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Case Study – Methods, Examples and Guide
Table of Contents
A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.
It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.
Types of Case Study
Types and Methods of Case Study are as follows:
Single-Case Study
A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.
For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.
Multiple-Case Study
A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.
For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.
Exploratory Case Study
An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.
For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.
Descriptive Case Study
A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.
For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.
Instrumental Case Study
An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.
For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.
Case Study Data Collection Methods
Here are some common data collection methods for case studies:
Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.
Observations
Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.
Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.
Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.
Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.
How to conduct Case Study Research
Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:
- Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
- Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
- Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
- Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
- Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
- Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
- Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.
Examples of Case Study
Here are some examples of case study research:
- The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
- The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
- The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
- The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
- The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.
Application of Case Study
Case studies have a wide range of applications across various fields and industries. Here are some examples:
Business and Management
Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.
Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.
Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.
Social Sciences
Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.
Law and Ethics
Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.
Purpose of Case Study
The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.
The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.
Case studies can also serve other purposes, including:
- Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
- Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
- Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
- Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
Advantages of Case Study Research
There are several advantages of case study research, including:
- In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
- Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
- Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
- Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
- Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
- Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.
Limitations of Case Study Research
There are several limitations of case study research, including:
- Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
- Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
- Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
- Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
- Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
- Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.
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- What Is a Case Study? | Definition, Examples & Methods
What Is a Case Study? | Definition, Examples & Methods
Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.
A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.
A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .
Table of contents
When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.
A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.
Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.
You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.
Research question | Case study |
---|---|
What are the ecological effects of wolf reintroduction? | Case study of wolf reintroduction in Yellowstone National Park |
How do populist politicians use narratives about history to gain support? | Case studies of Hungarian prime minister Viktor Orbán and US president Donald Trump |
How can teachers implement active learning strategies in mixed-level classrooms? | Case study of a local school that promotes active learning |
What are the main advantages and disadvantages of wind farms for rural communities? | Case studies of three rural wind farm development projects in different parts of the country |
How are viral marketing strategies changing the relationship between companies and consumers? | Case study of the iPhone X marketing campaign |
How do experiences of work in the gig economy differ by gender, race and age? | Case studies of Deliveroo and Uber drivers in London |
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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:
- Provide new or unexpected insights into the subject
- Challenge or complicate existing assumptions and theories
- Propose practical courses of action to resolve a problem
- Open up new directions for future research
TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.
Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.
Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.
However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.
Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.
While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:
- Exemplify a theory by showing how it explains the case under investigation
- Expand on a theory by uncovering new concepts and ideas that need to be incorporated
- Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions
To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.
There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.
Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.
The aim is to gain as thorough an understanding as possible of the case and its context.
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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.
How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .
Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).
In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Normal distribution
- Degrees of freedom
- Null hypothesis
- Discourse analysis
- Control groups
- Mixed methods research
- Non-probability sampling
- Quantitative research
- Ecological validity
Research bias
- Rosenthal effect
- Implicit bias
- Cognitive bias
- Selection bias
- Negativity bias
- Status quo bias
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What is a Case Study in Research? Definition, Methods, and Examples
Case study methodology offers researchers an exciting opportunity to explore intricate phenomena within specific contexts using a wide range of data sources and collection methods. It is highly pertinent in health and social sciences, environmental studies, social work, education, and business studies. Its diverse applications, such as advancing theory, program evaluation, and intervention development, make it an invaluable tool for driving meaningful research and fostering positive change.[ 1]
Table of Contents
What is a Case Study?
A case study method involves a detailed examination of a single subject, such as an individual, group, organization, event, or community, to explore and understand complex issues in real-life contexts. By focusing on one specific case, researchers can gain a deep understanding of the factors and dynamics at play, understanding their complex relationships, which might be missed in broader, more quantitative studies.
When to do a Case Study?
A case study design is useful when you want to explore a phenomenon in-depth and in its natural context. Here are some examples of when to use a case study :[ 2]
- Exploratory Research: When you want to explore a new topic or phenomenon, a case study can help you understand the subject deeply. For example , a researcher studying a newly discovered plant species might use a case study to document its characteristics and behavior.
- Descriptive Research: If you want to describe a complex phenomenon or process, a case study can provide a detailed and comprehensive description. For instance, a case study design could describe the experiences of a group of individuals living with a rare disease.
- Explanatory Research: When you want to understand why a particular phenomenon occurs, a case study can help you identify causal relationships. A case study design could investigate the reasons behind the success or failure of a particular business strategy.
- Theory Building: Case studies can also be used to develop or refine theories. By systematically analyzing a series of cases, researchers can identify patterns and relationships that can contribute to developing new theories or refining existing ones.
- Critical Instance: Sometimes, a single case can be used to study a rare or unusual phenomenon, but it is important for theoretical or practical reasons. For example , the case of Phineas Gage, a man who survived a severe brain injury, has been widely studied to understand the relationship between the brain and behavior.
- Comparative Analysis: Case studies can also compare different cases or contexts. A case study example involves comparing the implementation of a particular policy in different countries to understand its effectiveness and identifying best practices.
How to Create a Case Study – Step by Step
Step 1: select a case .
Careful case selection ensures relevance, insight, and meaningful contribution to existing knowledge in your field. Here’s how you can choose a case study design :[ 3]
- Define Your Objectives: Clarify the purpose of your case study and what you hope to achieve. Do you want to provide new insights, challenge existing theories, propose solutions to a problem, or explore new research directions?
- Consider Unusual or Outlying Cases: Focus on unusual, neglected, or outlying cases that can provide unique insights.
- Choose a Representative Case: Alternatively, select a common or representative case to exemplify a particular category, experience, or phenomenon.
- Avoid Bias: Ensure your selection process is unbiased using random or criteria-based selection.
- Be Clear and Specific: Clearly define the boundaries of your study design , including the scope, timeframe, and key stakeholders.
- Ethical Considerations: Consider ethical issues, such as confidentiality and informed consent.
Step 2: Build a Theoretical Framework
To ensure your case study has a solid academic foundation, it’s important to build a theoretical framework:
- Conduct a Literature Review: Identify key concepts and theories relevant to your case study .
- Establish Connections with Theory: Connect your case study with existing theories in the field.
- Guide Your Analysis and Interpretation: Use your theoretical framework to guide your analysis, ensuring your findings are grounded in established theories and concepts.
Step 3: Collect Your Data
To conduct a comprehensive case study , you can use various research methods. These include interviews, observations, primary and secondary sources analysis, surveys, and a mixed methods approach. The aim is to gather rich and diverse data to enable a detailed analysis of your case study .
Step 4: Describe and Analyze the Case
How you report your findings will depend on the type of research you’re conducting. Here are two approaches:
- Structured Approach: Follows a scientific paper format, making it easier for readers to follow your argument.
- Narrative Approach: A more exploratory style aiming to analyze meanings and implications.
Regardless of the approach you choose, it’s important to include the following elements in your case study :
- Contextual Details: Provide background information about the case, including relevant historical, cultural, and social factors that may have influenced the outcome.
- Literature and Theory: Connect your case study to existing literature and theory in the field. Discuss how your findings contribute to or challenge existing knowledge.
- Wider Patterns or Debates: Consider how your case study fits into wider patterns or debates within the field. Discuss any implications your findings may have for future research or practice.
What Are the Benefits of a Case Study
Case studies offer a range of benefits , making them a powerful tool in research.
1. In-Depth Analysis
- Comprehensive Understanding: Case studies allow researchers to thoroughly explore a subject, understanding the complexities and nuances involved.
- Rich Data: They offer rich qualitative and sometimes quantitative data, capturing the intricacies of real-life contexts.
2. Contextual Insight
- Real-World Application: Case studies provide insights into real-world applications, making the findings highly relevant and practical.
- Context-Specific: They highlight how various factors interact within a specific context, offering a detailed picture of the situation.
3. Flexibility
- Methodological Diversity: Case studies can use various data collection methods, including interviews, observations, document analysis, and surveys.
- Adaptability: Researchers can adapt the case study approach to fit the specific needs and circumstances of the research.
4. Practical Solutions
- Actionable Insights: The detailed findings from case studies can inform practical solutions and recommendations for practitioners and policymakers.
- Problem-Solving: They help understand the root causes of problems and devise effective strategies to address them.
5. Unique Cases
- Rare Phenomena: Case studies are particularly valuable for studying rare or unique cases that other research methods may not capture.
- Detailed Documentation: They document and preserve detailed information about specific instances that might otherwise be overlooked.
What Are the Limitations of a Case Study
While case studies offer valuable insights and a detailed understanding of complex issues, they have several limitations .
1. Limited Generalizability
- Specific Context: Case studies often focus on a single case or a small number of cases, which may limit the generalization of findings to broader populations or different contexts.
- Unique Situations: The unique characteristics of the case may not be representative of other situations, reducing the applicability of the results.
2. Subjectivity
- Researcher Bias: The researcher’s perspectives and interpretations can influence the analysis and conclusions, potentially introducing bias.
- Participant Bias: Participants’ responses and behaviors may be influenced by their awareness of being studied, known as the Hawthorne effect.
3. Time-Consuming
- Data Collection and Analysis: Gathering detailed, in-depth data requires significant time and effort, making case studies more time-consuming than other research methods.
- Longitudinal Studies: If the case study observes changes over time, it can become even more prolonged.
4. Resource Intensive
- Financial and Human Resources: Conducting comprehensive case studies may require significant financial investment and human resources, including trained researchers and participant access.
- Access to Data: Accessing relevant and reliable data sources can be challenging, particularly in sensitive or proprietary contexts.
5. Replication Difficulties
- Unique Contexts: A case study’s specific and detailed context makes it difficult to replicate the study exactly, limiting the ability to validate findings through repetition.
- Variability: Differences in contexts, researchers, and methodologies can lead to variations in findings, complicating efforts to achieve consistent results.
By acknowledging and addressing these limitations , researchers can enhance the rigor and reliability of their case study findings.
Key Takeaways
Case studies are valuable in research because they provide an in-depth, contextual analysis of a single subject, event, or organization. They allow researchers to explore complex issues in real-world settings, capturing detailed qualitative and quantitative data. This method is useful for generating insights, developing theories, and offering practical solutions to problems. They are versatile, applicable in diverse fields such as business, education, and health, and can complement other research methods by providing rich, contextual evidence. However, their findings may have limited generalizability due to the focus on a specific case.
Frequently Asked Questions
Q: What is a case study in research?
A case study in research is an impactful tool for gaining a deep understanding of complex issues within their real-life context. It combines various data collection methods and provides rich, detailed insights that can inform theory development and practical applications.
Q: What are the advantages of using case studies in research?
Case studies are a powerful research method, offering advantages such as in-depth analysis, contextual insights, flexibility, rich data, and the ability to handle complex issues. They are particularly valuable for exploring new areas, generating hypotheses, and providing detailed, illustrative examples that can inform theory and practice.
Q: Can case studies be used in quantitative research?
While case studies are predominantly associated with qualitative research, they can effectively incorporate quantitative methods to provide a more comprehensive analysis. A mixed-methods approach leverages qualitative and quantitative research strengths, offering a powerful tool for exploring complex issues in a real-world context. For example , a new medical treatment case study can incorporate quantitative clinical outcomes (e.g., patient recovery rates and dosage levels) along with qualitative patient interviews.
Q: What are the key components of a case study?
A case study typically includes several key components:
- Introductio n, which provides an overview and sets the context by presenting the problem statement and research objectives;
- Literature review , which connects the study to existing theories and prior research;
- Methodology , which details the case study design , data collection methods, and analysis techniques;
- Findings , which present the data and results, including descriptions, patterns, and themes;
- Discussion and conclusion , which interpret the findings, discuss their implications, and offer conclusions, practical applications, limitations, and suggestions for future research.
Together, these components ensure a comprehensive, systematic, and insightful exploration of the case.
References
- de Vries, K. (2020). Case study methodology. In Critical qualitative health research (pp. 41-52). Routledge.
- Fidel, R. (1984). The case study method: A case study. Library and Information Science Research , 6 (3), 273-288.
- Thomas, G. (2021). How to do your case study. How to do your case study , 1-320.
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- Case Study | Definition, Examples & Methods
Case Study | Definition, Examples & Methods
Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.
A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.
A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .
Table of contents
When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.
A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.
Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.
You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.
Research question | Case study |
---|---|
What are the ecological effects of wolf reintroduction? | Case study of wolf reintroduction in Yellowstone National Park in the US |
How do populist politicians use narratives about history to gain support? | Case studies of Hungarian prime minister Viktor Orbán and US president Donald Trump |
How can teachers implement active learning strategies in mixed-level classrooms? | Case study of a local school that promotes active learning |
What are the main advantages and disadvantages of wind farms for rural communities? | Case studies of three rural wind farm development projects in different parts of the country |
How are viral marketing strategies changing the relationship between companies and consumers? | Case study of the iPhone X marketing campaign |
How do experiences of work in the gig economy differ by gender, race, and age? | Case studies of Deliveroo and Uber drivers in London |
Prevent plagiarism, run a free check.
Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:
- Provide new or unexpected insights into the subject
- Challenge or complicate existing assumptions and theories
- Propose practical courses of action to resolve a problem
- Open up new directions for future research
Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.
If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible.
However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.
While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:
- Exemplify a theory by showing how it explains the case under investigation
- Expand on a theory by uncovering new concepts and ideas that need to be incorporated
- Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions
To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.
There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .
The aim is to gain as thorough an understanding as possible of the case and its context.
In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.
How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .
Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).
In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.
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The 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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What is case study research?
Last updated
8 February 2023
Reviewed by
Cathy Heath
Short on time? Get an AI generated summary of this article instead
Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.
Organization
Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.
Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take.
Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.
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- What are the different types of case study designs?
Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.
Here are the common types of case study design:
Explanatory
An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it.
Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”
Descriptive
An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand.
The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.
Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."
Exploratory
Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.
Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”
An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others.
In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”
This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study.
The researchers also get an in-depth look at a particular subject from different viewpoints. Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”
Critical instance
A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth.
The findings can then be used further to generalize whether they would also apply in a different environment. Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”
Instrumental
Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory.
For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”
Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making.
For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.”
- When do you use case studies?
Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.
They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.
- What are the steps to follow when conducting a case study?
1. Select a case
Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.
2. Create a theoretical framework
While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information.
It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.
3. Collect the data
Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.
4. Analyze your case
The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.
In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.
- What are some case study examples?
What are the best approaches for introducing our product into the Kenyan market?
How does the change in marketing strategy aid in increasing the sales volumes of product Y?
How can teachers enhance student participation in classrooms?
How does poverty affect literacy levels in children?
Case study topics
Case study of product marketing strategies in the Kenyan market
Case study of the effects of a marketing strategy change on product Y sales volumes
Case study of X school teachers that encourage active student participation in the classroom
Case study of the effects of poverty on literacy levels in children
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Case Study Research Method in Psychology
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).
The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.
The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.
The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.
Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.
This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.
There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.
Famous Case Studies
- Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
- Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
- Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
- Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
- Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.
Clinical Case Studies
- Studying the effectiveness of psychotherapy approaches with an individual patient
- Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
- Neuropsychological cases investigating brain injuries or disorders
Child Psychology Case Studies
- Studying psychological development from birth through adolescence
- Cases of learning disabilities, autism spectrum disorders, ADHD
- Effects of trauma, abuse, deprivation on development
Types of Case Studies
- Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
- Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
- Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
- Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
- Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
- Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.
Where Do You Find Data for a Case Study?
There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.
1. Primary sources
- Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
- Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
- Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.
2. Secondary sources
- News/Media – News coverage of events related to the case study.
- Academic articles – Journal articles, dissertations etc. that discuss the case.
- Government reports – Official data and records related to the case context.
- Books/films – Books, documentaries or films discussing the case.
3. Archival records
Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.
Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.
4. Organizational records
Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.
Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.
However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.
- Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
- Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
- School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.
How do I Write a Case Study in Psychology?
Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.
1. Introduction
- Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
- Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.
2. Case Presentation
- Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
- Include client demographics like age and gender, information about social relationships, and mental health history.
- Describe all physical, emotional, and/or sensory symptoms reported by the client.
- Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
- Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
- Clearly state the working diagnosis or clinical impression before transitioning to management.
3. Management and Outcome
- Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
- Present the results of the intervention,including any quantitative or qualitative data collected.
- For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.
4. Discussion
- Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
- Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
- Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
- Offer clinical implications, and suggest future research directions.
5. Additional Items
- Thank specific assistants for writing support only. No patient acknowledgments.
- References should directly support any key claims or quotes included.
- Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
- Provides detailed (rich qualitative) information.
- Provides insight for further research.
- Permitting investigation of otherwise impractical (or unethical) situations.
Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.
Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.
Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.
Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.
The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).
Limitations
- Lacking scientific rigor and providing little basis for generalization of results to the wider population.
- Researchers’ own subjective feelings may influence the case study (researcher bias).
- Difficult to replicate.
- Time-consuming and expensive.
- The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.
Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.
Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.
This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.
For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).
This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.
Breuer, J., & Freud, S. (1895). Studies on hysteria . Standard Edition 2: London.
Curtiss, S. (1981). Genie: The case of a modern wild child .
Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304
Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306
Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.
Harlow J. M. (1848). Passage of an iron rod through the head. Boston Medical and Surgical Journal, 39 , 389–393.
Harlow, J. M. (1868). Recovery from the Passage of an Iron Bar through the Head . Publications of the Massachusetts Medical Society. 2 (3), 327-347.
Money, J., & Ehrhardt, A. A. (1972). Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.
Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.
Further Information
- Case Study Approach
- Case Study Method
- Enhancing the Quality of Case Studies in Health Services Research
- “We do things together” A case study of “couplehood” in dementia
- Using mixed methods for evaluating an integrative approach to cancer care: a case study
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You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).
- Look around on the home page. Does anything seem interesting to you?
- How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
- What do the reviews say about the restaurant you've chosen?
- What was the most important factor for you in choosing this spot?
- You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
- Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
- Now go to any restaurant's page and try to leave a review (don't actually submit it).
What was the worst thing about your experience?
It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer
What other aspects of the experience could be improved?
Feedback from the owners would be nice
What did you like about the website?
The flow was good, lots of bright photos
What other comments do you have for the owner of the website?
I like that you can sort by what you are looking for and i like the idea of collections
You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.
- Please begin by downloading the app to your device.
- Choose Italian and get started with the first lesson (stop once you reach the first question).
- Now go all the way through the rest of the first lesson, describing your thoughts as you go.
- Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
- After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
- What other languages does the app offer? Do any of them interest you?
I felt like there could have been a little more of an instructional component to the lesson.
It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.
Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.
Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.
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What is a Case Study? Definition, Research Methods, Sampling and Examples
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What is a Case Study?
A case study is defined as an in-depth analysis of a particular subject, often a real-world situation, individual, group, or organization.
It is a research method that involves the comprehensive examination of a specific instance to gain a better understanding of its complexities, dynamics, and context.
Case studies are commonly used in various fields such as business, psychology, medicine, and education to explore and illustrate phenomena, theories, or practical applications.
In a typical case study, researchers collect and analyze a rich array of qualitative and/or quantitative data, including interviews, observations, documents, and other relevant sources. The goal is to provide a nuanced and holistic perspective on the subject under investigation.
The information gathered here is used to generate insights, draw conclusions, and often to inform broader theories or practices within the respective field.
Case studies offer a valuable method for researchers to explore real-world phenomena in their natural settings, providing an opportunity to delve deeply into the intricacies of a particular case. They are particularly useful when studying complex, multifaceted situations where various factors interact.
Additionally, case studies can be instrumental in generating hypotheses, testing theories, and offering practical insights that can be applied to similar situations. Overall, the comprehensive nature of case studies makes them a powerful tool for gaining a thorough understanding of specific instances within the broader context of academic and professional inquiry.
Key Characteristics of Case Study
Case studies are characterized by several key features that distinguish them from other research methods. Here are some essential characteristics of case studies:
- In-depth Exploration: Case studies involve a thorough and detailed examination of a specific case or instance. Researchers aim to explore the complexities and nuances of the subject under investigation, often using multiple data sources and methods to gather comprehensive information.
- Contextual Analysis: Case studies emphasize the importance of understanding the context in which the case unfolds. Researchers seek to examine the unique circumstances, background, and environmental factors that contribute to the dynamics of the case. Contextual analysis is crucial for drawing meaningful conclusions and generalizing findings to similar situations.
