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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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• 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• 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.

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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

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

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The 2021-2022 academic year marks the 100-year anniversary of the introduction of the case method at Harvard Business School. Today, the HBS case method is employed in the HBS MBA program, in Executive Education programs, and in dozens of other business schools around the world. As Dean Srikant Datar's says, the case method has withstood the test of time.

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The goal of a research proposal is twofold: to present and justify the need to study a research problem and to present the practical ways in which the proposed study should be conducted. The design elements and procedures for conducting research are governed by standards of the predominant discipline in which the problem resides, therefore, the guidelines for research proposals are more exacting and less formal than a general project proposal. Research proposals contain extensive literature reviews. They must provide persuasive evidence that a need exists for the proposed study. In addition to providing a rationale, a proposal describes detailed methodology for conducting the research consistent with requirements of the professional or academic field and a statement on anticipated outcomes and benefits derived from the study's completion.

Krathwohl, David R. How to Prepare a Dissertation Proposal: Suggestions for Students in Education and the Social and Behavioral Sciences . Syracuse, NY: Syracuse University Press, 2005.

How to Approach Writing a Research Proposal

Your professor may assign the task of writing a research proposal for the following reasons:

• Develop your skills in thinking about and designing a comprehensive research study;
• Learn how to conduct a comprehensive review of the literature to determine that the research problem has not been adequately addressed or has been answered ineffectively and, in so doing, become better at locating pertinent scholarship related to your topic;
• Improve your general research and writing skills;
• Practice identifying the logical steps that must be taken to accomplish one's research goals;
• Critically review, examine, and consider the use of different methods for gathering and analyzing data related to the research problem; and,
• Nurture a sense of inquisitiveness within yourself and to help see yourself as an active participant in the process of conducting scholarly research.

A proposal should contain all the key elements involved in designing a completed research study, with sufficient information that allows readers to assess the validity and usefulness of your proposed study. The only elements missing from a research proposal are the findings of the study and your analysis of those findings. Finally, an effective proposal is judged on the quality of your writing and, therefore, it is important that your proposal is coherent, clear, and compelling.

Regardless of the research problem you are investigating and the methodology you choose, all research proposals must address the following questions:

• What do you plan to accomplish? Be clear and succinct in defining the research problem and what it is you are proposing to investigate.
• Why do you want to do the research? In addition to detailing your research design, you also must conduct a thorough review of the literature and provide convincing evidence that it is a topic worthy of in-depth study. A successful research proposal must answer the "So What?" question.
• How are you going to conduct the research? Be sure that what you propose is doable. If you're having difficulty formulating a research problem to propose investigating, go here for strategies in developing a problem to study.

Common Mistakes to Avoid

• Failure to be concise . A research proposal must be focused and not be "all over the map" or diverge into unrelated tangents without a clear sense of purpose.
• Failure to cite landmark works in your literature review . Proposals should be grounded in foundational research that lays a foundation for understanding the development and scope of the the topic and its relevance.
• Failure to delimit the contextual scope of your research [e.g., time, place, people, etc.]. As with any research paper, your proposed study must inform the reader how and in what ways the study will frame the problem.
• Failure to develop a coherent and persuasive argument for the proposed research . This is critical. In many workplace settings, the research proposal is a formal document intended to argue for why a study should be funded.
• Sloppy or imprecise writing, or poor grammar . Although a research proposal does not represent a completed research study, there is still an expectation that it is well-written and follows the style and rules of good academic writing.
• Too much detail on minor issues, but not enough detail on major issues . Your proposal should focus on only a few key research questions in order to support the argument that the research needs to be conducted. Minor issues, even if valid, can be mentioned but they should not dominate the overall narrative.

Procter, Margaret. The Academic Proposal.  The Lab Report. University College Writing Centre. University of Toronto; Sanford, Keith. Information for Students: Writing a Research Proposal. Baylor University; Wong, Paul T. P. How to Write a Research Proposal. International Network on Personal Meaning. Trinity Western University; Writing Academic Proposals: Conferences, Articles, and Books. The Writing Lab and The OWL. Purdue University; Writing a Research Proposal. University Library. University of Illinois at Urbana-Champaign.

Structure and Writing Style

Beginning the Proposal Process

As with writing most college-level academic papers, research proposals are generally organized the same way throughout most social science disciplines. The text of proposals generally vary in length between ten and thirty-five pages, followed by the list of references. However, before you begin, read the assignment carefully and, if anything seems unclear, ask your professor whether there are any specific requirements for organizing and writing the proposal.

A good place to begin is to ask yourself a series of questions:

• What do I want to study?
• Why is the topic important?
• How is it significant within the subject areas covered in my class?
• What problems will it help solve?
• How does it build upon [and hopefully go beyond] research already conducted on the topic?
• What exactly should I plan to do, and can I get it done in the time available?

In general, a compelling research proposal should document your knowledge of the topic and demonstrate your enthusiasm for conducting the study. Approach it with the intention of leaving your readers feeling like, "Wow, that's an exciting idea and I can’t wait to see how it turns out!"

Most proposals should include the following sections:

I.  Introduction

In the real world of higher education, a research proposal is most often written by scholars seeking grant funding for a research project or it's the first step in getting approval to write a doctoral dissertation. Even if this is just a course assignment, treat your introduction as the initial pitch of an idea based on a thorough examination of the significance of a research problem. After reading the introduction, your readers should not only have an understanding of what you want to do, but they should also be able to gain a sense of your passion for the topic and to be excited about the study's possible outcomes. Note that most proposals do not include an abstract [summary] before the introduction.

Think about your introduction as a narrative written in two to four paragraphs that succinctly answers the following four questions :

• What is the central research problem?
• What is the topic of study related to that research problem?
• What methods should be used to analyze the research problem?
• Answer the "So What?" question by explaining why this is important research, what is its significance, and why should someone reading the proposal care about the outcomes of the proposed study?

II.  Background and Significance

This is where you explain the scope and context of your proposal and describe in detail why it's important. It can be melded into your introduction or you can create a separate section to help with the organization and narrative flow of your proposal. Approach writing this section with the thought that you can’t assume your readers will know as much about the research problem as you do. Note that this section is not an essay going over everything you have learned about the topic; instead, you must choose what is most relevant in explaining the aims of your research.

To that end, while there are no prescribed rules for establishing the significance of your proposed study, you should attempt to address some or all of the following:

• State the research problem and give a more detailed explanation about the purpose of the study than what you stated in the introduction. This is particularly important if the problem is complex or multifaceted .
• Present the rationale of your proposed study and clearly indicate why it is worth doing; be sure to answer the "So What? question [i.e., why should anyone care?].
• Describe the major issues or problems examined by your research. This can be in the form of questions to be addressed. Be sure to note how your proposed study builds on previous assumptions about the research problem.
• Explain the methods you plan to use for conducting your research. Clearly identify the key sources you intend to use and explain how they will contribute to your analysis of the topic.
• Describe the boundaries of your proposed research in order to provide a clear focus. Where appropriate, state not only what you plan to study, but what aspects of the research problem will be excluded from the study.
• If necessary, provide definitions of key concepts, theories, or terms.

III.  Literature Review

Connected to the background and significance of your study is a section of your proposal devoted to a more deliberate review and synthesis of prior studies related to the research problem under investigation . The purpose here is to place your project within the larger whole of what is currently being explored, while at the same time, demonstrating to your readers that your work is original and innovative. Think about what questions other researchers have asked, what methodological approaches they have used, and what is your understanding of their findings and, when stated, their recommendations. Also pay attention to any suggestions for further research.

Since a literature review is information dense, it is crucial that this section is intelligently structured to enable a reader to grasp the key arguments underpinning your proposed study in relation to the arguments put forth by other researchers. A good strategy is to break the literature into "conceptual categories" [themes] rather than systematically or chronologically describing groups of materials one at a time. Note that conceptual categories generally reveal themselves after you have read most of the pertinent literature on your topic so adding new categories is an on-going process of discovery as you review more studies. How do you know you've covered the key conceptual categories underlying the research literature? Generally, you can have confidence that all of the significant conceptual categories have been identified if you start to see repetition in the conclusions or recommendations that are being made.

NOTE: Do not shy away from challenging the conclusions made in prior research as a basis for supporting the need for your proposal. Assess what you believe is missing and state how previous research has failed to adequately examine the issue that your study addresses. Highlighting the problematic conclusions strengthens your proposal. For more information on writing literature reviews, GO HERE .

To help frame your proposal's review of prior research, consider the "five C’s" of writing a literature review:

• Cite , so as to keep the primary focus on the literature pertinent to your research problem.
• Compare the various arguments, theories, methodologies, and findings expressed in the literature: what do the authors agree on? Who applies similar approaches to analyzing the research problem?
• Contrast the various arguments, themes, methodologies, approaches, and controversies expressed in the literature: describe what are the major areas of disagreement, controversy, or debate among scholars?
• Critique the literature: Which arguments are more persuasive, and why? Which approaches, findings, and methodologies seem most reliable, valid, or appropriate, and why? Pay attention to the verbs you use to describe what an author says/does [e.g., asserts, demonstrates, argues, etc.].
• Connect the literature to your own area of research and investigation: how does your own work draw upon, depart from, synthesize, or add a new perspective to what has been said in the literature?

IV.  Research Design and Methods

This section must be well-written and logically organized because you are not actually doing the research, yet, your reader must have confidence that you have a plan worth pursuing . The reader will never have a study outcome from which to evaluate whether your methodological choices were the correct ones. Thus, the objective here is to convince the reader that your overall research design and proposed methods of analysis will correctly address the problem and that the methods will provide the means to effectively interpret the potential results. Your design and methods should be unmistakably tied to the specific aims of your study.

Describe the overall research design by building upon and drawing examples from your review of the literature. Consider not only methods that other researchers have used, but methods of data gathering that have not been used but perhaps could be. Be specific about the methodological approaches you plan to undertake to obtain information, the techniques you would use to analyze the data, and the tests of external validity to which you commit yourself [i.e., the trustworthiness by which you can generalize from your study to other people, places, events, and/or periods of time].

When describing the methods you will use, be sure to cover the following:

• Specify the research process you will undertake and the way you will interpret the results obtained in relation to the research problem. Don't just describe what you intend to achieve from applying the methods you choose, but state how you will spend your time while applying these methods [e.g., coding text from interviews to find statements about the need to change school curriculum; running a regression to determine if there is a relationship between campaign advertising on social media sites and election outcomes in Europe ].
• Keep in mind that the methodology is not just a list of tasks; it is a deliberate argument as to why techniques for gathering information add up to the best way to investigate the research problem. This is an important point because the mere listing of tasks to be performed does not demonstrate that, collectively, they effectively address the research problem. Be sure you clearly explain this.
• Anticipate and acknowledge any potential barriers and pitfalls in carrying out your research design and explain how you plan to address them. No method applied to research in the social and behavioral sciences is perfect, so you need to describe where you believe challenges may exist in obtaining data or accessing information. It's always better to acknowledge this than to have it brought up by your professor!

V.  Preliminary Suppositions and Implications

Just because you don't have to actually conduct the study and analyze the results, doesn't mean you can skip talking about the analytical process and potential implications . The purpose of this section is to argue how and in what ways you believe your research will refine, revise, or extend existing knowledge in the subject area under investigation. Depending on the aims and objectives of your study, describe how the anticipated results will impact future scholarly research, theory, practice, forms of interventions, or policy making. Note that such discussions may have either substantive [a potential new policy], theoretical [a potential new understanding], or methodological [a potential new way of analyzing] significance.   When thinking about the potential implications of your study, ask the following questions:

• What might the results mean in regards to challenging the theoretical framework and underlying assumptions that support the study?
• What suggestions for subsequent research could arise from the potential outcomes of the study?
• What will the results mean to practitioners in the natural settings of their workplace, organization, or community?
• Will the results influence programs, methods, and/or forms of intervention?
• How might the results contribute to the solution of social, economic, or other types of problems?
• Will the results influence policy decisions?
• In what way do individuals or groups benefit should your study be pursued?
• What will be improved or changed as a result of the proposed research?
• How will the results of the study be implemented and what innovations or transformative insights could emerge from the process of implementation?

NOTE:   This section should not delve into idle speculation, opinion, or be formulated on the basis of unclear evidence . The purpose is to reflect upon gaps or understudied areas of the current literature and describe how your proposed research contributes to a new understanding of the research problem should the study be implemented as designed.

ANOTHER NOTE : This section is also where you describe any potential limitations to your proposed study. While it is impossible to highlight all potential limitations because the study has yet to be conducted, you still must tell the reader where and in what form impediments may arise and how you plan to address them.

VI.  Conclusion

The conclusion reiterates the importance or significance of your proposal and provides a brief summary of the entire study . This section should be only one or two paragraphs long, emphasizing why the research problem is worth investigating, why your research study is unique, and how it should advance existing knowledge.

Someone reading this section should come away with an understanding of:

• Why the study should be done;
• The specific purpose of the study and the research questions it attempts to answer;
• The decision for why the research design and methods used where chosen over other options;
• The potential implications emerging from your proposed study of the research problem; and
• A sense of how your study fits within the broader scholarship about the research problem.

VII.  Citations

As with any scholarly research paper, you must cite the sources you used . In a standard research proposal, this section can take two forms, so consult with your professor about which one is preferred.

• References -- a list of only the sources you actually used in creating your proposal.
• Bibliography -- a list of everything you used in creating your proposal, along with additional citations to any key sources relevant to understanding the research problem.

In either case, this section should testify to the fact that you did enough preparatory work to ensure the project will complement and not just duplicate the efforts of other researchers. It demonstrates to the reader that you have a thorough understanding of prior research on the topic.

Most proposal formats have you start a new page and use the heading "References" or "Bibliography" centered at the top of the page. Cited works should always use a standard format that follows the writing style advised by the discipline of your course [e.g., education=APA; history=Chicago] or that is preferred by your professor. This section normally does not count towards the total page length of your research proposal.

