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Case Study – Methods, Examples and Guide

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Case study research: opening up research opportunities

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 30 December 2019

Issue publication date: 3 March 2020

The case study approach has been widely used in management studies and the social sciences more generally. However, there are still doubts about when and how case studies should be used. This paper aims to discuss this approach, its various uses and applications, in light of epistemological principles, as well as the criteria for rigor and validity.

Design/methodology/approach

This paper discusses the various concepts of case and case studies in the methods literature and addresses the different uses of cases in relation to epistemological principles and criteria for rigor and validity.

The use of this research approach can be based on several epistemologies, provided the researcher attends to the internal coherence between method and epistemology, or what the authors call “alignment.”

Originality/value

This study offers a number of implications for the practice of management research, as it shows how the case study approach does not commit the researcher to particular data collection or interpretation methods. Furthermore, the use of cases can be justified according to multiple epistemological orientations.

• Epistemology

Takahashi, A.R.W. and Araujo, L. (2020), "Case study research: opening up research opportunities", RAUSP Management Journal , Vol. 55 No. 1, pp. 100-111. https://doi.org/10.1108/RAUSP-05-2019-0109

Emerald Publishing Limited

Copyright © 2019, Adriana Roseli Wünsch Takahashi and Luis Araujo.

Published in RAUSP Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The case study as a research method or strategy brings us to question the very term “case”: after all, what is a case? A case-based approach places accords the case a central role in the research process ( Ragin, 1992 ). However, doubts still remain about the status of cases according to different epistemologies and types of research designs.

Despite these doubts, the case study is ever present in the management literature and represents the main method of management research in Brazil ( Coraiola, Sander, Maccali, & Bulgacov, 2013 ). Between 2001 and 2010, 2,407 articles (83.14 per cent of qualitative research) were published in conferences and management journals as case studies (Takahashi & Semprebom, 2013 ). A search on Spell.org.br for the term “case study” under title, abstract or keywords, for the period ranging from January 2010 to July 2019, yielded 3,040 articles published in the management field. Doing research using case studies, allows the researcher to immerse him/herself in the context and gain intensive knowledge of a phenomenon, which in turn demands suitable methodological principles ( Freitas et al. , 2017 ).

Our objective in this paper is to discuss notions of what constitutes a case and its various applications, considering epistemological positions as well as criteria for rigor and validity. The alignment between these dimensions is put forward as a principle advocating coherence among all phases of the research process.

This article makes two contributions. First, we suggest that there are several epistemological justifications for using case studies. Second, we show that the quality and rigor of academic research with case studies are directly related to the alignment between epistemology and research design rather than to choices of specific forms of data collection or analysis. The article is structured as follows: the following four sections discuss concepts of what is a case, its uses, epistemological grounding as well as rigor and quality criteria. The brief conclusions summarize the debate and invite the reader to delve into the literature on the case study method as a way of furthering our understanding of contemporary management phenomena.

2. What is a case study?

The debate over what constitutes a case in social science is a long-standing one. In 1988, Howard Becker and Charles Ragin organized a workshop to discuss the status of the case as a social science method. As the discussion was inconclusive, they posed the question “What is a case?” to a select group of eight social scientists in 1989, and later to participants in a symposium on the subject. Participants were unable to come up with a consensual answer. Since then, we have witnessed that further debates and different answers have emerged. The original question led to an even broader issue: “How do we, as social scientists, produce results and seem to know what we know?” ( Ragin, 1992 , p. 16).

An important step that may help us start a reflection on what is a case is to consider the phenomena we are looking at. To do that, we must know something about what we want to understand and how we might study it. The answer may be a causal explanation, a description of what was observed or a narrative of what has been experienced. In any case, there will always be a story to be told, as the choice of the case study method demands an answer to what the case is about.

A case may be defined ex ante , prior to the start of the research process, as in Yin’s (2015) classical definition. But, there is no compelling reason as to why cases must be defined ex ante . Ragin (1992 , p. 217) proposed the notion of “casing,” to indicate that what the case is emerges from the research process:

Rather than attempt to delineate the many different meanings of the term “case” in a formal taxonomy, in this essay I offer instead a view of cases that follows from the idea implicit in many of the contributions – that concocting cases is a varied but routine social scientific activity. […] The approach of this essay is that this activity, which I call “casing”, should be viewed in practical terms as a research tactic. It is selectively invoked at many different junctures in the research process, usually to resolve difficult issues in linking ideas and evidence.

In other words, “casing” is tied to the researcher’s practice, to the way he/she delimits or declares a case as a significant outcome of a process. In 2013, Ragin revisited the 1992 concept of “casing” and explored its multiple possibilities of use, paying particular attention to “negative cases.”

According to Ragin (1992) , a case can be centered on a phenomenon or a population. In the first scenario, cases are representative of a phenomenon, and are selected based on what can be empirically observed. The process highlights different aspects of cases and obscures others according to the research design, and allows for the complexity, specificity and context of the phenomenon to be explored. In the alternative, population-focused scenario, the selection of cases precedes the research. Both positive and negative cases are considered in exploring a phenomenon, with the definition of the set of cases dependent on theory and the central objective to build generalizations. As a passing note, it is worth mentioning here that a study of multiple cases requires a definition of the unit of analysis a priori . Otherwise, it will not be possible to make cross-case comparisons.

These two approaches entail differences that go beyond the mere opposition of quantitative and qualitative data, as a case often includes both types of data. Thus, the confusion about how to conceive cases is associated with Ragin’s (1992) notion of “small vs large N,” or McKeown’s (1999) “statistical worldview” – the notion that relevant findings are only those that can be made about a population based on the analysis of representative samples. In the same vein, Byrne (2013) argues that we cannot generate nomothetic laws that apply in all circumstances, periods and locations, and that no social science method can claim to generate invariant laws. According to the same author, case studies can help us understand that there is more than one ideographic variety and help make social science useful. Generalizations still matter, but they should be understood as part of defining the research scope, and that scope points to the limitations of knowledge produced and consumed in concrete time and space.

Thus, what defines the orientation and the use of cases is not the mere choice of type of data, whether quantitative or qualitative, but the orientation of the study. A statistical worldview sees cases as data units ( Byrne, 2013 ). Put differently, there is a clear distinction between statistical and qualitative worldviews; the use of quantitative data does not by itself means that the research is (quasi) statistical, or uses a deductive logic:

Case-based methods are useful, and represent, among other things, a way of moving beyond a useless and destructive tradition in the social sciences that have set quantitative and qualitative modes of exploration, interpretation, and explanation against each other ( Byrne, 2013 , p. 9).

Other authors advocate different understandings of what a case study is. To some, it is a research method, to others it is a research strategy ( Creswell, 1998 ). Sharan Merrian and Robert Yin, among others, began to write about case study research as a methodology in the 1980s (Merrian, 2009), while authors such as Eisenhardt (1989) called it a research strategy. Stake (2003) sees the case study not as a method, but as a choice of what to be studied, the unit of study. Regardless of their differences, these authors agree that case studies should be restricted to a particular context as they aim to provide an in-depth knowledge of a given phenomenon: “A case study is an in-depth description and analysis of a bounded system” (Merrian, 2009, p. 40). According to Merrian, a qualitative case study can be defined by the process through which the research is carried out, by the unit of analysis or the final product, as the choice ultimately depends on what the researcher wants to know. As a product of research, it involves the analysis of a given entity, phenomenon or social unit.

Thus, whether it is an organization, an individual, a context or a phenomenon, single or multiple, one must delimit it, and also choose between possible types and configurations (Merrian, 2009; Yin, 2015 ). A case study may be descriptive, exploratory, explanatory, single or multiple ( Yin, 2015 ); intrinsic, instrumental or collective ( Stake, 2003 ); and confirm or build theory ( Eisenhardt, 1989 ).

both went through the same process of implementing computer labs intended for the use of information and communication technologies in 2007;

both took part in the same regional program (Paraná Digital); and

they shared similar characteristics regarding location (operation in the same neighborhood of a city), number of students, number of teachers and technicians and laboratory sizes.

However, the two institutions differed in the number of hours of program use, with one of them displaying a significant number of hours/use while the other showed a modest number, according to secondary data for the period 2007-2013. Despite the context being similar and the procedures for implementing the technology being the same, the mechanisms of social integration – an idiosyncratic factor of each institution – were different in each case. This explained differences in their use of resource, processes of organizational learning and capacity to absorb new knowledge.

On the other hand, multiple case studies seek evidence in different contexts and do not necessarily require direct comparisons ( Stake, 2003 ). Rather, there is a search for patterns of convergence and divergence that permeate all the cases, as the same issues are explored in every case. Cases can be added progressively until theoretical saturation is achieved. An example is of a study that investigated how entrepreneurial opportunity and management skills were developed through entrepreneurial learning ( Zampier & Takahashi, 2014 ). The authors conducted nine case studies, based on primary and secondary data, with each one analyzed separately, so a search for patterns could be undertaken. The convergence aspects found were: the predominant way of transforming experience into knowledge was exploitation; managerial skills were developed through by taking advantages of opportunities; and career orientation encompassed more than one style. As for divergence patterns: the experience of success and failure influenced entrepreneurs differently; the prevailing rationality logic of influence was different; and the combination of styles in career orientation was diverse.

A full discussion of choice of case study design is outside the scope of this article. For the sake of illustration, we make a brief mention to other selection criteria such as the purpose of the research, the state of the art of the research theme, the time and resources involved and the preferred epistemological position of the researcher. In the next section, we look at the possibilities of carrying out case studies in line with various epistemological traditions, as the answers to the “what is a case?” question reveal varied methodological commitments as well as diverse epistemological and ontological positions ( Ragin, 2013 ).

3. Epistemological positioning of case study research

Ontology and epistemology are like skin, not a garment to be occasionally worn ( Marsh & Furlong, 2002 ). According to these authors, ontology and epistemology guide the choice of theory and method because they cannot or should not be worn as a garment. Hence, one must practice philosophical “self-knowledge” to recognize one’s vision of what the world is and of how knowledge of that world is accessed and validated. Ontological and epistemological positions are relevant in that they involve the positioning of the researcher in social science and the phenomena he or she chooses to study. These positions do not tend to vary from one project to another although they can certainly change over time for a single researcher.

Ontology is the starting point from which the epistemological and methodological positions of the research arise ( Grix, 2002 ). Ontology expresses a view of the world, what constitutes reality, nature and the image one has of social reality; it is a theory of being ( Marsh & Furlong, 2002 ). The central question is the nature of the world out there regardless of our ability to access it. An essentialist or foundationalist ontology acknowledges that there are differences that persist over time and these differences are what underpin the construction of social life. An opposing, anti-foundationalist position presumes that the differences found are socially constructed and may vary – i.e. they are not essential but specific to a given culture at a given time ( Marsh & Furlong, 2002 ).

Epistemology is centered around a theory of knowledge, focusing on the process of acquiring and validating knowledge ( Grix, 2002 ). Positivists look at social phenomena as a world of causal relations where there is a single truth to be accessed and confirmed. In this tradition, case studies test hypotheses and rely on deductive approaches and quantitative data collection and analysis techniques. Scholars in the field of anthropology and observation-based qualitative studies proposed alternative epistemologies based on notions of the social world as a set of manifold and ever-changing processes. In management studies since the 1970s, the gradual acceptance of qualitative research has generated a diverse range of research methods and conceptions of the individual and society ( Godoy, 1995 ).

The interpretative tradition, in direct opposition to positivism, argues that there is no single objective truth to be discovered about the social world. The social world and our knowledge of it are the product of social constructions. Thus, the social world is constituted by interactions, and our knowledge is hermeneutic as the world does not exist independent of our knowledge ( Marsh & Furlong, 2002 ). The implication is that it is not possible to access social phenomena through objective, detached methods. Instead, the interaction mechanisms and relationships that make up social constructions have to be studied. Deductive approaches, hypothesis testing and quantitative methods are not relevant here. Hermeneutics, on the other hand, is highly relevant as it allows the analysis of the individual’s interpretation, of sayings, texts and actions, even though interpretation is always the “truth” of a subject. Methods such as ethnographic case studies, interviews and observations as data collection techniques should feed research designs according to interpretivism. It is worth pointing out that we are to a large extent, caricaturing polar opposites rather characterizing a range of epistemological alternatives, such as realism, conventionalism and symbolic interactionism.

If diverse ontologies and epistemologies serve as a guide to research approaches, including data collection and analysis methods, and if they should be regarded as skin rather than clothing, how does one make choices regarding case studies? What are case studies, what type of knowledge they provide and so on? The views of case study authors are not always explicit on this point, so we must delve into their texts to glean what their positions might be.

Two of the cited authors in case study research are Robert Yin and Kathleen Eisenhardt. Eisenhardt (1989) argues that a case study can serve to provide a description, test or generate a theory, the latter being the most relevant in contributing to the advancement of knowledge in a given area. She uses terms such as populations and samples, control variables, hypotheses and generalization of findings and even suggests an ideal number of case studies to allow for theory construction through replication. Although Eisenhardt includes observation and interview among her recommended data collection techniques, the approach is firmly anchored in a positivist epistemology:

Third, particularly in comparison with Strauss (1987) and Van Maanen (1988), the process described here adopts a positivist view of research. That is, the process is directed toward the development of testable hypotheses and theory which are generalizable across settings. In contrast, authors like Strauss and Van Maanen are more concerned that a rich, complex description of the specific cases under study evolve and they appear less concerned with development of generalizable theory ( Eisenhardt, 1989 , p. 546).

This position attracted a fair amount of criticism. Dyer & Wilkins (1991) in a critique of Eisenhardt’s (1989) article focused on the following aspects: there is no relevant justification for the number of cases recommended; it is the depth and not the number of cases that provides an actual contribution to theory; and the researcher’s purpose should be to get closer to the setting and interpret it. According to the same authors, discrepancies from prior expectations are also important as they lead researchers to reflect on existing theories. Eisenhardt & Graebner (2007 , p. 25) revisit the argument for the construction of a theory from multiple cases:

A major reason for the popularity and relevance of theory building from case studies is that it is one of the best (if not the best) of the bridges from rich qualitative evidence to mainstream deductive research.

Although they recognize the importance of single-case research to explore phenomena under unique or rare circumstances, they reaffirm the strength of multiple case designs as it is through them that better accuracy and generalization can be reached.

Likewise, Robert Yin emphasizes the importance of variables, triangulation in the search for “truth” and generalizable theoretical propositions. Yin (2015 , p. 18) suggests that the case study method may be appropriate for different epistemological orientations, although much of his work seems to invoke a realist epistemology. Authors such as Merrian (2009) and Stake (2003) suggest an interpretative version of case studies. Stake (2003) looks at cases as a qualitative option, where the most relevant criterion of case selection should be the opportunity to learn and understand a phenomenon. A case is not just a research method or strategy; it is a researcher’s choice about what will be studied:

Even if my definition of case study was agreed upon, and it is not, the term case and study defy full specification (Kemmis, 1980). A case study is both a process of inquiry about the case and the product of that inquiry ( Stake, 2003 , p. 136).

Later, Stake (2003 , p. 156) argues that:

[…] the purpose of a case report is not to represent the world, but to represent the case. […] The utility of case research to practitioners and policy makers is in its extension of experience.