- Holistic Perspective: Rather than focusing on isolated variables, case studies take a holistic approach to studying a phenomenon. Researchers consider a wide range of factors and their interrelationships, aiming to capture the richness and complexity of the case. This holistic perspective helps in providing a more complete understanding of the subject.
- Qualitative and/or Quantitative Data: Case studies can incorporate both qualitative and quantitative data, depending on the research question and objectives. Qualitative data often include interviews, observations, and document analysis, while quantitative data may involve statistical measures or numerical information. The combination of these data types enhances the depth and validity of the study.
- Longitudinal or Retrospective Design: Case studies can be designed as longitudinal studies, where the researcher follows the case over an extended period, or retrospective studies, where the focus is on examining past events. This temporal dimension allows researchers to capture changes and developments within the case.
- Unique and Unpredictable Nature: Each case study is unique, and the findings may not be easily generalized to other situations. The unpredictable nature of real-world cases adds a layer of authenticity to the study, making it an effective method for exploring complex and dynamic phenomena.
- Theory Building or Testing: Case studies can serve different purposes, including theory building or theory testing. In some cases, researchers use case studies to develop new theories or refine existing ones. In others, they may test existing theories by applying them to real-world situations and assessing their explanatory power.
Understanding these key characteristics is essential for researchers and practitioners using case studies as a methodological approach, as it helps guide the design, implementation, and analysis of the study.
Key Components of a Case Study
A well-constructed case study typically consists of several key components that collectively provide a comprehensive understanding of the subject under investigation. Here are the key components of a case study:
- Provide an overview of the context and background information relevant to the case. This may include the history, industry, or setting in which the case is situated.
- Clearly state the purpose and objectives of the case study. Define what the study aims to achieve and the questions it seeks to answer.
- Clearly identify the subject of the case study. This could be an individual, a group, an organization, or a specific event.
- Define the boundaries and scope of the case study. Specify what aspects will be included and excluded from the investigation.
- Provide a brief review of relevant theories or concepts that will guide the analysis. This helps place the case study within the broader theoretical context.
- Summarize existing literature related to the subject, highlighting key findings and gaps in knowledge. This establishes the context for the current case study.
- Describe the research design chosen for the case study (e.g., exploratory, explanatory, descriptive). Justify why this design is appropriate for the research objectives.
- Specify the methods used to gather data, whether through interviews, observations, document analysis, surveys, or a combination of these. Detail the procedures followed to ensure data validity and reliability.
- Explain the criteria for selecting the case and any sampling considerations. Discuss why the chosen case is representative or relevant to the research questions.
- Describe how the collected data will be coded and categorized. Discuss the analytical framework or approach used to identify patterns, themes, or trends.
- If multiple data sources or methods are used, explain how they complement each other to enhance the credibility and validity of the findings.
- Present the key findings in a clear and organized manner. Use tables, charts, or quotes from participants to illustrate the results.
- Interpret the results in the context of the research objectives and theoretical framework. Discuss any unexpected findings and their implications.
- Provide a thorough interpretation of the results, connecting them to the research questions and relevant literature.
- Acknowledge the limitations of the study, such as constraints in data collection, sample size, or generalizability.
- Highlight the contributions of the case study to the existing body of knowledge and identify potential avenues for future research.
- Summarize the key findings and their significance in relation to the research objectives.
- Conclude with a concise summary of the case study, its implications, and potential practical applications.
- Provide a complete list of all the sources cited in the case study, following a consistent citation style.
- Include any additional materials or supplementary information, such as interview transcripts, survey instruments, or supporting documents.
By including these key components, a case study becomes a comprehensive and well-rounded exploration of a specific subject, offering valuable insights and contributing to the body of knowledge in the respective field.
Sampling in a Case Study Research
Sampling in case study research involves selecting a subset of cases or individuals from a larger population to study in depth. Unlike quantitative research where random sampling is often employed, case study sampling is typically purposeful and driven by the specific objectives of the study. Here are some key considerations for sampling in case study research:
- Criterion Sampling: Cases are selected based on specific criteria relevant to the research questions. For example, if studying successful business strategies, cases may be selected based on their demonstrated success.
- Maximum Variation Sampling: Cases are chosen to represent a broad range of variations related to key characteristics. This approach helps capture diversity within the sample.
- Selecting Cases with Rich Information: Researchers aim to choose cases that are information-rich and provide insights into the phenomenon under investigation. These cases should offer a depth of detail and variation relevant to the research objectives.
- Single Case vs. Multiple Cases: Decide whether the study will focus on a single case (single-case study) or multiple cases (multiple-case study). The choice depends on the research objectives, the complexity of the phenomenon, and the depth of understanding required.
- Emergent Nature of Sampling: In some case studies, the sampling strategy may evolve as the study progresses. This is known as theoretical sampling, where new cases are selected based on emerging findings and theoretical insights from earlier analysis.
- Data Saturation: Sampling may continue until data saturation is achieved, meaning that collecting additional cases or data does not yield new insights or information. Saturation indicates that the researcher has adequately explored the phenomenon.
- Defining Case Boundaries: Clearly define the boundaries of the case to ensure consistency and avoid ambiguity. Consider what is included and excluded from the case study, and justify these decisions.
- Practical Considerations: Assess the feasibility of accessing the selected cases. Consider factors such as availability, willingness to participate, and the practicality of data collection methods.
- Informed Consent: Obtain informed consent from participants, ensuring that they understand the purpose of the study and the ways in which their information will be used. Protect the confidentiality and anonymity of participants as needed.
- Pilot Testing the Sampling Strategy: Before conducting the full study, consider pilot testing the sampling strategy to identify potential challenges and refine the approach. This can help ensure the effectiveness of the sampling method.
- Transparent Reporting: Clearly document the sampling process in the research methodology section. Provide a rationale for the chosen sampling strategy and discuss any adjustments made during the study.
Sampling in case study research is a critical step that influences the depth and richness of the study’s findings. By carefully selecting cases based on specific criteria and considering the unique characteristics of the phenomenon under investigation, researchers can enhance the relevance and validity of their case study.
Case Study Research Methods With Examples
- Interviews:
- Interviews involve engaging with participants to gather detailed information, opinions, and insights. In a case study, interviews are often semi-structured, allowing flexibility in questioning.
- Example: A case study on workplace culture might involve conducting interviews with employees at different levels to understand their perceptions, experiences, and attitudes.
- Observations:
- Observations entail direct examination and recording of behavior, activities, or events in their natural setting. This method is valuable for understanding behaviors in context.
- Example: A case study investigating customer interactions at a retail store may involve observing and documenting customer behavior, staff interactions, and overall dynamics.
- Document Analysis:
- Document analysis involves reviewing and interpreting written or recorded materials, such as reports, memos, emails, and other relevant documents.
- Example: In a case study on organizational change, researchers may analyze internal documents, such as communication memos or strategic plans, to trace the evolution of the change process.
- Surveys and Questionnaires:
- Surveys and questionnaires collect structured data from a sample of participants. While less common in case studies, they can be used to supplement other methods.
- Example: A case study on the impact of a health intervention might include a survey to gather quantitative data on participants’ health outcomes.
- Focus Groups:
- Focus groups involve a facilitated discussion among a group of participants to explore their perceptions, attitudes, and experiences.
- Example: In a case study on community development, a focus group might be conducted with residents to discuss their views on recent initiatives and their impact.
- Archival Research:
- Archival research involves examining existing records, historical documents, or artifacts to gain insights into a particular phenomenon.
- Example: A case study on the history of a landmark building may involve archival research, exploring construction records, historical photos, and maintenance logs.
- Longitudinal Studies:
- Longitudinal studies involve the collection of data over an extended period to observe changes and developments.
- Example: A case study tracking the career progression of employees in a company may involve longitudinal interviews and document analysis over several years.
- Cross-Case Analysis:
- Cross-case analysis compares and contrasts multiple cases to identify patterns, similarities, and differences.
- Example: A comparative case study of different educational institutions may involve analyzing common challenges and successful strategies across various cases.
- Ethnography:
- Ethnography involves immersive, in-depth exploration within a cultural or social setting to understand the behaviors and perspectives of participants.
- Example: A case study using ethnographic methods might involve spending an extended period within a community to understand its social dynamics and cultural practices.
- Experimental Designs (Rare):
- While less common, experimental designs involve manipulating variables to observe their effects. In case studies, this might be applied in specific contexts.
- Example: A case study exploring the impact of a new teaching method might involve implementing the method in one classroom while comparing it to a traditional method in another.
These case study research methods offer a versatile toolkit for researchers to investigate and gain insights into complex phenomena across various disciplines. The choice of methods depends on the research questions, the nature of the case, and the desired depth of understanding.
Best Practices for a Case Study in 2024
Creating a high-quality case study involves adhering to best practices that ensure rigor, relevance, and credibility. Here are some key best practices for conducting and presenting a case study:
- Clearly articulate the purpose and objectives of the case study. Define the research questions or problems you aim to address, ensuring a focused and purposeful approach.
- Choose a case that aligns with the research objectives and provides the depth and richness needed for the study. Consider the uniqueness of the case and its relevance to the research questions.
- Develop a robust research design that aligns with the nature of the case study (single-case or multiple-case) and integrates appropriate research methods. Ensure the chosen design is suitable for exploring the complexities of the phenomenon.
- Use a variety of data sources to enhance the validity and reliability of the study. Combine methods such as interviews, observations, document analysis, and surveys to provide a comprehensive understanding of the case.
- Clearly document and describe the procedures for data collection to enhance transparency. Include details on participant selection, sampling strategy, and data collection methods to facilitate replication and evaluation.
- Implement measures to ensure the validity and reliability of the data. Triangulate information from different sources to cross-verify findings and strengthen the credibility of the study.
- Clearly define the boundaries of the case to avoid scope creep and maintain focus. Specify what is included and excluded from the study, providing a clear framework for analysis.
- Include perspectives from various stakeholders within the case to capture a holistic view. This might involve interviewing individuals at different organizational levels, customers, or community members, depending on the context.
- Adhere to ethical principles in research, including obtaining informed consent from participants, ensuring confidentiality, and addressing any potential conflicts of interest.
- Conduct a rigorous analysis of the data, using appropriate analytical techniques. Interpret the findings in the context of the research questions, theoretical framework, and relevant literature.
- Offer detailed and rich descriptions of the case, including the context, key events, and participant perspectives. This helps readers understand the intricacies of the case and supports the generalization of findings.
- Communicate findings in a clear and accessible manner. Avoid jargon and technical language that may hinder understanding. Use visuals, such as charts or graphs, to enhance clarity.
- Seek feedback from colleagues or experts in the field through peer review. This helps ensure the rigor and credibility of the case study and provides valuable insights for improvement.
- Connect the case study findings to existing theories or concepts, contributing to the theoretical understanding of the phenomenon. Discuss practical implications and potential applications in relevant contexts.
- Recognize that case study research is often an iterative process. Be open to revisiting and refining research questions, methods, or analysis as the study progresses. Practice reflexivity by acknowledging and addressing potential biases or preconceptions.
By incorporating these best practices, researchers can enhance the quality and impact of their case studies, making valuable contributions to the academic and practical understanding of complex phenomena.
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- Open access
- Published: 27 June 2011
The case study approach
- Sarah Crowe 1 ,
- Kathrin Cresswell 2 ,
- Ann Robertson 2 ,
- Guro Huby 3 ,
- Anthony Avery 1 &
- Aziz Sheikh 2
BMC Medical Research Methodology volume 11 , Article number: 100 ( 2011 ) Cite this article
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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.
Peer Review reports
Introduction
The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.
The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.
This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].
What is a case study?
A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.
Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.
These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].
What are case studies used for?
According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.
Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].
How are case studies conducted?
Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.
Defining the case
Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].
For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.
Selecting the case(s)
The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.
For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.
In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.
The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.
It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.
In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.
Collecting the data
In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].
Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.
In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.
Analysing, interpreting and reporting case studies
Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.
The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].
Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.
When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].
What are the potential pitfalls and how can these be avoided?
The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.
Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].
Conclusions
The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.
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Acknowledgements
We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.
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What Is a Case Study?
Weighing the pros and cons of this method of research
Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Cara Lustik is a fact-checker and copywriter.
Verywell / Colleen Tighe
- Pros and Cons
What Types of Case Studies Are Out There?
Where do you find data for a case study, how do i write a psychology case study.
A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.
The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.
While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.
At a Glance
A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.
What Are the Benefits and Limitations of Case Studies?
A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.
One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:
- Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
- Gives researchers the chance to collect information on why one strategy might be chosen over another
- Permits researchers to develop hypotheses that can be explored in experimental research
On the other hand, a case study can have some drawbacks:
- It cannot necessarily be generalized to the larger population
- Cannot demonstrate cause and effect
- It may not be scientifically rigorous
- It can lead to bias
Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.
It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.
Case Study Examples
There have been a number of notable case studies in the history of psychology. Much of Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:
- Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
- Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
- Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.
Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.
This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.
There are a few different types of case studies that psychologists and other researchers might use:
- Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
- Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
- Explanatory case studies : These are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
- Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
- Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
- Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.
The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.
The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.
There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:
- Archival records : Census records, survey records, and name lists are examples of archival records.
- Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
- Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
- Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
- Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
- Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.
If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.
Here is a general outline of what should be included in a case study.
Section 1: A Case History
This section will have the following structure and content:
Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.
Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.
Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.
Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.
Section 2: Treatment Plan
This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.
- Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
- Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
- Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
- Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.
This section of a case study should also include information about the treatment goals, process, and outcomes.
When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research.
In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?
Need More Tips?
Here are a few additional pointers to keep in mind when formatting your case study:
- Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
- Read examples of case studies to gain an idea about the style and format.
- Remember to use APA format when citing references .
Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011;11:100.
Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100
Gagnon, Yves-Chantal. The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.
Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Research Writing and Analysis
- NVivo Group and Study Sessions
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Writing a Case Study
What is a case study?
A Case study is:
- An in-depth research design that primarily uses a qualitative methodology but sometimes includes quantitative methodology.
- Used to examine an identifiable problem confirmed through research.
- Used to investigate an individual, group of people, organization, or event.
- Used to mostly answer "how" and "why" questions.
What are the different types of case studies?
Descriptive | This type of case study allows the researcher to: | How has the implementation and use of the instructional coaching intervention for elementary teachers impacted students’ attitudes toward reading? |
Explanatory | This type of case study allows the researcher to: | Why do differences exist when implementing the same online reading curriculum in three elementary classrooms? |
Exploratory | This type of case study allows the researcher to:
| What are potential barriers to student’s reading success when middle school teachers implement the Ready Reader curriculum online? |
Multiple Case Studies or Collective Case Study | This type of case study allows the researcher to: | How are individual school districts addressing student engagement in an online classroom? |
Intrinsic | This type of case study allows the researcher to: | How does a student’s familial background influence a teacher’s ability to provide meaningful instruction? |
Instrumental | This type of case study allows the researcher to: | How a rural school district’s integration of a reward system maximized student engagement? |
Note: These are the primary case studies. As you continue to research and learn
about case studies you will begin to find a robust list of different types.
Who are your case study participants?
|
This type of study is implemented to understand an individual by developing a detailed explanation of the individual’s lived experiences or perceptions.
|
| This type of study is implemented to explore a particular group of people’s perceptions. |
| This type of study is implemented to explore the perspectives of people who work for or had interaction with a specific organization or company. |
| This type of study is implemented to explore participant’s perceptions of an event. |
What is triangulation ?
Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.
How to write a Case Study?
When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.
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The case study approach
Sarah crowe.
1 Division of Primary Care, The University of Nottingham, Nottingham, UK
Kathrin Cresswell
2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK
Ann Robertson
3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK
Anthony Avery
Aziz sheikh.
The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.
Introduction
The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.
The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.
This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables Tables1, 1 , ,2, 2 , ,3 3 and and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].
Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]
Minority ethnic people experience considerably greater morbidity from asthma than the White majority population. Research has shown however that these minority ethnic populations are likely to be under-represented in research undertaken in the UK; there is comparatively less marginalisation in the US. |
To investigate approaches to bolster recruitment of South Asians into UK asthma studies through qualitative research with US and UK researchers, and UK community leaders. |
Single intrinsic case study |
Centred on the issue of recruitment of South Asian people with asthma. |
In-depth interviews were conducted with asthma researchers from the UK and US. A supplementary questionnaire was also provided to researchers. |
Framework approach. |
Barriers to ethnic minority recruitment were found to centre around: |
1. The attitudes of the researchers' towards inclusion: The majority of UK researchers interviewed were generally supportive of the idea of recruiting ethnically diverse participants but expressed major concerns about the practicalities of achieving this; in contrast, the US researchers appeared much more committed to the policy of inclusion. |
2. Stereotypes and prejudices: We found that some of the UK researchers' perceptions of ethnic minorities may have influenced their decisions on whether to approach individuals from particular ethnic groups. These stereotypes centred on issues to do with, amongst others, language barriers and lack of altruism. |
3. Demographic, political and socioeconomic contexts of the two countries: Researchers suggested that the demographic profile of ethnic minorities, their political engagement and the different configuration of the health services in the UK and the US may have contributed to differential rates. |
4. Above all, however, it appeared that the overriding importance of the US National Institute of Health's policy to mandate the inclusion of minority ethnic people (and women) had a major impact on shaping the attitudes and in turn the experiences of US researchers'; the absence of any similar mandate in the UK meant that UK-based researchers had not been forced to challenge their existing practices and they were hence unable to overcome any stereotypical/prejudicial attitudes through experiential learning. |
Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]
Health work forces globally are needing to reorganise and reconfigure in order to meet the challenges posed by the increased numbers of people living with long-term conditions in an efficient and sustainable manner. Through studying the introduction of General Practitioners with a Special Interest in respiratory disorders, this study aimed to provide insights into this important issue by focusing on community respiratory service development. |
To understand and compare the process of workforce change in respiratory services and the impact on patient experience (specifically in relation to the role of general practitioners with special interests) in a theoretically selected sample of Primary Care Organisations (PCOs), in order to derive models of good practice in planning and the implementation of a broad range of workforce issues. |
Multiple-case design of respiratory services in health regions in England and Wales. |
Four PCOs. |
Face-to-face and telephone interviews, e-mail discussions, local documents, patient diaries, news items identified from local and national websites, national workshop. |
Reading, coding and comparison progressed iteratively. |
1. In the screening phase of this study (which involved semi-structured telephone interviews with the person responsible for driving the reconfiguration of respiratory services in 30 PCOs), the barriers of financial deficit, organisational uncertainty, disengaged clinicians and contradictory policies proved insurmountable for many PCOs to developing sustainable services. A key rationale for PCO re-organisation in 2006 was to strengthen their commissioning function and those of clinicians through Practice-Based Commissioning. However, the turbulence, which surrounded reorganisation was found to have the opposite desired effect. |
2. Implementing workforce reconfiguration was strongly influenced by the negotiation and contest among local clinicians and managers about "ownership" of work and income. |
3. Despite the intention to make the commissioning system more transparent, personal relationships based on common professional interests, past work history, friendships and collegiality, remained as key drivers for sustainable innovation in service development. |
It was only possible to undertake in-depth work in a selective number of PCOs and, even within these selected PCOs, it was not possible to interview all informants of potential interest and/or obtain all relevant documents. This work was conducted in the early stages of a major NHS reorganisation in England and Wales and thus, events are likely to have continued to evolve beyond the study period; we therefore cannot claim to have seen any of the stories through to their conclusion. |
Example of a case study investigating the introduction of the electronic health records[ 5 ]
Healthcare systems globally are moving from paper-based record systems to electronic health record systems. In 2002, the NHS in England embarked on the most ambitious and expensive IT-based transformation in healthcare in history seeking to introduce electronic health records into all hospitals in England by 2010. |
To describe and evaluate the implementation and adoption of detailed electronic health records in secondary care in England and thereby provide formative feedback for local and national rollout of the NHS Care Records Service. |
A mixed methods, longitudinal, multi-site, socio-technical collective case study. |
Five NHS acute hospital and mental health Trusts that have been the focus of early implementation efforts. |
Semi-structured interviews, documentary data and field notes, observations and quantitative data. |
Qualitative data were analysed thematically using a socio-technical coding matrix, combined with additional themes that emerged from the data. |
1. Hospital electronic health record systems have developed and been implemented far more slowly than was originally envisioned. |
2. The top-down, government-led standardised approach needed to evolve to admit more variation and greater local choice for hospitals in order to support local service delivery. |
3. A range of adverse consequences were associated with the centrally negotiated contracts, which excluded the hospitals in question. |
4. The unrealistic, politically driven, timeline (implementation over 10 years) was found to be a major source of frustration for developers, implementers and healthcare managers and professionals alike. |
We were unable to access details of the contracts between government departments and the Local Service Providers responsible for delivering and implementing the software systems. This, in turn, made it difficult to develop a holistic understanding of some key issues impacting on the overall slow roll-out of the NHS Care Record Service. Early adopters may also have differed in important ways from NHS hospitals that planned to join the National Programme for Information Technology and implement the NHS Care Records Service at a later point in time. |
Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]
There is a need to reduce the disease burden associated with iatrogenic harm and considering that healthcare education represents perhaps the most sustained patient safety initiative ever undertaken, it is important to develop a better appreciation of the ways in which undergraduate and newly qualified professionals receive and make sense of the education they receive. | |
---|---|
To investigate the formal and informal ways pre-registration students from a range of healthcare professions (medicine, nursing, physiotherapy and pharmacy) learn about patient safety in order to become safe practitioners. | |
Multi-site, mixed method collective case study. | |
: Eight case studies (two for each professional group) were carried out in educational provider sites considering different programmes, practice environments and models of teaching and learning. | |
Structured in phases relevant to the three knowledge contexts: | |
Documentary evidence (including undergraduate curricula, handbooks and module outlines), complemented with a range of views (from course leads, tutors and students) and observations in a range of academic settings. | |
Policy and management views of patient safety and influences on patient safety education and practice. NHS policies included, for example, implementation of the National Patient Safety Agency's , which encourages organisations to develop an organisational safety culture in which staff members feel comfortable identifying dangers and reporting hazards. | |
The cultures to which students are exposed i.e. patient safety in relation to day-to-day working. NHS initiatives included, for example, a hand washing initiative or introduction of infection control measures. | |
1. Practical, informal, learning opportunities were valued by students. On the whole, however, students were not exposed to nor engaged with important NHS initiatives such as risk management activities and incident reporting schemes. | |
2. NHS policy appeared to have been taken seriously by course leaders. Patient safety materials were incorporated into both formal and informal curricula, albeit largely implicit rather than explicit. | |
3. Resource issues and peer pressure were found to influence safe practice. Variations were also found to exist in students' experiences and the quality of the supervision available. | |
The curriculum and organisational documents collected differed between sites, which possibly reflected gatekeeper influences at each site. The recruitment of participants for focus group discussions proved difficult, so interviews or paired discussions were used as a substitute. |
What is a case study?