Develop a Research Proposal: Writing the Proposal. Office of Library Information Services. Baltimore County Public Schools; Heath, M. Teresa Pereira and Caroline Tynan. “Crafting a Research Proposal.” The Marketing Review 10 (Summer 2010): 147-168; Jones, Mark. “Writing a Research Proposal.” In MasterClass in Geography Education: Transforming Teaching and Learning . Graham Butt, editor. (New York: Bloomsbury Academic, 2015), pp. 113-127; Juni, Muhamad Hanafiah. “Writing a Research Proposal.” International Journal of Public Health and Clinical Sciences 1 (September/October 2014): 229-240; Krathwohl, David R. How to Prepare a Dissertation Proposal: Suggestions for Students in Education and the Social and Behavioral Sciences . Syracuse, NY: Syracuse University Press, 2005; Procter, Margaret. The Academic Proposal. The Lab Report. University College Writing Centre. University of Toronto; Punch, Keith and Wayne McGowan. "Developing and Writing a Research Proposal." In From Postgraduate to Social Scientist: A Guide to Key Skills . Nigel Gilbert, ed. (Thousand Oaks, CA: Sage, 2006), 59-81; Wong, Paul T. P. How to Write a Research Proposal. International Network on Personal Meaning. Trinity Western University; Writing Academic Proposals: Conferences , Articles, and Books. The Writing Lab and The OWL. Purdue University; Writing a Research Proposal. University Library. University of Illinois at Urbana-Champaign.

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Blog Business How to Present a Case Study like a Pro (With Examples)

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

Written by: Danesh Ramuthi Sep 07, 2023

Okay, let’s get real: case studies can be kinda snooze-worthy. But guess what? They don’t have to be!

In this article, I will cover every element that transforms a mere report into a compelling case study, from selecting the right metrics to using persuasive narrative techniques.

And if you’re feeling a little lost, don’t worry! There are cool tools like Venngage’s Case Study Creator to help you whip up something awesome, even if you’re short on time. Plus, the pre-designed case study templates are like instant polish because let’s be honest, everyone loves a shortcut.

Click to jump ahead: 

What is a case study presentation?

What is the purpose of presenting a case study, how to structure a case study presentation, how long should a case study presentation be, 5 case study presentation examples with templates, 6 tips for delivering an effective case study presentation, 5 common mistakes to avoid in a case study presentation, how to present a case study faqs.

A case study presentation involves a comprehensive examination of a specific subject, which could range from an individual, group, location, event, organization or phenomenon.

They’re like puzzles you get to solve with the audience, all while making you think outside the box.

Unlike a basic report or whitepaper, the purpose of a case study presentation is to stimulate critical thinking among the viewers. 

The primary objective of a case study is to provide an extensive and profound comprehension of the chosen topic. You don’t just throw numbers at your audience. You use examples and real-life cases to make you think and see things from different angles.

The primary purpose of presenting a case study is to offer a comprehensive, evidence-based argument that informs, persuades and engages your audience.

Here’s the juicy part: presenting that case study can be your secret weapon. Whether you’re pitching a groundbreaking idea to a room full of suits or trying to impress your professor with your A-game, a well-crafted case study can be the magic dust that sprinkles brilliance over your words.

Think of it like digging into a puzzle you can’t quite crack . A case study lets you explore every piece, turn it over and see how it fits together. This close-up look helps you understand the whole picture, not just a blurry snapshot.

It’s also your chance to showcase how you analyze things, step by step, until you reach a conclusion. It’s all about being open and honest about how you got there.

Besides, presenting a case study gives you an opportunity to connect data and real-world scenarios in a compelling narrative. It helps to make your argument more relatable and accessible, increasing its impact on your audience.

One of the contexts where case studies can be very helpful is during the job interview. In some job interviews, you as candidates may be asked to present a case study as part of the selection process.

Having a case study presentation prepared allows the candidate to demonstrate their ability to understand complex issues, formulate strategies and communicate their ideas effectively.

The way you present a case study can make all the difference in how it’s received. A well-structured presentation not only holds the attention of your audience but also ensures that your key points are communicated clearly and effectively.

In this section, let’s go through the key steps that’ll help you structure your case study presentation for maximum impact.

Let’s get into it. 

Open with an introductory overview 

Start by introducing the subject of your case study and its relevance. Explain why this case study is important and who would benefit from the insights gained. This is your opportunity to grab your audience’s attention.

Explain the problem in question

Dive into the problem or challenge that the case study focuses on. Provide enough background information for the audience to understand the issue. If possible, quantify the problem using data or metrics to show the magnitude or severity.

Detail the solutions to solve the problem

After outlining the problem, describe the steps taken to find a solution. This could include the methodology, any experiments or tests performed and the options that were considered. Make sure to elaborate on why the final solution was chosen over the others.

Key stakeholders Involved

Talk about the individuals, groups or organizations that were directly impacted by or involved in the problem and its solution. 

Stakeholders may experience a range of outcomes—some may benefit, while others could face setbacks.

For example, in a business transformation case study, employees could face job relocations or changes in work culture, while shareholders might be looking at potential gains or losses.

Discuss the key results & outcomes

Discuss the results of implementing the solution. Use data and metrics to back up your statements. Did the solution meet its objectives? What impact did it have on the stakeholders? Be honest about any setbacks or areas for improvement as well.

Include visuals to support your analysis

Visual aids can be incredibly effective in helping your audience grasp complex issues. Utilize charts, graphs, images or video clips to supplement your points. Make sure to explain each visual and how it contributes to your overall argument.

Pie charts illustrate the proportion of different components within a whole, useful for visualizing market share, budget allocation or user demographics.

This is particularly useful especially if you’re displaying survey results in your case study presentation.

Stacked charts on the other hand are perfect for visualizing composition and trends. This is great for analyzing things like customer demographics, product breakdowns or budget allocation in your case study.

Consider this example of a stacked bar chart template. It provides a straightforward summary of the top-selling cake flavors across various locations, offering a quick and comprehensive view of the data.

Not the chart you’re looking for? Browse Venngage’s gallery of chart templates to find the perfect one that’ll captivate your audience and level up your data storytelling.

Recommendations and next steps

Wrap up by providing recommendations based on the case study findings. Outline the next steps that stakeholders should take to either expand on the success of the project or address any remaining challenges.

Acknowledgments and references

Thank the people who contributed to the case study and helped in the problem-solving process. Cite any external resources, reports or data sets that contributed to your analysis.

Feedback & Q&A session

Open the floor for questions and feedback from your audience. This allows for further discussion and can provide additional insights that may not have been considered previously.

Closing remarks

Conclude the presentation by summarizing the key points and emphasizing the takeaways. Thank your audience for their time and participation and express your willingness to engage in further discussions or collaborations on the subject.

Well, the length of a case study presentation can vary depending on the complexity of the topic and the needs of your audience. However, a typical business or academic presentation often lasts between 15 to 30 minutes. 

This time frame usually allows for a thorough explanation of the case while maintaining audience engagement. However, always consider leaving a few minutes at the end for a Q&A session to address any questions or clarify points made during the presentation.

When it comes to presenting a compelling case study, having a well-structured template can be a game-changer. 

It helps you organize your thoughts, data and findings in a coherent and visually pleasing manner. 

Not all case studies are created equal and different scenarios require distinct approaches for maximum impact. 

To save you time and effort, I have curated a list of 5 versatile case study presentation templates, each designed for specific needs and audiences. 

Here are some best case study presentation examples that showcase effective strategies for engaging your audience and conveying complex information clearly.

1 . Lab report case study template

Ever feel like your research gets lost in a world of endless numbers and jargon? Lab case studies are your way out!

Think of it as building a bridge between your cool experiment and everyone else. It’s more than just reporting results – it’s explaining the “why” and “how” in a way that grabs attention and makes sense.

This lap report template acts as a blueprint for your report, guiding you through each essential section (introduction, methods, results, etc.) in a logical order.

Want to present your research like a pro? Browse our research presentation template gallery for creative inspiration!

2. Product case study template

It’s time you ditch those boring slideshows and bullet points because I’ve got a better way to win over clients: product case study templates.

Instead of just listing features and benefits, you get to create a clear and concise story that shows potential clients exactly what your product can do for them. It’s like painting a picture they can easily visualize, helping them understand the value your product brings to the table.

Grab the template below, fill in the details, and watch as your product’s impact comes to life!

3. Content marketing case study template

In digital marketing, showcasing your accomplishments is as vital as achieving them. 

A well-crafted case study not only acts as a testament to your successes but can also serve as an instructional tool for others. 

With this coral content marketing case study template—a perfect blend of vibrant design and structured documentation, you can narrate your marketing triumphs effectively.

4. Case study psychology template

Understanding how people tick is one of psychology’s biggest quests and case studies are like magnifying glasses for the mind. They offer in-depth looks at real-life behaviors, emotions and thought processes, revealing fascinating insights into what makes us human.

Writing a top-notch case study, though, can be a challenge. It requires careful organization, clear presentation and meticulous attention to detail. That’s where a good case study psychology template comes in handy.

Think of it as a helpful guide, taking care of formatting and structure while you focus on the juicy content. No more wrestling with layouts or margins – just pour your research magic into crafting a compelling narrative.

5. Lead generation case study template

Lead generation can be a real head-scratcher. But here’s a little help: a lead generation case study.

Think of it like a friendly handshake and a confident resume all rolled into one. It’s your chance to showcase your expertise, share real-world successes and offer valuable insights. Potential clients get to see your track record, understand your approach and decide if you’re the right fit.

No need to start from scratch, though. This lead generation case study template guides you step-by-step through crafting a clear, compelling narrative that highlights your wins and offers actionable tips for others. Fill in the gaps with your specific data and strategies, and voilà! You’ve got a powerful tool to attract new customers.

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

So, you’ve spent hours crafting the perfect case study and are now tasked with presenting it. Crafting the case study is only half the battle; delivering it effectively is equally important. 

Whether you’re facing a room of executives, academics or potential clients, how you present your findings can make a significant difference in how your work is received. 

Forget boring reports and snooze-inducing presentations! Let’s make your case study sing. Here are some key pointers to turn information into an engaging and persuasive performance:

• Know your audience : Tailor your presentation to the knowledge level and interests of your audience. Remember to use language and examples that resonate with them.
• Rehearse : Rehearsing your case study presentation is the key to a smooth delivery and for ensuring that you stay within the allotted time. Practice helps you fine-tune your pacing, hone your speaking skills with good word pronunciations and become comfortable with the material, leading to a more confident, conversational and effective presentation.
• Start strong : Open with a compelling introduction that grabs your audience’s attention. You might want to use an interesting statistic, a provocative question or a brief story that sets the stage for your case study.
• Be clear and concise : Avoid jargon and overly complex sentences. Get to the point quickly and stay focused on your objectives.
• Use visual aids : Incorporate slides with graphics, charts or videos to supplement your verbal presentation. Make sure they are easy to read and understand.
• Tell a story : Use storytelling techniques to make the case study more engaging. A well-told narrative can help you make complex data more relatable and easier to digest.

Ditching the dry reports and slide decks? Venngage’s case study templates let you wow customers with your solutions and gain insights to improve your business plan. Pre-built templates, visual magic and customer captivation – all just a click away. Go tell your story and watch them say “wow!”

Nailed your case study, but want to make your presentation even stronger? Avoid these common mistakes to ensure your audience gets the most out of it:

Overloading with information

A case study is not an encyclopedia. Overloading your presentation with excessive data, text or jargon can make it cumbersome and difficult for the audience to digest the key points. Stick to what’s essential and impactful. Need help making your data clear and impactful? Our data presentation templates can help! Find clear and engaging visuals to showcase your findings.

Lack of structure

Jumping haphazardly between points or topics can confuse your audience. A well-structured presentation, with a logical flow from introduction to conclusion, is crucial for effective communication.

Ignoring the audience

Different audiences have different needs and levels of understanding. Failing to adapt your presentation to your audience can result in a disconnect and a less impactful presentation.

Poor visual elements

While content is king, poor design or lack of visual elements can make your case study dull or hard to follow. Make sure you use high-quality images, graphs and other visual aids to support your narrative.

Not focusing on results

A case study aims to showcase a problem and its solution, but what most people care about are the results. Failing to highlight or adequately explain the outcomes can make your presentation fall flat.

How to start a case study presentation?

Starting a case study presentation effectively involves a few key steps:

• Grab attention : Open with a hook—an intriguing statistic, a provocative question or a compelling visual—to engage your audience from the get-go.
• Set the stage : Briefly introduce the subject, context and relevance of the case study to give your audience an idea of what to expect.
• Outline objectives : Clearly state what the case study aims to achieve. Are you solving a problem, proving a point or showcasing a success?
• Agenda : Give a quick outline of the key sections or topics you’ll cover to help the audience follow along.
• Set expectations : Let your audience know what you want them to take away from the presentation, whether it’s knowledge, inspiration or a call to action.

How to present a case study on PowerPoint and on Google Slides?

Presenting a case study on PowerPoint and Google Slides involves a structured approach for clarity and impact using presentation slides :

• Title slide : Start with a title slide that includes the name of the case study, your name and any relevant institutional affiliations.
• Introduction : Follow with a slide that outlines the problem or situation your case study addresses. Include a hook to engage the audience.
• Objectives : Clearly state the goals of the case study in a dedicated slide.
• Findings : Use charts, graphs and bullet points to present your findings succinctly.
• Analysis : Discuss what the findings mean, drawing on supporting data or secondary research as necessary.
• Conclusion : Summarize key takeaways and results.
• Q&A : End with a slide inviting questions from the audience.

What’s the role of analysis in a case study presentation?

The role of analysis in a case study presentation is to interpret the data and findings, providing context and meaning to them. 

It helps your audience understand the implications of the case study, connects the dots between the problem and the solution and may offer recommendations for future action.

Is it important to include real data and results in the presentation?

Yes, including real data and results in a case study presentation is crucial to show experience,  credibility and impact. Authentic data lends weight to your findings and conclusions, enabling the audience to trust your analysis and take your recommendations more seriously

How do I conclude a case study presentation effectively?

To conclude a case study presentation effectively, summarize the key findings, insights and recommendations in a clear and concise manner. 

End with a strong call-to-action or a thought-provoking question to leave a lasting impression on your audience.

What’s the best way to showcase data in a case study presentation ?

The best way to showcase data in a case study presentation is through visual aids like charts, graphs and infographics which make complex information easily digestible, engaging and creative. 

Don’t just report results, visualize them! This template for example lets you transform your social media case study into a captivating infographic that sparks conversation.

Choose the type of visual that best represents the data you’re showing; for example, use bar charts for comparisons or pie charts for parts of a whole. 

Ensure that the visuals are high-quality and clearly labeled, so the audience can quickly grasp the key points. 

Keep the design consistent and simple, avoiding clutter or overly complex visuals that could distract from the message.

Choose a template that perfectly suits your case study where you can utilize different visual aids for maximum impact. 

Need more inspiration on how to turn numbers into impact with the help of infographics? Our ready-to-use infographic templates take the guesswork out of creating visual impact for your case studies with just a few clicks.