Still according to Stake (2003 , pp. 140-141), to do justice to complex views of social phenomena, it is necessary to analyze the context and relate it to the case, to look for what is peculiar rather than common in cases to delimit their boundaries, to plan the data collection looking for what is common and unusual about facts, what could be valuable whether it is unique or common:

Reflecting upon the pertinent literature, I find case study methodology written largely by people who presume that the research should contribute to scientific generalization. The bulk of case study work, however, is done by individuals who have intrinsic interest in the case and little interest in the advance of science. Their designs aim the inquiry toward understanding of what is important about that case within its own world, which is seldom the same as the worlds of researchers and theorists. Those designs develop what is perceived to be the case’s own issues, contexts, and interpretations, its thick descriptions . In contrast, the methods of instrumental case study draw the researcher toward illustrating how the concerns of researchers and theorists are manifest in the case. Because the critical issues are more likely to be know in advance and following disciplinary expectations, such a design can take greater advantage of already developed instruments and preconceived coding schemes.

The aforementioned authors were listed to illustrate differences and sometimes opposing positions on case research. These differences are not restricted to a choice between positivism and interpretivism. It is worth noting that Ragin’s (2013 , p. 523) approach to “casing” is compatible with the realistic research perspective:

In essence, to posit cases is to engage in ontological speculation regarding what is obdurately real but only partially and indirectly accessible through social science. Bringing a realist perspective to the case question deepens and enriches the dialogue, clarifying some key issues while sweeping others aside.

cases are actual entities, reflecting their operations of real causal mechanism and process patterns;

case studies are interactive processes and are open to revisions and refinements; and

social phenomena are complex, contingent and context-specific.

Ragin (2013 , p. 532) concludes:

Lurking behind my discussion of negative case, populations, and possibility analysis is the implication that treating cases as members of given (and fixed) populations and seeking to infer the properties of populations may be a largely illusory exercise. While demographers have made good use of the concept of population, and continue to do so, it is not clear how much the utility of the concept extends beyond their domain. In case-oriented work, the notion of fixed populations of cases (observations) has much less analytic utility than simply “the set of relevant cases,” a grouping that must be specified or constructed by the researcher. The demarcation of this set, as the work of case-oriented researchers illustrates, is always tentative, fluid, and open to debate. It is only by casing social phenomena that social scientists perceive the homogeneity that allows analysis to proceed.

In summary, case studies are relevant and potentially compatible with a range of different epistemologies. Researchers’ ontological and epistemological positions will guide their choice of theory, methodologies and research techniques, as well as their research practices. The same applies to the choice of authors describing the research method and this choice should be coherent. We call this research alignment , an attribute that must be judged on the internal coherence of the author of a study, and not necessarily its evaluator. The following figure illustrates the interrelationship between the elements of a study necessary for an alignment ( Figure 1 ).

In addition to this broader aspect of the research as a whole, other factors should be part of the researcher’s concern, such as the rigor and quality of case studies. We will look into these in the next section taking into account their relevance to the different epistemologies.

4. Rigor and quality in case studies

Traditionally, at least in positivist studies, validity and reliability are the relevant quality criteria to judge research. Validity can be understood as external, internal and construct. External validity means identifying whether the findings of a study are generalizable to other studies using the logic of replication in multiple case studies. Internal validity may be established through the theoretical underpinning of existing relationships and it involves the use of protocols for the development and execution of case studies. Construct validity implies defining the operational measurement criteria to establish a chain of evidence, such as the use of multiple sources of evidence ( Eisenhardt, 1989 ; Yin, 2015 ). Reliability implies conducting other case studies, instead of just replicating results, to minimize the errors and bias of a study through case study protocols and the development of a case database ( Yin, 2015 ).

Several criticisms have been directed toward case studies, such as lack of rigor, lack of generalization potential, external validity and researcher bias. Case studies are often deemed to be unreliable because of a lack of rigor ( Seuring, 2008 ). Flyvbjerg (2006 , p. 219) addresses five misunderstandings about case-study research, and concludes that:

[…] a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one.

theoretical knowledge is more valuable than concrete, practical knowledge;

the case study cannot contribute to scientific development because it is not possible to generalize on the basis of an individual case;

the case study is more useful for generating rather than testing hypotheses;

the case study contains a tendency to confirm the researcher’s preconceived notions; and

it is difficult to summarize and develop general propositions and theories based on case studies.

These criticisms question the validity of the case study as a scientific method and should be corrected.

The critique of case studies is often framed from the standpoint of what Ragin (2000) labeled large-N research. The logic of small-N research, to which case studies belong, is different. Cases benefit from depth rather than breadth as they: provide theoretical and empirical knowledge; contribute to theory through propositions; serve not only to confirm knowledge, but also to challenge and overturn preconceived notions; and the difficulty in summarizing their conclusions is because of the complexity of the phenomena studies and not an intrinsic limitation of the method.

Thus, case studies do not seek large-scale generalizations as that is not their purpose. And yet, this is a limitation from a positivist perspective as there is an external reality to be “apprehended” and valid conclusions to be extracted for an entire population. If positivism is the epistemology of choice, the rigor of a case study can be demonstrated by detailing the criteria used for internal and external validity, construct validity and reliability ( Gibbert & Ruigrok, 2010 ; Gibbert, Ruigrok, & Wicki, 2008 ). An example can be seen in case studies in the area of information systems, where there is a predominant orientation of positivist approaches to this method ( Pozzebon & Freitas, 1998 ). In this area, rigor also involves the definition of a unit of analysis, type of research, number of cases, selection of sites, definition of data collection and analysis procedures, definition of the research protocol and writing a final report. Creswell (1998) presents a checklist for researchers to assess whether the study was well written, if it has reliability and validity and if it followed methodological protocols.

In case studies with a non-positivist orientation, rigor can be achieved through careful alignment (coherence among ontology, epistemology, theory and method). Moreover, the concepts of validity can be understood as concern and care in formulating research, research development and research results ( Ollaik & Ziller, 2012 ), and to achieve internal coherence ( Gibbert et al. , 2008 ). The consistency between data collection and interpretation, and the observed reality also help these studies meet coherence and rigor criteria. Siggelkow (2007) argues that a case study should be persuasive and that even a single case study may be a powerful example to contest a widely held view. To him, the value of a single case study or studies with few cases can be attained by their potential to provide conceptual insights and coherence to the internal logic of conceptual arguments: “[…] a paper should allow a reader to see the world, and not just the literature, in a new way” ( Siggelkow, 2007 , p. 23).

Interpretative studies should not be justified by criteria derived from positivism as they are based on a different ontology and epistemology ( Sandberg, 2005 ). The rejection of an interpretive epistemology leads to the rejection of an objective reality: “As Bengtsson points out, the life-world is the subjects’ experience of reality, at the same time as it is objective in the sense that it is an intersubjective world” ( Sandberg, 2005 , p. 47). In this event, how can one demonstrate what positivists call validity and reliability? What would be the criteria to justify knowledge as truth, produced by research in this epistemology? Sandberg (2005 , p. 62) suggests an answer based on phenomenology:

This was demonstrated first by explicating life-world and intentionality as the basic assumptions underlying the interpretative research tradition. Second, based on those assumptions, truth as intentional fulfillment, consisting of perceived fulfillment, fulfillment in practice, and indeterminate fulfillment, was proposed. Third, based on the proposed truth constellation, communicative, pragmatic, and transgressive validity and reliability as interpretative awareness were presented as the most appropriate criteria for justifying knowledge produced within interpretative approach. Finally, the phenomenological epoché was suggested as a strategy for achieving these criteria.

From this standpoint, the research site must be chosen according to its uniqueness so that one can obtain relevant insights that no other site could provide ( Siggelkow, 2007 ). Furthermore, the view of what is being studied is at the center of the researcher’s attention to understand its “truth,” inserted in a given context.

The case researcher is someone who can reduce the probability of misinterpretations by analyzing multiple perceptions, searches for data triangulation to check for the reliability of interpretations ( Stake, 2003 ). It is worth pointing out that this is not an option for studies that specifically seek the individual’s experience in relation to organizational phenomena.

In short, there are different ways of seeking rigor and quality in case studies, depending on the researcher’s worldview. These different forms pervade everything from the research design, the choice of research questions, the theory or theories to look at a phenomenon, research methods, the data collection and analysis techniques, to the type and style of research report produced. Validity can also take on different forms. While positivism is concerned with validity of the research question and results, interpretivism emphasizes research processes without neglecting the importance of the articulation of pertinent research questions and the sound interpretation of results ( Ollaik & Ziller, 2012 ). The means to achieve this can be diverse, such as triangulation (of multiple theories, multiple methods, multiple data sources or multiple investigators), pre-tests of data collection instrument, pilot case, study protocol, detailed description of procedures such as field diary in observations, researcher positioning (reflexivity), theoretical-empirical consistency, thick description and transferability.

5. Conclusions

The central objective of this article was to discuss concepts of case study research, their potential and various uses, taking into account different epistemologies as well as criteria of rigor and validity. Although the literature on methodology in general and on case studies in particular, is voluminous, it is not easy to relate this approach to epistemology. In addition, method manuals often focus on the details of various case study approaches which confuse things further.

Faced with this scenario, we have tried to address some central points in this debate and present various ways of using case studies according to the preferred epistemology of the researcher. We emphasize that this understanding depends on how a case is defined and the particular epistemological orientation that underpins that conceptualization. We have argued that whatever the epistemological orientation is, it is possible to meet appropriate criteria of research rigor and quality provided there is an alignment among the different elements of the research process. Furthermore, multiple data collection techniques can be used in in single or multiple case study designs. Data collection techniques or the type of data collected do not define the method or whether cases should be used for theory-building or theory-testing.

Finally, we encourage researchers to consider case study research as one way to foster immersion in phenomena and their contexts, stressing that the approach does not imply a commitment to a particular epistemology or type of research, such as qualitative or quantitative. Case study research allows for numerous possibilities, and should be celebrated for that diversity rather than pigeon-holed as a monolithic research method.

The interrelationship between the building blocks of research

Byrne , D. ( 2013 ). Case-based methods: Why We need them; what they are; how to do them . Byrne D. In D Byrne. and C.C Ragin (Eds.), The SAGE handbooks of Case-Based methods , pp. 1 – 10 . London : SAGE Publications Inc .

Creswell , J. W. ( 1998 ). Qualitative inquiry and research design: choosing among five traditions , London : Sage Publications .

Coraiola , D. M. , Sander , J. A. , Maccali , N. & Bulgacov , S. ( 2013 ). Estudo de caso . In A. R. W. Takahashi , (Ed.), Pesquisa qualitativa em administração: Fundamentos, métodos e usos no Brasil , pp. 307 – 341 . São Paulo : Atlas .

Dyer , W. G. , & Wilkins , A. L. ( 1991 ). Better stories, not better constructs, to generate better theory: a rejoinder to Eisenhardt . The Academy of Management Review , 16 , 613 – 627 .

Eisenhardt , K. ( 1989 ). Building theory from case study research . Academy of Management Review , 14 , 532 – 550 .

Eisenhardt , K. M. , & Graebner , M. E. ( 2007 ). Theory building from cases: Opportunities and challenges . Academy of Management Journal , 50 , 25 – 32 .

Flyvbjerg , B. ( 2006 ). Five misunderstandings about case-study research . Qualitative Inquiry , 12 , 219 – 245 .

Freitas , J. S. , Ferreira , J. C. A. , Campos , A. A. R. , Melo , J. C. F. , Cheng , L. C. , & Gonçalves , C. A. ( 2017 ). Methodological roadmapping: a study of centering resonance analysis . RAUSP Management Journal , 53 , 459 – 475 .

Gibbert , M. , Ruigrok , W. , & Wicki , B. ( 2008 ). What passes as a rigorous case study? . Strategic Management Journal , 29 , 1465 – 1474 .

Gibbert , M. , & Ruigrok , W. ( 2010 ). The “what” and “how” of case study rigor: Three strategies based on published work . Organizational Research Methods , 13 , 710 – 737 .

Godoy , A. S. ( 1995 ). Introdução à pesquisa qualitativa e suas possibilidades . Revista de Administração de Empresas , 35 , 57 – 63 .

Grix , J. ( 2002 ). Introducing students to the generic terminology of social research . Politics , 22 , 175 – 186 .

Marsh , D. , & Furlong , P. ( 2002 ). A skin, not a sweater: ontology and epistemology in political science . In D Marsh. , & G Stoker , (Eds.), Theory and Methods in Political Science , New York, NY : Palgrave McMillan , pp. 17 – 41 .

McKeown , T. J. ( 1999 ). Case studies and the statistical worldview: Review of King, Keohane, and Verba’s designing social inquiry: Scientific inference in qualitative research . International Organization , 53 , 161 – 190 .

Merriam , S. B. ( 2009 ). Qualitative research: a guide to design and implementation .

Ollaik , L. G. , & Ziller , H. ( 2012 ). Distintas concepções de validade em pesquisas qualitativas . Educação e Pesquisa , 38 , 229 – 241 .

Picoli , F. R. , & Takahashi , A. R. W. ( 2016 ). Capacidade de absorção, aprendizagem organizacional e mecanismos de integração social . Revista de Administração Contemporânea , 20 , 1 – 20 .

Pozzebon , M. , & Freitas , H. M. R. ( 1998 ). Pela aplicabilidade: com um maior rigor científico – dos estudos de caso em sistemas de informação . Revista de Administração Contemporânea , 2 , 143 – 170 .

Sandberg , J. ( 2005 ). How do we justify knowledge produced within interpretive approaches? . Organizational Research Methods , 8 , 41 – 68 .

Seuring , S. A. ( 2008 ). Assessing the rigor of case study research in supply chain management. Supply chain management . Supply Chain Management: An International Journal , 13 , 128 – 137 .

Siggelkow , N. ( 2007 ). Persuasion with case studies . Academy of Management Journal , 50 , 20 – 24 .

Stake , R. E. ( 2003 ). Case studies . In N. K. , Denzin , & Y. S. , Lincoln (Eds.). Strategies of Qualitative Inquiry , London : Sage Publications . pp. 134 – 164 .

Takahashi , A. R. W. , & Semprebom , E. ( 2013 ). Resultados gerais e desafios . In A. R. W. , Takahashi (Ed.), Pesquisa qualitativa em administração: Fundamentos, métodos e usos no brasil , pp. 343 – 354 . São Paulo : Atlas .

Ragin , C. C. ( 1992 ). Introduction: Cases of “what is a case? . In H. S. , Becker , & C. C. Ragin and (Eds). What is a case? Exploring the foundations of social inquiry , pp. 1 – 18 .

Ragin , C. C. ( 2013 ). Reflections on casing and Case-Oriented research . In D , Byrne. , & C. C. Ragin (Eds.), The SAGE handbooks of Case-Based methods , London : SAGE Publications , pp. 522 – 534 .

Yin , R. K. ( 2015 ). Estudo de caso: planejamento e métodos , Porto Alegre : Bookman .

Zampier , M. A. , & Takahashi , A. R. W. ( 2014 ). Aprendizagem e competências empreendedoras: Estudo de casos de micro e pequenas empresas do setor educacional . RGO Revista Gestão Organizacional , 6 , 1 – 18 .

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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.

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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

Diez Roux AV. Complex systems thinking and current impasses in health disparities research. Am J Public Health. 2011;101(9):1627–34.

Article   Google Scholar  

Ogilvie D, Mitchell R, Mutrie N, M P, Platt S. Evaluating health effects of transport interventions: methodologic case study. Am J Prev Med 2006;31:118–126.

Walshe C. The evaluation of complex interventions in palliative care: an exploration of the potential of case study research strategies. Palliat Med. 2011;25(8):774–81.

Woolcock M. Using case studies to explore the external validity of ‘complex’ development interventions. Evaluation. 2013;19:229–48.

Cartwright N. Are RCTs the gold standard? BioSocieties. 2007;2(1):11–20.

Deaton A, Cartwright N. Understanding and misunderstanding randomized controlled trials. Soc Sci Med. 2018;210:2–21.