A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.
Definitions of a case study
Author | Definition |
---|---|
Stake[ ] | (p.237) |
Yin[ , , ] | (Yin 1999 p. 1211, Yin 1994 p. 13) |
• | |
• (Yin 2009 p18) | |
Miles and Huberman[ ] | (p. 25) |
Green and Thorogood[ ] | (p. 284) |
George and Bennett[ ] | (p. 17)" |
Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.
These are however not necessarily mutually exclusive categories. In the first of our examples (Table (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables Tables2, 2 , ,3 3 and and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].
What are case studies used for?
According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables Tables2 2 and and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.
Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].
Example of epistemological approaches that may be used in case study research
Approach | Characteristics | Criticisms | Key references |
---|---|---|---|
Involves questioning one's own assumptions taking into account the wider political and social environment. | It can possibly neglect other factors by focussing only on power relationships and may give the researcher a position that is too privileged. | Howcroft and Trauth[ ] Blakie[ ] Doolin[ , ] | |
Interprets the limiting conditions in relation to power and control that are thought to influence behaviour. | Bloomfield and Best[ ] | ||
Involves understanding meanings/contexts and processes as perceived from different perspectives, trying to understand individual and shared social meanings. Focus is on theory building. | Often difficult to explain unintended consequences and for neglecting surrounding historical contexts | Stake[ ] Doolin[ ] | |
Involves establishing which variables one wishes to study in advance and seeing whether they fit in with the findings. Focus is often on testing and refining theory on the basis of case study findings. | It does not take into account the role of the researcher in influencing findings. | Yin[ , , ] Shanks and Parr[ ] |
How are case studies conducted?
Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.
Defining the case
Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].
Example of a checklist for rating a case study proposal[ 8 ]
Clarity: Does the proposal read well? | |
Integrity: Do its pieces fit together? | |
Attractiveness: Does it pique the reader's interest? | |
The case: Is the case adequately defined? | |
The issues: Are major research questions identified? | |
Data Resource: Are sufficient data sources identified? | |
Case Selection: Is the selection plan reasonable? | |
Data Gathering: Are data-gathering activities outlined? | |
Validation: Is the need and opportunity for triangulation indicated? | |
Access: Are arrangements for start-up anticipated? | |
Confidentiality: Is there sensitivity to the protection of people? | |
Cost: Are time and resource estimates reasonable? |
For example, in our evaluation of the introduction of electronic health records in English hospitals (Table (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.
Selecting the case(s)
The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.
For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.
In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.
The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.
It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.
In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.
Collecting the data
In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table (Table2 2 )[ 4 ].
Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.
In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.
Analysing, interpreting and reporting case studies
Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.
The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table (Table4 4 )[ 6 ].
Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.
When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].
What are the potential pitfalls and how can these be avoided?
The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.
Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table (Table9 9 )[ 8 ].
Potential pitfalls and mitigating actions when undertaking case study research
Potential pitfall | Mitigating action |
---|---|
Selecting/conceptualising the wrong case(s) resulting in lack of theoretical generalisations | Developing in-depth knowledge of theoretical and empirical literature, justifying choices made |
Collecting large volumes of data that are not relevant to the case or too little to be of any value | Focus data collection in line with research questions, whilst being flexible and allowing different paths to be explored |
Defining/bounding the case | Focus on related components (either by time and/or space), be clear what is outside the scope of the case |
Lack of rigour | Triangulation, respondent validation, the use of theoretical sampling, transparency throughout the research process |
Ethical issues | Anonymise appropriately as cases are often easily identifiable to insiders, informed consent of participants |
Integration with theoretical framework | Allow for unexpected issues to emerge and do not force fit, test out preliminary explanations, be clear about epistemological positions in advance |
Stake's checklist for assessing the quality of a case study report[ 8 ]
1. Is this report easy to read? |
2. Does it fit together, each sentence contributing to the whole? |
3. Does this report have a conceptual structure (i.e. themes or issues)? |
4. Are its issues developed in a series and scholarly way? |
5. Is the case adequately defined? |
6. Is there a sense of story to the presentation? |
7. Is the reader provided some vicarious experience? |
8. Have quotations been used effectively? |
9. Are headings, figures, artefacts, appendices, indexes effectively used? |
10. Was it edited well, then again with a last minute polish? |
11. Has the writer made sound assertions, neither over- or under-interpreting? |
12. Has adequate attention been paid to various contexts? |
13. Were sufficient raw data presented? |
14. Were data sources well chosen and in sufficient number? |
15. Do observations and interpretations appear to have been triangulated? |
16. Is the role and point of view of the researcher nicely apparent? |
17. Is the nature of the intended audience apparent? |
18. Is empathy shown for all sides? |
19. Are personal intentions examined? |
20. Does it appear individuals were put at risk? |
Conclusions
The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.
Pre-publication history
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1471-2288/11/100/prepub
Acknowledgements
We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.
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- Open access
- Published: 14 October 2024
Private hospitals in low- and middle-income countries: a typology using the cluster method, the case of Morocco
- Saad Zbiri ORCID: orcid.org/0000-0001-7059-0577 1 , 2 , 3 , 4 ,
- Abdelali Belghiti Alaoui 1 , 3 ,
- Imad El Badisy 1 , 2 , 5 ,
- Najib Diouri 6 ,
- Sanaa Belabbes 1 , 2 , 3 ,
- Radouane Belouali 1 , 2 , 3 &
- Zakaria Belrhiti ORCID: orcid.org/0000-0002-0115-682X 1 , 2 , 3
BMC Health Services Research volume 24 , Article number: 1231 ( 2024 ) Cite this article
Metrics details
The private healthcare sector has become an essential component of healthcare systems globally. This interest has increased with the universal health coverage agenda. However, in most low- and middle-income countries, few classificatory studies of the private hospital sector were carried out.
This study describes the private hospital sector in a developing country setup and propose a typology that could facilitate the identification of its categories and the understanding of its organizational and strategic characteristics.
All private hospitals in Morocco as of December 31, 2021 including 397 facilities are included. Most hospitals are for-profit, poly-disciplinary, independent, commercial societies, have fewer than 30 beds or between 30 and 99 beds and are located in urban areas. Private hospitals have a median turnover of 9.8 million MAD and a median capital value of 2 million MAD. The clustering method identifies three main categories of private hospitals: for-profit hospitals with medium size and turnover, spread across the country but with a high concentration in large regions; not-for-profit hospitals, with medium to large size, high turnover, located in large regions and including university hospitals; and small for-profit hospitals with low turnover, independent ownership and wide distribution over the country. Three criteria have the most significant discriminatory power: ownership, size (beds, turnover) and mode of governance.
Conclusions
Private hospitals in Morocco are organized into three types according to three similarity criteria including ownership, size and governance. These criteria might be used as the basis for a common typology of private hospitals in Morocco and possibly in other low- and middle-income countries with similar contexts.
Peer Review reports
Academic interest in the private healthcare sector dates back to the 1960s with the emergence of private for-profit hospital companies in the United States heralding the birth of the hospital industry. This birth occurred during a period of rapid growth in health expenditure and allowed the creation of a new competitive environment allowing hospitals to develop independently from health policies [ 1 ]. To segment this new market, a first categorization of hospitals was imposed through the identification of three types of establishments in the private hospital sector depending on the ownership of the hospitals: not-for-profit hospitals, independent hospitals and investor-owned hospitals. This development also enabled hospital activity to establish itself as an industrial category in the United Nations’ international classification of economic activities (ISIC rev.3) from 1989 and subsequently in other industrial classifications.
In developing countries, the dynamics of healthcare demand and supply have led to a growing need for private healthcare facilities. The area where private sector involvement held the greatest potential was in hospital care [ 2 ]. More recently, healthcare reforms in various countries have sought to increase the role of the private healthcare sector to complete public sector activities. The general argument was that these reforms could maintain equity in healthcare financing while promoting efficiency by introducing and encouraging competition. Today, the private sector is increasingly serving as a partner to public health systems, particularly in the provision of clinical care [ 3 ].
Yet, mixed evidence still exists on the relationship between hospital ownership (private not- for-profit, for-profit and public) and the variable effects on hospital performance and quality of care, as well as on out-of-pocket payments. These variations are largely explained by differences in institutional context, including differences between markets, regions, data sources and over time [ 4 , 5 , 6 ].
An increasing interest has been paid by international organizations in redefining the role of the private sector in health reforms [ 1 ] and in progressing towards achieving public health objectives [ 7 ]. In the same line, the WHO proposed a definition and an initial global typology of the private healthcare sector in 2005 [ 7 ]. Recently, this growing interest increased with the advent of universal health coverage (UHC) which led to considering the private sector as lever for health systems strengthening in all low and middle-income countries (LMICs) [ 8 , 9 ].
Currently, the private sector provides almost 40% of all health care in the Latin American, African and Western Pacific regions, 57% in the South-East Asian region and 62% in the Eastern Mediterranean region. Weighted regional results indicate that 53% of hospital care is provided in the private for-profit sector [ 10 ]. In OECD countries, private hospitals, both for-profit and not-for-profit, have gained significant shares of the healthcare market. They now represent 77% of hospitals in the United States, 75% in Germany, 56% in Spain, 55% in France and 48% in Australia [ 11 ].
However, there are very few descriptions of the profile of private hospitals in LMICs. In the Philippines, Lavado et al. were able to cover nearly all aspects of hospital organization. But their study only focused on four regions [ 12 ]. To our knowledge, this is the only study identified in LMICs and that covers so many variables. This can be explained by the fact that in the Philippines the private hospital sector is highly developed. In addition, very few studies have been carried out using clustering methods applied to private hospitals in LMICs. Most of these studies do not use national databases and do not offer a typology [ 13 ]. Some studies have used hospital clustering methods based on the characteristics of health professionals and not on the characteristics of hospitals [ 14 ]. Others studies have attempted a typology focusing on the entire private sector and not just on private hospitals [ 15 ].
Thus, through its evolution, the private hospital sector become an important component of the healthcare system and of the reform dynamics. Despite this importance, the classification and typology of private hospitals have received little attention from policy makers and researchers. This study argues that it is not possible to adequately approach the analysis and development of the private sector without an empirical understanding of its overall organization and characteristics. In this context, and in order to advance the understanding and documentation of the private hospital sector, we propose the development of a typology to better describe and understand the private hospital market. This typology could thus facilitate the identification of categories of private hospitals sharing common organizational and strategic characteristics, which could be used to analyse the dynamics of change in this sector. The relevance of this typology is tested through its application for the documentation of the private hospital sector in Morocco.
Study context
The Moroccan health system is organized into several interconnected healthcare networks, combining a dense public healthcare network in 12 decentralized health regions, and an expanding private healthcare sector, located mainly in major urban areas, particularly on the Casablanca-Rabat axis. In 2021, the public sector comprises 2985 primary healthcare centers providing supportive, preventive and curative care [ 16 ]. Secondary and tertiary care are provided by a pyramid-shaped public hospital network including 165 local, regional and inter-regional hospitals (as well as 157 support facilities) accounting for a total of 26 771 beds and 13 682 medical practitioners. The public sector has a ratio of 1375 inhabitants per hospital bed. The private sector has over 12 534 beds and 14 199 medical practitioners [ 17 ]. Most private physicians are self-employed but some physicians are public-sector employees and are practicing in private hospitals (part-time practice or dual practice). The capital of private hospitals is funded by independent actors (mostly physicians) (86%), national and international financial holdings (8%), health insurance organizations (3,3%) and non-profit foundations (2,6%) [ 18 ].
Delimitation of the study object
The overall definition of what constitutes a “hospital” is similar in all countries, with specific characteristics inherent to each country’s history. The word “hospital” currently covers a wide range of institutions, from small rural facilities to large university clinics, from small community centers to giant complexes with multiple head offices and large numbers of employees [ 19 ]. In the United Nations classification of all economic activities (ISIC rev.4), hospitals are understood as “ human health institutions which have accommodation facilities and which engage in providing diagnostic and medical treatment to inpatients with any of a wide variety of medical conditions ” [ 20 ]. In the OECD classification of systems of health accounts, hospitals are defined as “ licensed establishments that are primarily engaged in providing medical , diagnostic and treatment services that include physician , nursing and other health services , to inpatients and the specialized accommodation services required by inpatients ” [ 21 ]. In some countries, health facilities must additionally have a minimum size, such as a number of beds and medical staff to ensure 24-hour access, in order to be registered as a hospital. Currently, the private hospital sector comes in many shapes and sizes, ranging from small physician-owned facilities to large, publicly traded, for-profit hospital chains [ 22 ].
In a similar vein, private hospitals are, according to the global health observatory of the WHO, “ hospitals not owned by government or to parastatal organizations ”. They include both private not-for-profit hospitals and private for-profit hospitals [ 10 ]. Thus, a private hospital is a non-state-owned health facility authorized to provide specialized inpatient and/or outpatient healthcare. It is sometimes called a private clinic (Morocco, Tunisia) or a private health establishment (France). In this work, these names will be used interchangeably.
Framework development
The rationale behind developing a typology of healthcare facilities is to allow for continuous refinement of actors, understanding and analysis of the healthcare system and systematic organizational analysis. It also facilitates comparison by referring to the same categorization criteria, and can be used as a regulatory framework. Typologies are thus a useful and widely used tool in understanding and describing health organizations. Thereby, the use of typologies makes it possible to reduce the complexity of “messy” empirical realities, allowing it to systematically analyse similarities and variations and to detect and interpret patterns [ 23 ].
To develop a framework to categorize private hospitals, we adopted an empirically-driven approach rather than a theoretically-driven approach. The empirical approach is not based on a theoretical pre-selection of relevant indicators to deduce ideal types but rather on empirical data [ 24 ]. We built our framework primarily on international classifications governing the hospital market as a health industry (United Nations, OECD, WHO, Research and Markets) (see Additional file 1 for more details), discussed and supplemented by empirical policy documents based on country experiences (Morocco, Tunisia, France, United States) (see Additional file 2 for more details). Therefore, the resulting typology corresponds to a real categorization (clusters) and not to a theoretical modelling. We adopted a single two-step approach, in which the cluster analysis followed the descriptive analysis and literature review. The steps of the typology building process are described in Fig. 1 .
Flow diagram of the typology building process
In this context, our narrative review of international classifications enabled us to identify nine criteria with 15 variables for the categorization of private hospitals (Table 1 ).
Collection of data
We collected data in Morocco during the year 2021 using two main databases:
The database of the Ministry of Health relating to the supply of care [ 25 ].
The private database of Inforisk SA. specialized in legal and financial information on companies. It is the richest and most up-to-date database on Moroccan companies. Its platform Charika.ma provides access to legal and financial information on more than 760 000 Moroccan companies and more than 500 million companies in 230 countries around the world.
These two databases have been cross-referenced, cleaned up and completed by collecting updated information through direct contact with private hospital key stakeholders. These collected data were the subject of a two-stage analysis. First, a descriptive statistical analysis to define the profiles of private hospitals in Morocco was based on seven criteria and ten variables, as shown in Table 1 , including hospital ownership, specialisation, number of beds, type of governance, declared turnover, capital, legal form, university status, population covered and region. Second, a cluster analysis was performed to identify a private hospital typology.
Cluster analysis
Factor, cluster and discriminant analyses are used to analyse data collected from private hospitals for the year 2021. Private hospital data were analysed using an unsupervised clustering approach, where nine variables with seven criteria were used to construct the clusters. These variables cover the main characteristics of private hospitals. The other two criteria including membership and performance, as well as six variables were not analysed, as the corresponding information is not available in the majority of private hospitals.
The method used to construct the clusters is a mixed method based on the combination of the k-means algorithm [ 26 ] and Gower’s distance measure [ 27 , 28 ].
In practice, a dissimilarity matrix between observations is first calculated. Then, this matrix is used as input to the k-means algorithm.
The k-means method aims to separate data points with different characteristics into different clusters and group data points with similar characteristics together. It is an iterative algorithm that, regardless of its starting point, converges to a solution. For each starting point, a different solution can be found. The calculations are repeated several times to select the best solution for the chosen criterion.
Iteratively, a starting point is chosen for the first iteration, which consists of matching observations with the centroids of the k clusters. After determining the distance of the observations from the k centroids, they are assigned to the closest one. The observations are then redistributed according to their distance from the new centroids, and so on, until convergence is achieved.
The optimal number of clusters can be determined using a variety of techniques. In our study, we used the elbow method [ 29 ]. It uses sum-of-square to evaluate the goodness of split. Then, an elbow plot of sum-of-square for k values ranging from two to N is created. As a rule, when k increases, the corresponding sum-of-square will decrease. A trade-off between k (i.e. number of clusters) and sum-of-square can be observed. Typically, the optimal value of k starts to flatten out and forms an elbow shape.
All the analysis was carried out using the R software [ 30 ].
Ethical and regulatory considerations
As the study is retrospective, based on anonymized data and purely observational, it was exempt from institutional review board approval according to the Moroccan legislation (law number 28 − 13) [ 31 ].
A total of 397 private hospitals are considered in this study and are thus included in the clustering algorithm. They represent all private hospitals in Morocco, called private clinics, as of December 31, 2021. “Establishments assimilated” to private clinics, such as dialysis centers and medical radiology centers, are not taken into consideration in this study because they do not correspond to our definition of a private hospital.