Related: 10+ Case Study Infographic Templates That Convert

Congrats on mastering the art of compelling case study presentations! This guide has equipped you with all the essentials, from structure and nuances to avoiding common pitfalls. You’re ready to impress any audience, whether in the boardroom, the classroom or beyond.

And remember, you’re not alone in this journey. Venngage’s Case Study Creator is your trusty companion, ready to elevate your presentations from ordinary to extraordinary. So, let your confidence shine, leverage your newly acquired skills and prepare to deliver presentations that truly resonate.

Go forth and make a lasting impact!

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Engaging stakeholders to retrospectively discern implementation strategies to support program evaluation: Proposed method and case study

Affiliations.

• 1 US Department of Veterans Affairs Medical Center, Central Arkansas Veterans Healthcare System, HSR&D Center of Innovation: Center for Mental Healthcare & Outcomes Research, 2200 Fort Roots Drive, North Little Rock, AR 72114, USA; University of Arkansas for Medical Sciences, College of Pharmacy, Division of Pharmaceutical Evaluation & Policy, 4301 W Markham St., Little Rock, AR 72205, USA. Electronic address: [email protected].
• 2 US Department of Veterans Affairs Medical Center, Central Arkansas Veterans Healthcare System, HSR&D Center of Innovation: Center for Mental Healthcare & Outcomes Research, 2200 Fort Roots Drive, North Little Rock, AR 72114, USA. Electronic address: [email protected].
• 3 US Department of Veterans Affairs Medical Center, Central Arkansas Veterans Healthcare System, HSR&D Center of Innovation: Center for Mental Healthcare & Outcomes Research, 2200 Fort Roots Drive, North Little Rock, AR 72114, USA; Saint Louis University, School of Social Work, 3500 Lindell Blvd., Saint Louis, MO 63103, USA. Electronic address: [email protected].
• 4 US Department of Veterans Affairs Medical Center, Central Arkansas Veterans Healthcare System, HSR&D Center of Innovation: Center for Mental Healthcare & Outcomes Research, 2200 Fort Roots Drive, North Little Rock, AR 72114, USA. Electronic address: [email protected].
• 5 US Department of Veterans Affairs Medical Center, Central Arkansas Veterans Healthcare System, HSR&D Center of Innovation: Center for Mental Healthcare & Outcomes Research, 2200 Fort Roots Drive, North Little Rock, AR 72114, USA. Electronic address: [email protected].
• 6 US Department of Veterans Affairs Medical Center, Central Arkansas Veterans Healthcare System, Geriatric Research, Education and Clinical Center, 2200 Fort Roots Drive, North Little Rock, AR 72114, USA; University of Arkansas for Medical Sciences, College of Medicine, Department of Psychiatry, 4301 W Markham St., Little Rock, AR 72205, USA. Electronic address: [email protected].
• PMID: 38183893
• DOI: 10.1016/j.evalprogplan.2023.102398

Background: Availability of evidence-based practices (EBPs) is critical for improving health care outcomes, but diffusion can be challenging. Implementation activities increase the adoption of EBPs and support sustainability. However, when implementation activities are a part of quality improvement processes, evaluation of the time and cost associated with these activities is challenged by the need for a correct classification of these activities to a known taxonomy of implementation strategies by implementation actors.

Design: Observational study of a four-stage, stakeholder-engaged process for identifying implementation activities and estimating the associated costs.

Results: A national initiative in the Veterans Health Administration (VHA) to improve Advance Care Planning (ACP) via Group Visits (ACP-GV) for rural veterans identified 49 potential implementation activities. Evaluators translated and reduced these to 14 strategies used across three groups with the aid of implementation actors. Data were collected to determine the total implementation effort and applied cost estimates to estimate the budget impact of implementation for VHA.

Limitations: Recall bias may influence the identification of potential implementation activities.

Conclusions: This process improved understanding of the implementation effort and allowed estimation of ACP-GV 's budget impact.

Implications: A four-stage, stakeholder-engaged methodology can be applied to other initiatives when a pragmatic evaluation of implementation efforts is needed.

Keywords: Budget impact analysis; Implementation; Program evaluation; Stakeholder engagement.

Published by Elsevier Ltd.

Publication types

• Observational Study
• Evidence-Based Practice* / methods
• Program Evaluation
• Quality Improvement
• Retrospective Studies

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Open Access

Peer-reviewed

Research Article

A novel method for multiple phenotype association studies based on genotype and phenotype network

Roles Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America

Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Xuewei Cao, 
• Shuanglin Zhang, 
• Qiuying Sha

• Published: May 10, 2024
• https://doi.org/10.1371/journal.pgen.1011245
• Reader Comments

This is an uncorrected proof.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

Author summary

Biological pleiotropy refers to a SNP or gene that has a direct biological influence on more than one phenotypic trait, which can offer significant insights in understanding the complex genotype-phenotype relationships. Network analyses provide an integrative approach to characterize complex genomic associations by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). Jointly analyzing multiple phenotypes and incorporating the genetic information into the phenotype clustering may increase the statistical power to discover the cross-phenotype association and pleiotropy. We evaluate our proposed multiple phenotype association tests based on network modules detected by GPN for 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue in the UK Biobank. From the post-GWAS analyses, we observe that the test based on GPN can identify more significantly enriched biological pathways than that without considering the network modules. Meanwhile, some of the uniquely identified SNPs by the test based on GPN are also colocalized in the eQTL study of the gene expression in the Muscle Skeletal tissue.

Citation: Cao X, Zhang S, Sha Q (2024) A novel method for multiple phenotype association studies based on genotype and phenotype network. PLoS Genet 20(5): e1011245. https://doi.org/10.1371/journal.pgen.1011245

Editor: Heather J. Cordell, Newcastle University, UNITED KINGDOM

Received: August 18, 2023; Accepted: March 29, 2024; Published: May 10, 2024

Copyright: © 2024 Cao 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: The UK Biobank data are accessed via https://www.ukbiobank.ac.uk/ . The GWAS catalog summary data are accessed via https://www.ebi.ac.uk/gwas/ . The SNP-gene associations in the Muscle Skeletal tissue are downloaded via https://gtexportal.org/home/ . Software The software for the proposed method is publicly available at https://github.com/xueweic/GPN . PLINK version 1.9 can be downloaded from https://www.cog-genomics.org/plink/1.9/ . LDSC: the command line tool for estimating heritability and genetic correlation from GWAS summary statistics can be downloaded from https://github.com/bulik/ldsc [ 27 ]. FUMA: the platform that can be used to annotate, prioritize, visualize and interpret GWAS results can be found from https://fuma.ctglab.nl/ [ 57 ]. DAVID: the functional tool can be found from https://david.ncifcrf.gov/ [ 65 , 66 ]. Cytoscape: an open source software platform for visualizing complex networks which can be accessed via https://cytoscape.org/ [ 71 ].

Funding: The work was in part funded by the Michigan Technological University Health Research Institute Fellowship program, the Portage Health Foundation Graduate Assistantship, and Graduate Dean Awards Advisory Panel. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) genetically associated with a wide range of complex human diseases and traits [ 1 , 2 ]. Over the past decade, more than 10,000 associations between SNPs and diseases/traits have been discovered [ 3 ]. Although GWAS have emerged as a common and powerful tool to detect the complexity of the genotype-phenotype associations, a common limitation of GWAS is that they focus on only a single phenotype at a time [ 4 – 7 ]. Joint analysis of multiple correlated phenotypes for GWAS may provide more power to identify and interpret pleiotropic loci, which are essential to understand pleiotropy in diseases and complex traits [ 4 , 8 , 9 ]. In brief, biological pleiotropy refers to a SNP or gene that has a direct biological influence on more than one phenotypic trait [ 10 ]. Biological pleiotropy can offer significant insights in understanding the complex genotype-phenotype relationships [ 2 ]. Therefore, multiple phenotypes are usually collected in many GWAS cohorts and jointly analyzing multiple phenotypes may increase statistical power to discover the cross-phenotype associations and pleiotropy [ 10 – 13 ].

Many statistical methods have been developed to jointly test the association between a SNP and multiple correlated phenotypes [ 14 ]. The most widely used methods for multiple phenotype association studies can be roughly classified into three categories: 1) statistical tests based on combining either the univariate test statistics or p-values, such as O’Brien’s method [ 15 ], adaptive Fisher’s combination (AFC) [ 16 ], aSPU [ 17 ], and others [ 18 ]; 2) multivariate analyses based on regression methods, such as multivariate analysis of variance (MANOVA) [ 19 ], reverse regression methods (MultiPhen) [ 20 ], linear mixed effect models (LMM) [ 21 ], and generalized estimating equations (GEE) [ 22 ]; and 3) dimension reduction methods, such as clustering linear combination (CLC) [ 12 ], canonical correlation analysis (CCA) [ 23 ], and principal components analysis (PCA) [ 24 , 25 ]. However, most phenotypes are influenced by many SNPs that act in concert to alter cellular function [ 26 ], the above mentioned methods are only based on phenotypic correlation without considering the genetic correlation among phenotypes. Therefore, these methods may loss statistical power to detect the true pleiotropic effects comparing the methods based on genetic architecture among complex diseases. To address this issue, numerous types of algorithms to investigate the genetic correlation among complex traits and diseases have been developed [ 27 – 29 ]. Many of these algorithms are often in conjunction with linkage disequilibrium (LD) information by using GWAS summary association data [ 28 ]. For example, cross-trait LD score regression has been developed to estimate genetic and phenotypic correlation that requires only GWAS summary statistics and is not biased by overlapping samples [ 27 ].

In 2007, a conceptually different approach based on the human disease network had been developed, exploring whether human complex traits and the corresponding genotypes might be related to each other at a higher level of cellular and organismal organization [ 30 ]. Network analyses provide an integrative approach to characterize complex genomic associations [ 31 ]. Over the past decade, network methodologies, particularly Weighted Gene Co-expression Network Analysis (WGCNA) [ 32 ], have become increasingly popular in genetic association studies. This popularity is due to their effectiveness in identifying complex patterns of gene expression and clarifying the relationships among genes. Researchers have applied WGCNA to unravel the genetic underpinnings of complex traits, focusing primarily on gene-gene interactions [ 33 , 34 ]. However, constructing networks that map the associations between phenotypes and genotypes can provide fresh perspectives, enabling the simultaneous analysis of multiple phenotypes and SNPs. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy [ 8 ]. Modules detected from human disease networks are useful in providing insights pertaining to biological functionality [ 35 ]. Therefore, community detection methods play a key role in understanding the global and local structures of disease interaction, in shedding light on association connections that may not be easily visible in the network topology [ 36 ]. Many community detection methods have been applied from social networks to human disease networks, such as Louvain’s method [ 8 ] with modularity as a measure and core module identification to identify small and structurally well-defined communities [ 35 ]. However, most community detection methods have been developed for unsigned networks [ 37 – 43 ].

To date, many biobanks, such as the UK Biobank [ 44 ], aggregate data across tens of thousands of phenotypes and provide a great opportunity to construct the human disease network and perform joint analyses of multiple correlated phenotypes. The electronic health record (EHR)-driven genomic research (EDGR) workflow is the most popular way to analyze multiple diagnosis codes in Biobank data, at its core, which is the use of EHR data for genomic research in the investigation of population-wide genomic characterization [ 45 ]. In most EHR systems, the whole phenome can be divided into numerous phenotypic categories according to the first few characters of the International Classification of Disease (ICD) billing codes [ 46 ]. However, the ICD-based categories are based on the underlying cause of death rather than on the shared genetic architecture among all complex diseases and traits. Meanwhile, the phenotypes in large biobanks usually have extremely unbalanced case-control ratios. Therefore, linking phenotypes, especially EHR-derived phenotypes, with genotypes in a network is also very important to examine the genetic architecture of complex diseases and traits.

Overview of methods

In this paper, we develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN; Fig 1A ). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to phenotypes with extremely unbalanced case-control ratios for large-scale biobank datasets since the saddlepoint approximation [ 47 ] is used to test the association between genotype and phenotype with extremely unbalanced case-control ratio. After projecting genotypes into phenotypes, the genetic correlation of phenotypes can be calculated based on the shared associations among all genotypes ( Fig 1B ). We then apply a powerful community detection method to partition phenotypes into disjoint network modules using the hierarchical clustering method and the number of modules is determined by perturbation ( Fig 1C ) [ 48 ]. The phenotypes in each network module share the same genetic information. After partitioning phenotypes into disjoint network modules, a statistical method for multiple phenotype association studies can be applied to test the association between phenotypes in each module and a SNP, then a Bonferroni correction can be used to test if all phenotypes are associated with a SNP ( Fig 1D ). To jointly analyze the association between multiple phenotypes in each module with a SNP, we use six multiple phenotype association tests, including ceCLC [ 49 ], CLC [ 12 ], HCLC [ 50 ], MultiPhen [ 20 ], O’Brien [ 15 ], and Omnibus [ 12 ]. The advantage of the association test based on network modules detected by GPN is that phenotypes in a network module are highly correlated based on the genetic architecture, therefore, the association test is more powerful to identify pleiotropic SNPs. After we obtain the GWAS signals from the previous steps, post-GWAS analyses can be applied to understand the high level of biological mechanism, such as pathway/tissue enrichment analysis and colocalization of GWAS signals and eQTL analysis in the specific disease-associated tissue ( Fig 1E–1G ). The construction of GPN, community detection method, and six multiple phenotype association tests have been implemented in R, which is an open-source software and publicly available on GitHub: https://github.com/xueweic/GPN .

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a . Construction of GPN. Each phenotype (yellow square) and each SNP form a directed edge which represents the strength of the association, where the red dashed line indicates that the minor allele of the SNP is a protective allele to the phenotype, and the blue dashed line indicates that the minor allele of the SNP is a risk allele to the phenotype. b . Construction of PPN, which is the one-mode projection of GPN on phenotypes. c . The community detection method is used to partition phenotypes into disjoint network modules. d . Multiple phenotype association tests based on the network modules detected by GPN. e . GWAS signals are identified by a multiple phenotype association test with or without considering network modules. f . Functional enrichment analysis based on the detected GWAS signals and the publicly available functional database. g . Colocalization of GWAS signals and eQTL analysis. (All networks are generated by an open source software platform, Cytoscape 0.9.2, which can be accessed via https://cytoscape.org/ [ 51 ]; Other figures are generated by an open source software, R 4.2.2, which can be accessed via https://www.r-project.org/ ).

https://doi.org/10.1371/journal.pgen.1011245.g001

Simulation studies

We first use extensive simulation studies to validate multiple phenotype association studies based on the newly proposed GPN. In the simulation studies, we assess the type I error rate and power with different numbers of phenotypes (60, 80, and 100), different types of phenotypes along with different sample sizes: (i) mixture phenotypes are half quantitative and half qualitative with balanced case-control ratios for sample sizes of 2,000 and 4,000, and (ii) binary phenotypes are all qualitative but with extremely unbalanced case-control ratios for sample sizes of 10,000 and 20,000. Similar to the simulation models introduced in Sha et al. [ 12 ], we generate six different models (see Data Simulation).