Salway S, Green J. Towards a critical complex systems approach to public health. Crit Public Health. 2017;27(5):523–4.

Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2018;16(1):95.

Bonell C, Warren E, Fletcher A. Realist trials and the testing of context-mechanism-outcome configurations: a response to Van Belle et al. Trials. 2016;17:478.

Pallmann P, Bedding AW, Choodari-Oskooei B. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16:29.

Curran G, Bauer M, Mittman B, Pyne J, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26. https://doi.org/10.1097/MLR.0b013e3182408812 .

Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015 [cited 2020 Jun 27];350. Available from: https://www.bmj.com/content/350/bmj.h1258 .

Evans RE, Craig P, Hoddinott P, Littlecott H, Moore L, Murphy S, et al. When and how do ‘effective’ interventions need to be adapted and/or re-evaluated in new contexts? The need for guidance. J Epidemiol Community Health. 2019;73(6):481–2.

Shoveller J. A critical examination of representations of context within research on population health interventions. Crit Public Health. 2016;26(5):487–500.

Treweek S, Zwarenstein M. Making trials matter: pragmatic and explanatory trials and the problem of applicability. Trials. 2009;10(1):37.

Rosengarten M, Savransky M. A careful biomedicine? Generalization and abstraction in RCTs. Crit Public Health. 2019;29(2):181–91.

Green J, Roberts H, Petticrew M, Steinbach R, Goodman A, Jones A, et al. Integrating quasi-experimental and inductive designs in evaluation: a case study of the impact of free bus travel on public health. Evaluation. 2015;21(4):391–406.

Canguilhem G. The normal and the pathological. New York: Zone Books; 1991. (1949).

Google Scholar  

Hawe P, Shiell A, Riley T. Theorising interventions as events in systems. Am J Community Psychol. 2009;43:267–76.

King G, Keohane RO, Verba S. Designing social inquiry: scientific inference in qualitative research: Princeton University Press; 1994.

Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629.

Yin R. Enhancing the quality of case studies in health services research. Health Serv Res. 1999;34(5 Pt 2):1209.

CAS   PubMed   PubMed Central   Google Scholar  

Raine R, Fitzpatrick R, Barratt H, Bevan G, Black N, Boaden R, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res. 2016 [cited 2020 Jun 30];4(16). Available from: https://www.journalslibrary.nihr.ac.uk/hsdr/hsdr04160#/abstract .

Craig P, Di Ruggiero E, Frohlich KL, E M, White M, Group CCGA. Taking account of context in population health intervention research: guidance for producers, users and funders of research. NIHR Evaluation, Trials and Studies Coordinating Centre; 2018.

Grant RL, Hood R. Complex systems, explanation and policy: implications of the crisis of replication for public health research. Crit Public Health. 2017;27(5):525–32.

Mahoney J. Strategies of causal inference in small-N analysis. Sociol Methods Res. 2000;4:387–424.

Turner S. Major system change: a management and organisational research perspective. In: Rosalind Raine, Ray Fitzpatrick, Helen Barratt, Gywn Bevan, Nick Black, Ruth Boaden, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res. 2016;4(16) 2016. https://doi.org/10.3310/hsdr04160.

Ragin CC. Using qualitative comparative analysis to study causal complexity. Health Serv Res. 1999;34(5 Pt 2):1225.

Hanckel B, Petticrew M, Thomas J, Green J. Protocol for a systematic review of the use of qualitative comparative analysis for evaluative questions in public health research. Syst Rev. 2019;8(1):252.

Schneider CQ, Wagemann C. Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis: Cambridge University Press; 2012. 369 p.

Flyvbjerg B. Five misunderstandings about case-study research. Qual Inq. 2006;12:219–45.

Tsoukas H. Craving for generality and small-N studies: a Wittgensteinian approach towards the epistemology of the particular in organization and management studies. Sage Handb Organ Res Methods. 2009:285–301.

Stake RE. The art of case study research. London: Sage Publications Ltd; 1995.

Mitchell JC. Typicality and the case study. Ethnographic research: A guide to general conduct. Vol. 238241. 1984.

Gerring J. What is a case study and what is it good for? Am Polit Sci Rev. 2004;98(2):341–54.

May C, Mort M, Williams T, F M, Gask L. Health technology assessment in its local contexts: studies of telehealthcare. Soc Sci Med 2003;57:697–710.

McGill E. Trading quality for relevance: non-health decision-makers’ use of evidence on the social determinants of health. BMJ Open. 2015;5(4):007053.

Greenhalgh T. We can’t be 100% sure face masks work – but that shouldn’t stop us wearing them | Trish Greenhalgh. The Guardian. 2020 [cited 2020 Jun 27]; Available from: https://www.theguardian.com/commentisfree/2020/jun/05/face-masks-coronavirus .

Hammersley M. So, what are case studies? In: What’s wrong with ethnography? New York: Routledge; 1992.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Med Res Methodol. 2011;11(1):100.

Luck L, Jackson D, Usher K. Case study: a bridge across the paradigms. Nurs Inq. 2006;13(2):103–9.

Yin RK. Case study research and applications: design and methods: Sage; 2017.

Hyett N, A K, Dickson-Swift V. Methodology or method? A critical review of qualitative case study reports. Int J Qual Stud Health Well-Being. 2014;9:23606.

Carolan CM, Forbat L, Smith A. Developing the DESCARTE model: the design of case study research in health care. Qual Health Res. 2016;26(5):626–39.

Greenhalgh T, Annandale E, Ashcroft R, Barlow J, Black N, Bleakley A, et al. An open letter to the BMJ editors on qualitative research. Bmj. 2016;352.

Thomas G. A typology for the case study in social science following a review of definition, discourse, and structure. Qual Inq. 2011;17(6):511–21.

Lincoln YS, Guba EG. Judging the quality of case study reports. Int J Qual Stud Educ. 1990;3(1):53–9.

Riley DS, Barber MS, Kienle GS, Aronson JK, Schoen-Angerer T, Tugwell P, et al. CARE guidelines for case reports: explanation and elaboration document. J Clin Epidemiol. 2017;89:218–35.

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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” for case studies: why we need high-quality examples of global implementation research

• Blythe Beecroft   ORCID: orcid.org/0000-0002-6254-421X 1 ,
• Rachel Sturke 1 ,
• Gila Neta 2 &
• Rohit Ramaswamy 3  

Implementation Science Communications volume  3 , Article number:  15 ( 2022 ) Cite this article

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Rigorous and systematic documented examples of implementation research in global contexts can be a valuable resource and help build research capacity. In the context of low- and middle-income countries (LMICs), there is a need for practical examples of how to conduct implementation studies. To address this gap, Fogarty’s Center for Global Health Studies in collaboration with the Cincinnati Children's Hospital Medical Center and the National Cancer Institute is commissioning a collection of implementation science case studies in LMICs that describe key components of conducting implementation research, including how to select, adapt, and apply implementation science models, theories, and frameworks to these settings; develop and test implementation strategies; and evaluate implementation processes and outcomes. The case studies describe implementation research in various disease areas in LMICs around the world. This commentary highlights the value of case study methods commonly used in law and business schools as a source of “thick” (i.e., context-rich) description and a teaching tool for global implementation researchers. It addresses the independent merit of case studies as an evaluation approach for disseminating high-quality research in a format that is useful to a broad range of stakeholders. This commentary finally describes an approach for developing high-quality case studies of global implementation research, in order to be of value to a broad audience of researchers and practitioners.

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Contributions to the literature

Reinforcing the need for “thick” (i.e., context-rich) descriptions of implementation studies

Highlighting the utility of case studies as a dissemination strategy for researchers, practitioners, and policymakers

Articulating the value of detailed case studies as a teaching tool for global implementation researchers

Describing a method for developing high-quality case studies of global implementation research

Research capacity for implementation science remains limited in low- and middle-income countries (LMICs). Various stakeholders, including NIH-funded implementation researchers and practitioners, often inquire about how to apply implementation science methods and have requested additional resources and training to support implementation capacity building. This is in part due to a dearth of practical examples for both researchers and practitioners of how to select, adapt, and apply implementation science models, theories, and frameworks to these settings; how to evaluate implementation processes and outcomes; and how to develop and test implementation strategies. The need for detailed documentation of implementation research in all settings has been well established, and guidelines for documentation of implementation research studies have been created [ 1 , 2 ]. But the mere availability of checklists and guidelines in and of themselves does not result in comprehensive documentation that is useful for learning, as has been pointed out by many systematic reviews of implementation science and quality improvement studies ([ 3 , 4 ]). It has also been observed that documentation alone is not enough, and there is a need for mentors to translate abstract theories into context-appropriate research designs and practice approaches [ 5 ]. Because of the especially acute shortage of mentors and coaches in LMIC settings, we propose that documentation with “thick” descriptions that go beyond checklists and guidelines are needed to make the field more useful to emerging professionals [ 6 ]. We suggest that the case study method intended to “explore the space between the world of theory and the experience of practice” [ 7 ] that has been used successfully for over a century by law and business schools as a teaching aid can be of value to develop detailed narratives of implementation research projects. In this definition, we are not referring to the case study as a qualitative research method [ 8 ], but as a rich and detailed method of retrospective documentation to aid teaching, practice, and research. In this context, our case studies are akin to “single-institution or single-patient descriptions” [ 9 ] called “case reports” or “case examples” in other fields. As these terms are rarely used in global health, we have used the words “case studies” in this paper but reiterate that they do not refer to case study research designs.

Fogarty’s Center for Global Health Studies (CGHS) in collaboration with the Cincinnati Children's Hospital Medical Center and the National Cancer Institute (NCI) is commissioning a collection of implementation science case studies that describe implementation research focusing on various disease areas in different (LMIC) contexts around the world. These case study descriptions will provide guidance on the process of conducting implementation science studies and will highlight the impact these studies have had on practice and policy in global health contexts. This brief note makes a case for using case studies to document and disseminate implementation research, describes the CGHS approach to case study development and poses evaluation questions that need to be answered to better understand the utility of case studies. This effort is intended to develop a set of useful examples for LMIC researchers, practitioners, and policymakers, but also to assess and improve the use of case studies as a tested documentation methodology in implementation research.

The “case” for case studies

A preliminary landscape analysis of the field conducted by CGHS found that there are not many descriptions of global implementation science projects in a case study format in the peer-reviewed or gray literature, and those that exist are embedded in the content of academic teaching materials. There is not a cohesive collection, especially relating to health, that illustrates how implementation research has been conducted in varied organizations, countries, or disease areas. This new collection will add value in three different ways: as a dissemination strategy, as a tool for capacity building, and as a vehicle for stimulating better research.

Case studies as a dissemination strategy

Case studies have independent merit as an evaluation approach for disseminating high-quality research in a format that is useful to a broad range of stakeholders. The Medical Research Council (MRC) has recommended process evaluation as a useful approach to examine complex implementation, mechanisms of impact, and context [ 10 ]. Guidelines on documentation of implementation recommend that researchers should provide “detailed descriptions of interventions (and implementation strategies) in published papers, clarify assumed change processes and design principles, provide access to manuals and protocols that provide information about the clinical interventions or implementation strategies, and give detailed descriptions of active control conditions” [ 1 ]. Case studies can be thought of as a form of post hoc process evaluation, to disseminate how the delivery of an intervention is achieved, the mechanisms by which implementation strategies produce change, or how context impacts implementation and related outcomes.

Case studies as a capacity building tool

In addition, case studies can address the universal recognition of the need for more capacity building in Implementation Science , especially in LMIC settings. Case studies have been shown to address common pedagogical challenges in helping students learn by allowing students to dissect and explore limitations, adaptations, and utilization of theories, thereby creating a bridge between theories presented in a classroom and their application in the field [ 11 ]. A recent learning needs assessment for implementation researchers, practitioners, and policymakers in LMICs conducted by Turner et al. [ 12 ] reflected a universal consensus on the need for context-specific knowledge about how to apply implementation science in practice, delivered in an interactive format supported by mentorship. A collection of case studies is a valuable and scalable resource to meet this need.

Using case studies to strengthen implementation research

Descriptions of research using studies can illustrate not just whether implementation research had an impact on practice and policy, but how, why, under what circumstances, and to whom, which is the ultimate goal of generating generalizable knowledge about how to implement. Using diverse cases to demonstrate how a variety of research designs have been used to answer complex implementation questions provides researchers with a palette of design options and examples of their use. A framework developed by Minary et al. [ 13 ] illustrates the wide variety of research designs that are useful for complex interventions, depending on whether the emphasis is on internal and external validity or whether knowledge about content and process or about outcomes is more important. A collection of case studies would be invaluable to researchers seeking to develop appropriate designs for their work. In addition, the detailed documentation provided through these case descriptions will hopefully motivate researchers to document their own studies better using the guidelines described earlier.

Developing and testing the case study creation process: the CGHS approach

Writing case studies that satisfy the objectives described above is an implementation science undertaking in itself that requires the engagement of a variety of stakeholders and planned implementation strategies. The CGHS team responsible for commissioning the case studies began this process in 2017 and followed the approach detailed below to test the process of case study development.

Conducted 25+ consultations with various implementation science experts on gaps in the field and the relevance of global case studies

Convened a 15-member Steering Committee Footnote 1 of implementation scientists with diverse expertise, from various academic institutions and NIH institutes to serve as technical experts and to help guide the development and execution of the project

Developed a case study protocol in partnership with the Steering Committee to guide the inclusion of key elements in the case studies

Commissioned two pilot cases Footnote 2 to assess the feasibility and utility of the case study protocol and elicited feedback on the writing experience and how it could be improved as the collection expands

Led an iterative pilot writing process where each case study writing team developed several drafts, which were reviewed by CGHS staff and a designated member of the Steering Committee

Truncated and adjusted the protocol in response to input from the pilot case study authors teams

Developed a comprehensive grid with the Steering Committee, outlining the key dimensions of implementation science that are significant and would be important areas of focus for future case studies. The grid will be used to evaluate potential case applicants and is intended to help foster diversity of focus and content, in addition to geography

Implementing the process: the call for case studies

In March of 2021, CGHS issued a closed call for case studies to solicit applications from a pool of researchers. Potential applicants completed the comprehensive grid in addition to a case study proposal. Applicants will go through a three-tier screening and review process. CGHS will initially screen the applications for completeness to ensure all required elements are present. Each case study application will then be reviewed by two Steering Committee members for content and scientific rigor and given a numerical score based on the selection criteria. Finally, the CGHS team will screen the applications to ensure diversity of implementation elements, geography, and disease area. Approximately 10 case studies will be selected for development in an iterative process. Each case team will present their case drafts to the Steering Committee, which will collectively workshop the drafts in multiple sittings, drawing on the committee’s implementation science expertise. Once case study manuscripts are accepted by the Steering Committee, they will be submitted to Implementation Science Communications for independent review by the journal. CGHS intends for the case studies to be published collectively, but on a rolling basis as they are accepted for publication.

Future research: evaluating the effectiveness of the case study approach

This commentary has put forth arguments for the potential value of case studies for documenting implementation research for researchers, practitioners, and policymakers. Case studies not only provide a way to underscore how implementation science can advance practice and policy in LMICs, but also offer guidance on how to conduct implementation research tailored to global contexts. However, there is little empirical evidence about the validity of these arguments. The creation of this body of case studies will allow us to study why, how, how often, and by whom these case studies are used. This is a valuable opportunity to learn and use that information to better inform future use of this approach as a capacity-building or dissemination strategy.