Private hospital characteristics
The distribution of the characteristics of the study hospitals are presented in Table 2 . Regarding hospital ownership, the majority (93.2%) are for-profit hospitals and 6.8% are not-for-profit hospitals. However, the latter represent 17.3% of the total bed capacity of private hospitals due to the importance of their size. 18.6% of the study hospitals are mono-disciplinary while 81.4% are poly-disciplinary. 50.4% of private hospitals have less than 30 beds and barely 5.8% have 100 beds or more. In terms of geographical distribution, among the 12 regions in Morocco, 5 regions include 79.3% of private hospitals (Casablanca-Settat, Rabat-Sale-Kenitra, Marrakech-Safi, Fes-Meknes and Tanger-Tetouan-Al Hoceima). The Casablanca-Settat region alone includes 30% of private hospitals and 40% of their beds, at a time when two regions have none. Also, the majority of private hospitals are located in urban areas (89.4% versus 10.6%). In terms of governance, the majority (86.6%) are independent hospitals while 7.3%, 2.8% and 3.3% are corporate group, health network and under tutelage private hospitals, respectively. The turnover of the study hospitals ranges from 6.5 thousand to 178 million Moroccan dirhams (MAD), with a median of 9.8 million MAD and a mean of 16 million MAD. Regarding the value of capital, nearly 55% of private hospitals report it among their data. It varies from 10 thousand to 775 million MAD with an average of 8.3 million and a median of 2 million MAD. The range of variation in turnover and capital is too wide between private hospitals. This shows that the declaration of these values is most likely not well regulated. Finally, regarding their legal form, 92.2% of hospitals are commercial societies (SA, SARL, SARLAU or SNC), 1% are civil societies (SCP), 3.5% are non-governmental organizations (NGOs) or foundations and 3.3% have other legal forms.
Number of clusters
One of the most difficult problems in any applied cluster analysis is determining the number of clusters in which to group the data. Our clustering separation metrics recommend three as the number of clusters that convey most information. This is consistent with the aspect of the elbow plot, which shows that the additional contribution to the variance explained is smaller after three clusters (Fig. 2 ). Following the elbow plot and the majority rule, the number of clusters was set to three.
Cluster separation according to the number of groups in the clustering algorithm
Private hospital categories
The hospital profiles identified by the k-means algorithm are presented in Table 3 . The hospitals included in each cluster have different patterns.
Cluster 1 consists of 169 private hospitals. All hospitals are for-profit and most of them are poly-disciplinary (91.7%). The number of beds is average with 93.5% hospitals having between 30 and 99 beds, and only 6.5% having 100 beds or more. In terms of governance, hospitals are mainly independent (88.8%) and 11.2% are part of corporate groups. The turnover of the hospitals ranges from 492 thousand to 87.7 million MAD, with a mean of 19.2 million MAD and a median of 14.5 million MAD. The most common legal form is commercial society (99.4%) while only one hospital is a civil society owned hospital. All hospitals are non-teaching centers and the majority of them are located in the urban area (94.7%). Hospitals are located in the region of Casablanca-Settat (37.3%), the region of Rabat-Sale-Kenitra (16%), the region of Fes-Meknes (11.2%), the region of Oriental (9.5%), the region of Marrakech-Safi (8.9%), the region of Tanger-Tetouan-Al Hoceima (6.5%), the region of Souss-Massa (6.5%), the region of Beni Mellal-Khenifra (3%) and the region of Draa-Tafilalet (1.2%).
Cluster 2 is the smallest cluster including 32 private hospitals. The majority of hospitals are not-for-profit (84.4%) and poly-disciplinary (87.5%). The number of beds is medium to high with 37.3% of hospitals having 100 beds or more, 50% having between 30 and 99 beds and only 12.5% having less than 30 beds. In terms of governance, most hospitals are under tutelage (40.6%) or in network (34.4%) and only few hospitals are independent (12.5%) or part of corporate groups (12.5%). Hospital turnover is high and ranges from 2.9 to 178 million MAD, with a mean of 54.5 million MAD and a median of 37.6 million MAD. In terms of hospital legal form, most hospitals are NGOs or foundations (43.8%) or had other forms (40.6%), few hospitals are commercial societies (15.6%) and none is a civil society. 12.5% of hospitals are teaching centers. Most hospitals are in the urban area (81.3%) and mainly concentrated in the region of Casablanca-Settat (43.8%), followed by the region of Tanger-Tetouan-Al Hoceima (21.9%), the region of Marrakech-Safi (12.5%) and the region of Rabat-Sale-Kenitra (9.4%).
Cluster 3 is the largest cluster with 196 private hospitals. All hospitals are for-profit and mono-disciplinary (28.6%) or poly-disciplinary (71.4%). The number of beds is low with all hospitals having less than 30 beds. Regarding governance, most hospitals are independent (96.9%) and only 3.1% are part of corporate groups. The turnover of the hospitals is low and ranges from 6.5 thousand to 55.9 million MAD, with a mean of 7 million MAD and a median of 7.5 million MAD. The most common legal form is commercial society (98.5%) and only few hospitals are civil societies (1.5%). All hospitals are non-teaching centers. 86.2% of hospitals serve a predominantly urban population and 13.8% serve a predominantly rural population. Hospitals are well distributed over the national territory with 21.4% in the region of Casablanca-Settat, 19.4% in the region of Rabat-Sale-Kenitra, 12.8% in the region of Marrakech-Safi, 11.2% in the region of Fes-Meknes, 11.2% in the region of Tanger-Tetouan-Al Hoceima, 10.2% in the region of Beni Mellal-Khenifra, 9.2% in the region of Souss-Massa, 3.1% in the region of Oriental, 1% in the region of Draa-Tafilalet and 0.5% in the region of Guelmim-Oued Noun.
Therefore, three categories of private hospitals stand out. A first type which groups together for-profit hospitals, rather poly-disciplinary and independent, mainly commercial companies, with medium size and turnover. They are spread over the whole territory, but with a high concentration in large regions. A second type of private hospitals, which is mainly made up of non-profit hospitals, rather poly-disciplinary, in network or under tutelage, with medium to large size and high turnover. They are concentrated in large regions and may be university hospitals. A third type of hospitals is a group of private for-profit hospitals, rather independent, with small size and low turnover and well distributed over the territory.
Thus, the Table 3 shows that the variables which have the most significant discriminatory power in this segmentation are: the property, the number of beds, the turnover and the governance. The characteristics relating to the other variables studied are practically found in the three clusters. These four discriminating variables refer to three criteria (Table 1 ): the ownership, size of hospitals (beds and turnover) and governance. These criteria can be used for the classification of private hospitals in LMICs which often do not have large databases of private health facilities.
Sensitivity analysis
Figure 3 displays the distribution of hospitals in the three clusters. Observations (i.e. hospitals) are represented by points and variables have been transformed into two dimensions using principal component analysis. This representation shows the distribution of observations in the three clusters, represented on a Euclidean space. This sensitivity check confirms that the three private hospital clusters are distinct. We can thus consider that our clustering algorithm used is robust.
Distribution of observations in the three clusters
The private health sector plays an important and growing role in the healthcare systems of the LMICs. But significant gaps remain about its characteristics and development. Due to various demographic and epidemiological changes, the public sector has been overwhelmed with the demand for healthcare services, especially services provided by hospitals. This forced changes in health markets, which led to significant increases in private sector participation in healthcare delivery [ 3 ]. The international community, including the WHO, lacks a common framework for analysing the private health sector. It also lacks an evidence base from which to develop guidance on the types of services and activities where it might have a role or comparative advantage in strengthening health systems [ 9 ]. Whatever approach is taken to overcoming this obstacle, it must begin by understanding the rules that govern the organization and functioning of the private care sector.
Our study focuses on private hospitals, which are health facilities that contain a lot of equipment and consume a lot of resources, and whose use is costly for users and for the UHC. In Morocco, there are 397 private hospitals with 14 502 beds in 2021. They thus represent 70.6% of all hospitals (public and private) and 35.2% of the national capacity in hospital beds (excluding military hospitals). In addition, the private hospital sector is the preferred recourse for beneficiaries of compulsory health insurance [ 32 ]. Since 2022, this health insurance has been generalized to the entire Moroccan population. This makes knowledge of the private hospital sector useful for achieving universal access to healthcare and for regulating health insurance.
Almost all private hospital classifications have a legal rather than an empirical basis. To understand the organization and real characteristics of the private hospital sector in Morocco, we adopted a two-step approach in our study: (i) a descriptive approach based on a statistical analysis including ten variables identified through a review of the literature on hospital classification; and (ii) an unsupervised learning approach based on clustering algorithm (k-means) to identify hidden patterns or groupings in our empirical data.
The first stage of analysis is the most common in the literature. It allowed us to better define the profile of private hospitals since it was based on ten variables, while the private hospital data collected by the Ministry of Health is based on only three variables: the ownership, the number of beds and the geographical site. Compared to Morocco, Tunisia has the same basis for describing the private hospital sector, with an additional criterion relating to specialization [ 33 ]. Due to lack of data, our descriptive analysis of private hospitals did not cover variables relating to human resources, medical technology and use of services, as is the case in the OECD database (OECD.stat) considered among the most important health databases [ 11 ]. Despite its importance, OECD.stat remains a general health database. Indeed, compared to our typology, the OECD database does not include key variables such as turnover, capital, governance and ownership, which are essential to describe the profile of private hospitals. This means that our analysis is more specific to the private hospital sector than that of the OECD. As for the WHO, its global health observatory has no data on private hospitals and its description of the hospital sector is limited to the overall density of beds [ 10 ]. Thus, the results of our descriptive analysis provide a better understanding of the profile of private hospitals and their differential characteristics compared to public hospitals. They help identify gaps in healthcare coverage and guide healthcare planning. This better description of hospital profiles also makes it possible to draw cross-national comparisons. However, despite its importance, the private hospital sector is not considered in the analysis of healthcare systems at the global level.
According to the second stage of analysis, it was a question of building a typology by using a grouping algorithm based on our empirical data covering the entire national territory. The clustering method showed that it is possible to build a solid segmentation with only three criteria of similarity: ownership, hospital size (beds and turnover) and governance. Hospital size related criterion can be enriched by the use of data on staff, technology and on patients when they exist. In our study, we identified three main types of private hospitals. The first type is made up of for-profit, independent or group hospitals, which are medium in size and turnover. The second type is made up of large private, not-for-profit, under tutelage or networked hospitals with high turnover. The third type is made up of small, for-profit, independent, low-turnover hospitals.
In high-income countries, hospital clustering aimed to analyse trends in hospital organization (vertical and horizontal integration) [ 34 ]. Some countries have even applied it to the public hospital sector [ 35 ]. Some high-income countries categorize private hospitals according to their function (general or specialist care; acute or long-term) or according to their legal status or ownership, such as in the United States, France or Germany. In the United States, due to the development of hospital groups, hospital segmentation is organized into systems and networks to inform health policy and practice, as well as research on the performance of different health groups and clusters [ 36 ]. In Morocco, the creation of hospital groups is a relatively recent phenomenon. Although it concerns 41 private hospitals, it did not impose itself as a cluster whatever the number k of groups explored with k-means algorithm.
Thus, this single two-step approach to analyse the private hospital sector, adopted by our study, appear to be comprehensive. The descriptive approach makes it possible to define the private hospital general profile based on specific variables identified and tested. As for the unsupervised learning approach, it makes it possible to build clusters of hospitals on the basis of attributes of similarities identified in a more objective way by clustering algorithm. This enables to systematically analyse similarities and variation as well as detect and interpret patterns for a better understanding of the dynamics of this sector which is still poorly understood in LMICs.
Our findings may have several implications for public authorities. This study provides valuable insights into the private hospital market in Morocco, which could be used to inform policy and decision-making in the healthcare sector. Understanding the characteristics and emerging typology of private hospitals can help to better regulate the private sector and to better plan its complementarity with the public sector. In addition, both in Morocco and in similar LMICs, private hospitals play an increasing role in the healthcare system, providing a substantial portion of healthcare services to the population, and playing a crucial role in achieving UHC. Clarke et al. highlight the importance of the private sector in UHC and consider that it is necessary to have political commitment and effective regulation to optimize its potential [ 9 ]. Fallah and Bazrafshan conducted a systematic scoping review of evidence from developing countries, examining the participation of private hospitals in delivering services towards UHC [ 37 ]. Siddiqi et al. draw lessons from country experiences to understand the role of the private sector in delivering health benefit packages of UHC [ 38 ]. These studies collectively underscore the significance of the private sector in the context of UHC, while also highlighting challenges and lessons learned from country experiences. Similarly, Jeurissen et al. suggested the role of public support and reimbursement strategies within UHC reforms in promoting the growth of the private sector in the United States, the United Kingdom, Germany, and the Netherlands [ 22 ]. These lessons could inspire private sector growth in Morocco, from small physician-owned private hospitals to private hospital networks.
Overall, these studies highlight the heterogeneity of the private hospital sector in LMICs and the need to consider its diversity when designing health policies and regulations. In addition, the majority of these studies were descriptive and did not include a clustering algorithm of the data. Our study is the first in Morocco and among the few in LMICs to identify the typology of private hospitals using an unsupervised clustering approach. Another strength of our study is the use of a database that includes all private hospitals in the country. Our study also used a robust methodology and two-stage approach to categorize a new typology of the private sector hospitals that presents high contextual relevance for the ongoing implementation of UHC policy and health system reform in Morocco. In addition, the ten variables used considered a wide range of hospital characteristics that, to our knowledge, have never been analysed before in Morocco and in the LMICs. However, our study may have some limitations. A limitation is that our analysis did not include some important hospital characteristics, such as staffing, technology and utilization, due to the unavailability of these data. This shows the importance of feeding the routine information system with data from the private sector. Another limitation of our study is that our literature review was not comprehensive, but was based on a narrative review of typologies adopted by international organizations.
In LMICs, the private hospital sector is not sufficiently known. The data available to define its profile are insufficient to provide an understanding of its organization and the dynamics of its development. LMIC governments will find it difficult to define the role of this sector in improving access to healthcare and in moving towards UHC. The national information systems of these countries must therefore be strengthened and enriched with data on the private healthcare sector. In the meantime, studies similar to our can be considered to position and regulate the private hospital sector. We consider that the results of our study offer a starting point for adapting the regulation and governance of this sector in Morocco and in LMICs. However, our single case study is not sufficient to test the transferability of our typology. Further research needs to be carried out in other contexts to judge its generalizability to other similar LMICs contexts.
In Morocco, the routine information system has few variables to describe the profile of the private hospital sector while it represents more than a third of all hospitals. We explored the characteristics and typology of the private hospital sector in Morocco using ten variables. Our results show that the private hospital sector is organized into three types of hospitals according to three similarity criteria: ownership, size (beds and turnover) and governance. This typology contributes to the understanding of the development dynamics of this sector and can guide efforts to plan and regulate the supply and demand of care in the current context of the generalization of compulsory health insurance which makes the private sector open to beneficiaries of all medical coverage schemes. Our study has several policy implications. It offers policymakers with a typology of private facilities that may guide the formulation of policies to enhance the growth of the private sector in order to improve regulation, supervision and the design of appropriate policies and incentives to increase quality of care, performance and accessibility of care within the context of UHC extension and decentralization reform.
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The data that support the results of this study cannot be publicly available because the source of data did not provide permission to do so.
Abbreviations
International Standard Industrial Classification of All Economic Activities
Low and Middle-Income Countries
Moroccan Dirhams
Organisation for Economic Co-operation and Development
Universal Health Coverage
World Health Organization
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ABA and ND provided the data. IEB and SZ analysed the data. SZ and ABA wrote the manuscript. ZB, RB and SB edited the manuscript. All authors approved the final manuscript.
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Zbiri, S., Belghiti Alaoui, A., El Badisy, I. et al. Private hospitals in low- and middle-income countries: a typology using the cluster method, the case of Morocco. BMC Health Serv Res 24 , 1231 (2024). https://doi.org/10.1186/s12913-024-11660-2
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Research Article
Periodontitis and pre-eclampsia among pregnant women in Rwanda: A case-control study
Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing
* E-mail: [email protected]
Affiliation College of Medicine and Health Sciences, School of Dentistry, University of Rwanda, Kigali, Rwanda
Roles Conceptualization, Methodology, Supervision, Writing – review & editing
Affiliation College of Medicine and Health Sciences, School of Public Health, University of Rwanda, Kigali, Rwanda
Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing
Affiliation Peninsula Dental School, University of Plymouth, Plymouth, United Kingdom
Affiliation Centre for Human Genetics, College of Medicine and Health Sciences, School of Medicine and Pharmacy, University of Rwanda, Kigali, Rwanda
Affiliation Karolinska Institutet, Stockholm, Sweden
Affiliation College of Medicine and Health Sciences, School of Medicine and Pharmacy, University of Rwanda, Kigali, Rwanda
- Agnes Gatarayiha,
- Joseph Ntaganira,
- Zoe Brookes,
- Léon Mutesa,
- Anders Gustafsson,
- Stephen Rulisa
- Published: October 14, 2024
- https://doi.org/10.1371/journal.pone.0312103
- Peer Review
- Reader Comments
Introduction
Several studies have indicated that the presence of periodontitis during pregnancy could increase the risk of developing pre-eclampsia, thereby negatively influencing pregnancy outcomes for both the mother and child. Notably, despite the high prevalence of both periodontitis and adverse pregnancy outcomes in Rwanda, there exists a crucial evidence gap concerning the precise relationship between periodontitis and pre-eclampsia.
The aim of this study was to assess the association between periodontitis and pre-eclampsia amongst pregnant women in Rwanda.
Methods and materials
Employing an unmatched 1:2 case-control design, we studied 52 pre-eclamptic and 104 non-pre-eclamptic pregnant women aged ≥18 years at two referral hospitals in Rwanda. Pre-eclampsia was defined as a systolic blood pressure ≥ 140 and diastolic blood pressure ≥ 90 mm Hg, diagnosed after 20 weeks of gestation and proteinuria of ≥300mL in 24 hours of urine collection. Periodontitis was defined as the presence of two or more teeth with one or more sites with a pocket depth ≥ 4mm and clinical attachment loss >3 mm at the same site, assessed through clinical attachment loss measurement. Bivariate analysis and logistic regression were used to estimate Odds ratio (ORs) and 95% confidence interval.
The prevalence of periodontitis was significantly higher among women with pre-eclampsia, compared to pregnant women without pre-eclampsia, at 90.4% and 55.8%, respectively (p< 0.001). Pregnant Women with periodontitis were 3.85 times more likely to develop pre-eclampsia after controlling for relevant confounders (adjusted Odds Ratio [aOR] = 3.85, 95%CI = 1.14–12.97, p<0.05).
This study results indicates that periodontitis is significantly associated with pre-eclampsia among pregnant women in Rwanda. These findings suggest that future research should explore whether enhancing periodontal health during pregnancy could contribute to reducing pre-eclampsia in this specific population.
Citation: Gatarayiha A, Ntaganira J, Brookes Z, Mutesa L, Gustafsson A, Rulisa S (2024) Periodontitis and pre-eclampsia among pregnant women in Rwanda: A case-control study. PLoS ONE 19(10): e0312103. https://doi.org/10.1371/journal.pone.0312103
Editor: Feriha Fatima Khidri, Liaquat University of Medical and Health Sciences, PAKISTAN
Received: December 13, 2023; Accepted: October 1, 2024; Published: October 14, 2024
Copyright: © 2024 Gatarayiha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data Availability Statement The data that support the findings from this study are publicly available at Zenodo via https://zenodo.org/records/10928051 .
Funding: This research received funding from the Capacity Building for Female Scientists in East Africa Africa program (CaFe-SEA) under the East African Consortium for Clinical Research (EACCR) partner’s institution funded by EDCTP and University of Rwanda-Sweden collaboration. These had no other involvement for this study.
Competing interests: The authors have declared that no competing interests exist.
Pre-eclampsia is a major complication of pregnancy characterized by gestational hypertension and associated with significant maternal and fetal risks, including impaired fetal development, maternal morbidity and mortality [ 1 ]. Globally, the prevalence of pre-eclampsia ranges from 2% to 8% of pregnancies [ 2 , 3 ], with a prevalence of 4.1% in developing countries like Sub-Saharan Africa [ 4 ]. In Rwanda, recent studies have reported a prevalence of 2.3% in Kigali teaching hospitals [ 5 ] and approximately 3% in rural areas [ 6 ], highlighting the ongoing challenge posed by this condition.