Type I error rates

Tables A-F in S1 Text summarize the estimated type I error rates of six multiple phenotype association tests for mixture phenotypes under models 1–6, respectively. “N.O.” represents the type I error rates of multiple phenotype association tests being calculated without considering network modules; “NET” presents the type I error rates of the tests being evaluated by considering network modules detected by GPN. Based on 500 Monte-Carlo (MC) runs which is the same as 10 6 replicates, the 95% confidence intervals (Cis) for type I error rates divided by nominal significance levels 0.001 and 0.0001 are (0.938, 1.062) and (0.804,1.196), respectively. The bold-faced values indicate that the values are beyond the upper bounds of the 95% Cis. We can see that almost all of the estimated type I error rates of ceCLC, CLC, HCLC, and Omnibus tests are within 95% Cis. However, O’Brien in NET has inflated type I error rates under model 6. MultiPhen has inflated type I error rates for the sample size of 2,000. If the sample size is 4000, MultiPhen in N.O. also inflates type I error rates, but MultiPhen in NET can control type I error rates for the significance level is 0.0001. Tables G-L in S1 Text summarize the estimated type I error rates of six multiple phenotype association tests for binary phenotypes with extremely unbalanced case-control ratios under models 1–6. Similar to Tables A-F in S1 Text , ceCLC, CLC, HCLC, and Omnibus have corrected type I error rates at almost all simulation settings. However, O’Brien in NET has inflated type I error rates and MultiPhen has inflated type I error rates at all scenarios. If there is no clusters of the phenotypes, we also see that only MultiPhen has inflated type I error rates and other five multiple phenotype association tests have well-controlled type I error rates ( Table M in S1 Text ).

Power comparisons

For power comparisons, we consider 100 causal SNPs for models 1–4 and 200 causal SNPs for models 5–6 (see Data Simulation). In each of the simulation models, the power is evaluated using 10 MC runs which is the same as 1,000 replicates for models 1–4 and 2,000 replicates for models 5–6. Meanwhile, the power is evaluated at the Bonferroni corrected significance level of 0.05 based on the number of causal SNPs in each MC run.

Fig 2 ( Fig A in S1 Text ) shows the power of six multiple phenotype association tests under six simulation models for different effect sizes with a total of 80 mixture phenotypes and a sample size of 4,000 (2,000). From Figs 2 and A in S1 Text , we can see that: (i) All tests in NET (filled by the dashed line) are much more powerful than those in N.O., indicating that tests based on network modules detected by GPN are more powerful than the tests without considering network modules. Since the community detection method can partition phenotypes into different network modules based on shared genetic architecture, the phenotypes can be clustered in the same module if they have higher genetic correlations. In particular, the power of O’Brien [ 15 ] increases a lot in the case of a SNP affecting phenotypes in different directions. (ii) ceCLC is more powerful than other tests in both N.O. and NET under the six simulation models. (iii) As sample size increases, the power of all multiple phenotype association tests increases. We also perform power comparisons for a total of 60 and 100 mixture phenotypes with 2,000 and 4,000 sample sizes for different effect sizes under the six simulation models ( Figs B-E in S1 Text ), respectively. We observe that the patterns of the power are similar to those observed in Figs 2 and A in S1 Text .

The number of mixture phenotypes (half continuous phenotypes and half binary phenotypes with balanced case-control ratios) is 80 and the sample size is 4,000. The power of all of the six tests is evaluated using 10 MC runs. (Figure is generated by an open source software, R 4.2.2, which can be accessed via https://www.r-project.org/ ).

https://doi.org/10.1371/journal.pgen.1011245.g002

To mimic phenotypes in the UK Biobank, we also consider the case with all phenotypes being binary with extremely unbalanced case-control ratios. The phenotypes are generated based on extremely unbalanced case-control ratios which are randomly selected from the set of case-control ratios with cases greater than 200 from UK Biobank ICD-10 code level 3 phenotypes (see Real Dataset; case-control ratios belong to [0.000658,0.03937]). In this simulation, we consider a total of 60, 80, and 100 phenotypes along with two sample sizes, 10,000 and 20,000. Figs F-K in S1 Text show the power comparisons of the six tests under six simulation models. Fig L in S1 Text shows the power comparisons of the six tests under the models that mimic real data cluster structures. The patterns of power comparisons for binary phenotypes and for the models that mimic real data cluster structure are similar to those observed in Figs 2 and A-E in S1 Text .

Real data analysis based on UK Biobank

Furthermore, we apply the newly proposed multiple phenotype association test based on network modules detected by GPN to a set of diseases of the musculoskeletal system and connective tissue across more than 300,000 individuals from the UK Biobank.

Network module detection

We construct GPN based on 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue with 288,647 SNPs in autosomal chromosomes in the UK Biobank. Due to all phenotypes in our analysis being extremely unbalanced, the strength of the association between phenotype and genotype is calculated by the saddlepoint approximation [ 47 ]. After the construction of GPN, we apply a powerful community detection method and these 72 phenotypes are partitioned into 8 disjoint network modules ( Fig 3 ). There are 2–37 phenotypes in each module.

The blocks with different color indicate different modules, where the values in the legend represent the number of phenotypes in each network module. The labels of phenotypes are listed in the form of ICD-10 code and the corresponding diseases can be found in the UK Biobank. The connection between two phenotypes represents the absolutely value of the weight greater than 40. (The graph was prepared by Cytoscape 0.9.2, which can be accessed via https://cytoscape.org/ )

https://doi.org/10.1371/journal.pgen.1011245.g003

We can see that the network modules are not consistent with the ICD-based categories which are based on the underlying cause of death rather than the shared genetic architecture among all complex diseases. For example, Fig 3 shows three phenotypes, M32.9 Systemic lupus erythematosus, M35.0 Sicca syndrome, and M65.3 Trigger finger, are detected in network module III (in red). However, these three phenotypes do not belong to the same ICD-category (Data-Field 41202 in UK Biobank), where M35.0 is one of the diseases in the other systemic involvement of connective tissue (M35) and M65.3 belongs to the synovitis and tenosynovitis (M65). To investigate the genetic correlation among these three phenotypes, we use the saddlepoint approximation to test the association between each phenotype and each SNP. As shown in Fig M in S1 Text , the Manhattan plots for the three phenotypes in network module III (M32.9, M35.0, and M65.3) have a similar pattern. Although the synovitis and tenosynovitis (M65.9) and M65.3 belong to the same ICD code category (M65), the Manhattan plot of M65.9 shows that there are no SNPs significantly associated with this phenotype and the genetic correlation between M65.9 and M65.3 is not strong. Therefore, we can conclude that the community detection method based on our proposed GPN can partition phenotypes into different categories based on the shared genetic architecture.

Furthermore, we apply the hierarchical clustering method to compare the genetic correlation of phenotypes obtained by our proposed GPN and that estimated by LDSC [ 27 ]. Figs N-O in S1 Text show that dendrograms of hierarchical clustering method based on the genetic correlation of phenotypes obtained by GPN, and the phenotypic or genetic correlation estimated by LDSC, respectively. In Fig N in S1 Text , the cluster results of the phenotypic correlation estimated by LDSC are similar to that of the genetic correlation based on GPN, but GPN can separately identify two highly genetic correlated phenotypes, ankylosing spondylitis (M45) and ankylosing spondylitis with site unspecified (M45.X9). However, the cluster results of the genetic correlation estimated by LDSC are different from those obtained by GPN. Some phenotypes in the same UK Biobank level 1 category can be clustered in the same group by GPN but not by LDSC ( Fig O in S1 Text ).

Interpretation of the association test.

We apply five multiple phenotype association tests (ceCLC, CLC, HCLC, O’Brien, and Omnibus) to test the association between 72 EHR-derived phenotypes and each of 288,647 SNPs in the UK Biobank. MultiPhen is not considered here since it has inflated type I error rates, especially for the phenotypes with extremely unbalanced case-control ratios.

First, we apply the five tests in N.O. to test the association between 72 phenotypes and each SNP. We use the commonly used genome-wide significance level 5×10 −8 . Fig 4A shows the Venn diagram of the number of SNPs identified by the five tests. There are 11 SNPs identified by all five tests. ceCLC identifies 647 SNPs with 32 unique SNPs not being identified by other four tests. Among the 32 novel SNPs, two SNPs, rs13107325 (p-value = 4.6×10 −10 ) and rs443198 (p-value = 1.73×10 −11 ), are significantly associated with at least one of the 72 phenotypes reported in the GWAS catalog ( Table N in S1 Text ). rs13107325 is reported to be associated with osteoarthritis (M19.9) [ 52 ] and rotator cuff syndrome (M75.1) [ 53 ]. Meanwhile, rs13107325 is mapped to gene SLC39A8 that is also reported to be significantly associated with multisite chronic pain (M25.5) [ 54 ]. rs443198 is mapped to gene NOTCH4 which is associated with systemic sclerosis (M34) [ 55 ]. Moreover, the mapped gene NOTCH4 is one of the most important genes reported to be associated with multiple diseases in the disease category of the musculoskeletal system and connective tissue, such as rheumatoid arthritis (M06.9) [ 56 ], psoriatic arthritis (M07.3) [ 57 ], Takayasu arteritis (M31.4) [ 58 ], systemic lupus erythematosus (M32.9) [ 59 ], and appendicular lean mass (M62.9) [ 60 ]. We map these 32 unique SNPs into genes with 20 kb upstream and 20 kb downstream regions. There are 27 out of 32 SNPs with corresponding mapped genes associated with 14 phenotypes reported in the GWAS catalog ( Table N in S1 Text ). These 14 phenotypes and corresponding ICD-10 codes are summarized in Table O in S1 Text .

( a ) and in NET ( b ). The number below each method indicates the total number of SNPs identified by the corresponding method. (Figure is generated by an open source software, R 4.2.2, which can be accessed via https://www.r-project.org/ ).

https://doi.org/10.1371/journal.pgen.1011245.g004

Next, we test the associations between phenotypes in each of the eight network modules detected by the GPN and each SNP. Then, we adjust the p-value of each method for testing the association between a SNP and all of the 72 phenotypes by Bonferroni correction. We adopt the commonly used genome-wide significance level 5×10 −8 . Fig 4B shows that all tests can identify more SNPs comparing with the number of SNPs identified in N.O. ceCLC in NET identifies 980 SNPs, where 647 SNPs are identified in N.O. Meanwhile, there are 950 SNPs identified by HCLC, 949 SNPs by CLC, and 891 SNPs by Omnibus, where the corresponding results in N.O. are 354 SNPs, 808 SNPs, and 634 SNPs, respectively. In particular, the number of SNPs identified by O’Brien in NET is increased a lot, where there are 948 SNPs identified in NET and only 57 SNPs identified in N.O. As the results shown in Fig 4B , there are 807 overlapped SNPs identified by all five tests in NET which is much larger than 11 overlapped SNPs identified in N.O.

To compare the difference between the tests in N.O. and in NET, we summarize the number of overlapping SNPs identified by each method in N.O. and NET in Fig P in S1 Text . We observe that most SNPs identified in N.O. can be identified in NET. Meanwhile, tests in NET can identify much more SNPs than those in N.O. As mentioned previously, the advantage of the tests based on the network modules detected by GPN is that we can identify potential pleiotropic SNPs and also interpret SNP effects on which network modules based on the shared genetic architecture. Notably, we also investigate the smallest p-value obtained by each of the eight phenotypic modules for each of the 980 SNPs identified by ceCLC. For example, 396 SNPs have the smallest p-values for testing the association with network module III. Based on the results of the univariate score test corrected for saddlepoint approximation (SPAtest) ( Fig M in S1 Text ), 104 SNPs are significantly associated with at least one phenotype in module III. All of these 104 SNPs can be identified by ceCLC, HCLC, and Omnibus in NET and 103 SNPs can be identified by CLC and O’Brien in NET. The results show that the tests based on network modules can detect potential pleiotropic loci which can not be detected by the univariate test. Fig Q in S1 Text shows the QQ plots and inflation factors in each of the eight network modules for six tests in the real data analysis. We see that the inflation factors for all approaches are close to 1.

Pathway enrichment analysis.

ceCLC is more powerful than the other four tests in simulations and also can identify more SNPs in real data analysis, therefore, we only perform the post-GWAS analyses of the SNPs identified by ceCLC. There are 191 mapped genes containing at least one of the 647 SNPs identified by ceCLC in N.O. and 252 mapped genes containing at least one of the 980 SNPs identified by ceCLC in NET. In this study, significantly enriched pathways are identified by those genes with false discovery rate (FDR) < 0.05.

From the pathway enrichment analyses, we observe that ceCLC based on the network modules identifies more significantly enriched pathways than that without considering network modules. Fig 5 shows that 16 pathways are significantly enriched by 191 mapped genes in N.O. and 29 pathways are significantly enriched by 252 mapped genes in NET, where all of the 16 pathways identified in N.O. are also identified in NET. Two pathways identified in N.O. and NET, rheumatoid arthritis (hsa05323; FDR = 8.72×10 −3 in N.O. and FDR = 6.48×10 −8 in NET) and systemic lupus erythematosus (hsa05322; FDR = 4.25×10 −19 in N.O. and FDR = 1.02×10 −40 in NET) showed in Fig 5 , are related to the diseases of the musculoskeletal system and connective tissue. For example, osteopetrosis (M19.9) and rheumatoid arthritis (M06.9) are related to the rheumatoid arthritis pathway. Meanwhile, the pathway related to at least one of the 72 phenotypes, hematopoietic cell lineage (hsa04640; FDR = 1.08×10 −5 ), is only identified in NET. Notably, DBGET system ( https://www.genome.jp/dbget-bin/www_bget?hsa05322 ) reports that there are two pathways related to systemic lupus erythematosus: antigen processing and presentation (hsa04612; FDR = 4.83×10 −3 in N.O. and FDR = 2.82×10 −16 in NET) identified in both N.O. and NET and cell adhesion molecule (hsa04514; FDR = 1.04×10 −5 ) only identified in NET.