Conclusions

Similar to their use in law and business, case study descriptions of implementation research could be an important mechanism to counteract the paucity of training programs and mentors to meet the demands of global health researchers. If the evaluation results indicate that the case study creation process produces useful products that enhance learning to improve future implementation research, a mechanism needs to be put in place to create more case studies than the small set that can be generated through this initiative. There will be a need to create a set of documentation guidelines that complement those that currently exist and a mechanism to solicit, review, publish, and disseminate case studies from a wide variety of researchers and practitioners. Journals such as Implementation Science or Implementation Science Communications can facilitate this effort by either creating a new article type or by considering a new journal with a focus on rigorous and systematic case study descriptions of implementation research and practice. An example that could serve as a guide is BMJ Open Quality , which is a peer-reviewed, open-access journal focused on healthcare improvement. In addition to original research and systematic reviews, the journal publishes two article types: Quality Improvement Report and Quality Education Report to document healthcare quality improvement programs and training. The journal offers resources for authors to document their work rigorously. Recently, a new journal titled BMJ Open Quality South Asia has been released to disseminate regional research. We hope that our efforts in sponsoring and publishing these cases, and in setting up a process to support their creation, will make an important contribution to the field and become a mechanism for sharing knowledge that accelerates the growth of implementation science in LMIC settings.

Availability of data and materials

Not applicable.

Rohit Ramaswamy, CCHMC, Gila Neta, NCI NIH, Theresa Betancourt, BC, Ross Brownson, WASU, David Chambers, NCI NIH, Sharon Straus, University of Toronto, Greg Aarons, UCSD, Bryan Weiner, UW, Sonia Lee, NICHD NIH, Andrea Horvath Marques, NIMH NIH, Susannah Allison, NIMH NIH, Suzy Pollard, NIMH NIH, Chris Gordon, NIMH NIH, Kenny Sherr, UW, Usman Hamdani, HDR Foundation Pakistan, Linda Kupfer, FIC NIH

The first pilot case was led by the Human Development Research Foundation (HDRF) in Pakistan and examines scaling up evidenced-based care for children with developmental disorders in rural Pakistan. The second pilot was led by Boston College and investigates alternate delivery platforms and implementation models for bringing evidence-based behavioral Interventions to scale for youth facing adversity in Sierra Leone to close the mental health treatment gap.

Abbreviations

Low- and middle-income countries

Center for Global Health Studies

National Cancer Institute

Medical Research Council

Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Implementation Sci. 2013;8(139) [cited 2021 May];. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-8-139 .

Pinnock H, Barwick M, Carpenter CR, Eldridge S, Grandes G, Griffiths CJ, et al. for the StaRI group. Standards for Reporting Implementation Studies (StaRI) statement. BMJ. 2017;356:i6795 [cited 2021Sept]. Available from: https://www.bmj.com/content/356/bmj.i6795 .

Article   Google Scholar  

Brouwers MC, De Vito C, Bahirathan L, Carol A, Carroll JC, Cotterchio M, et al. What implementation interventions increase cancer screening rates? A systematic review. Implementation Sci. 2011; [cited 2021 Sept];6:111. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3197548/ .

Nadeem E, Gleacher A, Beidas RS. Consultation as an implementation strategy for evidence-based practices across multiple contexts: unpacking the black box. Adm Policy Ment Health. 2013;40(6):439–50. [cited 2021 Sept]. Available from: https://link.springer.com/article/10.1007%2Fs10488-013-0502-8 .

Darnell D, Dorsey CN, Melvin A, Chi J, Lyon AR, Lewis CC. A content analysis of dissemination and implementation science resource initiatives: what types of resources do they offer to advance the field? Implement Sci. 2017;12(1):137 [cited 2021 Sept];. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-017-0673-x .

Geertz C. Thick description: toward an interpretive theory of culture In: The interpretation of cultures: selected essays. New York: Basic Books; 1973. p. 3–32.

Breslin M, Buchanan R. On the case study method of research and teaching in design. Design Issues. 2008;24(1):36–40 [cited 2021 Sept]. Available from: http://www.jstor.org/stable/25224148 .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Med Res Methodol. 2011;11:100. [cited 2021 Sept]. Available from. https://doi.org/10.1186/1471-2288-11-100 .

Article   PubMed   PubMed Central   Google Scholar  

Alpi KM, Evans JJ. Distinguishing case study as a research method from case reports as a publication type. J Med Libr Assoc. 2019;107(1):1–5 Jan [cited 2021 Sept]. Available from: https://jmla.pitt.edu/ojs/jmla/article/view/615 .

Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015;350:h1258. March [cited 2021 May]. Available from: https://www.bmj.com/content/350/bmj.h1258 . https://doi.org/10.1136/bmj.h1258 .

Velenchik AD. The case method as a strategy for teaching policy analysis to undergraduates. J. Econ. Educ. 1995;26(1):29–38.

Turner MW, Bogdewic S, Agha E, Blanchard C, Sturke R, Pettifor A, et al. Learning needs assessment for multi-stakeholder implementation science training in LMIC settings: findings and recommendations. Implement Sci Commun. 2021;2(134) [cited 2021 May]. Available from: https://implementationsciencecomms.biomedcentral.com/articles/10.1186/s43058-021-00238-2 .

Minary L, Trompette J, Kivits J, Cambon L, Tarquinio C, Alla F. Which design to evaluate complex interventions? Toward a methodological framework through a systematic review. BMC Med Res Methodol. 2019;19(92) [cited 2021 May]. Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0736-6 .

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Beecroft, B., Sturke, R., Neta, G. et al. The “case” for case studies: why we need high-quality examples of global implementation research. Implement Sci Commun 3 , 15 (2022). https://doi.org/10.1186/s43058-021-00227-5

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The case study approach

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

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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.

Yin RK: Case study research, design and method. 2009, London: Sage Publications Ltd., 4

Google Scholar  

Keen J, Packwood T: Qualitative research; case study evaluation. BMJ. 1995, 311: 444-446.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sheikh A, Halani L, Bhopal R, Netuveli G, Partridge M, Car J, et al: Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma. PLoS Med. 2009, 6 (10): 1-11.

Article   Google Scholar  

Pinnock H, Huby G, Powell A, Kielmann T, Price D, Williams S, et al: The process of planning, development and implementation of a General Practitioner with a Special Interest service in Primary Care Organisations in England and Wales: a comparative prospective case study. Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D (NCCSDO). 2008, [ http://www.sdo.nihr.ac.uk/files/project/99-final-report.pdf ]

Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T, et al: Prospective evaluation of the implementation and adoption of NHS Connecting for Health's national electronic health record in secondary care in England: interim findings. BMJ. 2010, 41: c4564-

Pearson P, Steven A, Howe A, Sheikh A, Ashcroft D, Smith P, the Patient Safety Education Study Group: Learning about patient safety: organisational context and culture in the education of healthcare professionals. J Health Serv Res Policy. 2010, 15: 4-10. 10.1258/jhsrp.2009.009052.

Article   PubMed   Google Scholar  

van Harten WH, Casparie TF, Fisscher OA: The evaluation of the introduction of a quality management system: a process-oriented case study in a large rehabilitation hospital. Health Policy. 2002, 60 (1): 17-37. 10.1016/S0168-8510(01)00187-7.

Stake RE: The art of case study research. 1995, London: Sage Publications Ltd.

Sheikh A, Smeeth L, Ashcroft R: Randomised controlled trials in primary care: scope and application. Br J Gen Pract. 2002, 52 (482): 746-51.

PubMed   PubMed Central   Google Scholar  

King G, Keohane R, Verba S: Designing Social Inquiry. 1996, Princeton: Princeton University Press

Doolin B: Information technology as disciplinary technology: being critical in interpretative research on information systems. Journal of Information Technology. 1998, 13: 301-311. 10.1057/jit.1998.8.

George AL, Bennett A: Case studies and theory development in the social sciences. 2005, Cambridge, MA: MIT Press

Eccles M, the Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG): Designing theoretically-informed implementation interventions. Implementation Science. 2006, 1: 1-8. 10.1186/1748-5908-1-1.

Article   PubMed Central   Google Scholar  

Netuveli G, Hurwitz B, Levy M, Fletcher M, Barnes G, Durham SR, Sheikh A: Ethnic variations in UK asthma frequency, morbidity, and health-service use: a systematic review and meta-analysis. Lancet. 2005, 365 (9456): 312-7.

Sheikh A, Panesar SS, Lasserson T, Netuveli G: Recruitment of ethnic minorities to asthma studies. Thorax. 2004, 59 (7): 634-

CAS   PubMed   PubMed Central   Google Scholar  

Hellström I, Nolan M, Lundh U: 'We do things together': A case study of 'couplehood' in dementia. Dementia. 2005, 4: 7-22. 10.1177/1471301205049188.

Som CV: Nothing seems to have changed, nothing seems to be changing and perhaps nothing will change in the NHS: doctors' response to clinical governance. International Journal of Public Sector Management. 2005, 18: 463-477. 10.1108/09513550510608903.

Lincoln Y, Guba E: Naturalistic inquiry. 1985, Newbury Park: Sage Publications

Barbour RS: Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?. BMJ. 2001, 322: 1115-1117. 10.1136/bmj.322.7294.1115.

Mays N, Pope C: Qualitative research in health care: Assessing quality in qualitative research. BMJ. 2000, 320: 50-52. 10.1136/bmj.320.7226.50.

Mason J: Qualitative researching. 2002, London: Sage

Brazier A, Cooke K, Moravan V: Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study. Integr Cancer Ther. 2008, 7: 5-17. 10.1177/1534735407313395.

Miles MB, Huberman M: Qualitative data analysis: an expanded sourcebook. 1994, CA: Sage Publications Inc., 2

Pope C, Ziebland S, Mays N: Analysing qualitative data. Qualitative research in health care. BMJ. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.

Cresswell KM, Worth A, Sheikh A: Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak. 2010, 10 (1): 67-10.1186/1472-6947-10-67.

Article   PubMed   PubMed Central   Google Scholar  

Malterud K: Qualitative research: standards, challenges, and guidelines. Lancet. 2001, 358: 483-488. 10.1016/S0140-6736(01)05627-6.

Article   CAS   PubMed   Google Scholar  

Yin R: Case study research: design and methods. 1994, Thousand Oaks, CA: Sage Publishing, 2

Yin R: Enhancing the quality of case studies in health services research. Health Serv Res. 1999, 34: 1209-1224.

Green J, Thorogood N: Qualitative methods for health research. 2009, Los Angeles: Sage, 2

Howcroft D, Trauth E: Handbook of Critical Information Systems Research, Theory and Application. 2005, Cheltenham, UK: Northampton, MA, USA: Edward Elgar

Book   Google Scholar  

Blakie N: Approaches to Social Enquiry. 1993, Cambridge: Polity Press

Doolin B: Power and resistance in the implementation of a medical management information system. Info Systems J. 2004, 14: 343-362. 10.1111/j.1365-2575.2004.00176.x.

Bloomfield BP, Best A: Management consultants: systems development, power and the translation of problems. Sociological Review. 1992, 40: 533-560.

Shanks G, Parr A: Positivist, single case study research in information systems: A critical analysis. Proceedings of the European Conference on Information Systems. 2003, Naples

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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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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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The “case” for case studies: why we need high-quality examples of global implementation research

Blythe beecroft.

1 Fogarty International Center, US National Institutes of Health, Bethesda, USA

Rachel Sturke

2 National Cancer Institute, US National Institutes of Health, Bethesda, USA

Rohit Ramaswamy

3 Cincinnati Children’s Hospital Medical Center, Cincinnati, USA

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Rigorous and systematic documented examples of implementation research in global contexts can be a valuable resource and help build research capacity. In the context of low- and middle-income countries (LMICs), there is a need for practical examples of how to conduct implementation studies. To address this gap, Fogarty’s Center for Global Health Studies in collaboration with the Cincinnati Children's Hospital Medical Center and the National Cancer Institute is commissioning a collection of implementation science case studies in LMICs that describe key components of conducting implementation research, including how to select, adapt, and apply implementation science models, theories, and frameworks to these settings; develop and test implementation strategies; and evaluate implementation processes and outcomes. The case studies describe implementation research in various disease areas in LMICs around the world. This commentary highlights the value of case study methods commonly used in law and business schools as a source of “thick” (i.e., context-rich) description and a teaching tool for global implementation researchers. It addresses the independent merit of case studies as an evaluation approach for disseminating high-quality research in a format that is useful to a broad range of stakeholders. This commentary finally describes an approach for developing high-quality case studies of global implementation research, in order to be of value to a broad audience of researchers and practitioners.

Contributions to the literature

• Reinforcing the need for “thick” (i.e., context-rich) descriptions of implementation studies
• Highlighting the utility of case studies as a dissemination strategy for researchers, practitioners, and policymakers
• Articulating the value of detailed case studies as a teaching tool for global implementation researchers
• Describing a method for developing high-quality case studies of global implementation research

Research capacity for implementation science remains limited in low- and middle-income countries (LMICs). Various stakeholders, including NIH-funded implementation researchers and practitioners, often inquire about how to apply implementation science methods and have requested additional resources and training to support implementation capacity building. This is in part due to a dearth of practical examples for both researchers and practitioners of how to select, adapt, and apply implementation science models, theories, and frameworks to these settings; how to evaluate implementation processes and outcomes; and how to develop and test implementation strategies. The need for detailed documentation of implementation research in all settings has been well established, and guidelines for documentation of implementation research studies have been created [ 1 , 2 ]. But the mere availability of checklists and guidelines in and of themselves does not result in comprehensive documentation that is useful for learning, as has been pointed out by many systematic reviews of implementation science and quality improvement studies ([ 3 , 4 ]). It has also been observed that documentation alone is not enough, and there is a need for mentors to translate abstract theories into context-appropriate research designs and practice approaches [ 5 ]. Because of the especially acute shortage of mentors and coaches in LMIC settings, we propose that documentation with “thick” descriptions that go beyond checklists and guidelines are needed to make the field more useful to emerging professionals [ 6 ]. We suggest that the case study method intended to “explore the space between the world of theory and the experience of practice” [ 7 ] that has been used successfully for over a century by law and business schools as a teaching aid can be of value to develop detailed narratives of implementation research projects. In this definition, we are not referring to the case study as a qualitative research method [ 8 ], but as a rich and detailed method of retrospective documentation to aid teaching, practice, and research. In this context, our case studies are akin to “single-institution or single-patient descriptions” [ 9 ] called “case reports” or “case examples” in other fields. As these terms are rarely used in global health, we have used the words “case studies” in this paper but reiterate that they do not refer to case study research designs.

Fogarty’s Center for Global Health Studies (CGHS) in collaboration with the Cincinnati Children's Hospital Medical Center and the National Cancer Institute (NCI) is commissioning a collection of implementation science case studies that describe implementation research focusing on various disease areas in different (LMIC) contexts around the world. These case study descriptions will provide guidance on the process of conducting implementation science studies and will highlight the impact these studies have had on practice and policy in global health contexts. This brief note makes a case for using case studies to document and disseminate implementation research, describes the CGHS approach to case study development and poses evaluation questions that need to be answered to better understand the utility of case studies. This effort is intended to develop a set of useful examples for LMIC researchers, practitioners, and policymakers, but also to assess and improve the use of case studies as a tested documentation methodology in implementation research.

The “case” for case studies

A preliminary landscape analysis of the field conducted by CGHS found that there are not many descriptions of global implementation science projects in a case study format in the peer-reviewed or gray literature, and those that exist are embedded in the content of academic teaching materials. There is not a cohesive collection, especially relating to health, that illustrates how implementation research has been conducted in varied organizations, countries, or disease areas. This new collection will add value in three different ways: as a dissemination strategy, as a tool for capacity building, and as a vehicle for stimulating better research.