According to the American College of Obstetrics and Gynecology (ACOG), pre-eclampsia is defined as hypertension (≥ 140/90 mm Hg) and proteinuria (300 mg or more per 24-hour urine collection) or, in the absence of proteinuria, new-onset hypertension with associated complications, such as thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, severe headache unresponsive to medication, or visual disturbances [ 3 , 7 ]. This condition typically manifests after 20 weeks of gestation [ 3 , 7 ], underscoring the importance of early detection and management.
Pre-eclampsia contributes to approximately 50,000 maternal deaths globally each year [ 8 ]. In low- and middle-income countries, it is responsible for 10–15% of maternal deaths [ 9 ], with Rwanda experiencing a rate of 13% [ 10 ]. The condition’s multifactorial etiology includes factors such as endothelial dysfunction, systemic inflammation, and various risk factors like primiparity, family history, uterine malformation, pre-existing hypertension, diabetes, obesity, placental abnormalities, renal disease [ 11 , 12 ]. Recent research has highlighted the role of systemic inflammation and endothelial dysfunction in the pathogenesis of pre-eclampsia [ 13 – 15 ]. Systemic inflammation, often exacerbated conditions such as periodontitis, has been proposed as a key mechanism contributing to the development of pre-eclampsia [ 14 , 16 ].
Periodontitis, a chronic inflammatory disease caused by localized bacterial infections, results in the destruction of periodontal tissues and can lead to tooth loss [ 17 , 18 ]. Globally, severe periodontitis affects approximately 616 million people [ 19 , 20 ], with a higher prevalence of 13.5% reported in central Sub-Saharan Africa [ 21 ]. In Rwanda, a national oral health survey found a high prevalence of periodontitis in general population, with 60% of people experiencing dental calculus and 34.2% having dental debris [ 22 ]. Furthermore, periodontitis is found in up to 40% of pregnant women [ 23 ], and a growing body of evidence suggests a compelling link between periodontitis and adverse pregnancy outcomes, including pre-eclampsia [ 24 – 27 ]. Studies have reported associations with adverse pregnancy outcomes such as preterm births, low birth weight and pre-eclampsia [ 28 – 30 ]. For instance, research in Korea found a significant association between periodontitis and an increased risk of pre-eclampsia [ 31 ], while studies in Tanzania revealed a substantial association with high odds of pre-eclampsia (aOR = 4.12; 95% CI: 2.20–7.90) [ 32 ].
The mechanistic relationship between periodontitis and pre-eclampsia involves several key pathways. Chronic systemic inflammation resulting from periodontitis may exacerbate the inflammatory processes involved in pre-eclampsia. Periodontal disease can lead to elevated levels of inflammatory markers such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α), which may contribute to endothelial dysfunction and impaired placental blood flow, thereby increasing the risk of pre-eclampsia [ 33 , 34 ]. This connection underscores the need for further research to explore these inflammatory markers and their role in the pathogenesis of pre-eclampsia. Systematic review from 30 studies confirms that pre-eclampsia is a significant risk factor for periodontitis, and even suggests that is more pronounced in lower-middle-income countries [ 35 , 36 ].
Despite the growing body of evidence, most research on the relationship between periodontal disease and pre-eclampsia has been in developed countries, creating a significant knowledge gap for low and middle income countries. In Rwanda, where both pre-eclampsia and periodontal disease are prevalent, there is a need for localized studies to better understand this association. This study aims to investigate the association between periodontitis and pre-eclampsia in Rwanda. By doing so, it seeks to provide valuable insights into how these conditions interact in the context of Rwanda, contributing to improved health outcomes and advancing of knowledge in this critical area.
Study design
An unmatched 1:2 case control design was used to comprehensively investigate the association between periodontitis and pre-eclampsia among pregnant women in Rwanda. The case group comprised pregnant women diagnosed with pre-eclampsia, while the control group consisted of normotensive pregnant women.
Sample and procedure
To determine the sample size, we utilized G* Power Software, aiming for a significance level (α\alpha) of 0.05 and a target power of 85%, which is a common standard for many studies. to determine the necessary number of participants. The effect size was set at 0.50, representing a medium to large odds ratio (OR) of approximately 2.61. Based on this effect size and power level, the required total sample size was 156 participants, with 52 women with pre-eclampsia (case) and 104 women without pre-eclampsia (control) [ 37 ], ( Fig 1 ). Pre-eclampsia, in this context, was defined as a systolic blood pressure ≥ 140 mm Hg and diastolic blood pressure ≥ 90 mm Hg diagnosed after 20 weeks of gestation, in women who were normotensive previously. Pre-eclamptic women also had proteinuria, with of urine protein concentration of ≥ 300 mg over 24 hours urine collection [ 7 , 38 ].
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For the inclusion criteria, this study includes singleton pregnant women at 20 weeks’ gestation or beyond, aged 18 years and older, and with non-history of hypertension previously before the current pregnancy. Furthermore, selection was limited to women attending antenatal care services or admitted to maternity wards at designated hospitals (i.e., The University teaching Hospital of Kigali (CHUK) and Ruhengeri Hospital) during the study period (August 1, 2021, to February 25, 2022). On the other hand, the exclusion criteria included women with a history of placental abnormalities, or recent periodontal treatment requiring antibiotic or antiseptic mouthwashes during the 4 weeks preceding the study. However, women with their first pregnancy were not excluded in our study despite if they were diagnosed by healthcare providers with any potential placental abnormalities during ante natal care after a thorough initial assessment during medical history, physical examination, and laboratory test results. Therefore, they were included unless present with such identification placental abnormalities during assessment. Furthermore, for periodontal treatment requiring antibiotic or antiseptic mouthwashes, 4-weeks period is usually used as a standard (practical approach, and is reported in other studies [ 39 ], because it allows adequate time for any residual effect of antibiotics or mouthwashes to dissolve. Antibiotics and mouthwashes can both can alter oral microbiome/bacteria, affecting periodontal health, hence inclusion could potentially affect the study outcomes.
Participants with dentures or orthodontic braces, and those with a history of in vitro fertilization were also excluded. Orthodontic braces themselves are not directly associated with pre-eclampsia, however, potential bad oral hygiene related to them is crucial, as this may cause chronic inflammation from the accumulated oral bacteria, which in turn could contribute to the development of preeclampsia during pregnancy. Thus we excluded patients with orthodontic braces during our study. This stringent selection process aims to ensure a homogeneous study population, minimizing potential confounding factors.
Our study adhered strictly to ethical guidelines, ensuring participant well-being and rights. Ethical clearance was granted by the Rwanda National Ethics Committee (Approval No. 154/RNEC/2021). Participants provided informed consent, fully briefed on the study’s aims, potential risks, and benefits. They retained the right to withdraw at any time without affecting their medical care, underscoring our commitment to their autonomy and the confidentiality of their responses.
Data collection measurements and procedure
The data collection was conducted by two calibrated dental therapists who was blinded to the study group allocations of the participants. They conducted personalized interviews to gather comprehensive information on the participants’ medical histories and socio-demographic characteristics, smoking and alcohol usage, and specific pre-eclampsia details including blood pressure, antihypertensive drug dosage, protein concentration and maternal clinical features such as oedema, headache, and visual disturbance. Simultaneously, physical examinations, encompassing blood pressure measurements and details on proteinuria were conducted during obstetrics and gynecology consultation. For the detection of proteinuria, Urine Protein Test Strips by Siemens Healthcare Diagnostics (Tarrytown, NY, USA) were employed. These were also obtained through local medical suppliers in Rwanda to measure the presence of protein in urine samples. This information was sourced from participants’ medical records by trained clinician/nurse at the study hospitals.
Periodontal measurement
For the periodontal examination, we assessed all teeth (excluding third molars) for the case and control groups. Periodontal measurements included the recording of probing pocket depth (PPD), gingival recession and clinical attachment loss (CAL) in millimeters (mm) at six sites surrounding each tooth (buccal, mesio-buccal, distal-buccal, lingual, mesio-lingual, and disto-lingual), using the University of North Carolina (UNC)-15 probe manufactured by Hu-Friedy (Chicago, IL, USA). The PPD was measured as the distance from the gingival margin to the apical portion of the gingival sulcus. CAL was derived from the aggregate of gingival recession and PPD measures, though gingival recession itself was not independently measured. Additionally, the measurement from the cemento-enamel junction (CEJ) to the base of the pocket/sulcus was taken to ascertain clinical attachment loss.
Classification criteria for periodontitis severity
In defining periodontitis for this study, involvement had to include two or more teeth, each with at least one site demonstrating a probing pocket depth ≥ 4mm and clinical attachment loss >3 mm at the same site [ 14 ]. Reflecting on the constraints imposed by the absence of radiographs and building upon the methodology of previous similar studies [ 40 , 41 ], we classified mild periodontitis as CAL of 4–5 mm and moderate/severe CAL ≥ 6 mm across at least two different teeth. This classification scheme enabled the safe and precise categorization of periodontitis severity among our participants without resorting to the use of radiograph. Due to the concerns for the safety of pregnant women participating in our study, we did not perform radiographic examinations for the purpose of research. This decision was made to protect the participants as recommended by the ethic committee and to maintain the ethical integrity of our research findings. Our approach acknowledges the challenges of applying an internationally recognized specific diagnosis of periodontal disease without such imaging, as might be recommended in guidelines from entities like the European Federation of Periodontology (EFP) consensus [ 42 ].
Data analysis
All Analyses were conducted using SPSS (Statistical Package for the Social Sciences) Version 25. Binary variables including location, smoking, and alcohol consumption status, and medical pregnancy history were described using the frequencies and percentages. In contrast, continuous variables such as age, and weight, were described using mean and standard deviation.
A chi-square test was conducted for bivariate analysis to assess whether the difference in terms of proportion between dependent variables (pre-eclampsia) and other independent variables (periodontitis, socio demographic characteristics, medical, dental, and pregnant conditions) are statistically significant. Additionally, the Mann-Whitney U test was employed to assess the difference in mean ranks for continuous variables between pre-eclamptic and non-preeclamptic women due to the non-normal distribution of the data.
Logistic regression analysis was conducted to examine the association between potential risk factors and pre-eclampsia while controlling for potential confounders. The covariates were chosen based on a purposeful selection process begun by a univariate analysis of each variable. Covariates were included based on results from Wald tests; those with p-values below.25 were retained. The results were reported as crude odd ratios (cOR) and adjusted odd ratios (aORs) with their 95% confidence intervals. All statistically significant variables in a bivariate analysis were included in the logistic regression model. P-values of < 0.05 were considered as statistically significant.
Socio-demographic characteristics
This study included 156 pregnant women, comprising 52 women with pre-eclampsia and 104 women without the condition. The mean age of all participants was 30.4 years (SD = 4.9), with significant age differences observed between non-pre-eclamptic (29.6 years) and pre-eclamptic (32.0 years) groups (P = 0.01). The gestational age averaged 30.7±19.6 weeks across the cohort.
Regarding pregnancy and health history, 17.9% of participants were in their first pregnancy, 75% had 2–4 pregnancies and 7.1% had more than four pregnancies ( Table 1 ). The average weight of women in pre-eclamptic group was significantly higher than in the non-pre-eclamptic group (74.5±9.2 kg versus 68.5±6.3 kg). A majority (79.5%) attended fewer than four antenatal care visits. Additionally, 8.7% of the non-pre-eclamptic group and 25.0% of the pre-eclamptic group reported alcohol consumption (P = 0.015).
https://doi.org/10.1371/journal.pone.0312103.t001
Regarding smoking status, 2.8% of non-pre-eclamptic women were smokers compared to none in the pre-eclamptic group (P = 0.358). Although smoking rates were low, this difference may warrant further investigation. Information on oral hygiene revealed that 3.8% of non-pre-eclamptic women and 13.5% of pre-eclamptic women had visited a dental service in the previous 6 months (P = 0.027). Only a small fraction of women in both groups visited for scaling teeth (2.9% vs. 9.6%, P = 0.072).
Comparison of periodontal parameters between pre-eclamptic and non-pre-eclamptic group
The analysis, conducted using the Mann-Whitney test for non-normally distributed continuous variables (PPD and CAL) and the chi-square test for categorical variables, revealed significant difference in periodontal health between groups with and without pre-eclampsia ( Table 2 ). Compared to the non-pre-eclamptic group, women with pre-eclampsia significantly higher mean PPD (4.2±1.1 mm vs. 3.62±0.8 mm) and CAL (5.2±1.4 mm vs. 4.0±1.1 mm), with p<0.0001 for both measures. Additionally, the incidence of periodontitis was markedly greater in the pre-eclamptic population (90.4%) compared to the non-pre-eclamptic group (55.8%, p<0.001). Specifically, moderate to severe periodontitis (CAL >6 mm) was significantly more common in the pre-eclamptic group (42.3%) than in the non-pre-eclamptic group (13.5%, p<0.001). Furthermore, bleeding on probing was more prevalent in pre-eclamptic women (88.5%) as compared to non-pre-eclamptic women (65.4%), with this finding nearing statistical significance (p<0.056).
https://doi.org/10.1371/journal.pone.0312103.t002
Factors associated with pre-eclampsia
The chi-square significantly showed differences between the groups in the variables including age, weight, study location, alcohol consumption, smoking, diabetes, periodontal Health Variables, cardiovascular disease, parity, edema, headache, visual disturbances, use of anti-hypertensive medication. Table 3 presents the findings from a binary logistic regression analysis exploring the association between periodontitis and pre-eclampsia among pregnant women. Initial crude analysis showed that women with periodontitis were 7.5 times more likely to develop pre-eclampsia (OR = 7.46, 95%CI = 2.74–20.26; p<0.05). This association remained significant after adjusting for confounders (aOR = 3.85, 95% CI = 1.14–12.97, p<0.05), indicating that pregnant women with periodontitis were approximately 3.85 times more likely to develop pre-eclampsia than those without periodontitis, after adjusting for confounders. For moderate/severe periodontitis, the association with pre-eclampsia was significant in the unadjusted model (cOR: 4.71, 95% CI: 2.15–10.36, p<0.05) but became non-significant after adjustment for confounders (aOR: 1.74, 95% CI: 0.68–5.27). Crude analysis also demonstrated that other risk factors such as study location, alcohol consumption, age, dental services visits in the previous 6 months, primiparity, experiencing edema and headache were associated with pre-eclampsia. Notably, variables such as study site location, experiencing edema, and headache also remained significantly associated with pre-eclampsia after controlling for confounders.
https://doi.org/10.1371/journal.pone.0312103.t003
The findings of this study support the hypothesis that periodontitis is associated with pre-eclampsia in two selected referral hospitals in Rwanda. Our observation of a notably higher prevalence of periodontitis (90.4%), including moderate/ severe periodontitis cases, among women with pre-eclampsia aligns with several global studies. For instance, a study done in India found a similar association, where women with periodontitis had a significantly higher risk of developing pre-eclampsia standing at 90% [ 43 ]. This pattern is also observed in studies from Iran at 98% [ 15 ] and Turkey at 74.3% [ 44 ], reinforcing the potential global relevance of this association.
The prevalence of periodontitis in our study population significantly exceeded that of the general adult population in Rwanda (39.6%), [ 45 ].This elevated prevalence among pre-eclamptic women mirrors findings from studies in the USA, where pregnant women with periodontitis were found to have higher rates of pre-eclampsia compared to those without periodontitis [ 16 ]. Similarly, a Tanzanian study demonstrated a strong association between periodontal disease and adverse pregnancy outcomes, including pre-eclampsia [ 32 ].
In contrast, some studies have reported conflicting results. For example, a study conducted in Norway found no significant association between periodontitis and pre-eclampsia after adjusting for confounders [ 46 ]. Additionally, a study from Japan reported similar findings, where the link between periodontal disease and pre-eclampsia was not statistically significant [ 47 ]. These discrepancies could be due to differences in study populations, periodontal disease definitions, or variations in healthcare access and practices across different countries.
Our multivariate logistical regression analysis was adjusted for potential confounders such as age, alcohol consumption, primiparity, edema, and headache. The resulting odds ratio of 3.85, indicates that women with moderate/severe periodontitis are over three times more likely to develop pre-eclampsia, which is consistent with a meta-analysis that reported an overall odds ratio of 3.18 [ 35 ]. Interestingly, this meta-analysis found that the association was even stronger in studies conducted in low- and middle-income countries (OR = 6.70), [ 35 ], underscoring the importance of context-specific research.
In terms of clinical indicators, our study found significantly higher mean periodontal pocket depth (PPD) and clinical attachment loss (CAL) in the pre-eclamptic group, which aligns with the findings from studies in Brazil and India. In Brazil, researchers observed that pre-eclamptic women had greater PPD and CAL compared to their non-pre-eclamptic counterparts [ 48 ]. Similarly, an Indian study highlighted significant differences in periodontal measurements between pre-eclamptic and non-pre-eclamptic women, further supporting the association between periodontal health and pregnancy outcomes [ 49 ]. These findings suggest that the severity of periodontal disease may be a critical factor in the development of pre-eclampsia.
Univariate analysis in our study identified association between pre-eclampsia and various risk factors, including age, study location, alcohol consumption during pregnancy, visit to dental services, edema, headache, primiparity, pregnant women’s weight and visual disturbances.
The final multivariate logistic regression confirmed age, study location, edema and headache as significant risks factors for pre-eclampsia. These results are consistent with findings from studies done in Chile and Bangladesh, which identified similar risk factors for pre-eclampsia, including maternal age and pre-existing hypertension [ 50 , 51 ]. Additionally, studies in Sweden and South Africa emphasized the role of socio-economic factors in pre-eclampsia risk [ 52 , 53 ], suggesting that future research in Rwanda should also consider these variables.
Interestingly, our study found a significant association between dental service visits and pre-eclampsia, which contrasts with findings from Boggess et al, that reported no significant link between dental visits and pre-eclampsia [ 54 ]. This difference might be explained by varying access to dental care and differences in dental service utilization during pregnancy across different healthcare systems. Furthermore, our study did not find an association between tobacco consumption and pre-eclampsia, a finding that diverges from studies in Sweden and the USA, which reported smoking as a significant risk factor for pre-eclampsia [ 48 , 55 ].
Potential mechanisms for the link between periodontitis and pre-eclampsia include the translocation of periodontal pathogens to the placenta, leading to localized inflammation and oxidative stress, which may contribute to endothelial dysfunction—a key feature of pre-eclampsia [ 35 ]. This theory is supported by studies that detected periodontal bacteria in placental tissues of pre-eclamptic women, suggesting a direct pathogenic role [ 35 , 56 ]. Moreover, the hypothesis that repeated bacteremia exposes decidual tissues to periodontal bacteria, has been proposed as another pathway, although the exact mechanism remain to be fully elucidated [ 15 , 50 ]. Despite these plausible explanations, the multifactorial nature of both conditions necessitates further research to clarify these pathways and determine whether improving periodontal health can reduce pre-eclampsia risk.
Strength and limitations of our study
Our study is the first to explore association between periodontitis and pre-eclampsia among pregnant women in Rwanda, offering valuable insights into this important public health issue. However, the small sample size and omission of certain socio-demographic characteristics may limit the generalizability of our findings. Future research should aim to collect individual-level data on socioeconomic factors to deepen our understanding. Additionally, the absence of dental radiographs due to the safety concern, a limitation noted in similar studies [ 40 – 42 ], restricted our ability to assess of bone loss radiographically and provide a universally accepted diagnosis of periodontal disease. While we adjusted for known confounders in our multivariate logistic regression model, residual confounding may still exist due to the lack of detailed endothelial function measures or accurate quantifications of placental insufficiency. These limitations highlight the need for future studies to include larger sample sizes, comprehensive socio-economic data, additional inflammatory markers, and advanced diagnostic methods to strengthen the observed associations and provide a more robust understanding of the relationship between periodontitis and pre-eclampsia.
This study establishes a robust association between periodontitis and pre-eclampsia among pregnant women in Rwanda, even after accounting for potential confounders. Our findings indicate that pregnant women with periodontitis are approximately three times more likely to develop pre-eclampsia compared to their counterparts. This significant association underscores the impact of periodontal health on maternal outcomes and highlights the importance of integrating oral health care into prenatal care practices, as interventions to manage periodontitis once pregnancy may be too late; non-surgical therapy for example does not reduce adverse pregnancy outcomes [ 57 ].