( a ) and NET ( b ). The red marked pathways denote the pathways related to the diseases of the musculoskeletal system and connective tissue. There are 191 genes in N.O. and 252 genes in NET that are applied to the pathway enrichment analysis. (Figure is generated by an open source software, R 4.2.2, which can be accessed via https://www.r-project.org/ ).

https://doi.org/10.1371/journal.pgen.1011245.g005

Meanwhile, the above five pathways related to the diseases of the musculoskeletal system and connective tissue contain more enriched genes identified by ceCLC in NET than the enriched genes identified in N.O. For example, 43 SNPs within six mapped genes identified by ceCLC in N.O. are enriched in rheumatoid arthritis pathway, including ATP6V1G2 , HLA-DRA , LTB , TNF , HLA-DRB1 , and HLA-DQA1 ; and 111 SNPs within 12 mapped genes in NET are enriched in this pathway, including HLA-DMA , HLA-DMB , ATP6V1G2 , HLA-DRA , LTB , HLA-DOA , TNF , HLA-DOB , HLA-DQA2 , HLA-DRB1 , HLA-DQA1 , and HLA-DQB1 . Compared with the results of ceCLC in N.O., the test based on network modules identifies six more enriched genes, especially, gene HLA-DMB (including rs241458; p-value = 7.09×10 −9 ) and gene HLA-DOA (including rs3097646; p-value = 5.50×10 −9 ) that have not been reported in the GWAS catalog.

Tissue enrichment analysis.

To further investigate the biological mechanism, we use FUMA [ 61 ] to annotate 191 mapped genes in N.O. and 252 mapped genes in NET in terms of biological context. Due to these mapped genes associated with at least one phenotype in the diseases of the musculoskeletal system and connective tissue, we can test if these mapped genes are enriched in the relevant-tissue based on FUMA. Fig R in S1 Text shows the ordered enriched tissues based on the mapped genes identified by ceCLC in N.O. and NET. We observe that the mapped genes identified by ceCLC in N.O. are most enriched in brain-related tissue ( Fig R(a) in S1 Text ). Nevertheless, Fig R(b) in S1 Text shows that the mapped genes identified by ceCLC in NET are significantly enriched in the Muscle-Skeletal tissue with p-value < 0.05. The construction of GPN is benefit to multiple phenotype association studies by clustering the related phenotypes based on the genetic information. Notably, the identified SNPs are more likely to be within the same relevant biological context.

Colocalization of GWAS and eQTL analysis.

We perform the colocalization analysis on the 33 unique SNPs identified by ceCLC ( Table N in S1 Text ; one SNP in NET and 32 SNPs in N.O.) and all SNP-gene association pairs in the Muscle Skeletal tissue reported in GTEx. Fig S in S1 Text shows the colocalization signals with the uniquely identified SNPs by ceCLC that are selected to be the lead SNPs in the colocalization analysis. NET identifies one unique SNP, rs4148866, which is mapped to gene ABCB9 . Even if gene ABCB9 has no reported associations with any diseases of the musculoskeletal system and connective tissue in the GWAS Catalog, the Bayesian posterior probability of colocalization analysis for shared variant of significant SNPs identified by ceCLC and gene expression in the Muscle Skeletal tissue (PP H4 ) is 98.4%. The higher value of PP H4 indicates that gene ABCB9 and Muscle Skeletal tissue play an important role in the disease mechanism due to the same variant responsible for a GWAS locus and also affecting gene expression [ 62 ]. Among 32 unique SNPs identified by ceCLC in N.O., there are two SNPs, rs34333163 and rs6916921, selected to be the lead SNPs ( Fig S in S1 Text ). Both of them are reported in the GWAS Catalog that have associations with at least one of the diseases in the musculoskeletal system. However, the PP H4 values for the corresponding genes SLC38A8 and ATP6V1G2 are lower than 50%.

In this paper, we propose a novel method for multiple phenotype association studies based on genotype and phenotype network. The construction of a bipartite signed network, GPN, is to link genotypes with phenotypes using the evidence of associations. To understand pleiotropy in diseases and complex traits and explore the genetic correlation among phenotypes, we project genotypes into phenotypes based on the GPN. We also apply a powerful community detection method to detect the network modules based on the shared genetic architecture. In contrast to previous community detection methods for disease networks, the applied method benefits from exploring the biological functionality interactions of diseases based on the signed network. Furthermore, we apply several multiple phenotype association tests to test the association between phenotypes in each network module and a SNP. Extensive simulation studies show that all multiple phenotype association tests based on network modules have corrected type I error rates if the corresponding test is a valid test for testing the association between a SNP and phenotypes without considering network modules. Most tests in NET are much more powerful than those in N.O. Meanwhile, we evaluate the performance of the association tests based on network modules detected by GPN through a set of 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue across more than 300,000 samples from the UK Biobank. Compared with the tests in N.O., all tests based on network modules can identify more potentially pleiotropic SNPs and ceCLC can identify more SNPs than other methods.

In addition, the construction of GPN does not require access to individual-level genotypes and phenotypes data, which only requires association evidence between each genotype and each phenotype. Therefore, when individual-level data are not available, this evidence can be obtained from GWAS summary statistics, such as the effect sizes (odds ratios for binary phenotypes) and corresponding p-values. The development of GPN can also be applied to omics studies, such as constructing a GPN that incorporates expression Quantitative Trait Locus (eQTLs) and gene expressions. However, in the context of numerous omics studies, the sample sizes are not very large. We have broadened our simulation analysis to include the same six simulation models, specifically targeting scenarios with the number of phenotypes of 60 and the sample size of 1,000. We observe similar results as simulations with larger sample sizes: the tests in NET are much more powerful than those in N.O ( Fig T in S1 Text ). Meanwhile, the simulation studies show that the powerful network community detection method can correctly partition phenotypes into several disjoint network modules based on the shared genetic architecture. Since the determination of the number of network modules in community detection method is independent of the association tests [ 48 ], we only need to perform the perturbation procedure once in real data analyses. In our real data analysis with 72 phenotypes and 288,647 SNPs, it only takes 1.5 hour with 1,000 perturbations to obtain the optimal number of network modules on a macOS (2.7 GHz Quad-Core Intel Core i7, 16 GB memory).

In this paper, the multiple phenotype association test based on the network module uses association information twice. We first use association information to detect communities and to cluster phenotypes into different groups, then we use the association information to perform the multiple phenotype association test. One may doubt whether the multiple phenotype association test has inflated type I error rates by using the association information twice. However, the community detection uses association information between all SNPs and all phenotypes, while the multiple phenotype association test only considers one SNP. Based on our simulation studies, the first time use of association information only affects the multiple phenotype association test slightly and is not enough to affect the type I error rates.

In summary, the proposed GPN provides a new insight to investigate the genetic correlation among phenotypes. Especially when the phenotypes have extremely unbalanced case-control ratios, the weight of an edge in the signed bipartite network can be calculated based on the saddlepoint approximation. The power of multiple phenotype association tests based on network modules detected by GPN are improved by incorporating the genetic information into the phenotypic clustering. Therefore, the proposed method can be applied to large-scale data across multiple related traits and diseases (i.e., biobanks data set, etc.).

Consider a sample with n unrelated individuals, indexed by i = 1,⋯, n . Suppose each individual has a total of K phenotypes and M SNPs. Let Y = ( y ik ) be an n × K matrix of K phenotypes, where y ik denotes the phenotype value of the i th individual for the k th phenotype. The phenotypes can be both quantitative and qualitative, especially for phenotypes with extremely unbalanced case-control ratios. Let G = ( g im ) be an n × M matrix of genotypes, where g im represents the genotypic score of the i th individual at the m th SNP which is the number of minor alleles that the i th individual carries at the SNP.

Construction of the genotype and phenotype network

Here, W kl is the genetic correlation between the k th and l th phenotypes based on the association strengths T km for k = 1,⋯, K and m = 1,⋯, M . Thus, the PPN is also a signed network.

Community detection method

We can use the identified C network modules to further investigate the associations between phenotypes in each network module and SNPs.

Multiple phenotype association tests

After we obtain C network modules for the phenotypes, we apply a multiple phenotype association test to identify the association between phenotypes in each of the C network modules and a SNP. Any multiple phenotype association test can be applied here. In this article, we apply six commonly used multiple phenotype association tests to each network module, including ceCLC [ 49 ], CLC [ 12 ], HCLC [ 50 ], MultiPhen [ 20 ], O’Brien [ 15 ], and Omnibus [ 12 ] (see details in Text A in S1 Text ), then a Bonferroni correction is used to adjust for multiple testing for the C network modules to test if all phenotypes in the C network modules associated with a SNP.

Data simulation

To generate a qualitative disease affection status, we use a liability threshold model based on a quantitative phenotype and its case-control ratio. Let n a and n C denote the number of affected individuals and the number of non-affected individuals. For a given case-control ratio r and sample size N , n c = N /( r +1) and n a = rN /( r +1). An individual is defined to be affected if the individual’s phenotype is in the top n a of all phenotypes. For each phenotype, the case-control ratio is randomly chosen from a set S . The set S contains all case-control ratios with the number of cases greater than 200 from UK Biobank ICD-10 code level 3 phenotypes.

Based on the factor model, we consider different numbers of phenotypes, 60, 80, and 100, and different sample sizes. For mixture phenotypes, the sample sizes are 2,000 and 4,000; for binary phenotypes, the sample sizes are 10,000 and 20,000. We consider six simulation models ( Text B and Table P in S1 Text ) with M = 2,000 and MAF ∼ U (0.05,0.5). The calculations of the type I error rates and power of multiple phenotype association test in N.O. and in NET are summarized in Text C in S1 Text .

Real dataset

The UK Biobank is a population-based cohort study with a wide variety of genetic and phenotypic information [ 67 ]. It includes ~ 500K people from all around the United Kingdom who were aged between 40 and 69 when recruited in 2006–2010 [ 44 , 68 ]. Following the genotype and phenotype preprocess introduced in Liang et al. [ 50 ], there are 288,647 SNPs and 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue for 322,607 individuals are kept in our real data analysis [ 69 ] ( Text D and Fig U in S1 Text ). Among the 72 phenotypes, lumbar and other intervertebral disk disorders with myelopathy (M51.0) has the smallest case-control ratio 0.000658 with 212 cases and 322,395 controls; Gonarthrosis (M17.9) has the largest case-control ratio 0.03937 with 12,218 cases and 310,389 controls. Therefore, all of the phenotypes we considered in our analysis have extremely unbalanced case-control ratios. Furthermore, each phenotype is adjusted by 13 covariates, including age, sex, genotyping array, and the first 10 genetic principal components (PCs) [ 65 ]. The analysis is performed based on the adjusted phenotypes.

Correlation analysis

To compare the genetic and phenotypic correlations among the 72 EHR-derived phenotypes, we apply cross-triat LDSC regression [ 27 ] to obtain the genetic correlation and phenotypic correlation which can provide useful etiological insights [ 27 ]. GWAS summary statistics are generated from the association between phenotype and genotype which are calculated by the saddlepoint approximation. We use the precomputed LD scores of European individuals in the 1000 Genomes project for high-quality HapMap3 SNPs (‘eur_w_ld_chr’). For the phenotypic correlation, we consider 70 phenotypes excluding M79.6 (Enthesopathy of lower limb) and M67.8 (Other specified disorders of synovium and tendon), since the heritabilities of these two phenotypes estimated by LDSC are out of bounds. For the genetic correlation, we only consider 52 phenotypes exlcuding 20 phenotypes, where the heritabilities of these phenotypes are not significantly different from zero. We apply the K-means hierarchical clustering method to compare the correlations of phenotypes obtained by our proposed GPN and LDSC.

Post-GWAS analyses

To better understand the biological functions behind the SNPs identified by one multiple phenotype association test, we identify the pathways in which the identified SNPs are involved. We use the functional annotation tool named Database for Annotation, Visualization, and Integrated Discovery bioinformatics resource (DAVID: https://david.ncifcrf.gov/ ) [ 70 , 71 ] for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A mapped gene used in the pathway enrichment analysis denotes the gene that includes at least one identified SNPs with a 20kb window region. The biological pathways with FDR < 0.05 and enriched gene count > 2 are considered statistically significant [ 72 ].

To prioritize and interpret the GWAS signals and identify lead SNPs, tissue enrichment analyses are performed using the Functional Mapping and Annotation (FUMA: https://fuma.ctglab.nl/ ) [ 61 ] platform and the GWAS signals from one multiple phenotype association test in N.O. and in NET, respectively. FUMA first performs a genic aggregation analysis of GWAS association signals to calculate gene-wise association signals using MAGMA, which is a commonly used generalized gene-set analysis of GWAS summary statistics [ 73 ]. Then, it subsequently tests whether tissues and cell types are enriched for expression of the genes with gene-wise association signals. For tissue enrichment analysis, we use 30 general tissue types in GTEx v8 reference set ( https://gtexportal.org/home/ ).

Colocalization analysis.

As most associated variants are noncoding, it is expected that they influence disease risk through altering gene expression or splicing [ 74 ]. The colocalization analysis is a way to identify the association of a GWAS SNP and a gene expression QTL that are colocalized. We perform colocalization analysis using the ‘coloc’ package in R [ 62 ], a Bayesian statistical methodology that tests pairwise colocalization of eQTLs with unique identified SNPs by ceCLC in NET and N.O. from the UK Biobank dataset. The SNP-gene associations in the Muscle Skeletal tissue are downloaded from GTEx v7. We use the default setting of the prior probabilities, p 1 = p 2 = 10 −4 and p 12 = 10 −5 , for a causal variant in an eQTL or a GWAS SNP and a shared causal variant between eQTL and GWAS SNP, respectively.

Supporting information

S1 text. supplemental texts, tables, and figures..

https://doi.org/10.1371/journal.pgen.1011245.s001

Acknowledgments

Part of this research has been conducted using the UK Biobank Resource under application number 102999 and the NHGRI-EBI GWAS Catalog. High-Performance Computing Shared Facility (Superior) at Michigan Technological University was used in obtaining results presented in this publication. Some parts of this work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges-2 system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center (PSC).