Case studies as a dissemination strategy

Case studies have independent merit as an evaluation approach for disseminating high-quality research in a format that is useful to a broad range of stakeholders. The Medical Research Council (MRC) has recommended process evaluation as a useful approach to examine complex implementation, mechanisms of impact, and context [ 10 ]. Guidelines on documentation of implementation recommend that researchers should provide “detailed descriptions of interventions (and implementation strategies) in published papers, clarify assumed change processes and design principles, provide access to manuals and protocols that provide information about the clinical interventions or implementation strategies, and give detailed descriptions of active control conditions” [ 1 ]. Case studies can be thought of as a form of post hoc process evaluation, to disseminate how the delivery of an intervention is achieved, the mechanisms by which implementation strategies produce change, or how context impacts implementation and related outcomes.

Case studies as a capacity building tool

In addition, case studies can address the universal recognition of the need for more capacity building in Implementation Science , especially in LMIC settings. Case studies have been shown to address common pedagogical challenges in helping students learn by allowing students to dissect and explore limitations, adaptations, and utilization of theories, thereby creating a bridge between theories presented in a classroom and their application in the field [ 11 ]. A recent learning needs assessment for implementation researchers, practitioners, and policymakers in LMICs conducted by Turner et al. [ 12 ] reflected a universal consensus on the need for context-specific knowledge about how to apply implementation science in practice, delivered in an interactive format supported by mentorship. A collection of case studies is a valuable and scalable resource to meet this need.

Using case studies to strengthen implementation research

Descriptions of research using studies can illustrate not just whether implementation research had an impact on practice and policy, but how, why, under what circumstances, and to whom, which is the ultimate goal of generating generalizable knowledge about how to implement. Using diverse cases to demonstrate how a variety of research designs have been used to answer complex implementation questions provides researchers with a palette of design options and examples of their use. A framework developed by Minary et al. [ 13 ] illustrates the wide variety of research designs that are useful for complex interventions, depending on whether the emphasis is on internal and external validity or whether knowledge about content and process or about outcomes is more important. A collection of case studies would be invaluable to researchers seeking to develop appropriate designs for their work. In addition, the detailed documentation provided through these case descriptions will hopefully motivate researchers to document their own studies better using the guidelines described earlier.

Developing and testing the case study creation process: the CGHS approach

Writing case studies that satisfy the objectives described above is an implementation science undertaking in itself that requires the engagement of a variety of stakeholders and planned implementation strategies. The CGHS team responsible for commissioning the case studies began this process in 2017 and followed the approach detailed below to test the process of case study development.

• Conducted 25+ consultations with various implementation science experts on gaps in the field and the relevance of global case studies
• Convened a 15-member Steering Committee 1 of implementation scientists with diverse expertise, from various academic institutions and NIH institutes to serve as technical experts and to help guide the development and execution of the project
• Developed a case study protocol in partnership with the Steering Committee to guide the inclusion of key elements in the case studies
• Commissioned two pilot cases 2 to assess the feasibility and utility of the case study protocol and elicited feedback on the writing experience and how it could be improved as the collection expands
• Led an iterative pilot writing process where each case study writing team developed several drafts, which were reviewed by CGHS staff and a designated member of the Steering Committee
• Truncated and adjusted the protocol in response to input from the pilot case study authors teams
• Developed a comprehensive grid with the Steering Committee, outlining the key dimensions of implementation science that are significant and would be important areas of focus for future case studies. The grid will be used to evaluate potential case applicants and is intended to help foster diversity of focus and content, in addition to geography

Implementing the process: the call for case studies

In March of 2021, CGHS issued a closed call for case studies to solicit applications from a pool of researchers. Potential applicants completed the comprehensive grid in addition to a case study proposal. Applicants will go through a three-tier screening and review process. CGHS will initially screen the applications for completeness to ensure all required elements are present. Each case study application will then be reviewed by two Steering Committee members for content and scientific rigor and given a numerical score based on the selection criteria. Finally, the CGHS team will screen the applications to ensure diversity of implementation elements, geography, and disease area. Approximately 10 case studies will be selected for development in an iterative process. Each case team will present their case drafts to the Steering Committee, which will collectively workshop the drafts in multiple sittings, drawing on the committee’s implementation science expertise. Once case study manuscripts are accepted by the Steering Committee, they will be submitted to Implementation Science Communications for independent review by the journal. CGHS intends for the case studies to be published collectively, but on a rolling basis as they are accepted for publication.

Future research: evaluating the effectiveness of the case study approach

This commentary has put forth arguments for the potential value of case studies for documenting implementation research for researchers, practitioners, and policymakers. Case studies not only provide a way to underscore how implementation science can advance practice and policy in LMICs, but also offer guidance on how to conduct implementation research tailored to global contexts. However, there is little empirical evidence about the validity of these arguments. The creation of this body of case studies will allow us to study why, how, how often, and by whom these case studies are used. This is a valuable opportunity to learn and use that information to better inform future use of this approach as a capacity-building or dissemination strategy.

Conclusions

Similar to their use in law and business, case study descriptions of implementation research could be an important mechanism to counteract the paucity of training programs and mentors to meet the demands of global health researchers. If the evaluation results indicate that the case study creation process produces useful products that enhance learning to improve future implementation research, a mechanism needs to be put in place to create more case studies than the small set that can be generated through this initiative. There will be a need to create a set of documentation guidelines that complement those that currently exist and a mechanism to solicit, review, publish, and disseminate case studies from a wide variety of researchers and practitioners. Journals such as Implementation Science or Implementation Science Communications can facilitate this effort by either creating a new article type or by considering a new journal with a focus on rigorous and systematic case study descriptions of implementation research and practice. An example that could serve as a guide is BMJ Open Quality , which is a peer-reviewed, open-access journal focused on healthcare improvement. In addition to original research and systematic reviews, the journal publishes two article types: Quality Improvement Report and Quality Education Report to document healthcare quality improvement programs and training. The journal offers resources for authors to document their work rigorously. Recently, a new journal titled BMJ Open Quality South Asia has been released to disseminate regional research. We hope that our efforts in sponsoring and publishing these cases, and in setting up a process to support their creation, will make an important contribution to the field and become a mechanism for sharing knowledge that accelerates the growth of implementation science in LMIC settings.

Acknowledgements

The findings and conclusions in this manuscript are those of the authors and do not necessarily represent any official position or policy of the US National Institutes of Health or the US Department of Health and Human Services or any other institutions with which authors are affiliated.

Abbreviations

Authors’ contributions

BB, RS, and GN contributed to the conceptualization of the manuscript with leadership from RR. BB and RS drafted the main text. RR and GN reviewed and contributed additional content to further develop the text. All authors have read and agreed to the contents of the final draft of the manuscript.

Availability of data and materials

Declarations.

The authors declare that they have no competing interests.

1 Rohit Ramaswamy, CCHMC, Gila Neta, NCI NIH, Theresa Betancourt, BC, Ross Brownson, WASU, David Chambers, NCI NIH, Sharon Straus, University of Toronto, Greg Aarons, UCSD, Bryan Weiner, UW, Sonia Lee, NICHD NIH, Andrea Horvath Marques, NIMH NIH, Susannah Allison, NIMH NIH, Suzy Pollard, NIMH NIH, Chris Gordon, NIMH NIH, Kenny Sherr, UW, Usman Hamdani, HDR Foundation Pakistan, Linda Kupfer, FIC NIH

2 The first pilot case was led by the Human Development Research Foundation (HDRF) in Pakistan and examines scaling up evidenced-based care for children with developmental disorders in rural Pakistan. The second pilot was led by Boston College and investigates alternate delivery platforms and implementation models for bringing evidence-based behavioral Interventions to scale for youth facing adversity in Sierra Leone to close the mental health treatment gap.

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Affiliations.

• 1 Research Institute of Eco-Friendly Livestock Science, Institute of Green-Bio Science and Technology, Seoul National University, Pyeongchang, 25354, South Korea. [email protected].
• 2 Research Institute of Eco-Friendly Livestock Science, Institute of Green-Bio Science and Technology, Seoul National University, Pyeongchang, 25354, South Korea.
• 3 Department of Agricultural Biotechnology, College of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, South Korea.
• 4 Department of Internal Medicine, Digestive Disease Center and Research Institute, Soon Chun Hyang University School of Medicine, Bucheon, 14584, South Korea.
• 5 Research Institute of Eco-Friendly Livestock Science, Institute of Green-Bio Science and Technology, Seoul National University, Pyeongchang, 25354, South Korea. [email protected].
• 6 Graduate School of International Agricultural Technology, Seoul National University, Pyeongchang, 25354, South Korea. [email protected].
• PMID: 39223323
• PMCID: PMC11368911
• DOI: 10.1038/s41598-024-71370-x

Bacterial culturomics is a set of techniques to isolate and identify live bacteria from complex microbial ecosystems. Despite its potential to revolutionize microbiome research, bacterial culturomics has significant challenges when applied to human gut microbiome studies due to its labor-intensive nature. Therefore, we established a streamlined culturomics approach with minimal culture conditions for stool sample preincubation. We evaluated the suitability of non-selective medium candidates for maintaining microbial diversity during a 30-day incubation period based on 16S rRNA gene amplicon analysis. Subsequently, we applied four culture conditions (two preincubation media under an aerobic/anaerobic atmosphere) to isolate gut bacteria on a large scale from eight stool samples of healthy humans. We identified 8141 isolates, classified into 263 bacterial species, including 12 novel species candidates. Our analysis of cultivation efficiency revealed that seven days of aerobic and ten days of anaerobic incubation captured approximately 91% and 95% of the identified species within each condition, respectively, with a synergistic effect confirmed when selected preincubation media were combined. Moreover, our culturomics findings expanded the coverage of gut microbial diversity compared to 16S rRNA gene amplicon sequencing results. In conclusion, this study demonstrated the potential of a streamlined culturomics approach for the efficient isolation of gut bacteria from human stool samples. This approach might pave the way for the broader adoption of culturomics in human gut microbiome studies, ultimately leading to a more comprehensive understanding of this complex microbial ecosystem.

Keywords: 16S rRNA gene amplicon analysis; Gut microbiota; Medium; Preincubation; Streamlined culturomics.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

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• Lewis, W. H., Tahon, G., Geesink, P., Sousa, D. Z. & Ettema, T. J. G. Innovations to culturing the uncultured microbial majority. Nat Rev Microbiol.19, 225–240. 10.1038/s41579-020-00458-8 (2021). 10.1038/s41579-020-00458-8 - DOI - PubMed
• Oulas, A. et al. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinform Biol Insights.9, 75–88. 10.4137/BBI.S12462 (2015). 10.4137/BBI.S12462 - DOI - PMC - PubMed
• Wang, W. L. et al. Application of metagenomics in the human gut microbiome. World J. Gastroenterol.21, 803–814. 10.3748/wjg.v21.i3.803 (2015). 10.3748/wjg.v21.i3.803 - DOI - PMC - PubMed
• Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature.568, 499–504. 10.1038/s41586-019-0965-1 (2019). 10.1038/s41586-019-0965-1 - DOI - PMC - PubMed
• Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol.39, 105–114. 10.1038/s41587-020-0603-3 (2021). 10.1038/s41587-020-0603-3 - DOI - PMC - PubMed
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• Introduction
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Distributions for sex (A), decedents (B), race and ethnicity (C), and dual-eligibility (D). Beneficiary race and ethnicity was determined using the Research Triangle Institute race code; Other and unknown race and ethnicity category includes Asian and Pacific Islander, American Indian or Alaska Native, and any race or ethnicity not otherwise specified. ASR indicates age-standardized rate.

eAppendix. Literature Review Protocol

eTable 1.  ICD-10-CM Codes and Prescription Drugs Used in the CCW and 21 Unique Researcher-Developed Claims-Based Dementia Identification Algorithms

eTable 2. Characteristics of Beneficiaries Categorized Into Each Tier of ICD-10-CM Codes (as Defined by Frequency of Use Across the CCW and Researcher-Developed Algorithms) and NDCs

eTable 3 . Raw and Age-Adjusted Characteristics of Beneficiaries Identified as Having Highly Likely ADRD, Likely ADRD, Possible ADRD, and No Evidence of ADRD

eTable 4. Beneficiary Age Distribution in the Full Sample, Within LTC Users and Non-Users, and Within Decedents and Non-Decedents

eTable 5. New Subcodes Associated With F01, F02, and F03 That Went Into Effect in October 2022

eReferences

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Gianattasio KZ , Wachsmuth J , Murphy R, et al. Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare. JAMA Netw Open. 2024;7(9):e2427610. doi:10.1001/jamanetworkopen.2024.27610

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Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare

• 1 NORC at the University of Chicago, Bethesda, Maryland
• 2 Department of Epidemiology, George Washington University School of Public Health, Washington, DC

Question   How many Medicare beneficiaries have diagnostic codes or drug prescriptions indicating Alzheimer disease and related dementias (ADRD) using a refined case definition, and what are the characteristics of these beneficiaries?

Findings   This cross-sectional study of more than 60 million Medicare beneficiaries identified 7.2% with evidence of highly likely ADRD, 1.9% with likely ADRD, and 4.3% with possible ADRD. Beneficiaries with evidence of ADRD were older, more frail, more likely to use long-term care, and more likely to die than those without evidence of ADRD; these differences persisted after age-standardization.

Meaning   In this cross-sectional study, more than 5.4 million Medicare beneficiaries (9.1%) had evidence of likely or highly likely ADRD in 2019; pending validation, this case definition can be adopted provisionally for national surveillance of persons with diagnosed dementia in the Medicare system.

Importance   Lack of a US dementia surveillance system hinders efforts to support and address disparities among persons living with Alzheimer disease and related dementias (ADRD).

Objective   To review diagnosis and prescription drug code ADRD identification algorithms to develop and implement case definitions for national surveillance.

Design, Setting, and Participants   In this cross-sectional study, a systematic literature review was conducted to identify unique International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and prescription drug codes used by researchers to identify ADRD in administrative records. Code frequency of use, characteristics of beneficiaries identified by codes, and expert and author consensus around code definitions informed code placement into categories indicating highly likely, likely, and possible ADRD. These definitions were applied cross-sectionally to 2017 to 2019 Medicare fee-for-service (FFS) claims and Medicare Advantage (MA) encounter data to classify January 2019 Medicare enrollees. Data analysis was conducted from September 2022 to March 2024.

Exposures   ICD-10-CM and national drug codes in FFS claims or MA encounters.

Main Outcomes and Measures   The primary outcome was counts and rates of beneficiaries meeting each case definition. Category-specific age, sex, race and ethnicity, MA enrollment, dual-eligibility, long-term care utilization, mortality, and rural residence distributions, as well as frailty scores and FFS monthly expenditures were also analyzed. Beneficiary characteristics were compared across categories, and age-standardized to minimize confounding by age.

Results   Of the 60 000 869 beneficiaries included (50 853 806 aged 65 years or older [84.8%]; 32 567 891 female [54.3%]; 5 555 571 Hispanic [9.3%]; 6 318 194 non-Hispanic Black [10.5%]; 44 384 980 non-Hispanic White [74.0%]), there were 4 312 496 (7.2%) with highly likely ADRD, 1 124 080 (1.9%) with likely ADRD, and 2 572 176 (4.3%) with possible ADRD, totaling more than 8.0 million with diagnostic evidence of at least possible ADRD. These beneficiaries were older, more frail, more likely to be female, more likely to be dual-eligible, more likely to use long-term care, and more likely to die in 2019 compared with beneficiaries with no evidence of ADRD. These differences became larger when moving from the possible ADRD group to the highly likely ADRD group. Mean (SD) FFS monthly spending was $2966 ($4921) among beneficiaries with highly likely ADRD compared with $936 ($2952) for beneficiaries with no evidence of ADRD. Differences persisted after age standardization.