Despite some limitations, our research contributes valuable insights into the association between periodontal disease and pre-eclampsia. The evidence suggests that improving oral health could be a viable strategy to reduce the risk of pre-eclampsia, emphasizing the need for preventive interventions. The findings have important implications for maternal and fetal health, particularly in resource-limited settings where access to comprehensive dental care may be limited. Future research should address regional variations in healthcare access, socio-economic factors, and the definitions of periodontal disease to better understand these relationships.
Supporting information
S1 checklist. strobe statement—checklist of items that should be included in reports of case-control studies ..
https://doi.org/10.1371/journal.pone.0312103.s001
Acknowledgments
The authors sincerely wish to thank all women for their kind cooperation. Authors would wish to recognize the research assistants for their contributions during data collection.
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The PHEM-B toolbox of methods for incorporating the influences on Behaviour into Public Health Economic Models
- Hazel Squires 1 ,
- Michael P. Kelly 2 ,
- Nigel Gilbert 3 ,
- Falko Sniehotta 4 ,
- Robin C. Purshouse 1 ,
- Leandro Garcia 5 ,
- Penny Breeze 1 ,
- Alan Brennan 1 ,
- Benjamin Gardner 3 ,
- Sophie Bright 1 ,
- Alastair Fischer 6 ,
- Alison Heppenstall 7 ,
- Joanna Davan Wetton 8 ,
- Monica Hernandez-Alava 1 ,
- Jennifer Boyd 9 ,
- Charlotte Buckley 1 ,
- Ivo Vlaev 10 ,
- Robert Smith 1 , 11 ,
- Ali Abbas 2 ,
- Roger Gibb 12 ,
- Madeleine Henney 1 ,
- Esther Moore 1 &
- Angel M. Chater 8
BMC Public Health volume 24 , Article number: 2794 ( 2024 ) Cite this article
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It is challenging to predict long-term outcomes of interventions without understanding how they work. Health economic models of public health interventions often do not incorporate the many determinants of individual and population behaviours that influence long term effectiveness. The aim of this paper is to draw on psychology, sociology, behavioural economics, complexity science and health economics to: (a) develop a toolbox of methods for incorporating the influences on behaviour into public health economic models (PHEM-B); and (b) set out a research agenda for health economic modellers and behavioural/ social scientists to further advance methods to better inform public health policy decisions.
A core multidisciplinary group developed a preliminary toolbox from a published review of the literature and tested this conceptually using a case study of a diabetes prevention simulation. The core group was augmented by a much wider group that covered a broader range of multidisciplinary expertise. We used a consensus method to gain agreement of the PHEM-B toolbox. This included a one-day workshop and subsequent reviews of the toolbox.
The PHEM-B toolbox sets out 12 methods which can be used in different combinations to incorporate influences on behaviours into public health economic models: collaborations between modellers and behavioural scientists, literature reviewing, application of the Behaviour Change Intervention Ontology, systems mapping, agent-based modelling, differential equation modelling, social network analysis, geographical information systems, discrete event simulation, theory-informed statistical and econometric analyses, expert elicitation, and qualitative research/process tracing. For each method, we provide a description with key references, an expert consensus on the circumstances when they could be used, and the resources required.
Conclusions
This is the first attempt to rigorously and coherently propose methods to incorporate the influences on behaviour into health economic models of public health interventions. It may not always be feasible or necessary to model the influences on behaviour explicitly, but it is essential to develop an understanding of the key influences. Changing behaviour and maintaining that behaviour change could have different influences; thus, there could be benefits in modelling these separately. Future research is needed to develop, collaboratively with behavioural scientists, a suite of more robust health economic models of health-related behaviours, reported transparently, including coding, which would allow model reuse and adaptation.
Peer Review reports
Public health interventions and health economic modelling
Public health interventions may include actions or activities that aim to make a person or population change behaviours to improve their health. They are often multi-component and operate within complex systems, which means that there is not a clear boundary around the system and the sum of individual intervention effects is not equal to the outcomes at the population level due to the interactions between heterogeneous individuals and the influence of their environment. Thus, effects of public health interventions may be non-linear, and sometimes unexpected, at the macro level, with the wider context impacting intervention effectiveness [ 1 ].
Health economic models are used to predict the difference in costs and effectiveness between current practice and alternative interventions, usually over an individual’s lifetime, to capture all impacts of the interventions to inform policy decisions about how best to spend limited resources [ 2 , 3 ]. The benefit of these models is that they can synthesise evidence from a range of sources and simulate possible future costs and outcomes of alternative interventions. Existing health economic models do not typically incorporate the determinants of individual and population behaviours that influence long term effectiveness, yet it is essential to understand how public health interventions work in order to attempt to predict long-term outcomes of interventions.
Health economic models include simple arithmetic calculations, cohort state transition models, and individual level simulations [ 4 ]. More flexible individual-level health economic models can be useful when decision makers want to understand the different impacts of interventions upon individuals or different groups of the population to reduce health inequalities [ 5 ] or when outcomes depend on time or event-dependent interactions [ 6 ]. Typically, within these models, a population is synthesised to match the characteristics of the real population of interest, so that every individual has their own attributes (e.g. age, socioeconomic status, Body Mass Index (BMI)), which can be updated over time [ 7 ]. These models can then be used to estimate the incidence and progression of diseases and health conditions using epidemiological risk equations, to estimate mortality, to assign different resource use, costs and utilities, to test the impact of alternative interventions, and to report outcomes by relevant subgroups.
Why is it useful to understand the influences on behaviour to predict behaviour and long-term outcomes of public health interventions?
The effectiveness of public health interventions is dependent upon human behaviour. Behaviour is complex and multifaceted, and shaped by many influences which change over time. In some health economic models of public health interventions, only biological risk factors for disease, such as BMI, are incorporated [ 8 ], without including the contributing behaviours such as eating and physical activity. In others, behavioural risk factors (e.g., smoking) are included, but the influences upon these behaviours, such as those related to capability (e.g. knowledge/behavioural regulation), opportunity (e.g. environmental context, social influences) and motivation (e.g. beliefs, emotion, reinforcement), are not explicitly considered [ 9 ]. The impact of the intervention is generally estimated based on a single study or a meta-analysis of the effectiveness of an intervention [ 3 ] and effectiveness evidence is often limited to 6 – 12 months follow up [ 10 , 11 , 12 ]. There is a dearth of evidence about behavioural maintenance resulting from interventions and there are no standard approaches for estimating the impacts of the intervention beyond the end of study follow up. Assumptions range from maintaining the effectiveness over an individual’s lifetime, to reverting to the outcomes of the comparator either immediately or gradually over some time period [ 13 , 14 ]. These assumptions are usually based on little theory or evidence, and generally do not vary by individual characteristics or intervention type.
Figure 1 shows some potential alternative modelling assumptions beyond study follow up for a hypothetical intervention which reduces BMI (either by increasing physical activity or improving diet). The cost-effectiveness results are based on the average differences between usual care and the interventions, which may be dramatically different depending on which assumptions are chosen [ 13 ]. For simplicity, this hypothetical example shows only usual care and one intervention, with three alternative modelling assumptions for the cohort. However, there are often multiple interventions to compare, and it may not be appropriate to make the same extrapolation assumptions for each intervention or each individual, given that there are many factors that will affect behaviours over time.
Illustration of the importance of assumptions beyond study follow up
legend: The three blue/ purple dashed lines each represent an alternative modelling assumption about the effectiveness of the intervention beyond the study period: Body Mass Index (BMI) returning to usual-care levels within 1 year; BMI slowly increasing post-intervention, at the same rate as those under usual care; or BMI remaining at the lower post-intervention level for 5 years and then returning to what would be predicted under the usual care scenario over a further 3 years
During model development, assumptions about the effectiveness of the interventions are often treated as an ‘add on’ to the main modelling of the current system. This may be because health economic modelling traditions were developed for the evaluation of clinical interventions, where effectiveness evidence tends to come from randomised controlled trials and the reasons why an intervention works or does not work will not affect policy decisions. Yet, for public health economic evaluation, the benefits of developing a more detailed individual-level simulation model are likely thwarted by the basic assumptions about the intervention effectiveness over the long-term. It is not advisable to attempt to predict future outcomes without any understanding of the mechanism of action – i.e., the processes through which an intervention affects behaviour, such as by increasing motivation [ 15 ]. This is particularly important when policy makers are comparing the cost-effectiveness of several alternative interventions which are made up of different behaviour change techniques, some of which are more likely to lead to maintenance of a new behaviour than others [ 16 ].
In addition, interventions may affect different behaviours (e.g., eating or physical activity) or different elements of a behaviour domain (e.g., eating fruit and vegetables or salt intake). Intervention strategies may include taxation policies, environmental changes, service provision, or education. Each of these intervention strategies has different mechanisms of action, with some depending more upon changing individual factors, and others on changing the external environment. If the same extrapolation assumptions are made for all interventions this could lead to inappropriate comparisons between the interventions. Moreover, different individuals may be more likely to maintain a behaviour than others, according to some environmental influences, biological factors or psychological attributes, and subsequent inequalities are likely to be important to decision makers. Thus, the underlying evidence, the choice of assumptions, and appropriate representation of uncertainty related to these aspects, is fundamental for informing policy decisions.
Behavioural theories and frameworks
Research undertaken within other disciplines including psychology, sociology, complexity science and behavioural economics can help to inform assumptions about behaviour beyond intervention study follow up. Many theories and frameworks have been developed to explain human behaviour. These link a set of biological, psychological, social and/or environmental factors to behaviour, offering a bio-psycho-social understanding of behaviour. Such factors can be thought of as potential intervention targets for change and mechanisms of action. The effectiveness of interventions for changing and maintaining behaviour will depend on which influences are targeted, and to what extent changes in these influences (e.g., knowledge), and the behaviour change techniques (e.g., providing information) and intervention strategies (e.g., education) used impact on behaviour. However, there is no one accepted behavioural theory, and there are multiple mechanisms of action and behaviour change techniques [ 17 ]. Michie [ 18 ] collated 83 behavioural theories which could inform the development of behavioural interventions. Within this review, the three theories for which the most published papers (more than 50%) were identified were: (i) the Transtheoretical Model of Behaviour Change (which includes progression and feedback loops through the stages precontemplation, contemplation, preparation, action and maintenance, allowing for relapse) [ 19 ]; (ii) The Theory of Planned Behaviour (which links attitude, subjective norms, perceived behavioural control and intentions to behaviour) [ 20 ]; and (iii) Social Cognitive Theory (which links the interaction between the individual, environment and behaviour with behavioural capability, observational learning, reinforcement, expectations and self-efficacy) [ 21 ].
Recent literature encourages the use of behavioural theories to inform public health intervention development to understand what works for whom in which contexts [ 22 ]. However, many existing studies do not report their theoretical basis [ 23 ] and a narrative synthesis of nine systematic reviews found no difference in the effectiveness of interventions that were theory-based versus non-theory-based [ 24 ]. The study authors suggested that this could be due to limitations with the theories used, (with two of the most widely used having had calls to be retired [ 25 , 26 ]) or issues with fidelity and the way in which they were applied.
The Behaviour Change Wheel developed by Michie et al. [ 27 ] has become an important framework for developing interventions, because it provides a coherent and comprehensive approach. It was developed based upon a systematic review of the literature and subsequently tested for reliability. The Behaviour Change Wheel includes influences on behaviour at the hub, with a wide range of intervention types and policy options set out in the middle and outer layers of the wheel. There are then tables to facilitate a systematic selection of intervention strategies and behaviour change techniques according to a behavioural analysis. The influences on behaviour at the hub are conceptualised through the COM-B model of behaviour, where Behaviour can be explained by a combination of Capability, Opportunity and Motivation. Capability includes physical (e.g., skills/ strength) and psychological (e.g., knowledge, memory, attention and decision making, behavioural regulation) factors; Motivation includes reflective (e.g. beliefs, intentions, identity) and automatic (e.g. emotion, reinforcement) factors; and Opportunity includes physical (e.g. environmental context and resources) and social (e.g. norms, culture) influences.
Any theory that is used to develop the interventions is generally ignored when they are evaluated within a health economic model. This is partly because health economic modellers tend to use methods developed for the evaluation of clinical interventions, and partly due to limitations of existing behavioural theories, including data limitations to support them and the fidelity with which they are implemented in the interventions. To predict the long-term effectiveness of interventions, it is important to understand the precise content and context of the interventions [ 1 ]. Combining behavioural theory with modelling, as has been done in other fields such as natural resource management [ 28 ], could help to understand the longer-term impacts of a range of interventions with different mechanisms of action upon individuals with different attributes.
Why might it be important to consider the influence of social networks and the broader environment?
While many of these behavioural theories focus primarily on individual psychology, there are a range of theories which suggest that behaviour is influenced by others, including our perception of others, and/ or the broader environment, which are discussed in this section.
There is evidence that people do not consider all possible outcomes systematically when making many behavioural decisions (as is assumed in standard economic theory of rational choice). Instead, to cope with complex choices, people use heuristics which are strategies that enable faster decision making whilst only using some information [ 29 , 30 ]. These heuristics have been shown to lead to predictable patterns of behaviour. Theory associated with ‘nudging’ [ 31 ] – that is, ‘any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives’ – has been extensively used to develop behaviour change interventions [ 32 ]. Nudge theory focuses on automatic mechanisms (non-reflective decision making) and how the context can affect these in positive ways, although it does not exclude reflective mechanisms (deliberate, highly cognitive decision making). The mnemonic MINDSPACE (Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitment and Ego) has been used to set out the most powerful contextual influences on automatic behaviours [ 32 ].
Social structure is about the patterns of social relationships within a population. An individual’s behaviour will impact upon these social relationships, and at the same time their social connections will affect their behaviour [ 33 ]. For example, a physically active person may join a running club and make friends with other runners, which might increase the amount the person runs. It has been suggested that friends and family influence weight-related behaviours and body weight [ 34 ]. People may influence each other through physical or online networks; for instance, social media is often used as a platform to influence behaviour [ 35 , 36 ]. The interaction of many individuals within a social structure can alter that structure and change social norms. The theory associated with social structure [ 33 ] is consistent with the theory of complex adaptive systems. Complex adaptive systems are made up of heterogeneous interacting elements and it is the relationships between these interacting elements which lead to potentially unexpected outcomes [ 37 ]. Public health interventions tend to operate within complex adaptive systems [ 38 ].
Social Norms Theory proposes that behaviour is influenced by perceptions of behavioural norms [ 39 ]. This may lead to the new behaviour becoming the social norm, thus changing the behaviour at the population level. Alternatively, social norms may make it harder for a new behaviour to diffuse and become the norm. It is therefore important to consider these interactions, rather than focusing solely upon the individual, to make more reasonable predictions.
Social Identity Theory proposes that each individual self-categorises based on their perceived membership of social groups (e.g., researcher, mother, runner) [ 40 ]. The extent to which they feel they share characteristics with other members will, in part, determine the influence of the group on their behaviours. The social connection to others affects health and wellbeing through these influences on behaviour, as well as directly through being able to count on social contacts (e.g., close connections in a running club may affect maintenance, frequency and length of exercise). These social connections are largely ignored when determining health-related outcomes within health economic models, yet their impact on mortality has been shown to be greater than obesity, blood pressure and physical inactivity [ 41 ]. Social Identity Theory and the accompanying empirical evidence suggests that the focus on the individual to improve population health and wellbeing is insufficient [ 40 ]. In addition, the theory suggests that maintenance of healthy behaviour changes will be facilitated when an individual shifts from an identity for which the previous behavioural pattern was central to an alternative identity more supportive of the new behaviour (e.g., smoker to non-smoker) [ 42 ].
The life course approach recognises that over a lifetime, individuals accumulate health losses and benefits, and that at key forks in the life course, such as becoming pregnant or beginning work, these can be magnified and substantially affect future trajectories [ 43 ]. Thus, interventions may be more effective if they are targeted at specific stages of the life course. The life course approach recognises that by making changes to the environment and social norms, inequalities may be reduced, which may impact on life course trajectories, and this could benefit the whole population and future generations [ 44 ].
Ecological frameworks have been developed which draw upon multiple theories to combine both individual and environmental influences for different health-related behaviours [ 45 ]. These frameworks recognise the interactions of influences across levels; however, they do not generally include quantifiable constructs. In public health it has long been recognised that interventions work best if they are provided at multiple levels: the individual, the community and the population [ 46 ]. This has been best demonstrated for smoking policy which has been highly successful during the past five decades [ 45 ]; for example, stop smoking services provided to individuals, training programs for practitioners at the community level and a ban in public places at the population level [ 47 ]. It is important to consider the interaction between individual level factors and changes in the broader social and environmental context. The structure of the system is a key driver of its behaviour [ 37 , 48 ]. We therefore need to be able to assess the potential impacts of changing the environment in addition to being able to assess individual-level interventions. Health economic modelling can provide a method for comparing and combining these interventions within one framework to explore their impacts, if the broader contexts that influence behaviour are incorporated within the models.
Summary of key influences on behaviour
Figure 2 summarises key influences on health-related behaviours and health economic outcomes as discussed in the above sections. In Fig. 2 , those elements within the dark grey shading are typically included within health economic models of public health interventions, whilst those within the light grey shading are not [ 49 , 50 , 51 , 52 , 53 ]. Individual health-related behaviours span both the light and dark grey shading because they are sometimes explicitly included. There are copious amounts of information about the influences on behaviour from psychology, sociology and behavioural economics; however, understanding what evidence, theories and methods are useful (or not) for health economic modelling is not feasible within an applied research project, particularly if it is undertaken within the timeframes of a decision-making process.
Influences on health-related behaviours and outcomes
Aim of the paper
The primary aim of this paper is to draw on psychology, sociology, behavioural economics, complexity science and health economics to propose a practical toolbox of methods for incorporating the influences on behaviour into health economic models. For each method, we provide a description, an expert consensus on the circumstances when they could be used, how the method can bring behavioural influences into the model, the minimum resource requirements and key references. This will provide a useful resource for modellers to identify and incorporate appropriate methods and behavioural theory within health economic models of public health interventions, to better inform resource allocation decisions.
However, there are weaknesses in current behavioural theories, methods and the data available to inform such models (see Background). Thus, we also aim to set out an agenda for further research for both health economic modellers and behavioural scientists. Since this work is multidisciplinary, a glossary is provided in Supplementary Material (“Glossary”), to clarify technical terminology. This paper is accompanied by a website which provides a more interactive version of the toolbox [ 54 ].
A consensus approach between experts from multiple disciplines was used to progress current methods. First, our multidisciplinary core team (HS, MK, NG, FS, RCP) undertook a literature review to identify and review all existing methods to incorporate health-related behaviour within simulation models across disciplines [ 55 ]. Based upon this, we developed a draft methods toolbox for incorporating behavioural theory within health economic models. We used an existing health economic model of diabetes prevention [ 8 ] initially to test and develop the toolbox conceptually. We used an agent-based model which investigated how behavioural theory can be used to predict population-level alcohol consumption patterns [ 56 ] to consider what would be necessary to develop such a simulation into a health economic model. Both are described in Supplementary material (“The School for Public Health Research (SPHR) diabetes prevention model” and “The agent-based model of alcohol consumption” respectively). We used these models because they were developed by some of the co-authors, so key elements of the model development process were known.
The core group was augmented by a wider group that covered a broader range of multidisciplinary expertise. We held a one-day in-person workshop with all 20 co-authors in February 2023 to develop the toolbox further. The multidisciplinary experts, based in the United Kingdom and Germany, were chosen according to their input into behavioural/ social science training courses, authoring key texts in their field or identification from the core multidisciplinary group as being a leader in their field. The group includes health economic modellers, health psychologists, behavioural and public health researchers, sociologists, behavioural economists, complexity scientists and agent-based modellers, a member of the patient and public involvement group, and policy makers. We circulated an early draft of this paper before the workshop. Within the workshop, we discussed how behavioural/ social science can inform and be informed by health economic models, the general structure of the paper, the potential barriers to the methods being used, and an agenda for future research. We used multidisciplinary breakout groups to discuss topics in more depth, followed by sharing key points and whole group discussions. Each breakout group had a designated note taker and a worksheet for all other authors to contribute notes. We narratively shared the outcomes of the workshop with the whole group and updated the toolbox accordingly. We subsequently collaboratively and iteratively developed improved versions of the toolbox, until qualitative agreement was reached between all authors.