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A novel ORESTE approach for MAGDM incorporating probabilistic interval-valued linguistic information: case studies in higher education quality and the energy industry

• Original Article
• Published: 14 May 2024

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• Jing Guo   ORCID: orcid.org/0000-0001-8628-5622 1 , 2 ,
• Xianjun Zhu 1 , 3 , 4 &
• Hui Li 1 , 5  

Multi-attribute group decision making (MAGDM) is a pivotal tool in diverse evaluations. However, existing approaches often overlook attribute ambiguity and interrelationships, leading to unreliable outcomes. This article introduces a novel MAGDM support scheme that extends the widely accepted ORESTE (organísation, rangement et Synthese dèdonnees relarionnelles, in French) method to the context of Probabilistic Hesitant Interval-Value Sets (PHIVSs). PHIVSs integrate Hesitant Fuzzy Linguistic Terms (HFLTs) and Probabilistic Linguistic Term Sets (PLTSs) and transform conventional linguistic terms into interval-based expressions. This augmentation significantly extends their applicability in MAGDM scenarios, particularly those marked by elevated uncertainty. The conventional ORESTE model, a standard MAGDM tool, encounters limitations in intricate scenarios, resulting in data loss and necessitating more adaptive solutions. Our integrated PHIVS approach overcomes these challenges by incorporating fuzzy representation into ORESTE, enabling robust MAGDM solutions. Preferences are classified into three intensities based on likelihoods, establishing a structured Preference Intensity Relation (PIR). PIR effectively discerns among alternatives, elucidating preferences, indifference, or incomparability. This distinction proves invaluable in complex and uncertain decision-making across diverse domains. A key innovation of our approach lies in the unexplored application of PHIVS and ORESTE in MAGDM. Utilizing probability measures for PHIVSs, we establishes precise binary connections among alternatives for enhancing assessment and prioritization. The representation of evaluations with PHIVS and integration of likelihood measures offer an efficient solution for intricate MAGDM problems, particularly those laden with uncertainty. To illustrate the utility of our approach, we provide two comprehensive examples. These examples showcase the practicality and effectiveness of our approach in real-world assessments, highlighting its significance in advancing decision-making methodologies.

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Acknowledgements

This research was financially supported by Universities Philosophy and Social Science Researches in Jiangsu Province (No. 2020SJA0534), Natural Science Research of Jiangsu Higher Education Institutions of China (No. 22KJD520002), Research on Modern Educational Technology in Jiangsu Province (2022-R-101356) and Research Initiation Fund for High-level Talents of Jinling Institute Technology (Nos. jit-b-201817, jit-b-201906), China Postdoctoral Science Foundation (No. 2020T130129ZX).

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Xianjun Zhu

Research Center for New Technology in Intelligent Equipment Nanjing University, Nanjing, 210093, People’s Republic of China

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People’s Republic of China

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Guo, J., Zhu, X. & Li, H. A novel ORESTE approach for MAGDM incorporating probabilistic interval-valued linguistic information: case studies in higher education quality and the energy industry. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02202-7

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ORIGINAL RESEARCH article

Evaluation of the urban living lab in heis towards education for sustainable development (e-ull-heis) provisionally accepted.

• 1 University of Guayaquil, Ecuador
• 2 Universitat Politecnica de Catalunya, Spain

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This study explores the implementation of Urban Living Labs (ULLs) in Higher Education Institutions (HEIs) to promote Education for Sustainable Development (ESD). It adopts a methodology that integrates a mixed approach, combining literature review, validation with experts in the field and analysis of case studies. A structured evaluation tool is proposed based on three constructs: Synergy, Strategy and Pedagogy, which cover the essential characteristics of the three thematic axes: ULLs, ESD and HEIs, through seven indicators. This tool is applied to examine the effective-ness of ULLs in promoting sustainable practices within the university context. The results, vali-dated through experts, exploratory factor analysis and Cronbach's alpha coefficient, demonstrate the reliability and consistency of the evaluative indicators, highlighting the crucial role of ULLs in the integration of sustainability in the curriculum, experiential learning, and the impact social and community. This approach allowed the identification of successful practices and common challenges in the implementation of ULL, as well as the development of a framework of indicators adapted to the specific needs of HEIs. The study concludes by emphasizing the transformative potential of ULLs in HEIs to advance towards sustainable urban transitions, underscoring the need for robust evaluative tools to optimize the contribution of higher education to global sustainable development.

Keywords: Education for Sustainable Development (ESD)1, Higher Education Institutions (HEIs) 2, Urban Living Labs (ULL) 3, assessment tool4, urban innovation5, interdisciplinarity6, curriculum integration7, experiential learning8

Received: 04 Apr 2024; Accepted: 14 May 2024.

Copyright: © 2024 Morales, Segalás and Masseck. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Ivetheyamel Morales, University of Guayaquil, Guayaquil, Ecuador

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• Published: 09 May 2024

Evaluation of integrated community case management of the common childhood illness program in Gondar city, northwest Ethiopia: a case study evaluation design

• Mekides Geta 1 ,
• Geta Asrade Alemayehu 2 ,
• Wubshet Debebe Negash 2 ,
• Tadele Biresaw Belachew 2 ,
• Chalie Tadie Tsehay 2 &
• Getachew Teshale 2  

BMC Pediatrics volume  24 , Article number:  310 ( 2024 ) Cite this article

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Integrated Community Case Management (ICCM) of common childhood illness is one of the global initiatives to reduce mortality among under-five children by two-thirds. It is also implemented in Ethiopia to improve community access and coverage of health services. However, as per our best knowledge the implementation status of integrated community case management in the study area is not well evaluated. Therefore, this study aimed to evaluate the implementation status of the integrated community case management program in Gondar City, Northwest Ethiopia.

A single case study design with mixed methods was employed to evaluate the process of integrated community case management for common childhood illness in Gondar town from March 17 to April 17, 2022. The availability, compliance, and acceptability dimensions of the program implementation were evaluated using 49 indicators. In this evaluation, 484 mothers or caregivers participated in exit interviews; 230 records were reviewed, 21 key informants were interviewed; and 42 observations were included. To identify the predictor variables associated with acceptability, we used a multivariable logistic regression analysis. Statistically significant variables were identified based on the adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value. The qualitative data was recorded, transcribed, and translated into English, and thematic analysis was carried out.

The overall implementation of integrated community case management was 81.5%, of which availability (84.2%), compliance (83.1%), and acceptability (75.3%) contributed. Some drugs and medical equipment, like Cotrimoxazole, vitamin K, a timer, and a resuscitation bag, were stocked out. Health care providers complained that lack of refreshment training and continuous supportive supervision was the common challenges that led to a skill gap for effective program delivery. Educational status (primary AOR = 0.27, 95% CI:0.11–0.52), secondary AOR = 0.16, 95% CI:0.07–0.39), and college and above AOR = 0.08, 95% CI:0.07–0.39), prescribed drug availability (AOR = 2.17, 95% CI:1.14–4.10), travel time to the to the ICCM site (AOR = 3.8, 95% CI:1.99–7.35), and waiting time (AOR = 2.80, 95% CI:1.16–6.79) were factors associated with the acceptability of the program by caregivers.

Conclusion and recommendation

The overall implementation status of the integrated community case management program was judged as good. However, there were gaps observed in the assessment, classification, and treatment of diseases. Educational status, availability of the prescribed drugs, waiting time and travel time to integrated community case management sites were factors associated with the program acceptability. Continuous supportive supervision for health facilities, refreshment training for HEW’s to maximize compliance, construction clean water sources for HPs, and conducting longitudinal studies for the future are the forwarded recommendation.

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Integrated Community Case Management (ICCM) is a critical public health strategy for expanding the coverage of quality child care services [ 1 , 2 ]. It mainly concentrated on curative care and also on the diagnosis, treatment, and referral of children who are ill with infectious diseases [ 3 , 4 ].

Based on the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) recommendations, Ethiopia adopted and implemented a national policy supporting community-based treatment of common childhood illnesses like pneumonia, Diarrhea, uncomplicated malnutrition, malaria and other febrile illness and Amhara region was one the piloted regions in late 2010 [ 5 ]. The Ethiopian primary healthcare units, established at district levels include primary hospitals, health centers (HCs), and health posts (HPs). The HPs are run by Health Extension Workers (HEWs), and they have function of monitoring health programs and disease occurrence, providing health education, essential primary care services, and timely referrals to HCs [ 6 , 7 ]. The Health Extension Program (HEP) uses task shifting and community ownership to provide essential health services at the first level using the health development army and a network of woman volunteers. These groups are organized to promote health and prevent diseases through community participation and empowerment by identifying the salient local bottlenecks which hinder vital maternal, neonatal, and child health service utilization [ 8 , 9 ].

One of the key steps to enhance the clinical case of health extension staff is to encourage better growth and development among under-five children by health extension. Healthy family and neighborhood practices are also encouraged [ 10 , 11 ]. The program also combines immunization, community-based feeding, vitamin A and de-worming with multiple preventive measures [ 12 , 13 ]. Now a days rapidly scaling up of ICCM approach to efficiently manage the most common causes of morbidity and mortality of children under the age of five in an integrated manner at the community level is required [ 14 , 15 ].

Over 5.3 million children are died at a global level in 2018 and most causes (75%) are preventable or treatable diseases such as pneumonia, malaria and diarrhea [ 16 ]. About 99% of the global burden of mortality and morbidity of under-five children which exists in developing countries are due to common childhood diseases such as pneumonia, diarrhea, malaria and malnutrition [ 17 ].

In 2013, the mortality rate of under-five children in Sub-Saharan Africa decreased to 86 deaths per 1000 live birth and estimated to be 25 per 1000live births by 2030. However, it is a huge figure and the trends are not sufficient to reach the target [ 18 ]. About half of global under-five deaths occurred in sub-Saharan Africa. And from the top 26 nations burdened with 80% of the world’s under-five deaths, 19 are in sub-Saharan Africa [ 19 ].

To alleviate the burden, the Ethiopian government tries to deliver basic child care services at the community level by trained health extension workers. The program improves the health of the children not only in Ethiopia but also in some African nations. Despite its proven benefits, the program implementation had several challenges, in particular, non-adherence to the national guidelines among health care workers [ 20 ]. Addressing those challenges could further improve the program performance. Present treatment levels in sub-Saharan Africa are unacceptably poor; only 39% of children receive proper diarrhea treatment, 13% of children with suspected pneumonia receive antibiotics, 13% of children with fever receive a finger/heel stick to screen for malaria [ 21 ].

To improve the program performance, program gaps should be identified through scientific evaluations and stakeholder involvement. This evaluation not only identify gaps but also forward recommendations for the observed gaps. Furthermore, the implementation status of ICCM of common childhood illnesses has not been evaluated in the study area yet. Therefore, this work aimed to evaluate the implementation status of integrated community case management program implementation in Gondar town, northwest Ethiopia. The findings may be used by policy makers, healthcare providers, funders and researchers.

Method and material

Evaluation design and settings.

A single-case study design with concurrent mixed-methods evaluation was conducted in Gondar city, northwest Ethiopia, from March 17 to April 17, 2022. The evaluability assessment was done from December 15–30, 2021. Both qualitative and quantitative data were collected concurrently, analyzed separately, and integrated at the result interpretation phase.

The evaluation area, Gondar City, is located in northwest Ethiopia, 740 km from Addis Ababa, the capital city of the country. It has six sub-cities and thirty-six kebeles (25 urban and 11 rural). In 2019, the estimated total population of the town was 338,646, and 58,519 (17.3%) were under-five children. In the town there are eight public health centers and 14 health posts serving the population. All health posts provide ICCM service for more than 70,852 populations.

Evaluation approach and dimensions

Program stakeholders.

The evaluation followed a formative participatory approach by engaging the potential stakeholders in the program. Prior to the development of the proposal, an extensive discussion was held with the Gondar City Health Department to identify other key stakeholders in the program. Service providers at each health facility (HCs and HPs), caretakers of sick children, the Gondar City Health Office (GCHO), the Amhara Regional Health Bureau (ARHB), the Minister of Health (MoH), and NGOs (IFHP and Save the Children) were considered key stakeholders. During the Evaluability Assessment (EA), the stakeholders were involved in the development of evaluation questions, objectives, indicators, and judgment criteria of the evaluation.

Evaluation dimensions

The availability and acceptability dimensions from the access framework [ 22 ] and compliance dimension from the fidelity framework [ 23 ] were used to evaluate the implementation of ICCM.

Population and samplings

All under-five children and their caregivers attended at the HPs; program implementers (health extension workers, healthcare providers, healthcare managers, PHCU focal persons, MCH coordinators, and other stakeholders); and ICCM records and registries in the health posts of Gondar city administration were included in the evaluation. For quantitative data, the required sample size was proportionally allocated for each health post based on the number of cases served in the recent one month. But the qualitative sample size was determined by data saturation, and the samples were selected purposefully.

The data sources and sample size for the compliance dimension were all administrative records/reports and ICCM registration books (230 documents) in all health posts registered from December 1, 2021, to February 30, 2022 (three months retrospectively) included in the evaluation. The registries were assessed starting from the most recent registration number until the required sample size was obtained for each health post.

The sample size to measure the mothers’/caregivers’ acceptability towards ICCM was calculated by taking prevalence of caregivers’ satisfaction on ICCM program p  = 74% from previously similar study [ 24 ] and considering standard error 4% at 95% CI and 10% non- responses, which gave 508. Except those who were seriously ill, all caregivers attending the ICCM sites during data collection were selected and interviewed consecutively.

The availability of required supplies, materials and human resources for the program were assessed in all 14HPs. The data collectors observed the health posts and collected required data by using a resources inventory checklist.

A total of 70 non-participatory patient-provider interactions were also observed. The observations were conducted per each health post and for health posts which have more than one health extension workers one of them were selected randomly. The observation findings were used to triangulate the findings obtained through other data collection techniques. Since people may act accordingly to the standards when they know they are observed for their activities, we discarded the first two observations from analysis. It is one of the strategies to minimize the Hawthorne effect of the study. Finally a total of 42 (3 in each HPs) observations were included in the analysis.

Twenty one key informants (14 HEWs, 3 PHCU focal person, 3 health center heads and one MCH coordinator) were interviewed. These key informants were selected since they are assumed to be best teachers in the program. Besides originally developed key informant interview questions, the data collectors probed them to get more detail and clear information.

Variables and measurement

The availability of resources, including trained healthcare workers, was examined using 17 indicators, with weighted score of 35%. Compliance was used to assess HEWs’ adherence to the ICCM treatment guidelines by observing patient-provider interactions and conducting document reviews. We used 18 indicators and a weighted value of 40%.