Conclusions and Relevance   This cross-sectional study of 2019 Medicare beneficiaries identified more than 5.4 million Medicare beneficiaries with evidence of at least likely ADRD in 2019 using the diagnostic case definition. Pending validation against clinical and other methods of ascertainment, this approach can be adopted provisionally for national surveillance.

Surveillance is a fundamental public health activity. Lack of a US dementia surveillance system hinders public health efforts to support persons living with Alzheimer disease and related dementias (ADRD), address health disparities, and plan ADRD care resources.

Medicare administrative data are an attractive source upon which to build a dementia surveillance system and are commonly used to identify persons living with ADRD, but a consensus diagnostic code case definition does not exist. Perhaps the most widely used definition (the Centers for Medicare and Medicaid Services [CMS] Chronic Conditions Warehouse [CCW] algorithm) uses 22 International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes, from the commonly accepted G30.X (Alzheimer disease) and F01.XX (vascular dementia), to less specific codes such as R54 (age-related physical debility). 1 In contrast, most researcher-developed ICD-10 - CM –based algorithms exclude R54, but may include codes such as G31.0 (frontotemporal dementia) that are not in the CCW algorithm. 2 - 4 Moreover, while some algorithms use Medicare Part D data to identify prescriptions for Alzheimer disease–related drugs, 5 - 8 most do not.

The impact of using different ICD-10-CM or prescription codes on the number of people identified or their characteristics is unknown. Because ICD-10-CM codes are used for billing (rather than diagnostic) purposes, specific codes may not be sensitive nor specific to dementia, and coding practices may differ systematically by health care practice, patient characteristics, and geography.

We examined how choices of ICD-10-CM and prescription drug codes used to identify persons with clinically recognized ADRD in Medicare fee-for-service (FFS) claims and Medicare Advantage (MA) encounter data affect dementia prevalence estimates and characteristics of the people identified. We synthesized this information to develop a new case definition using diagnostic and prescription drug codes that can be applied to administrative data to support surveillance of persons with diagnosed dementia in the Medicare system.

This cross-sectional study was deemed exempt from review and the requirement of informed consent by the NORC Institutional Review Board. The reporting of this research follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We searched PubMed for articles published from 2012 to 2022, with all-cause dementia or ADRD as a primary exposure or primary outcome, or where the research population of interest was persons living with all-cause dementia or ADRD (eAppendix in Supplement 1 ). We found 28 studies utilizing 20 distinct researcher-developed ICD-10-CM or prescription drug code algorithms in addition to the CCW algorithm (eTable 1 in Supplement 1 ). 2 - 21

We extracted 43 ICD-10-CM codes and 5 prescription drugs across algorithms ( Table 1 and eTable 1 in Supplement 1 ). We shared the codes with 3 clinicians (2 neurology clinicians and 1 geriatrics clinician) who provide care to persons living with dementia, who recommended excluding 8 codes deemed to not indicate dementia ( Table 1 ). We grouped the remaining codes into tiers by use frequency (tier 1, ≥15 algorithms; tier 2, 10-14 algorithms; tier 3, 5-9 algorithms; and tier 4, 1-4 algorithms). We designated prescriptions for ADRD-targeting drugs as indicated by National Drug Codes (NDC) without presence of an ADRD ICD-10-CM code as tier 5.

We used 100% of the 2017 to 2019 Medicare FFS inpatient, outpatient, carrier, skilled nursing facility, home health agency, and hospice claims; MA inpatient, outpatient, carrier, skilled nursing facility, and home health agency encounter data; and Medicare Part D prescription drug event (PDE) data. We used the minimum dataset (MDS 3.0) to identify long-term care (LTC) utilization. We limited analysis to Medicare beneficiaries with at least Part A (the premium-free Medicare benefit) enrollment in January 2019, nonmissing sex, and a valid US state or territory code based on the Medicare beneficiary summary files. We did not exclude beneficiaries based on age or lack of Part B enrollment because our aim was to identify all people in the Medicare system with evidence of ADRD.

To categorize beneficiaries with or without evidence of dementia as of 2019, we conducted a cross-sectional analysis of January 2017 to December 2019 FFS and MA data to identify all claims and encounters with a relevant ICD-10-CM code listed in any position, and all PDE claims for a relevant NDC. We classified beneficiaries hierarchically, first with a tier 1 ICD-10-CM code, then with a tier 2 code among remaining beneficiaries, and so on, identifying only the incremental beneficiaries in each tier if they had not been classified earlier. We compared distributions of age, sex, race and ethnicity (as indicated by the Research Triangle Institute race code 22 ), MA enrollment, LTC use, and 2019 mortality across tiers. Race and ethnicity categories included American Indian or Alaska Native, Asian or Pacific Islander, Hispanic, non-Hispanic Black, non-Hispanic White, unknown, and other (defined as any race or ethnicity not otherwise specified); race and ethnicity were included because existing evidence shows that there are disparities in dementia prevalence across race and ethnicity groups. We compared cross-tier beneficiary frailty using a claims-based frailty index (CFI), 23 an adapted CFI that excludes ADRD codes in tiers 1 to 4, and per-member-per-month (PMPM) spending, averaged across all months of 2019 FFS coverage.

Using these data (eTable 2 in Supplement 1 ), we found that beneficiaries in tiers 1 and 2 were older, more frail, more likely to be female, in LTC, and die than those in tiers 3 to 5. There were minimal differences in race and ethnicity across tiers, with exception of a higher-than-expected representation of Hispanic and Asian and Pacific Islander beneficiaries in tier 5; however, the overall size of the sample categorized as tier 5 was very small, at just 0.1% (52 338 of 60 000 869 beneficiaries). Based on the findings from the cross-tier comparison and author consensus, we further aggregated codes into 3 categories with decreasing confidence of having a true ADRD diagnosis: a highly likely ADRD category requiring at least 2 claims or encounters on different dates with ICD-10-CM codes from tiers 1 or 2; a likely ADRD category requiring 1 claim or encounter with an ICD-10-10-CM code from tiers 1 or 2; and a possible ADRD category requiring at least 1 claim or encounter with an ICD-10-CM or NDC code from tiers 3, 4, or 5 over a 3-year lookback period. We categorized beneficiaries and reevaluated group demographics, health insurance type, frailty and mortality, and rural residency. We then computed prevalence of highly likely, likely, and possible ADRD within population subgroups defined by these characteristics. We age-standardized to the full analytical population to evaluate differences unconfounded by age.

All analyses were conducted in SAS Enterprise Guide 7.1 and SAS Studio version 3.81 (SAS Institute). Data analysis was conducted from September 2022 to March 2024.

Of 64 430 729 2019 Medicare beneficiaries, we excluded 3 940 831 due to lack of Part A enrollment in January, 8 due to missing sex, and 489 021 due to a nonvalid US state or territory code, resulting in a total of 60 000 869 beneficiaries (50 853 806 aged 65 years or older [84.8%]; 32 567 891 female [54.3%]; 5 555 571 Hispanic [9.3%]; 6 318 194 non-Hispanic Black [10.5%]; 44 384 980 non-Hispanic White [74.0%]) included in the study sample. Of all beneficiaries, 11 502 479 (19.2%) had Medicaid dual-eligibility, while 23 607 426 (39.3%) had MA. Mean (SD) FFS PMPM spending in 2019 was $1220 ($3426) ( Table 2 ).

We identified 4 312 496 beneficiaries (7.2%) as having highly likely ADRD, and 1 124 080 (1.9%) as having likely ADRD ( Table 3 ). The proportion of beneficiaries with highly likely ADRD increased to 8.1% (4 125 639 beneficiaries) after limiting age to 65 years or older, and to 8.8% (4 093 008 beneficiaries) when further limiting to those with both Parts A and B enrollment. The proportion of beneficiaries with likely ADRD increased to 2.1% (996 379 beneficiaries) after these restrictions. Compared with those with likely ADRD, those with highly likely ADRD were older and more frail, more likely to be female and dual-eligible, had over 3 times the rate of LTC utilization (681 923 of 4 312 496 beneficiaries [15.8%] vs 51 332 of 1 124 080 beneficiaries [4.6%]), and almost double the rate of death (828 366 of 4 312 496 beneficiaries [19.2%] vs 129 705 of 1 124 080 beneficiaries [11.5%]). We identified an additional 2 572 176 beneficiaries (4.3%) as having possible ADRD; this percentage increased to 4.8% (2 231 673 beneficiaries) after restricting to beneficiaries aged 65 years or older with Parts A and B enrollment. The possible ADRD group was younger and healthier (lower CFI, mortality, and LTC utilization) than those with highly likely or likely ADRD but was older and less healthy than those with no evidence of ADRD (51 992 117 beneficiaries). Mean (SD) PMPM spending was approximately 3 times as high in the ADRD groups (ranging from $2559 [$2952] among those with possible ADRD to $2966 [$4921] among those with highly likely ADRD) as that of the no ADRD group ($936 [$2952]). Age standardization narrowed differences in sex distribution and death rates, widened differences in race and ethnicity distribution and dual-eligible rates, and had minimal impact on differences in MA enrollment, LTC utilization, and frailty ( Figure and eTable 3 in Supplement 1 ). FFS spending increased slightly for all categories after age standardization.

The proportion of beneficiaries with any evidence of ADRD increased with age, from 6.5% (1 931 517 of 29 878 739 beneficiaries) among beneficiaries aged 65 to 74 years to 42.5% (2 544 205 of 5 983 967) among those aged 85 years or older, with the largest increase seen in the percentage of those with highly likely ADRD (2.6% [770 296 of 29 878 739 beneficiaries] to 29.1% [1 739 705 of 5 983 967 beneficiaries]) ( Table 4 ). Prevalence of any ADRD was higher in females than in males but was similar between non-Hispanic White (5 950 598 beneficiaries [13.4%]), non-Hispanic Black (892 541 beneficiaries [14.1%]), and Hispanic (792 948 beneficiaries [14.3%]) beneficiaries. Those with LTC use were substantially more likely to have ADRD than those with no LTC (681 923 of 937 248 beneficiaries [72.8%] vs 3 630 573 of 59 063 621 beneficiaries [6.1%] categorized as highly likely). Similarly, prevalence of highly likely or likely ADRD was much higher in decedents (958 071 of 2 285 257 beneficiaries [41.9%]) than nondecedents (4 478 505 of 57 715 612 beneficiaries [7.7%]) and in those who were dual-eligible (1 917 434 of 11 502 479 beneficiaries [16.7%]) than among those who were not (3 519 142 of 48 498 390 beneficiaries [7.2%]). MA beneficiaries had a higher prevalence of highly likely or likely ADRD (2 296 154 of 23 607 426 beneficiaries [9.7%]) than FFS beneficiaries (3 140 422 of 36 393 443 beneficiaries [8.6%]), and any evidence of ADRD (4 595 211 of 23 607 426 beneficiaries [14.5%] for MA vs 4 593 211 of 36 393 443 beneficiaries [12.6%] for FFS).

Age-standardizing subgroups to the age distribution of the Medicare population resulted in changes in ADRD prevalence estimates in some groups ( Table 4 ). Relative differences in ADRD prevalence narrowed across sex but widened across race and ethnicity groups. Most notably, non-Hispanic White beneficiaries became less likely to have any evidence of ADRD (12.9% across categories), while racial and ethnic minority groups became more likely to have evidence of ADRD (non-Hispanic Black beneficiaries, 16.5%; Hispanic beneficiaries, 15.3%). Among non-Hispanic Black beneficiaries, age standardization resulted in a substantial increase in the proportion of those with highly likely or likely ADRD (9.9% to 12.0%). Age standardization also reduced ADRD prevalence among LTC users (from 72.8% to 62.8% with highly likely ADRD) and decedents (from 36.2% to 23.8% with highly likely ADRD) but had minimal impact in ADRD prevalence among non–LTC users and nondecedents; this is because LTC-users and decedent groups were heavily skewed toward older ages, while the age distribution of the non–LTC users and nondecedent groups mimicked that of the general Medicare population (eTable 4 in Supplement 1 ).

Among 2019 Medicare beneficiaries in this cross-sectional study, we identified approximately 4.3 million (7.2%) with highly likely ADRD, 1.1 million (1.9%) with likely ADRD, and 2.6 million (4.3%) with possible ADRD, for a total of more than 8.0 million (13.4%) in any category. Specifically, we developed new diagnosis and NDC code ADRD case definitions informed by a systematic review of previous algorithms, author and expert input, and analyses of Medicare data. The review identified 43 ICD-10-CM codes and 5 prescription drugs used by the CCW and 20 researcher-developed algorithms to identify ADRD in Medicare data. We divided codes into categories that were likely to indicate ADRD vs those that were possibly ADRD based on past frequency of use by other researchers, characteristics of beneficiaries identified by codes, and author and expert consensus around code definitions. We then added a highly likely category to describe beneficiaries who received 2 or more likely codes on different dates of service. We posit that these categories are superior to previous definitions for provisional use in surveillance systems, but caution that validation is necessary. To our knowledge, this is the first application of claims identification algorithms to all-age FFS and MA beneficiaries. We have used this case definition to compute provisional national-, state-, and county-level estimates of ADRD prevalence and incidence in 2020 Medicare and published them on our dementia surveillance website. 24 Estimates will be refined pending validation and updated with additional years of data as they become available.

Our 3-level case definition is novel in that it was driven by researcher-consensus as well as data analysis and identifies dementia with varying degrees of certainty. Of note, ICD-10-CM codes used to identify possible ADRD have lower researcher consensus and less specific code descriptions (ie, do not contain dementia or Alzheimer ). Use of the possible ADRD codes may reflect physician uncertainty about a dementia diagnosis or medical events involving ADRD-like symptoms in patients without underlying dementia. 25 - 27 Our definition also excludes several previously used codes that were determined to not indicate ADRD by expert clinicians. Compared with the commonly used CCW algorithm, which similarly uses a 3-year look-back period, our case definition is more specific when limited to the highly likely and likely categories, but broader when also including the possible ADRD category. The CCW algorithm estimated prevalence of 10.7% in 2019 Medicare FFS beneficiaries 28 falls between our estimates for FFS beneficiaries of 8.6% for highly likely or likely ADRD and 12.6% for all 3 categories.

Importantly, we saw expected and meaningful differences between beneficiaries identified in each ADRD category. Moving from the no evidence of ADRD to the highly likely ADRD groups, beneficiaries became progressively older and more frail and had greater rates of dual-eligibility, LTC use, and death, which is consistent with prior research. 29 - 35 Notably, prevalence of highly likely ADRD was 29.1% in beneficiaries aged 85 years or older, 72.8% in LTC users, and 36.2% in decedents, compared with 7.2% in the general Medicare population. Higher rates of dual-eligibility in ADRD groups may be driven by ADRD beneficiaries spending down assets to qualify for Medicaid and obtain LTC coverage. These differences persisted after age standardization and lend confidence to our case definitions.

Application of our case definitions also showed disparities in diagnosis rates by race in the expected direction—higher dementia risk among non-Hispanic Black beneficiaries relative to non-Hispanic White beneficiaries 36 , 37 —after age standardization to account for lower life expectancy among non-Hispanic Black individuals. 38 However, because non-Hispanic Black individuals also have a greater risk of under-diagnosis of ADRD than non-Hispanic White individuals, 39 disparities in true underlying rates may be higher than observed. Additionally, we found higher-than-expected representation of Hispanic and Asian and Pacific Islander beneficiaries among those that had an ADRD-targeting drug without diagnostic ( ICD-10-CM ) evidence. We hypothesize that differences in cultural perceptions around dementia and cognitive decline (eg, memory loss as a normal aging process) 40 , 41 may result in lower utilization of diagnosis codes when providers suspect dementia. Using PDE claims may result in higher and more accurate rates of ADRD among Hispanic and Asian and Pacific Islander individuals despite the overall small number of beneficiaries identified by PDE claims alone.