We used the Behaviour Change Wheel [ 27 ] (described in the Background) to set out the requirements of interventions to encourage modellers to use behavioural theory within health economic models (see Supplementary Material, “A COM-B analysis of the use of a toolbox by modellers to incorporate the influences of behaviour into health economic models”). The content of this paper was designed to meet some of these requirements, there is an accompanying website to make the toolbox more accessible [ 54 ], and the remainder of these requirements are described within the agenda for further research (see Discussion). The ‘Results’ section of the paper sets out the toolbox, which is made up of a decision framework for choosing which methods might be appropriate (Table 1 ), a tabulated summary description of all the methods included in Table 1 (Table 2 ) and a full narrative description of each method and when to use (with each numbered heading describing a different method). The intention is that the user will employ the decision algorithm (Table 1 ) to make decisions about appropriate methods and then access the summary table (Table 2 ) and the narrative description to find out more about the appropriate methods as needed.
A decision framework to help modellers incorporate the influences on behaviour within their health economic models of public health interventions is shown in Table 1 below. This is divided into 2 main sections: Part 1 includes approaches that should always be considered if feasible for understanding the problem and for choosing and describing interventions. Part 2 includes approaches for developing the model structure, dependent on the characteristics of the behaviour(s) of interest and the type of question being assessed within the model. The user should consider each question in Part 2 in turn, and methods are not mutually exclusive, so for example it may be appropriate to use a combination of agent-based modelling (ABM) and econometric analyses. Table 2 shows summary information about each of the methods included within Table 1 , including what the method is, when and why it could be used, how it brings behavioural influences into the model, minimum data or input required, and key references about the method. The use of each method will be dependent upon practical considerations, in particular the time available within the decision-making process. However, since this would be a factor for all the methods outlined, it has not been explicitly stated for each one. The methods are described in detail within the remainder of the paper. Many of the methods are highly resource intensive, and some approaches for reducing this, where possible, are outlined.
The combination of the decision framework and the description of the methods provides a toolbox for modellers to incorporate the Influences on Behaviour into Public Health Economic Modelling (‘the PHEM-B toolbox v1.0’). It is worth noting that the aim is not to identify all methods for public health economic modelling, but those which would allow the influences on behaviour to be incorporated to better predict the impacts of public health interventions.
Method 1: Collaboration between health economic modellers and behavioural/ social scientists throughout
Collaboration is about working together towards a shared goal to produce better outcomes than could be achieved individually. There would be a benefit to greater collaboration between behavioural and social scientists and health economic modellers in a cyclical process of intervention development and evaluation [ 57 , 58 ]. This would align with the latest guidance for developing and evaluating complex interventions [ 1 ]. Collaboration could enable better understanding by health economic modellers and behavioural scientists about which elements of their research are important for achieving shared goals and how they can inform each other. Early health economic model development/ input may lead to the conclusion that it is not worthwhile to study the effectiveness of a public health intervention, or it could inform useful data collection for evaluation. Psychologists, sociologists and/ or behavioural economists could help modellers to understand the evidence, the behavioural theory used to develop interventions, and to help inform assumptions beyond study follow up where no or limited data exist.
The APEASE criteria has been developed for assessing interventions at any stage of development or evaluation [ 59 , 90 , 91 ]. This comprises Acceptability, Practicability, Effectiveness (and cost-effectiveness), Affordability, Spill-over effects, and Equity, and if any of these are not deemed by stakeholders to be met then the intervention should not be considered. Throughout this paper, stakeholders include members of the public affected by the interventions, people delivering the interventions and commissioners of the interventions. The APEASE criteria could be used to develop shared goals for collaboration. Within a health economic model, Effectiveness, Affordability, Side-effects and Equity can be explored, whilst it would not be worthwhile assessing interventions within a health economic model if they were not considered to be acceptable or practical by stakeholders.
Method 2: Reviewing the literature for (a) theories used to develop the interventions and (b) behaviour maintenance studies
Teams evaluating the effectiveness and cost-effectiveness of a set of interventions could identify which theories were applied (if any) when interventions were developed, if these are reported. The interventions and their influences could then be added to a behavioural systems map (see Method 4). This information can be presented to and discussed with policy makers and other relevant stakeholders to help select interventions for evaluation. Behavioural theory could be qualitatively utilised to inform long term assumptions within both cohort and individual level models. Where intervention studies present theory of change diagrams (identifying an intervention’s impact, outcomes, outputs, activities, and inputs and describing why interventions will create which outcomes) these could be used to inform long term assumptions.
Most existing theories and intervention studies focus upon behaviour change rather than behaviour maintenance (i.e. sustained behaviour over more than 6 months) [ 60 ]. The influences on behaviour change should not be assumed to be the same as those affecting behaviour maintenance. For the prevention of many non-communicable diseases, the goal is to maintain healthier behaviour over the longer term, to impact long term health outcomes. Different interventions are likely to work in different ways, and in different populations, and hence some interventions may be more likely to assist behavioural maintenance rather than others. Most studies report only the effectiveness of the interventions over 6 or 12 months. There are a small number of intervention studies which are designed to capture behaviour maintenance outcomes, as well as some meta-analyses of these studies, mainly for physical activity maintenance [ 16 , 61 , 92 , 93 , 94 , 95 ]. A literature search for relevant behaviour maintenance studies could be undertaken. It may be possible to apply these studies to inform model predictions; however, outcomes are still relatively short term (generally, a maximum of three years of data), and only average effects are reported. These data could be used directly, or to inform elicitation, or as a calibration target. Information specialist input would be required to design these literature searches.
Method 3: Application of the behaviour change intervention ontology to describe interventions to model
The behaviour change intervention ontology (BCIO) [ 63 ] has been developed to provide consistent language within which to describe interventions, including the mode of delivery, setting, source, schedule and dose, population, method of engagement, style of delivery, behaviour change techniques (the content of the interventions), mechanisms of action (how the interventions work), human behaviour and intervention fidelity [ 62 ]. The TIDieR Checklist has previously been developed to improve reporting of interventions [ 96 ], which includes many of these elements and may be less resource- intensive. However, the benefit of the BCIO is that for each label in the ontology there is a unique ID number. This can facilitate consistency between intervention development and evaluation, and across models and theories. It can also enable the synthesis of similar interventions within meta-analysis which can be computer automated, which can be used within the health economic modelling. Specifying the detail of the interventions in this way could also help in informing the behavioural systems map and understanding the potential long-term impacts of the interventions. It is yet to be determined whether the BCIO fully captures all elements of interventions, for example behavioural economic interventions; however, the intention is that the ontology will be updated accordingly within future versions when elements are found to be missing.
Where a set of interventions are being assessed in combination in the model, but effectiveness evidence is only available for the individual interventions, the content of the interventions and behavioural theory could be used to understand the mechanisms of action. If they operate through completely different mechanisms because their content is different, then assuming an additive effect may be appropriate; and if some overlap, then a multiplicative effect. In addition, based upon the themes identified for behaviour maintenance [ 60 ], maintenance motivation, resources, environmental support and self-efficacy are all required for behaviour maintenance. Thus, interventions which impact all of these, when combined, may substantially increase the probability of behaviour maintenance compared with any of the interventions alone. Since within a health economic model it is necessary to predict the impact of the interventions over the long term, this could be used to help choose plausible long-term assumptions of intervention combinations.
Method 4: Behavioural system mapping
Behavioural systems mapping is a developing method which uses systems thinking to diagrammatically represent relationships between the behaviour(s) of interest, interventions, actors, and the influences on behaviour, for the purpose of helping decision-makers or other stakeholders to understand systemic influences on behaviour(s) of interest [ 64 ] and the implications for interventions. Squires et al. [ 38 ] proposed the use of systems mapping to understand the problem needing to be modelled prior to developing the health economic model to understand relationships between factors and to help decide what to include and exclude from the model, but this framework did not consider influences on behaviour explicitly. To be able to model what may happen over time, modellers need to gain an understanding of the influences on the behaviour(s) and the impact of the intervention(s) upon the behaviour(s). Figure 2 within this paper and formal behavioural theories, alongside input from behavioural scientists, could be used to inform the development of a behavioural systems map. These could include both micro variables which describe individual attributes (e.g., motivation) and macro elements which describe aggregate level phenomenon (e.g., social network structures), and the potentially causal relationships between them. If the policy makers are aiming to reduce inequalities and/or consider intersectionality, then it will be important to consider the different influences impacting upon relevant groups of interest.
Describing these influences within a systems map will provide a tool for communication between behavioural and social scientists and health economic modellers and other stakeholders, increasing the understanding of the problem and facilitating the development of a useful model (see Supplementary material, “The School for Public Health Research (SPHR) diabetes prevention model”, for an example). Behavioural systems mapping can provide a transparent process through which to make decisions about what is included (or not) within the health economic model. It can also facilitate discussion about the social value judgements which may be made. The outcomes reported by intervention studies may limit what quantitative analyses can be done within the model, for example if only body mass index is reported rather than physical activity or dietary outcomes or the mechanisms of action associated with these. In addition, studies often do not report outcomes according to attributes associated with inequality (for example, by ethnic minority or gender) or intersectionality (for example, by Asian women). In these cases, behavioural systems mapping may provide a qualitative understanding of the micro and macro level influences, which can help to inform what to include and exclude from the model, assumptions beyond study follow up, and what further data collection may be useful.
Behavioural systems maps can be developed from participatory stakeholder input [ 66 ], and/or other sources including qualitative research such as interviews or quantitative analysis based on surveys [ 65 ]. These could either be developed at an early stage and could help to inform the choice of interventions to assess, or if the interventions have already been chosen, the theory used to develop the interventions (where available) could be incorporated.
Method 5: Agent based modelling
Agent-based modelling (ABM) is an individual-level simulation approach where the agents are given rules about their interactions with each other and their environment, which may depend on their individual characteristics [ 67 ]. It differs from typical microsimulation modelling because it is possible to incorporate interactions and feedback by explicitly modelling social networks and/ or the physical (built and natural) environment [ 71 ]. These interactions enable the analysis of emergent properties and tipping points where a new behaviour becomes the social norm over time. Whilst it is possible to model proximity to place within typical microsimulation models, more complex rules about the influence of social networks and the physical environment upon behaviour can be incorporated, as well as the possibility of changes to the environment as the behaviour of individuals changes. Individuals can be given different rules according to their psychological attributes or individual characteristics, which enables the impact of influences upon people’s behaviour to vary. In turn, this allows for more nuanced understanding of the equity impact of different interventions, including potential for disaggregation of results by intersectional subgroups. It is also possible to model multiple types of agents, for example, consumers and tobacco companies. There may be interactions between the behaviours of these different types of actors which lead to unexpected outcomes. It may be feasible to validate emergent population level impacts of the interventions with data.
If feasible given the resources available, ABMs are preferable over other modelling approaches when one or more of the following holds: (i) It would be useful to explore the impact of interactions between the behaviours of different stakeholders, such as the public and the tobacco industry; (ii) The model aims to assess the cost-effectiveness of interventions about access to places affecting public health, such as green spaces or food outlets; AND there is evidence of substantial interaction between the environment and behaviour; or (iii) There is evidence that social networks will substantially affect relevant outcomes (beyond what was reported in the intervention studies), which may include impacts on health inequalities AND it is unclear whether the interventions would be cost-effective without accounting for these additional impacts.
Models should be as simple as possible to capture the key drivers of the outcomes, and hence it is important to weigh up the benefits of developing an ABM and including the influences on behaviour within the model versus the time and resources required to build such a model. In some cases, there is very little evidence about the costs and/or effects of public health interventions in changing short term outcomes even at an aggregate level [ 3 , 97 ], hence more evidence needs to be collected before more complex models could be usefully parameterised for prediction. In addition, models are only useful if they are credible to stakeholders and policy makers. It may be that increased complexity may decrease credibility, and hence it will be beneficial to co-design models with stakeholders, discussing alternative modelling options, as well as reporting modelling methods and assumptions transparently [ 98 ].
ABMs require individual-level data about the population containing the key variables of interest and some evidence to inform the rules of the agents. One of the major advantages of ABM is its flexibility; any of the influences on behaviour outlined within Fig. 2 could be incorporated in an ABM if required, using a range of data, from qualitative to quantitative. Rules for the agents could be informed by (a combination of) behavioural systems mapping, qualitative research, heuristics, behavioural theory, statistical or econometric analyses, or secondary literature.
A few attempts have been made to incorporate psychological theory within ABMs of public health interventions [ 55 ]. These studies demonstrate the potential, but also the substantial time, skill and data requirements for such evaluations. Little justification is usually provided for the theory chosen, though some studies have undertaken systematic reviews [ 99 ] and conceptual modelling with expert input to identify the most appropriate theorical basis [ 100 ]. The only behavioural theory which has been quantified within a simulation model to date that considers behaviour maintenance explicitly is the Transtheoretical Model of Change [ 100 , 101 ]. This involves the stages: Precontemplation; Contemplation; Preparation; Action; and Maintenance, and it acknowledges the possibility of relapse. It has, however, been heavily criticised in the health psychology literature because it ignores habits and situational determinants of behaviour [ 26 ]. Buckley et al. [ 102 ] used dual process theory within a simulation model of alcohol consumption, with a habitual pathway and an intentional pathway, and a recognition that new habits in terms of alcohol consumption could be formed—a key determinant of behaviour maintenance [ 60 ]. Other simulation models that have quantified behavioural theory have updated the parameters at each time step but assumed the same mechanisms of behaviour maintenance as behaviour change [ 55 ]. The developers of the COM-B model have considered sustained behaviour change and suggest that changes to capability, opportunity and motivation must be mutually reinforcing for behaviour to be maintained [ 103 ].
It may be possible to utilise (or adapt) existing agent-based models of public health behaviours which use empirical data, and then incorporate the costs and effects of the interventions being assessed; however, many ABMs are developed to explore a population-level phenomenon over a short timeframe, so may not be easily modifiable for the purposes of health economic modelling (see the alcohol consumption example in the Supplementary Material, “The agent-based model of alcohol consumption”). Models built in modular form can be combined and reused, particularly if shared using open-source software. This would make it more feasible to build ABMs within the constraints of a policy making process. It would be possible to modify an existing individual level health economic model to incorporate the effects of social networks or the environment (see the diabetes prevention example in Supplementary material, “The School for Public Health Research (SPHR) diabetes prevention model”). A software architecture has recently been developed for mechanism-based social system modelling to incorporate behavioural and social theories within ABM [ 69 ]. Changes in macro level behaviours are generally determined by the interactions between micro level behaviours and macro level behaviours; however, it is also possible to use differential equations to represent macro level (behavioural) changes if they are not explained by the included micro level variables.
Method 6: Differential equation modelling
Differential equation models, including system dynamics models, use differential equations to capture the rates of change of a set of variables and the relationships between them [ 37 ]. These models are useful for describing the dynamics within the population, which may include how behaviour is influenced within a complex system [ 70 , 71 ]. However, it becomes more challenging to utilise this modelling method alone when decision makers would like to consider outcomes for many sociodemographic indicators, or when proximity to place is important in terms of behaviours. Differential equation models alone also cannot capture interactions between individuals in a way that would likely be useful for understanding the effect of interventions as they cannot explicitly capture different social network structures.
Method 7: Social network analysis
Social network analysis involves the collection and analysis of survey data about with whom individuals interact and their relationships, so that social or information networks can be specified using lines (relationships) and nodes (individuals/ organisations) [ 72 ]. At the same time behavioural outcomes of interest can be collected, such as alcohol consumption. The impact of the social networks upon behaviours can then be assessed.
It may be that intervention studies are undertaken within a population or community where the impacts of social networks are already (implicitly) captured, for example studies assessing the smoking ban in public places. Assuming additional impacts of the interventions due to social networks in this situation could lead to double counting if study follow up is sufficient. It is therefore important to understand the sample included within the intervention studies of interest; if it reflects the population and follow up is substantial then it may not be appropriate or worthwhile incorporating social networks explicitly. However, if social influences on behaviour would not have been captured by the intervention studies, then social network analysis could be considered. Undertaking social network analysis has the advantage that the relationship between the social network and the behaviour(s) of interest can be informed by the data collected.
Collecting data from the full network is ideal; however, practically this may not be possible. Egocentric network analysis, which can be done by collecting relationship data from individuals (or ‘egos’) from a sample of the population who may or may not be connected, may be more feasible and has been shown to provide reasonable results compared with a full network [ 74 ]. Most social network analysis assumes that social relationships are constant. Stochastic actor-based models for network dynamics allow social relationships to evolve over time as they would in practice [ 75 ]. This approach could be considered when an intervention might change social relationships, for example, if university students are given interventions to reduce binge drinking. It requires social network data for at least two time points, although preferably more.
A literature search for social network analysis associated with the behaviours of interest is recommended to first assess the benefits of including social network analysis and second for model parameterisation. Studies exist for some behaviours and outcomes which could be utilised [ 104 , 105 ] if the population is relevant. If there are no existing analyses and there are insufficient resources to undertake social network analysis, random networks, small world networks (all individuals linked by a small number of nodes) or scale free network (most people have a small number of connections, whilst a few have a large number) could be used within an ABM, and there are existing software packages to do this. However, these networks make ties at random or conditional on some characteristics and might not reflect the real social network, and assumptions are required about how individuals influence each other’s behaviours.
Method 8: Geographical Information System (GIS)
A GIS is essentially a spatial database that holds specifications of the spatial elements of a population (who lives where), or features of the physical landscape, such as roads or green spaces, or the built environment, such as food outlets [ 76 ]. Within ABMs, GIS information is often represented using one of two approaches; (i) raster data (that is, a large number of square cells, and then attributes are assigned to them; typically applied to environmental applications) or (ii) vector data (points, lines and polygons). The latter is the more ‘popular’ choice as this format (commonly termed shapefiles) allows the physical environment of roads, buildings and other urban features to be readily represented in fine granularity. With recent developments in popular platforms, such as Netlogo or GeoMason, it is possible to import shapefile layers directly into the platform and ABM simulations run directly within them [ 77 ].
Integrating GIS within ABMs may be useful for health economic modelling if an intervention being assessed is about access to certain places and there is substantial interaction between an element of the physical environment and behaviour, upon which there is some evidence. It could help to explore where places should be located to maximise cost-effectiveness and/ or reduce inequalities. Note, it is possible for ABMs to incorporate more abstract geographical elements in the absence of GIS to inform such policy decisions [ 106 ].
Undertaking GIS analysis requires geo-referenced data, many of which are online and open-source. These include OpenStreetMap (contains map data including roads, trails, cafes and railway stations) and Natural Earth Data (contains counties and points of interest) (see Crooks et al. [ 76 ] for a discussion of the different formats). There is also open-source software allowing individuals to create, edit, analyse and visualise the geographical data, which includes Quantum GIS and GRASS software. In addition, many of the platforms available for ABMs have the capability to process GIS data. For example, within NetLogo there is a GIS extension, and it is possible to import both raster and vector data [ 76 ].
Method 9: Discrete Event Simulation (DES)
DES is an individual level simulation approach where individuals interact with the system through a series of events, which may be resource constrained [ 78 ]. Within resource-constrained DES, queues can build up due to insufficient resources within the system, which can lead to long wait times. For health economic modelling, it is generally assumed that it would be feasible to implement interventions within the current system, with no additional physical resource requirements [ 79 ]. However, this may not always be the case, and limited resources within a system could affect individual behaviour and outcomes. For example, patients may decide not to utilise stop smoking services because the wait time is too long, particularly if they are less motivated to quit. Decision makers may want to evaluate the impacts of changing the physical resources, for example greater access to stop smoking services. Human behavioural theory could be used within a DES to model the staff that constrain the system (for example, staff may have long periods of sickness absence due to overwork which could lead to longer waiting lists and more overwork), or the individuals who use the system (for example, patients’ previous screening attendance may be a good predictor of future screening attendance). Thus, DES has advantages over other approaches for incorporating the influences on behaviour when behaviour and outcomes are influenced by physical resource constraints.