Mothers’ /caregivers’/ acceptance of ICCM service was examined using 14 indicators and had a weighted score of 25%. The indicators were developed with a five-point Likert scale (1: strongly disagree, 2: disagree, 3: neutral, 4: agree and 5: strongly agree). The cut off point for this categorization was calculated using the demarcation threshold formula: ( \(\frac{\text{t}\text{o}\text{t}\text{a}\text{l}\, \text{h}\text{i}\text{g}\text{h}\text{e}\text{s}\text{t}\, \text{s}\text{c}\text{o}\text{r}\text{e}-\,\text{t}\text{o}\text{t}\text{a}\text{l}\, \text{l}\text{o}\text{w}\text{e}\text{s}\text{t} \,\text{s}\text{c}\text{o}\text{r}\text{e}}{2}) +total lowest score\) ( 25 – 27 ). Those mothers/caregivers/ who scored above cut point (42) were considered as “satisfied”, otherwise “dissatisfied”. The indicators were adapted from the national ICCM and IMNCI implementation guideline and other related evaluations with the participation of stakeholders. Indicator weight was given by the stakeholders during EA. Indicators score was calculated using the formula \(\left(achieved \,in \%=\frac{indicator \,score \,x \,100}{indicator\, weight} \right)\) [ 26 , 28 ].

The independent variables for the acceptability dimension were socio-demographic and economic variables (age, educational status, marital status, occupation of caregiver, family size, income level, and mode of transport), availability of prescribed drugs, waiting time, travel time to ICCM site, home to home visit, consultation time, appointment, and source of information.

The overall implementation of ICCM was measured by using 49 indicators over the three dimensions: availability (17 indicators), compliance (18 indicators) and acceptability (14 indicators).

Program logic model

Based on the constructed program logic model and trained health care providers, mothers/caregivers received health information and counseling on child feeding; children were assessed, classified, and treated for disease, received follow-up; they were checked for vitamin A; and deworming and immunization status were the expected outputs of the program activities. Improved knowledge of HEWs on ICCM, increased health-seeking behavior, improved quality of health services, increased utilization of services, improved data quality and information use, and improved child health conditions are considered outcomes of the program. Reduction of under-five morbidity and mortality and improving quality of life in the society are the distant outcomes or impacts of the program (Fig.  1 ).

Integrated community case management of childhood illness program logic model in Gondar City in 2022

Data collection tools and procedure

Resource inventory and data extraction checklists were adapted from standard ICCM tool and check lists [ 29 ]. A structured interviewer administered questionnaire was adapted by referring different literatures [ 30 , 31 ] to measure the acceptability of ICCM. The key informant interview (KII) guide was also developed to explore the views of KIs. The interview questionnaire and guide were initially developed in English and translated into the local language (Amharic) and finally back to English to ensure consistency. All the interviews were done in the local language, Amharic.

Five trained clinical nurses and one BSC nurse were recruited from Gondar zuria and Wegera district as data collectors and supervisors, respectively. Two days training on the overall purpose of the evaluation and basic data collection procedures were provided prior to data collection. Then, both quantitative and qualitative data were gathered at the same time. The quantitative data were gathered from program documentation, charts of ICCM program visitors and, exit interview. Interviews with 21 KIIs and non-participatory observations of patient-provider interactions were used to acquire qualitative data. Key informant interviews were conducted to investigate the gaps and best practices in the implementation of the ICCM program.

A pretest was conducted to 26 mothers/caregivers/ at Maksegnit health post and appropriate modifications were made based on the pretest results. The data collectors were supervised and principal evaluator examined the completeness and consistency of the data on a daily basis.

Data management and analysis

For analysis, quantitative data were entered into epi-data version 4.6 and exported to Stata 14 software for analysis. Narration and tabular statistics were used to present descriptive statistics. Based on established judgment criteria, the total program implementation was examined and interpreted as a mix of the availability, compliance, and acceptability dimensions. To investigate the factors associated with ICCM acceptance, a binary logistic regression analysis was performed. During bivariable analysis, variables with p-values less than 0.25 were included in multivariable analysis. Finally, variables having a p-value less than 0.05 and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) were judged statistically significant. Qualitative data were collected recorded, transcribed into Amharic, then translated into English and finally coded and thematically analyzed.

Judgment matrix analysis

The weighted values of availability, compliance, and acceptability dimensions were 35, 40, and 25 based on the stakeholder and investigator agreement on each indicator, respectively. The judgment parameters for each dimension and the overall implementation of the program were categorized as poor (< 60%), fair (60–74.9%), good (75-84.9%), and very good (85–100%).

Availability of resources

A total of 26 HEWs were assigned within the fourteen health posts, and 72.7% of them were trained on ICCM to manage common childhood illnesses in under-five children. However, the training was given before four years, and they didn’t get even refreshment training about ICCM. The KII responses also supported that the shortage of HEWs at the HPs was the problem in implementing the program properly.

I am the only HEW in this health post and I have not been trained on ICCM program. So, this may compromise the quality of service and client satisfaction.(25 years old HEW with two years’ experience)

All observed health posts had ICCM registration books, monthly report and referral formats, functional thermometer, weighting scale and MUAC tape meter. However, timer and resuscitation bag was not available in all HPs. Most of the key informant finding showed that, in all HPs there was no shortage of guideline, registration book and recording tool; however, there was no OTP card in some health posts.

“Guideline, ICCM registration book for 2–59 months of age, and other different recording and reporting formats and booklet charts are available since September/2016. However, OTP card is not available in most HPs.”. (A 30 years male health center director)

Only one-fifth (21%) of HPs had a clean water source for drinking and washing of equipment. Most of Key-informant interview findings showed that the availability of infrastructures like water was not available in most HPs. Poor linkage between HPs, HCs, town health department, and local Kebele administer were the reason for unavailability.

Since there is no water for hand washing, or drinking, we obligated to bring water from our home for daily consumptions. This increases the burden for us in our daily activity. (35 years old HEW)

Most medicines, such as anti-malaria drugs with RDT, Quartem, Albendazole, Amoxicillin, vitamin A capsules, ORS, and gloves, were available in all the health posts. Drugs like zinc, paracetamol, TTC eye ointment, and folic acid were available in some HPs. However, cotrimoxazole and vitamin K capsules were stocked-out in all health posts for the last six months. The key informant also revealed that: “Vitamin K was not available starting from the beginning of this program and Cotrimoxazole was not available for the past one year and they told us they would avail it soon but still not availed. Some essential ICCM drugs like anti malaria drugs, De-worming, Amoxicillin, vitamin A capsules, ORS and medical supplies were also not available in HCs regularly.”(28 years’ Female PHCU focal)

The overall availability of resources for ICCM implementation was 84.2% which was good based on our presetting judgment parameter (Table  1 ).

Health extension worker’s compliance

From the 42 patient-provider interactions, we found that 85.7%, 71.4%, 76.2%, and 95.2% of the children were checked for body temperature, weight, general danger signs, and immunization status respectively. Out of total (42) observation, 33(78.6%) of sick children were classified for their nutritional status. During observation time 29 (69.1%) of caregivers were counseled by HEWs on food, fluid and when to return back and 35 (83.3%) of children were appointed for next follow-up visit. Key informant interviews also affirmed that;

“Most of our health extension workers were trained on ICCM program guidelines but still there are problems on assessment classification and treatment of disease based on guidelines and standards this is mainly due to lack refreshment training on the program and lack of continuous supportive supervision from the respective body.” (27years’ Male health center head)

From 10 clients classified as having severe pneumonia cases, all of them were referred to a health center (with pre-referral treatment), and from those 57 pneumonia cases, 50 (87.7%) were treated at the HP with amoxicillin or cotrimoxazole. All children with severe diarrhea, very severe disease, and severe complicated malnutrition cases were referred to health centers with a pre-referral treatment for severe dehydration, very severe febrile disease, and severe complicated malnutrition, respectively. From those with some dehydration and no dehydration cases, (82.4%) and (86.8%) were treated at the HPs for some dehydration (ORS; plan B) and for no dehydration (ORS; plan A), respectively. Moreover, zinc sulfate was prescribed for 63 (90%) of under-five children with some dehydration or no dehydration. From 26 malaria cases and 32 severe uncomplicated malnutrition and moderate acute malnutrition cases, 20 (76.9%) and 25 (78.1%) were treated at the HPs, respectively. Of the total reviewed documents, 56 (93.3%), 66 (94.3%), 38 (84.4%), and 25 (78.1%) of them were given a follow-up date for pneumonia, diarrhea, malaria, and malnutrition, respectively.

Supportive supervision and performance review meetings were conducted only in 10 (71.4%) HPs, but all (100%) HPs sent timely reports to the next supervisory body.

Most of the key informants’ interview findings showed that supportive supervision was not conducted regularly and for all HPs.

I had mentored and supervised by supportive supervision teams who came to our health post at different times from health center, town health office and zonal health department. I received this integrated supervision from town health office irregularly, but every month from catchment health center and last integrated supportive supervision from HC was on January. The problem is the supervision was conducted for all programs.(32 years’ old and nine years experienced female HEW)

Moreover, the result showed that there was poor compliance of HEWs for the program mainly due to weak supportive supervision system of managerial and technical health workers. It was also supported by key informants as:

We conducted supportive supervision and performance review meeting at different time, but still there was not regular and not addressed all HPs. In addition to this the supervision and review meeting was conducted as integration of ICCM program with other services. The other problem is that most of the time we didn’t used checklist during supportive supervision. (Mid 30 years old male HC director)

Based on our observation and ICCM document review, 83.1% of the HEWs were complied with the ICCM guidelines and judged as fair (Table  2 ).

Acceptability of ICCM program

Sociodemographic and obstetric characteristics of participants.

A total of 484 study participants responded to the interviewer-administered questionnaire with a response rate of 95.3%. The mean age of study participants was 30.7 (SD ± 5.5) years. Of the total caregivers, the majority (38.6%) were categorized under the age group of 26–30 years. Among the total respondents, 89.3% were married, and regarding religion, the majorities (84.5%) were Orthodox Christian followers. Regarding educational status, over half of caregivers (52.1%) were illiterate (unable to read or write). Nearly two-thirds of the caregivers (62.6%) were housewives (Table  3 ).

All the caregivers came to the health post on foot, and most of them 418 (86.4%) arrived within one hour. The majority of 452 (93.4%) caregivers responded that the waiting time to get the service was less than 30 min. Caregivers who got the prescribed drugs at the health post were 409 (84.5%). Most of the respondents, 429 (88.6%) and 438 (90.5%), received counseling services on providing extra fluid and feeding for their sick child and were given a follow-up date.

Most 298 (61.6%) of the caregivers were satisfied with the convenience of the working hours of HPs, and more than three-fourths (80.8%) were satisfied with the counseling services they received. Most of the respondents, 366 (75.6%), were satisfied with the appropriateness of waiting time and 431 (89%) with the appropriateness of consultation time. The majority (448 (92.6%) of caregivers were satisfied with the way of communicating with HEWs, and 269 (55.6%) were satisfied with the knowledge and competence of HEWs. Nearly half of the caregivers (240, or 49.6%) were satisfied with the availability of drugs at health posts.

The overall acceptability of the ICCM program was 75.3%, which was judged as good. A low proportion of acceptability was measured on the cleanliness of the health posts, the appropriateness of the waiting area, and the competence and knowledge of the HEWs. On the other hand, high proportion of acceptability was measured on appropriateness of waiting time, way of communication with HEWs, and the availability of drugs (Table  4 ).

Factors associated with acceptability of ICCM program

In the final multivariable logistic regression analysis, educational status of caregivers, availability of prescribed drugs, time to arrive, and waiting time were factors significantly associated with the satisfaction of caregivers with the ICCM program.

Accordingly, the odds of caregivers with primary education, secondary education, and college and above were 73% (AOR = 0.27, 95% CI: 0.11–0.52), 84% (AOR = 0.16, 95% CI: 0.07–0.39), and 92% (AOR = 0.08, 95% CI: 0.07–0.40) less likely to accept the program as compared to mothers or caregivers who were not able to read and write, respectively. The odds of caregivers or mothers who received prescribed drugs were 2.17 times more likely to accept the program as compared to their counters (AOR = 2.17, 95% CI: 1.14–4.10). The odds of caregivers or mothers who waited for services for less than 30 min were 2.8 times more likely to accept the program as compared to those who waited for more than 30 min (AOR = 2.80, 95% CI: 1.16–6.79). Moreover, the odds of caregivers/mothers who traveled an hour or less for service were 3.8 times more likely to accept the ICCM program as compared to their counters (AOR = 3.82, 95% CI:1.99–7.35) (Table  5 ).

Overall ICCM program implementation and judgment

The implementation of the ICCM program in Gondar city administration was measured in terms of availability (84.2%), compliance (83.1%), and acceptability (75.3%) dimensions. In the availability dimension, amoxicillin, antimalarial drugs, albendazole, Vit. A, and ORS were available in all health posts, but only six HPs had Ready-to-Use Therapeutic Feedings, three HPs had ORT Corners, and none of the HPs had functional timers. In all health posts, the health extension workers asked the chief to complain, correctly assessed for pneumonia, diarrhea, malaria, and malnutrition, and sent reports based on the national schedule. However, only 70% of caretakers counseled about food, fluids, and when to return, 66% and 76% of the sick children were checked for anemia and other danger signs, respectively. The acceptability level of the program by caretakers and caretakers’/mothers’ educational status, waiting time to get the service and travel time ICCM sites were the factors affecting its acceptability. The overall ICCM program in Gondar city administration was 81.5% and judged as good (Fig.  2 ).

Overall ICCM program implementation and the evaluation dimensions in Gondar city administration, 2022

The implementation status of ICCM was judged by using three dimensions including availability, compliance and acceptability of the program. The judgment cut of points was determined during evaluability assessment (EA) along with the stakeholders. As a result, we found that the overall implementation status of ICCM program was good as per the presetting judgment parameter. Availability of resources for the program implementation, compliance of HEWs to the treatment guideline and acceptability of the program services by users were also judged as good as per the judgment parameter.

This evaluation showed that most medications, equipment and recording and reporting materials available. This finding was comparable with the standard ICCM treatment guide line [ 10 ]. On the other hand trained health care providers, some medications like Zink, Paracetamol and TTC eye ointment, folic acid and syringes were not found in some HPs. However the finding was higher than the study conducted in SNNPR on selected health posts [ 33 ] and a study conducted in Soro district, southern Ethiopia [ 24 ]. The possible reason might be due to low interruption of drugs at town health office or regional health department stores, regular supplies of essential drugs and good supply management and distribution of drug from health centers to health post.

The result of this evaluation showed that only one fourth of health posts had functional ORT Corner which was lower compared to the study conducted in SNNPR [ 34 ]. This might be due poor coverage of functional pipe water in the kebeles and the installation was not set at the beginning of health post construction as reported from one of ICCM program coordinator.