Finally, also consistent with past research, 29 , 35 , 42 PMPM FFS spending was substantially higher for beneficiaries with evidence of ADRD compared with those with no evidence of ADRD. Medicare FFS PMPM spending was relatively similar across the highly likely, likely, and possible ADRD groups despite differences in frailty and mortality. Medicare FFS spending may not be generalizable to those with MA (for whom costs cannot be computed) and is only part of the economic story. Medicaid is the primary US payer of LTC; higher rates of dual-eligibility and LTC use among the highly likely ADRD group indicate that differences in total federal and state spending between the highly likely ADRD and other groups are likely larger. We also did not capture patient and family health–related out-of-pocket expenses and informal care costs ($203 117 in families caring for a patient living with dementia vs $102 955 in families caring for a patient without dementia over the last 7 years of the patient’s life 42 ), forgone wages, or other impacts on informal caregivers, and payments made by other assistance programs. Finally, we caution that our spending measure represents total Medicare FFS spending, rather than the incremental ADRD costs.

This study is limited by at least the following. First, our ADRD case definition was driven by researcher-consensus, and validation against other dementia ascertainment methods (including ascertainment based on in-person clinical and neuropsychological assessments) is necessary. Both over- and under-diagnosis of ADRD have been documented in Medicare claims, 35 , 39 and the 8.0 million beneficiaries identified as having some evidence of ADRD by our case definition will include some without ADRD, especially those in the possible category. Similarly, this method only captures documented cases of dementia in Medicare administrative records and cannot capture beneficiaries with unrecognized and/or undocumented ADRD. If we assume a 60% rate of undetected dementia in the US 43 our estimates would suggest an additional 12 million beneficiaries may be living with ADRD. Additionally, our data show a marginally higher rate of ADRD in MA than in FFS enrollees (14.5% vs 12.6% across the 3 categories), which may reflect beneficiary selection in MA plans, MA vs FFS differences in clinical ADRD assessment and diagnosis rates, differences in claims or encounter documentation, or a combination thereof. Given the rapid rise in MA participation (from 33% in 2017 to 51% in 2023) and variation in MA penetration across counties, 44 , 45 it is also important to understand potential differences in performance of this case definition between MA and FFS beneficiaries. As such, validation of this case definition against in-person clinical and other ascertainment methods to assess performance (including sensitivity, specificity, positive predictive value, and negative predictive value), separately for Medicare FFS and MA, is critical for refining and calibrating estimates to accurately capture the diagnosed prevalence and incidence of dementia. Pending validation, our case definitions should be considered provisional. Notably, we expect the possible ADRD category to identify a higher proportion of individuals who do not have ADRD. Thus, it is important to report the possible ADRD category separately from the likely and highly likely ADRD categories in research and surveillance efforts using these case definitions.

Second, evidence for ADRD documented in electronic health or insurance records outside the Medicare system is not captured by our method; this is particularly problematic for beneficiaries without Medicare Parts B or D (7.5% and 25.6% of Medicare enrollees, respectively 43 , 46 ). Third, we deliberately used data from 2017 to 2019 to avoid the COVID-19 pandemic years, which resulted in secular shocks, including excess senior deaths, forgone or deferred care, and increased telehealth, which may have impacted dementia diagnosis. Research is necessary to understand these effects but will necessarily be delayed pending new data. Fourth, Namzaric, a memantine and donepezil combination drug approved in 2014, was not included by any prescription-drug based identification strategy; while the impact of including this drug necessitates further investigation, we anticipate a negligible effect given that just 0.1% of the sample had an ADRD-targeting prescription drug without ICD-10-CM evidence. Similarly, ICD-10 -CM code updates from October 2022 added 29 highly specific codes each under code roots F01 (vascular dementia) F02 (dementia in other diseases classified elsewhere), and F03 (unspecified dementia) (eTable 5 in Supplement 1 ). 47 We recommend that applications of our approach to Medicare records beginning in October 2022 include these for identifying highly likely and likely ADRD. Fifth, in developing our case definitions, we only considered use of ICD-10-CM codes and prescription drugs but did not consider other criteria of existing ADRD-identification algorithms, including look-back period, types of claims or encounter data considered, number of claims or encounters with relevant ICD-10-CM codes required, and time elapsed between claims and encounters; sensitivity analyses around these different criteria are beyond the scope of this paper.

In this cross-sectional study, our novel case definition for ADRD identified approximately 5.4 million Medicare beneficiaries with evidence of at least likely ADRD in 2019. Pending validation against in-person clinical and other ascertainment methods, this definition can be adopted for provisional use in national surveillance efforts.

Accepted for Publication: June 17, 2024.

Published: September 3, 2024. doi:10.1001/jamanetworkopen.2024.27610

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

Corresponding Author: Kan Z. Gianattasio, PhD, NORC at the University of Chicago, 4350 East-West Hwy 8th Floor, Bethesda, MD 20814 ( [email protected] ).

Author Contributions: Mr Wachsmuth and Mr Murphy 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: Gianattasio, Hartzman, Wittenborn, Power, Rein.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gianattasio, Wachsmuth, Hartzman, Cutroneo, Wittenborn, Rein.

Critical review of the manuscript for important intellectual content: Gianattasio, Murphy, Hartzman, Montazer, Power, Rein.

Statistical analysis: Gianattasio, Wachsmuth, Murphy.

Obtained funding: Wittenborn, Rein.

Administrative, technical, or material support: Hartzman, Montazer, Cutroneo.

Supervision: Hartzman, Rein.

Conflict of Interest Disclosures: None reported.

Funding/Support: Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health (Award No. R01AG075730).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to thank Christina Prather, MD (George Washington University); Tania Alchalabi, MD (George Washington University); and Raymond Scott Turner, MD, PhD (Georgetown University), for providing critical review of the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes from a clinical perspective. Drs Prather, Alchalabi, and Turner did not receive compensation for their contributions to this work. We also wish to acknowledge the critical input into data processing and analysis decisions made by Qian Gu, PhD (KPMG); Carrie Bao, BS (KPMG); and Samuel Knisely, BA, (KPMG). Dr Gu, Ms Bao, and Mr Knisely received funding from the same National Institute on Aging grant (R01AG075730) that funded this study for their input.

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Sowing Seeds for Cleaner Air: How EPA Researchers are Addressing Air Pollution at Chicago Area Schools

Published September 3, 2024

EPA scientists are partnering with organizations in the greater-Chicago metropolitan area to assess the use of vegetative barriers in mitigating the effects of nearby roadway air pollution and improving air quality near schools. Vegetative barriers, such as roadside trees and foliage, can clean air by trapping pollutants on leaves and branches or forcing air up and away from people.

EPA’s Richard Baldauf and the EPA Chicago Vegetative Barrier Project team—Jennifer Tyler, Sheila Batka, Kathy Kowal, Yulissa Aguilar, and Megan Gavin— work with three Chicago-area schools located near major roadways in a study using trees and other vegetative barriers to protect schools from roadway air pollution.

“Chicago represents many cities across the U.S. where schools are located near large roadways,” said Baldauf, who is co-leading the study. “By properly planting vegetative barriers next to the schools, we hope to see improvements in air quality inside and outside the schools.”

EPA is collaborating on this project with the University of Illinois , the U.S. Forest Service , the City of Chicago , the Chicago-based Environmental Law and Policy Center and The Morton Arboretum . Researchers are also partnering with the Illinois Department of Transportation (IDOT) to measure noise levels outside the schools.

EPA scientists measured air quality inside and outside the schools during spring 2024. Though some trees have been planted at two schools by The Morton Arboretum and IDOT, more air quality measurements can be taken as the vegetation grows once the barriers are completely planted.

During the study, researchers use air sensors to measure pollutants commonly released in motor vehicle exhaust, including particulate matter, nitrogen dioxide and black carbon, which is a form of particulate matter. The researchers also collect meteorological data, including wind speed and direction, temperature, humidity, and precipitation measurements.

“Appropriately selected and maintained bushes and trees along roads have been shown to reduce air pollution exposures,” Baldauf said. “Plants used as vegetative barriers need to be suitable for all local weather situations and keep their foliage year-round so there are no gaps or large spaces.”

University of Illinois professor Dr. Mary Patricia McGuire and a small team of graduate students are working with The Morton Arboretum and the EPA to provide landscape architecture expertise to the project and design vegetation buffer prototypes that will be tested, refined and implemented at school sites.

“Collaboration across our team has been essential for bridging science and implementation,” McGuire noted. “As landscape architects, one of our key contributions is to work in the middle as translators and mediators between science, the site conditions, and the community. The visualizations we create allow the whole team, including the school community, to see, discuss and tweak the design together so that the designed buffer goes beyond air quality function to also address questions of aesthetics, safety, and other benefits of school greening.”

Tree and vegetation buffers are a fairly new design technology in American cities but could be a critical asset in the future as researchers expand their understanding of how to effectively integrate them into green infrastructures. EPA’s collaboration with local and university partners to pull in a breadth of knowledge and input is essential to identifying best practices for later vegetative barrier design and implementation.

EPA in Action

The project includes an educational and environmental awareness component. During their visits to the schools, the researchers led air quality educational sessions with students and demonstrated the air monitoring equipment being used in the study.

“Approximately 600 students participated in the educational sessions and showed a lot of interest in how the sensors worked, what they were measuring, and how the issue of air and noise pollution in their schools could be addressed,” Baldauf said. “Involving and informing students and teachers is a priority because this project relies on community collaboration.”

Just Keep Growing

Baldauf and his collaborators plan to continue conducting measurements after the vegetative barriers grow so the team can assess their impact on air and noise quality. Study results are expected to be available one year after completion of the vegetation sampling and will be shared with the participating schools.

By collecting air quality samples using air sensors and meteorological equipment, EPA scientists and their project partners aim to expand understanding of the ways that vegetative barriers can impact air quality and noise pollution near schools and other buildings, p romote learning and community engagement about air quality and trees, and create cleaner air for school communities.   

This article was written by Sarah Whichello, Oak Ridge Associated Universities Research participant with EPA. 

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Dynamic Bayesian networks for spatiotemporal modeling and its uncertainty in tradeoffs and synergies of ecosystem services: a case study in the Tarim River Basin, China

• ORIGINAL PAPER
• Published: 02 September 2024

Cite this article

• Yang Hu 1 , 2 ,
• Jie Xue 2 , 3 , 4 ,
• Jianping Zhao 1 ,
• Xinlong Feng 1 ,
• Huaiwei Sun 5 ,
• Junhu Tang 6 &
• Jingjing Chang 2  

Ecosystem services (ESs) refer to the benefits that humans obtain from ecosystems. These services are subject to environmental changes and human interventions, which introduce a significant level of uncertainty. Traditional ES modeling approaches often employ Bayesian networks, but they fall short in capturing spatiotemporal dynamic change processes. To address this limitation, dynamic Bayesian networks (DBNs) have emerged as stochastic models capable of incorporating uncertainty and capturing dynamic changes. Consequently, DBNs have found increasing application in ES modeling. However, the structure and parameter learning of DBNs present complexities within the field of ES modeling. To mitigate the reliance on expert knowledge, this study proposes an algorithm for structure and parameter learning, integrating the InVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) model with DBNs to develop a comprehensive understanding of the spatiotemporal dynamics and uncertainty of ESs in the Tarim River Basin, China from 2000 to 2020. The study further evaluates the tradeoffs and synergies among four key ecosystem services: water yield, habitat quality, sediment delivery ratio, and carbon storage and sequestration. The findings show that (1) the proposed structure learning and parameter learning algorithm for DBNs, including the hill-climb algorithm, linear analysis, the Markov blanket, and the EM algorithm, effectively address subjective factors that can influence model learning when dealing with uncertainty; (2) significant spatial heterogeneity is observed in the supply of ESs within the Tarim River Basin, with notable changes in habitat quality, water yield, and sediment delivery ratios occurring between 2000–2005, 2010–2015, and 2015–2020, respectively; (3) tradeoffs exist between water yield and habitat quality, as well as between soil conservation and carbon sequestration, while synergies are found among habitat quality, soil retention, and carbon sequestration. The land-use type emerges as the most influential factor affecting the tradeoffs and synergies of ESs. This study serves to validate the capacity of DBNs in addressing spatiotemporal dynamic changes and establishes an improved research methodology for ES modeling that considers uncertainty.

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Aguilera PA, Fernández A, Fernández R, Rumí R, Salmerón A (2011) Bayesian networks in environmental modelling. Environ Model Softw 26:1376–1388. https://doi.org/10.1016/j.envsoft.2011.06.004

Article   Google Scholar  

Barbier EB, Koch EW, Silliman BR et al (2008) Coastal ecosystem-based management with nonlinear ecological functions and values. Science 319(5861):321–323

Article   CAS   Google Scholar  

Bennett EM, Peterson GD, Gordon LJ (2009) Understanding relationships among multiple ecosystem services. Ecol Lett 12(12):1394–1404

Bicking S, Burkhard B, Kruse M, Müller F (2019) Bayesian belief network-based assessment of nutrient regulating ecosystem services in Northern Germany. PLoS ONE 14:e0216053. https://doi.org/10.1371/journal.pone.0216053

Chang JJ, Bai YX, Xue J, Gong L, Zeng FJ, Sun HW, Hu Y, Huang H, Ma YT (2023) Dynamic Bayesian networks with application in environmental modeling and management: a review. Environ Model Softw 170:105835

Costanza R, d’Arge R, De Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, O’Neill RV, Paruelo J, Raskin RG, Sutton P, Van Den Belt M (1998) The value of the world’s ecosystem services and natural capital. Ecol Econ 25:3–15. https://doi.org/10.1016/S0921-8009(98)00020-2

Costanza R, De Groot R, Sutton P, Van Der Ploeg S, Anderson SJ, Kubiszewski I, Farber S, Turner RK (2014) Changes in the global value of ecosystem services. Glob Environ Change 26:152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002

Daily GC (ed) (1997) Nature’s services: societal dependence on natural ecosystems. Island Press, Washington, DC

Google Scholar  

Dang KB, Windhorst W, Burkhard B, Müller F (2019) A Bayesian belief network—based approach to link ecosystem functions with rice provisioning ecosystem services. Ecol Ind 100:30–44. https://doi.org/10.1016/j.ecolind.2018.04.055

Das A, Das M, Houqe R, Pereira P (2023) Mapping ecosystem services for ecological planning and management: a case from a tropical planning region, Eastern India. Environ Sci Pollut Res 30:7543–7560. https://doi.org/10.1007/s11356-022-22732-3

Domínguez-Tejo E, Metternicht G (2019) An ecosystem-based approach and Bayesian modelling to inform coastal planning: a case study of Manly, Australia. Environ Sci Policy 101:72–86. https://doi.org/10.1016/j.envsci.2019.07.019

Feng Z, Jin X, Chen T, Wu J (2021) Understanding trade-offs and synergies of ecosystem services to support the decision-making in the Beijing–Tianjin–Hebei region. Land Use Policy 106:105446

Forio MAE, Villa-Cox G, Van Echelpoel W, Ryckebusch H, Lock K, Spanoghe P, Deknock A, De Troyer N, Nolivos-Alvarez I, Dominguez-Granda L, Speelman S, Goethals PLM (2020) Bayesian Belief Network models as trade-off tools of ecosystem services in the Guayas River Basin in Ecuador. Ecosyst Serv 44:101124. https://doi.org/10.1016/j.ecoser.2020.101124

