan.

Supervision: Turcano, Camerucci, Bower, Savica.

Conflict of Interest Disclosures: Dr Savica reported receiving support from the National Institute on Aging, the National Institute of Neurological Disorders and Stroke, the Parkinson’s Disease Foundation, Acadia Pharmaceuticals, and Michael J. Fox Foundation outside the submitted work.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank the staff of Neuroscience Publications at Barrow Neurological Institute for assistance with manuscript preparation.

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