Compliance of HEWs to the treatment guidelines in this evaluation was higher than the study done in southern Ethiopia (65.6%) [ 24 ]. This might be due to availability of essential drugs educational level of HEWs and good utilization of ICCM guideline and chart booklet by HEWs. The observations showed most of the sick children were assessed for danger sign, weight, and temperature respectively. This finding is lower than the study conducted in Rwanda [ 35 ]. This difference might be due to lack of refreshment training and regular supportive supervision for HEWs. This also higher compared to the study done in three regions of Ethiopia indicates that 88%, 92% and 93% of children classified as per standard for Pneumonia, diarrhea and malaria respectively [ 36 ]. The reason for this difference may be due to the presence of medical equipment and supplies including RDT kit for malaria, and good educational level of HEWs.

Moreover most HPs received supportive supervision and performance review meeting was conducted and all of them send reports timely to next level. The finding of this evaluation was lower than the study conducted on implementation evaluation of ICCM program southern Ethiopia [ 24 ] and study done in three regions of Ethiopia (Amhara, Tigray and SNNPR) [ 37 ]. This difference might be due sample size variation.

The overall acceptability of the ICCM program was less than the presetting judgment parameter but slightly higher compared to the study in southern Ethiopia [ 24 ]. This might be due to presence of essential drugs for treating children, reasonable waiting and counseling time provided by HEWs, and smooth communication between HEWs and caregivers. In contrast, this was lower than similar studies conducted in Wakiso district, Uganda [ 38 ]. The reason for this might be due to contextual difference between the two countries, inappropriate waiting area to receive the service and poor cleanness of the HPs in our study area. Low acceptability of caregivers to ICCM service was observed in the appropriateness of waiting area, availability of drugs, cleanness of health post, and competence of HEWs while high level of caregiver’s acceptability was consultation time, counseling service they received, communication with HEWs, treatment given for their sick children and interest to return back for ICCM service.

Caregivers who achieved primary, secondary, and college and above were more likely accept the program services than those who were illiterate. This may more educated mothers know about their child health condition and expect quality service from healthcare providers which is more likely reduce the acceptability of the service. The finding is congruent with a study done on implementation evaluation of ICCM program in southern Ethiopia [ 24 ]. However, inconsistent with a study conducted in wakiso district in Uganda [ 38 ]. The possible reason for this might be due to contextual differences between the two countries. The ICCM program acceptability was high in caregivers who received all prescribed drugs than those did not. Caregivers those waited less than 30 min for service were more accepted ICCM services compared to those more than 30 minutes’ waiting time. This finding is similar compared with the study conducted on implementation evaluation of ICCM program in southern Ethiopia [ 24 ]. In contrary, the result was incongruent with a survey result conducted by Ethiopian public health institute in all regions and two administrative cities of Ethiopia [ 39 ]. This variation might be due to smaller sample size in our study the previous one. Moreover, caregivers who traveled to HPs less than 60 min were more likely accepted the program than who traveled more and the finding was similar with the study finding in Jimma zone [ 40 ].

Strengths and limitations

This evaluation used three evaluation dimensions, mixed method and different data sources that would enhance the reliability and credibility of the findings. However, the study might have limitations like social desirability bias, recall bias and Hawthorne effect.

The implementation of the ICCM program in Gondar city administration was measured in terms of availability (84.2%), compliance (83.1%), and acceptability (75.3%) dimensions. In the availability dimension, amoxicillin, antimalarial drugs, albendazole, Vit. A, and ORS were available in all health posts, but only six HPs had Ready-to-Use Therapeutic Feedings, three HPs had ORT Corners, and none of the HPs had functional timers.

This evaluation assessed the implementation status of the ICCM program, focusing mainly on availability, compliance, and acceptability dimensions. The overall implementation status of the program was judged as good. The availability dimension is compromised due to stock-outs of chloroquine syrup, cotrimoxazole, and vitamin K and the inaccessibility of clean water supply in some health posts. Educational statuses of caregivers, availability of prescribed drugs at the HPs, time to arrive to HPs, and waiting time to receive the service were the factors associated with the acceptability of the ICCM program.

Therefore, continuous supportive supervision for health facilities, and refreshment training for HEW’s to maximize compliance are recommended. Materials and supplies shall be delivered directly to the health centers or health posts to solve the transportation problem. HEWs shall document the assessment findings and the services provided using the registration format to identify their gaps, limitations, and better performances. The health facilities and local administrations should construct clean water sources for health facilities. Furthermore, we recommend for future researchers and program evaluators to conduct longitudinal studies to know the causal relationship of the program interventions and the outcomes.

Data availability

Data will be available upon reasonable request from the corresponding author.

Abbreviations

Ethiopian Demographic and Health Survey

Health Center/Health Facility

Health Extension Program

Health Extension Workers

Health Post

Health Sector Development Plan

Integrated Community Case Management of Common Childhood Illnesses

Information Communication and Education

Integrated Family Health Program

Integrated Management of Neonatal and Childhood Illness

Integrated Supportive Supervision

Maternal and Child Health

Mid Upper Arm Circumference

Non-Government Organization

Oral Rehydration Salts

Outpatient Therapeutic program

Primary health care unit

Rapid Diagnostics Test

Ready to Use Therapeutic Foods

Sever Acute Malnutrition

South Nation Nationalities People Region

United Nations International Child Emergency Fund

World Health Organization

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Acknowledgements

We are very grateful to University of Gondar and Gondar town health office for its welcoming approaches. We would also like to thank all of the study participants of this evaluation for their information and commitment. Our appreciation also goes to the data collectors and supervisors for their unreserved contribution.

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All authors contributed to the preparation of the manuscript. M.G. conceived and designed the evaluation and performed the analysis then T.B.B., W.D.N., G.A.A., C.T.T. and G.T. revised the analysis. G.T. prepared the manuscript and all the authors revised and approved the final manuscript.

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Geta, M., Alemayehu, G.A., Negash, W.D. et al. Evaluation of integrated community case management of the common childhood illness program in Gondar city, northwest Ethiopia: a case study evaluation design. BMC Pediatr 24 , 310 (2024). https://doi.org/10.1186/s12887-024-04785-0

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Long Short-Term Memory Networks for Real-time Flood Forecast Correction: A Case Study for an Underperforming Hydrologic Model

Abstract. Flood forecasting systems play a key role in mitigating socio-economic damages caused by flooding events. The majority of these systems rely on process-based hydrologic models (PBHM), which are used to predict future river runoff. To enhance the forecast accuracy of these models, many operational flood forecasting systems implement error correction techniques, which is particularly important if the underlying hydrologic model is underperforming. Especially, AutoRegressive Integrated Moving Average (ARIMA) type models are frequently employed for this purpose. Despite their high popularity, numerous studies have pointed out potential shortcomings of these models, such as a decline in forecast accuracy with increasing lead time. To overcome the limitations presented by conventional ARIMA models, we propose a novel forecast correction technique based on a hindcast-forecast Long Short-Term Memory (LSTM) network. We showcase the effectiveness of the proposed approach by rigorously comparing its capabilities to those of an ARIMA model, utilizing one underperforming PBHM as a case study. Additionally, we test whether the LSTM benefits from the PBHM's results or if a similar accuracy can be reached by employing a standalone LSTM. Our investigations show that the proposed LSTM model significantly improves the PBHM's forecasts. Compared to ARIMA, the LSTM achieves a higher forecast accuracy for longer lead times. In terms of flood event runoff, the LSTM performs mostly on par with ARIMA in predicting the magnitude of the events. However, the LSTM majorly outperforms ARIMA in accurately predicting the timing of the peak runoff. Furthermore, our results provide no reliable evidence of whether the LSTM is able to extract information from the PBHM's results, given the widely equal performance of the proposed and standalone LSTM models.

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Model code for "Long Short-Term Memory Networks for Real-time Flood Forecast Correction: A Case Study for an Underperforming Hydrologic Model" Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider https://github.com/tug17/ForecastModel

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The case study approach

Sarah crowe.

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

Kathrin Cresswell

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

Ann Robertson

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

Anthony Avery

Aziz sheikh.

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

Introduction

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

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

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

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

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

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

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

What is a case study?

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

Definitions of a case study

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

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

What are case studies used for?

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

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

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

How are case studies conducted?

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

Defining the case

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

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Potential pitfalls and mitigating actions when undertaking case study research

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

Conclusions

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

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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

Pre-publication history

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

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

Acknowledgements

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

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Scientists are proposing a new way of understanding the genetics of Alzheimer’s that would mean that up to a fifth of patients would be considered to have a genetically caused form of the disease.

Currently, the vast majority of Alzheimer’s cases do not have a clearly identified cause. The new designation, proposed in a study published Monday, could broaden the scope of efforts to develop treatments, including gene therapy, and affect the design of clinical trials.

It could also mean that hundreds of thousands of people in the United States alone could, if they chose, receive a diagnosis of Alzheimer’s before developing any symptoms of cognitive decline, although there currently are no treatments for people at that stage.

The new classification would make this type of Alzheimer’s one of the most common genetic disorders in the world, medical experts said.

“This reconceptualization that we’re proposing affects not a small minority of people,” said Dr. Juan Fortea, an author of the study and the director of the Sant Pau Memory Unit in Barcelona, Spain. “Sometimes we say that we don’t know the cause of Alzheimer’s disease,” but, he said, this would mean that about 15 to 20 percent of cases “can be tracked back to a cause, and the cause is in the genes.”

The idea involves a gene variant called APOE4. Scientists have long known that inheriting one copy of the variant increases the risk of developing Alzheimer’s, and that people with two copies, inherited from each parent, have vastly increased risk.

The new study , published in the journal Nature Medicine, analyzed data from over 500 people with two copies of APOE4, a significantly larger pool than in previous studies. The researchers found that almost all of those patients developed the biological pathology of Alzheimer’s, and the authors say that two copies of APOE4 should now be considered a cause of Alzheimer’s — not simply a risk factor.

The patients also developed Alzheimer’s pathology relatively young, the study found. By age 55, over 95 percent had biological markers associated with the disease. By 65, almost all had abnormal levels of a protein called amyloid that forms plaques in the brain, a hallmark of Alzheimer’s. And many started developing symptoms of cognitive decline at age 65, younger than most people without the APOE4 variant.

“The critical thing is that these individuals are often symptomatic 10 years earlier than other forms of Alzheimer’s disease,” said Dr. Reisa Sperling, a neurologist at Mass General Brigham in Boston and an author of the study.

She added, “By the time they are picked up and clinically diagnosed, because they’re often younger, they have more pathology.”

People with two copies, known as APOE4 homozygotes, make up 2 to 3 percent of the general population, but are an estimated 15 to 20 percent of people with Alzheimer’s dementia, experts said. People with one copy make up about 15 to 25 percent of the general population, and about 50 percent of Alzheimer’s dementia patients.

The most common variant is called APOE3, which seems to have a neutral effect on Alzheimer’s risk. About 75 percent of the general population has one copy of APOE3, and more than half of the general population has two copies.

Alzheimer’s experts not involved in the study said classifying the two-copy condition as genetically determined Alzheimer’s could have significant implications, including encouraging drug development beyond the field’s recent major focus on treatments that target and reduce amyloid.

Dr. Samuel Gandy, an Alzheimer’s researcher at Mount Sinai in New York, who was not involved in the study, said that patients with two copies of APOE4 faced much higher safety risks from anti-amyloid drugs.

When the Food and Drug Administration approved the anti-amyloid drug Leqembi last year, it required a black-box warning on the label saying that the medication can cause “serious and life-threatening events” such as swelling and bleeding in the brain, especially for people with two copies of APOE4. Some treatment centers decided not to offer Leqembi, an intravenous infusion, to such patients.

Dr. Gandy and other experts said that classifying these patients as having a distinct genetic form of Alzheimer’s would galvanize interest in developing drugs that are safe and effective for them and add urgency to current efforts to prevent cognitive decline in people who do not yet have symptoms.

“Rather than say we have nothing for you, let’s look for a trial,” Dr. Gandy said, adding that such patients should be included in trials at younger ages, given how early their pathology starts.

Besides trying to develop drugs, some researchers are exploring gene editing to transform APOE4 into a variant called APOE2, which appears to protect against Alzheimer’s. Another gene-therapy approach being studied involves injecting APOE2 into patients’ brains.

The new study had some limitations, including a lack of diversity that might make the findings less generalizable. Most patients in the study had European ancestry. While two copies of APOE4 also greatly increase Alzheimer’s risk in other ethnicities, the risk levels differ, said Dr. Michael Greicius, a neurologist at Stanford University School of Medicine who was not involved in the research.

“One important argument against their interpretation is that the risk of Alzheimer’s disease in APOE4 homozygotes varies substantially across different genetic ancestries,” said Dr. Greicius, who cowrote a study that found that white people with two copies of APOE4 had 13 times the risk of white people with two copies of APOE3, while Black people with two copies of APOE4 had 6.5 times the risk of Black people with two copies of APOE3.

“This has critical implications when counseling patients about their ancestry-informed genetic risk for Alzheimer’s disease,” he said, “and it also speaks to some yet-to-be-discovered genetics and biology that presumably drive this massive difference in risk.”

Under the current genetic understanding of Alzheimer’s, less than 2 percent of cases are considered genetically caused. Some of those patients inherited a mutation in one of three genes and can develop symptoms as early as their 30s or 40s. Others are people with Down syndrome, who have three copies of a chromosome containing a protein that often leads to what is called Down syndrome-associated Alzheimer’s disease .

Dr. Sperling said the genetic alterations in those cases are believed to fuel buildup of amyloid, while APOE4 is believed to interfere with clearing amyloid buildup.

Under the researchers’ proposal, having one copy of APOE4 would continue to be considered a risk factor, not enough to cause Alzheimer’s, Dr. Fortea said. It is unusual for diseases to follow that genetic pattern, called “semidominance,” with two copies of a variant causing the disease, but one copy only increasing risk, experts said.

The new recommendation will prompt questions about whether people should get tested to determine if they have the APOE4 variant.

Dr. Greicius said that until there were treatments for people with two copies of APOE4 or trials of therapies to prevent them from developing dementia, “My recommendation is if you don’t have symptoms, you should definitely not figure out your APOE status.”

He added, “It will only cause grief at this point.”

Finding ways to help these patients cannot come soon enough, Dr. Sperling said, adding, “These individuals are desperate, they’ve seen it in both of their parents often and really need therapies.”

Pam Belluck is a health and science reporter, covering a range of subjects, including reproductive health, long Covid, brain science, neurological disorders, mental health and genetics. More about Pam Belluck

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