Fox WE, Medina-Cetina Z, Angerer J, Varela P, Ryang Chung J (2017) Water quality & natural resource management on military training lands in Central Texas: improved decision support via Bayesian Networks. Sustain Water Qual Ecol 9–10:39–52. https://doi.org/10.1016/j.swaqe.2017.03.001

Fu Q, Hou Y, Wang B, Bi X, Li B, Zhang X (2018) Scenario analysis of ecosystem service changes and interactions in a mountain-oasis-desert system: a case study in Altay Prefecture, China. Sci Rep 8:12939. https://doi.org/10.1038/s41598-018-31043-y

Furlan E, Slanzi D, Torresan S, Critto A, Marcomini A (2020) Multi-scenario analysis in the Adriatic Sea: a GIS-based Bayesian network to support maritime spatial planning. Sci Total Environ 703:134972. https://doi.org/10.1016/j.scitotenv.2019.134972

Gao J, Li F, Gao H, Zhou C, Zhang X (2017) The impact of land-use change on water-related ecosystem services: a study of the Guishui River Basin, Beijing, China. J Clean Prod 163:S148–S155. https://doi.org/10.1016/j.jclepro.2016.01.049

Grömping U (2006) Relative importance for linear regression in R : The package relaimpo . J Stat Soft. https://doi.org/10.18637/jss.v017.i01

Han H, Zhang J, Ma G, Zhang X, Bai Y (2018) Advances on impact of climate change on ecosystem services. J Nanjing for Univ (Nat Sci Ed) 61(02):184–190. https://doi.org/10.3969/j.issn.1000-2006.201706007

Hao R, Yu D, Sun Y, Shi M (2019) The features and influential factors of interactions among ecosystem services. Ecol Ind 101:770–779. https://doi.org/10.1016/j.ecolind.2019.01.080

Hernández-Guzmán R, Ruiz-Luna A, González C (2019) Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico. Remote Sens Appl Soc Environ 13:318–327. https://doi.org/10.1016/j.rsase.2018.12.005

Hou Y, Chen Y, Ding J, Li Z, Li Y, Sun F (2022) Ecological Impacts of land use change in the Arid Tarim river Basin of China. Remote Sens 14:1894. https://doi.org/10.3390/rs14081894

Hough RL, Towers W, Aalders I (2010) The risk of peat erosion from climate change: land management combinations—an assessment with Bayesian Belief Networks. Hum Ecol Risk Assess Int J 16:962–976. https://doi.org/10.1080/10807039.2010.511964

Huang H, Xue J, Feng X, Zhao J, Sun H, Hu Y, Ma Y (2024) Thriving arid oasis urban agglomerations: optimizing ecosystem services pattern under future climate change scenarios using dynamic Bayesian network. J Environ Manag 350:119612

Jäger WS, Christie EK, Hanea AM, Den Heijer C, Spencer T (2018) A Bayesian network approach for coastal risk analysis and decision making. Coast Eng 134:48–61. https://doi.org/10.1016/j.coastaleng.2017.05.004

Jia X, Fu B, Feng X, Hou G, Liu Y, Wang X (2014) The tradeoff and synergy between ecosystem services in the Grain-for-Green areas in Northern Shaanxi, China. Ecol Ind 43:103–113. https://doi.org/10.1016/j.ecolind.2014.02.028

Jiang C, Li D, Wang D, Zhang L (2016) Quantification and assessment of changes in ecosystem service in the Three-River Headwaters Region, China as a result of climate variability and land cover change. Ecol Ind 66:199–211. https://doi.org/10.1016/j.ecolind.2016.01.051

Jing L (2021) Research on optimization of spatial pattern of ecosystem service in Qinhuangdao. Hebei Agricultural University. https://doi.org/10.27109/d.cnki.ghbnu.2021.000524

Kragt ME (2009) A beginners guide to Bayesian network modelling for integrated catchment management Technical Report No. 9. https://api.semanticscholar.org/CorpusID:14470543

Landuyt D, Broekx S, D’hondt R, Engelen G, Aertsens J, Goethals PLM (2013) A review of Bayesian belief networks in ecosystem service modelling. Environ Model Softw 46:1–11. https://doi.org/10.1016/j.envsoft.2013.03.011

Landuyt D, Lemmens P, D’hondt R, Broekx S, Liekens I, De Bie T, Declerck SAJ, De Meester L, Goethals PLM (2014) An ecosystem service approach to support integrated pond management: a case study using Bayesian belief networks—highlighting opportunities and risks. J Environ Manag 145:79–87. https://doi.org/10.1016/j.jenvman.2014.06.015

Landuyt D, Van Der Biest K, Broekx S, Staes J, Meire P, Goethals PLM (2015) A GIS plug-in for Bayesian belief networks: towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping. Environ Model Softw 71:30–38. https://doi.org/10.1016/j.envsoft.2015.05.002

Landuyt D, Broekx S, Goethals PLM (2016) Bayesian belief networks to analyse trade-offs among ecosystem services at the regional scale. Ecol Ind 71:327–335. https://doi.org/10.1016/j.ecolind.2016.07.015

Lang Y, Song W (2018) Trade-off analysis of ecosystem services in a Mountainous Karst Area. China Water 10:300. https://doi.org/10.3390/w10030300

Liang J, Li S, Li X, Li X, Liu Q, Meng Q, Lin A, Li J (2021) Trade-off analyses and optimization of water-related ecosystem services (WRESs) based on land use change in a typical agricultural watershed, southern China. J Clean Prod 279:123851. https://doi.org/10.1016/j.jclepro.2020.123851

Liu S, Crossman ND, Nolan M, Ghirmay H (2013) Bringing ecosystem services into integrated water resources management. J Environ Manag 129:92–102. https://doi.org/10.1016/j.jenvman.2013.06.047

Liu L, Feng Q (2015) Advances in research of function and valuation of ecosystem services. Sci Cold Arid Reg 7(2):194-198. https://doi.org/10.3724/SP.J.1226.2015.00194

Liu SY, Hu NK, Zhang J, Lv ZC (2018) Spatiotemporal change of carbon storage in the Loess Plateau of northern Shaanxi, based on the invest model. Sci Cold Arid Reg 10(3):240–250. https://doi.org/10.3724/SP.J.1226.2018.00240

Ma YT, Xue J, Feng XL, Zhao JP, Tang JH, Sun HW, Chang JJ, Yan LK (2024) Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network. Sci Rep 14:17695. https://doi.org/10.1038/s41598-024-68523-3

Maes J, Egoh B, Willemen L, Liquete C, Vihervaara P, Schägner JP, Grizzetti B, Drakou EG, Notte AL, Zulian G, Bouraoui F, Luisa Paracchini M, Braat L, Bidoglio G (2012) Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst Serv 1:31–39. https://doi.org/10.1016/j.ecoser.2012.06.004

Marcot BG (2012) Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol Model 230:50–62. https://doi.org/10.1016/j.ecolmodel.2012.01.013

MEA (2005) Ecosystems and human well-being: synthesis. Island Press, Washington, DC

Molina J-L, Pulido-Velázquez D, García-Aróstegui JL, Pulido-Velázquez M (2013) Dynamic Bayesian networks as a decision support tool for assessing climate change impacts on highly stressed groundwater systems. J Hydrol 479:113–129. https://doi.org/10.1016/j.jhydrol.2012.11.038

Nadkarni S, Shenoy PP (2004) A causal mapping approach to constructing Bayesian networks. Decis Support Syst 38:259–281. https://doi.org/10.1016/S0167-9236(03)00095-2

Pham HV, Sperotto A, Torresan S, Acuña V, Jorda-Capdevila D, Rianna G, Marcomini A, Critto A (2019) Coupling scenarios of climate and land-use change with assessments of potential ecosystem services at the river basin scale. Ecosyst Serv 40:101045. https://doi.org/10.1016/j.ecoser.2019.101045

Pham HV, Sperotto A, Furlan E, Torresan S, Marcomini A, Critto A (2021) Integrating Bayesian Networks into ecosystem services assessment to support water management at the river basin scale. Ecosyst Serv 50:101300. https://doi.org/10.1016/j.ecoser.2021.101300

Renard D, Rhemtulla JM, Bennett EM (2015) Historical dynamics in ecosystem service bundles. Proc Natl Acad Sci USA 112:13411–13416. https://doi.org/10.1073/pnas.1502565112

Ronquist F (2004) Bayesian inference of character evolution. Trends Ecol Evol 19:475–481. https://doi.org/10.1016/j.tree.2004.07.002

Scutari M (2017) Understanding Bayesian networks with examples in R. University of Oxford

Scutari M (2010) Learning Bayesian Networks with the bnlearn R package

Scutari M, Graafland CE, Gutiérrez JM (2019) Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms. Int J Approx Reason 115:235–253. https://doi.org/10.1016/j.ijar.2019.10.003

Sharp R, Chaplin-Kramer R, Wood S, Guerry A, Tallis H, Ricketts T, Nelson E, Ennaanay D, Wolny S, Olwero N, Vigerstol K, Pennington D, Mendoza G, Aukema J, Foster J, Forrest J, Cameron DR, Arkema K, Lonsdorf E, Douglass J (2018) In:VEST User’s Guide. https://doi.org/10.13140/RG.2.2.32693.78567

Sheikholeslami R, Razavi S (2020) A fresh look at variography: measuring dependence and possible sensitivities across geophysical systems from any given data. Geophys Res Lett 47(20):e2020GL089829

Shen J, Li S, Liang Z, Liu L, Li D, Wu S (2020) Exploring the heterogeneity and nonlinearity of trade-offs and synergies among ecosystem services bundles in the Beijing-Tianjin-Hebei urban agglomeration. Ecosyst Serv 43:101103. https://doi.org/10.1016/j.ecoser.2020.101103

Sperotto A, Molina J-L, Torresan S, Critto A, Marcomini A (2017) Reviewing Bayesian Networks potentials for climate change impacts assessment and management: a multi-risk perspective. J Environ Manag 202:320–331. https://doi.org/10.1016/j.jenvman.2017.07.044

Sun Z, Müller D (2013) A framework for modeling payments for ecosystem services with agent-based models, Bayesian belief networks and opinion dynamics models. Environ Model Softw 45:15–28. https://doi.org/10.1016/j.envsoft.2012.06.007

Sun F, Wang Y, Chen Y, Li Y, Zhang Q, Qin J, Kayumba PM (2021) Historic and Simulated desert-oasis ecotone changes in the Arid Tarim River Basin, China. Remote Sens 13:647. https://doi.org/10.3390/rs13040647

Tolessa T, Senbeta F, Kidane M (2017) The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosyst Serv 23:47–54. https://doi.org/10.1016/j.ecoser.2016.11.010

Vallet A, Locatelli B, Levrel H, Wunder S, Seppelt R, Scholes RJ, Oszwald J (2018) Relationships between ecosystem services: comparing methods for assessing tradeoffs and synergies. Ecol Econ 150:96–106. https://doi.org/10.1016/j.ecolecon.2018.04.002

Van Jaarsveld AS, Biggs R, Scholes RJ (2005) Measuring conditions and trends in ecosystem services at multiple scales: the Southern African Millennium Ecosystem Assessment (SA f MA) experience. Philos Trans R Soc Lond B Biol Sci 360(1454):425–441

Voinov A, Bousquet F (2010) Modelling with stakeholders☆. Environ Model Softw 25:1268–1281. https://doi.org/10.1016/j.envsoft.2010.03.007

Wang Y, Dai E (2020) Spatial-temporal changes in ecosystem services and the trade-off relationship in mountain regions: a case study of Hengduan Mountain region in Southwest China. J Clean Prod 264:121573. https://doi.org/10.1016/j.jclepro.2020.121573

Wang C, Zhan J, Chu X, Liu W, Zhang F (2019) Variation in ecosystem services with rapid urbanization: a study of carbon sequestration in the Beijing–Tianjin–Hebei region, China. Phys Chem Earth Parts a/b/c 110:195–202. https://doi.org/10.1016/j.pce.2018.09.001

Wu L (2018) Tarim River Basin boundary dataset. National Glacier and Desert Science Data Center ( www.ncdc.ac . cn). https://cstr.cn/CSTR:11738.11.ncdc.Westdc.2020.338

Xue J, Gui D, Zhao Y, Lei J, Zeng F, Feng X, Mao D, Shareef M (2016) A decision-making framework to model environmental flow requirements in oasis areas using Bayesian networks. J Hydrol 540:1209–1222

Xue J, Gui D, Lei J, Zeng F, Mao D, Zhang Z (2017) Model development of a participatory Bayesian network for coupling ecosystem services into integrated water resources management. J Hydrol 554:50–65

Xue J, Lei JQ, Chang JJ, Zeng FJ, Zhang ZW, Sun HW (2022) A causal structure-based multiple-criteria decision framework for evaluating the waterrelated ecosystem service tradeoffs in a desert oasis region. J Hydrol Reg Stud 44:101226

Yang J, Huang X (2021) The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst Sci Data 13:3907–3925. https://doi.org/10.5194/essd-13-3907-2021

Yang S, Zhao W, Liu Y, Wang S, Wang J, Zhai R (2018) Influence of land use change on the ecosystem service trade-offs in the ecological restoration area: dynamics and scenarios in the Yanhe watershed, China. Sci Total Environ 644:556–566. https://doi.org/10.1016/j.scitotenv.2018.06.348

YuLiZhou YJZ, Tang C (2022) Spatial pattern optimization of ecosystem services based on Bayesian networks: a case of the Jing River Basin. Arid Land Geogr 45(4):1268–1280

Zeng L, Li J, Li T, Yang XN, Wang YZ (2018) Optimizing spatial patterns of water conservation ecosystem service based on Bayesian belief networks. Acta Geogr Sin 73(9):1809–1822. https://doi.org/10.11821/dlxb201809015

Ma YT, Xue J, Feng XL, Zhao JP, Tang JH, Sun HW, Chang JJ, Yan LK (2024) Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network Sci Rep 14: 17695. 10.1038/s41598-024-68523-3

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (42071259), the Tianshan Talents Program of Xinjiang Uygur Autonomous Region (2022TSYCJU0002), the original innovation project of the basic frontier scientific research program, Chinese Academy of Sciences (ZDBS-LY-DQC031), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2021D01E01), the water system evolution and risk assessment in arid regions for original innovation project of institute (2023–2025), and the Outstanding Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2019430) (2024-2026). We are also grateful to three anonymous referees for their constructive comments in this manuscript.

This work was supported by National Natural Science Foundation of China (Grant number: 42071259).

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College of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China

Yang Hu, Jianping Zhao & Xinlong Feng

State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China

Yang Hu, Jie Xue & Jingjing Chang

Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, 848300, Xinjiang, China

University of Chinese Academy of Sciences, Beijing, 100049, China

School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

Huaiwei Sun

College of Ecology and Environment, Xinjiang University, Urumqi, 830046, China

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Yang Hu: conceptualization, methodology, software, validation, formal analysis, writing—original draft. Jie Xue, Jianping Zhao, Xinlong Feng, and Huaiwei Sun: conceptualization, methodology, supervision, writing—review & editing. Junhu Tang and Jingjing Chang: data curation, visualization.

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Hu, Y., Xue, J., Zhao, J. et al. Dynamic Bayesian networks for spatiotemporal modeling and its uncertainty in tradeoffs and synergies of ecosystem services: a case study in the Tarim River Basin, China. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02805-0

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Accepted : 19 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s00477-024-02805-0

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