Currently, very few health economic DES models have been developed which incorporate behavioural theory [ 80 ]. There are lots of software options for DES, most of which provide a visual interface (e.g. Simul8 and Arena), which are helpful for sharing with stakeholders. DES requires information about timing of key events in the system, including arrival times, and quantity of constrained resources. Personal characteristics and psychological variables which would affect behaviour and outcomes of the people within the system would also ideally be incorporated.
Method 10: Theory-informed statistical and econometric analyses
Statistical analyses describe relationships between a set of variables, with econometric analyses involving the application of economic theory to formulate those relationships. There is a plethora of statistical and econometric methods available and so we do not attempt to cover them all here. Instead, the circumstances when these methods might be useful for incorporating the influences on behaviour into health economic models are described and references to papers discussing the key methods are provided. For any statistical approach chosen, these should be informed by available theory to have more confidence in results and avoid overfitting to the data.
Statistical methods could be used for incorporating the influences on behaviour within health economic models for the following (non-mutually exclusive) reasons:
To model the relationships between behaviours which influence each other;
To model the long-term impact of interventions upon behaviours;
To model population-level behaviours over time.
Econometric methods for modelling the relationships between behaviours could be considered when behaviours are highly likely to influence each other and the behaviours affect the same outcomes of interest to decision makers, for example, smoking and alcohol consumption [ 81 ]. Behaviours may be complements (decreasing one will decrease the other), substitutes (decreasing one will increase the other) or have no influence on each other. A longitudinal or repeated cross-sectional individual level data set with relevant variables and expertise in econometric analyses would be required to infer causal relationships between behaviours. The econometric analysis should highlight and discuss any necessary assumptions, especially those that cannot be tested.
Statistical analyses could be used to estimate the trajectories of behaviour and the impact of interventions upon a behavioural outcome where it is challenging to directly measure the longer term effects. Bianconcini and Bollen [ 82 ] describe a set of methods which can each be considered special cases of the Latent Variable-Autoregressive Latent Trajectory Model for longitudinal data analysis. Quantification of behavioural theories would be useful within a health economic model if: (i) the intervention aimed to change at least one variable within the theory (e.g., self-efficacy or social influences); or (ii) policy makers would like to explore targeting interventions at individuals with certain levels of a variable within a theory (e.g., level of physical resources or intention to quit). It should be noted that there is little time lag between changes in the mechanisms of action and behaviour, meaning it is not possible to predict future behaviour from current mechanisms of action in the same way that potential future disease can be predicted from current risk factors. In addition, data for the variables (e.g. a measure of motivation) are often not measured or reported within intervention studies. Increasingly, interventions may involve individuals reporting regular psychological, behavioural, health and economic outcomes on mobile phone apps. The use of such devices can provide many data points from individuals receiving an intervention, and could cheaply provide maintenance phase data, which would allow the longer-term impacts of the interventions to be better understood. Economic demand theory can be applied for statistically modelling the relationship between price and consumption where an intervention changes either supply or demand (e.g., implementing a soft drink tax). However, uncertainties around taxation policy impacts and long-term prediction should be highlighted; for example, there may be differences in the size of price changes observed in the data and those used for taxation policy changes. It is also important to consider heterogeneity in these relationships where possible.
Population-level behaviours may change over time because of ageing or external factors that affect the whole population (e.g., economic crisis, shift in social norms). These may affect different age groups differently and there may be cohort effects where behaviour varies according to birth year. Age Period Cohort analysis aims to understand and disentangle these effects using statistical analyses [ 84 ]. This may be useful where behaviour has been shown to change over time and it is likely that a behaviour is affected by all three effects, for example smoking. The analysis requires a longitudinal or repeat cross-sectional individual level dataset, with the relevant behaviours, age and external factors reported.
Method 11: Expert elicitation
Expert elicitation involves quantifying subjective and implicit expert knowledge and is useful when there is insufficient data available to quantify model parameters and their uncertainty [ 86 ]. Elicitation could be used to inform the parameters for the long-term model assumptions, particularly for the intervention effects, when there is a lack of quantitative data. Multiple experts should provide input [ 86 ], and these should include behavioural scientists. It is important to understand dependencies between elicited parameters (e.g., different points on a survival curve) so that the dependencies can be incorporated within the model explicitly [ 86 ].
In order to reduce bias, elicitation protocols should be followed [ 85 ]. Leading protocols include:
the Delphi method, where individual judgements are made, a summary of all the individual judgements is shared, before one or more additional rounds of providing judgements, followed by group summaries, until these are mathematically aggregated;
the Cooke protocol, where experts individually make judgements about uncertain quantities as well as quantities known to the researcher and then the uncertain judgements are weighted by their performance on the known quantities and mathematically aggregated; and
the Sheffield Elicitation Framework (SHELF) protocol, where individual judgements are made and then these are discussed with the group, including the reasons for differing opinions, until a consensus judgement is made.
For all of these protocols, questions should be piloted to ensure they are valid, intuitive and clear [ 86 ]. There are existing software and tutorials available to facilitate elicitation [ 85 ].
Method 12: Qualitative research/ process tracing
Qualitative research includes collecting and analysing data from observation, interviews or focus groups which can provide a richer understanding of how and why individuals behave in the way that they do in a set of hypothetical situations [ 87 ]. It can be used to inform model assumptions and may be able to provide more valid assumptions than those developed based on quantitative data alone. When making decisions about behaviours, people often use heuristics which are “strategies that ignore part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods” [ 29 , 30 ]. For instance, within an ABM, a heuristic decision tree may be used, where alternative cues affecting a decision are taken sequentially in order of importance, termed the fast and frugal approach [ 76 ], and these could be informed by qualitative research.
Process tracing is another way of describing the mechanisms between a cause (e.g., the intervention) and an outcome (e.g., the behaviour of interest), by observing how the intervention works within a set of individual cases [ 88 ]. Theory about why there is a relationship between a cause and outcome is developed and empirical observational data is collected to test and amend the theory within an iterative process. This could then be incorporated within a health economic model. However, this is a resource intensive process, and the theory should only be generalised very cautiously beyond the population within which it is tested.
Existing qualitative studies may be reviewed [ 89 ] or primary qualitative data collection and analyses could be undertaken where feasible. Data could be collected from a diverse set of people from the target population, or qualitative research may be useful for filling gaps in knowledge about the influences on behaviour for an understudied high-risk subgroup.
A new way of thinking
Health economic modelling should be founded in theory and use data to compare alternative interventions. Existing behavioural and social science is currently underutilised for making predictions within health economic models of public health interventions. A toolbox of methods for incorporating the influences on behaviour within health economic models has been developed and is made accessible via the accompanying website [ 54 ]. We anticipate that methods from the toolbox will be used in combination, and hybrid modelling approaches may be useful, including combining macro and micro level approaches. Within the behavioural and social sciences, theories and methods are constantly evolving, and it is anticipated that this toolbox will need to be updated as research within other disciplines develops.
There is much research being undertaken by behavioural scientists to develop public health interventions based upon behavioural theories and frameworks, but this is mostly not accessed by health economic modellers when evaluating interventions. In addition, health economic models are generally developed after an intervention has been tested and the data needed may not have been collected. There would be a benefit to greater collaboration between behavioural scientists and health economic modellers in a cyclical process of intervention development and evaluation, which could save costs and improve allocation of scarce resources.
Public health interventions operate within complex systems and as such it is essential to consider the importance of the interactions between individuals and with their context on predicting outcomes. Behavioural systems mapping can be used to understand these interactions and the potential relationships between interventions, psychological, social, biological and environmental mechanisms and behaviour, as well as for making transparent decisions about what to include and exclude from the health economic model and understanding the potential impacts of model simplifications [ 38 ]. Behavioural systems mapping could be useful for describing the different influences upon behaviour. It should be noted that complex models are not always necessary for modelling complex systems. For example, if an intervention is cost saving and has been shown to be effective, then it would not be necessary to develop such a model. In addition, there may not be sufficient time and/or data available. However, modelling complex systems can enable key influences on behaviour to be explored including the interactions between heterogenous individuals and their environment. When there is evidence of differential impacts according to relevant attributes, these models can be used to assess the impact of interventions on health inequalities. Whilst Big Data are becoming more accessible and may be useful to inform models, data alone cannot provide an understanding of all the relevant mechanisms and processes that are operating and should be accompanied by theory to make predictions.
It is important for policy makers to understand that it is typically not possible to predict, with any precision, long term outcomes within a complex system, and hence health economic models of such systems are unable to provide accurate cost-effectiveness estimates over a lifetime horizon for public health interventions. However, the process of model development and the model results can be informative in comparing and understanding different intervention options and facilitating decision making [ 107 ]. Health economic modellers should be clear in their reporting about the theories used, their assumptions, the limitations of the models and, the uncertainties within the model results. For ABMs and other individual-level simulations, there is a well-developed framework for model reporting [ 98 ].
Within health economic modelling, the main approach to quantifying uncertainty is probabilistic sensitivity analysis, where the uncertainty in model inputs is propagated through to model outputs [ 108 ]. It is recommended that structural uncertainties should be quantified within the probabilistic analysis where possible, and scenario analyses are run to explore alternative futures [ 109 ]. Within complex models, substantial uncertainties often exist within the model structures as well as the parameters, and it may not be feasible to quantify these. Machine learning has been used for exploring the impact of structural uncertainties, including comparing alternative behavioural theories [ 110 ]; however, this approach has not yet been used for health economic modelling. In addition, data may not exist for all variables of a behavioural theory. Calibration methods are likely to be required to inform unobserved parameters, within which uncertainty should be incorporated. Where possible, validation of each part of the model and the system level intermediate outcomes should be undertaken with different data from that used to build the model. Breeze et al. [ 71 ] provide a more detailed discussion of uncertainty analysis, validation and calibration within complex systems for economic evaluation; however further research is required in this area.
Developing modular models, where sections of code can be accessed as necessary, has the advantage of being able to be reused within other models, providing that code is shared within open-source software. This sharing between modellers can improve model building, verification, transparency and validation, as well as enabling faster model development. However, there are barriers to open code sharing which will need to be overcome [ 111 , 112 ]. There are different types of open-source licensing that can stipulate certain conditions, for example limitations on commercial use. Code sharing alongside clear model reporting would increase the feasibility of using these more complex modelling methods within a resource-constrained decision-making process. It could also allow models of different behaviours to be combined so that the interactions between different behaviours can be incorporated where this is important.
We emphasise that this paper aims to understand the interventions being evaluated so that appropriate assumptions about the long-term impacts of the interventions can be made. Ideally, modellers would understand the types of intervention (access to place, price changes, targeting individuals with specific characteristics/ psychological variables, targeting ‘influencers’ within a social network) and the sorts of evidence about intervention effectiveness (outcomes being reported, before and after study, number of data points, length of follow up, individual level data) which could change decisions about the methods employed. If this is not possible, then the model will need to be flexible to these considerations.
Agenda for further research
Currently, within most policy making arenas there is insufficient time and resources allocated to evaluating the cost-effectiveness of public health interventions, leading to very simple models of these complex systems being developed. Meanwhile, the studies of the interventions are very short term, not always clearly described, and with aggregate results presented. Therefore, there is a substantial further research agenda across fields to advance methods for incorporating the influences of behaviour into health economic models in order to better inform public health policy.
Develop collaborations between health economic modellers and behavioural/ social scientists to inform intervention development, to help understand at an earlier stage whether interventions are likely to be cost-effective and to ensure that useful outcomes for the health economic modelling are collected and reported. A process for working together effectively could be developed following the use of the toolbox within case studies. Health economic modellers could work with behavioural/ social scientists to understand how behaviour maintenance theories might best be utilised within health economic models, and what further research would be beneficial to improve long-term predictions of intervention effectiveness. The authors plan to set up a new network between modellers and behavioural scientists to encourage collaboration and to share resources.
Develop a consensus statement on the most appropriate behavioural theories for each health-related behaviour, ideally through collaboration between psychologists, sociologists and behavioural economists. Subsequently, develop and collect relevant standardised measures of behaviour and influences on behaviour, which use a consistent ontology. Collect longer term data where possible when evaluating the effectiveness of interventions. This could be done by using mobile phone apps or wearable sensors. Develop and test behaviour maintenance theories for different health-related behaviours.
Develop a suite of public health economic agent-based models which are built flexibly and reported open source, including coding, which would allow model reuse and adaptation. This would allow modellers who have limited resources and time within the decision-making process to build upon existing models. Standard social network structures and GIS data could be included which can be modified if both feasible and necessary. If these ABMs were built consistently across model behaviours, then they could link together if behaviours affect each other. Collaboration between health economic modellers and software engineers could improve model development efficiency and reuse. Exploiting recent advances in artificial intelligence may also facilitate this. Evaluate the benefits of the ABMs over standard health economic modelling approaches.
Develop methods for informing long term assumptions about intervention effectiveness. Test the appropriateness of developing expert panels and applying elicitation approaches to help inform structural assumptions and quantify parameters where there are no data. This could include lay people with relevant lived experience. Assess the feasibility of combining qualitative analysis with health economic modelling to inform behavioural assumptions. Explore the potential of utilising GP records (NHS digital) to assess the long-term effectiveness of interventions. Evaluate the benefits of these approaches.
Train modellers to utilise the new methods via short courses, webcasts, and workshops which would need to include an overview of the rationale and the methods, as well as demonstrating the use and outcomes with an example. Within the training, modellers could be given an opportunity to practice the methods using a simple example.
Public health intervention studies often have short-term follow up and they operate within dynamically complex systems. To model beyond the study data, it is essential to understand the influences upon behaviour, including the social determinants of health and health-related behaviours. A toolbox of methods has been developed as a starting point to help modellers incorporate the influences on behaviour into health economic models of public health interventions. The toolbox sets out when and why each method would be appropriate, and the minimum resources required. It may not always be feasible or necessary to model the influences on behaviour explicitly, but it is essential to develop an understanding of the key influences. Collaboration is needed between health economic modellers and behavioural/ social scientists throughout the process of intervention development and evaluation to help inform policy efficiently, and to generate approaches for utilising behaviour maintenance theories within health economic models. Further research is needed to develop a suite of more robust health economic models of health-related behaviours, reported transparently, including open-source model code, which would allow model reuse and adaption.
Availability of data and materials
All data generated or analysed during this study are included in this published article [and its supplementary information files]. The PHEM-B toolbox is also available in an accompanying website [ 54 ].
Abbreviations
Agent based model
Behaviour Change Intervention Ontology
Body Mass Index
Capability, Opportunity, Motivation-Behaviour
Discrete Event Simulation
Geographical Information System
Influences on Behaviour into Health Economic Models of Public Health interventions
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Acknowledgements
The authors would like to thank a patient and public involvement group who has informed this research. HS attended the UCL Introduction to Behaviour Change: Principles & Practice course which has helped inform the paper.
This study is funded by the NIHR Fellowship Programme (NIHR301406). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Nigel Gilbert’s contribution was supported by the ERSC through grant ES/Y001907/1 (CECAN). Robin C. Purshouse’s contribution was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Award Number R01AA024443 and Cancer Research UK under Award Number PRCRPG-Nov21\100002. Alison Heppenstall is funded by ESRC grants ES/L011921/1 and ES/S007105/1 and MRC MR/S037578/2; MC\_UU\_00022/5; SPHSU20. Sophie Bright, Robert Smith, Madeleine Henney and Esther Moore are funded via a Wellcome Trust PhD programme 218462/Z/19/Z.
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HS was responsible for the conception of the project and led the development of the toolbox. HS, MPK, NG, FS and RCP formed the core group who developed an early draft of possible methods and the workshop agenda. HS and AMC set out the requirements of behaviour change interventions to encourage modellers to use behavioural theory within health economic models which fed into the form of the paper and the agenda for further research. All authors (HS, MPK, NG, FS, RCP, LG, PB, AB, BG, SB, AF, AH, JDW, MHA, JB, CB, IV, RS, AA, RG, MH, EM, AMC) attended a workshop to develop the toolbox, reviewed multiple versions of the paper which involved substantial changes and approved the submitted version of the paper.
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Research on regional "dual carbon" targets and path planning based on mathematical modeling methods: a case study of southeast coastal China
- Published: 10 October 2024
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- Haiyang Shen 1 na1 ,
- Kai Guo 1 na1 ,
- Xuan Liao 1 ,
- Qimin Gao 1 ,
- Feng Wu 1 ,
- Fengwei Gu 1 &
- Zhichao Hu 1
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The growth of GDP and the increase in energy consumption have posed a challenge to crack the contradiction between development and emission reduction, and it is necessary to improve the efficiency of energy utilization and the proportion of non-fossil energy consumption. In this paper, to address the contradiction between development and emission reduction, based on the screening of regional population, GDP, energy consumption and carbon emission related indicators and indicator system, we have established Logistic Population Growth Model, Gray Prediction Model, Multiple Non-linear Regression Model and Extended STIRPAT Model, and realized the goal of "Dual Carbon" path planning. Mathematical models of energy efficiency and non-fossil energy share improvement under natural, baseline, and ambitious scenarios were constructed with the goal of realizing the "dual-carbon" path planning. The results show that under the dual-carbon target, the regional population is stagnant after 2025. In 2035, with the upgrading of the scenarios, the rate of carbon emissions gradually approaches 0, GDP doubles, and energy consumption decreases by 0.6%. In 2060, with the upgrading of the scenarios, the rate of carbon neutrality gradually rises, GDP is 12 times that of 2020, and energy consumption decreases by 4%, which is in line with the target vision. The study of regional dual-carbon targets and pathway planning based on mathematical modelling provides new ideas and methods for China and the world to crack the contradiction between development and emission reduction, which is of great significance for achieving carbon peak and carbon neutrality.
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Acknowledgements
This research was funded by Chinese Academy of Agricultural Sciences Innovation Project, Grant No. CAAS-31-NIAM.
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Haiyang Shen and Kai Guo have contributed equally to this work.
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Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, 210014, China
Haiyang Shen, Kai Guo, Xuan Liao, Qimin Gao, Feng Wu, Fengwei Gu & Zhichao Hu
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Shen, H., Guo, K., Liao, X. et al. Research on regional "dual carbon" targets and path planning based on mathematical modeling methods: a case study of southeast coastal China. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05513-5
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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 ...
The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case. It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed ...
Revised on November 20, 2023. A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are ...
Case study method is the most widely used method in academia for researchers interested in qualitative research (Baskarada, 2014). Research students select the case study as a method without understanding array of factors that can affect the outcome of their research.
Definition, Methods, and Examples. Case study methodology offers researchers an exciting opportunity to explore intricate phenomena within specific contexts using a wide range of data sources and collection methods. It is highly pertinent in health and social sciences, environmental studies, social work, education, and business studies.
The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case. Robert Yin, methodologist most associated with case study research, differentiates between descriptive, exploratory and explanatory case studies:
A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...
A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are sometimes also used.
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.
To conclude, there are two main objectives of this study. First is to provide a step-by-step guideline to research students for conducting case study. Second, an analysis of authors' multiple case studies is presented in order to provide an application of step-by-step guideline. This article has been divided into two sections.
1. Select a case. Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research. 2.
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The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies. Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.
A case study is defined as an in-depth analysis of a particular subject, often a real-world situation, individual, group, or organization. It is a research method that involves the comprehensive examination of a specific instance to gain a better understanding of its complexities, dynamics, and context.
Qualitative case study methodology provides tools for researchers to study complex phenomena within their contexts. When the approach is applied correctly, it becomes a valuable method for health ...
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 ...
A case study is an in-depth analysis of one individual or group. Learn more about how to write a case study, including tips and examples, and its importance in psychology. ... The Case Study as Research Method: A Practical Handbook. Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.
Case study method enables a researcher to closely examine the data within a specific context. In most cases, a case study method selects a small geograph ical area or a very li mited number. of ...
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As per Yin , case study is a form of social research inquiry where examining the context is particularly critical to understanding the case. We follow an inductive approach in exploring the effectiveness of this particular CoP within its specific context to identify a potential pattern for the use of CoPs to promote evaluative thinking in ...
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