how to analyze case study data

• Privacy Policy

Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Transformative Design – Methods, Types, Guide

Research Methods – Types, Examples and Guide

Focus Groups – Steps, Examples and Guide

Quasi-Experimental Research Design – Types...

Qualitative Research – Methods, Analysis Types...

Triangulation in Research – Types, Methods and...

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Qualitative case study data analysis: an example from practice

Affiliation.

• 1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
• PMID: 25976531
• DOI: 10.7748/nr.22.5.8.e1307

Aim: To illustrate an approach to data analysis in qualitative case study methodology.

Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.

Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.

Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.

Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.

Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.

Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.

PubMed Disclaimer

Similar articles

• Using Framework Analysis in nursing research: a worked example. Ward DJ, Furber C, Tierney S, Swallow V. Ward DJ, et al. J Adv Nurs. 2013 Nov;69(11):2423-31. doi: 10.1111/jan.12127. Epub 2013 Mar 21. J Adv Nurs. 2013. PMID: 23517523
• Rigour in qualitative case-study research. Houghton C, Casey D, Shaw D, Murphy K. Houghton C, et al. Nurse Res. 2013 Mar;20(4):12-7. doi: 10.7748/nr2013.03.20.4.12.e326. Nurse Res. 2013. PMID: 23520707
• Selection, collection and analysis as sources of evidence in case study research. Houghton C, Casey D, Smyth S. Houghton C, et al. Nurse Res. 2017 Mar 22;24(4):36-41. doi: 10.7748/nr.2017.e1482. Nurse Res. 2017. PMID: 28326917
• Qualitative case study methodology in nursing research: an integrative review. Anthony S, Jack S. Anthony S, et al. J Adv Nurs. 2009 Jun;65(6):1171-81. doi: 10.1111/j.1365-2648.2009.04998.x. Epub 2009 Apr 3. J Adv Nurs. 2009. PMID: 19374670 Review.
• Avoiding and identifying errors in health technology assessment models: qualitative study and methodological review. Chilcott J, Tappenden P, Rawdin A, Johnson M, Kaltenthaler E, Paisley S, Papaioannou D, Shippam A. Chilcott J, et al. Health Technol Assess. 2010 May;14(25):iii-iv, ix-xii, 1-107. doi: 10.3310/hta14250. Health Technol Assess. 2010. PMID: 20501062 Review.
• The lived experiences of fatigue among patients receiving haemodialysis in Oman: a qualitative exploration. Al-Naamani Z, Gormley K, Noble H, Santin O, Al Omari O, Al-Noumani H, Madkhali N. Al-Naamani Z, et al. BMC Nephrol. 2024 Jul 29;25(1):239. doi: 10.1186/s12882-024-03647-2. BMC Nephrol. 2024. PMID: 39075347 Free PMC article.
• How a National Organization Works in Partnership With People Who Have Lived Experience in Mental Health Improvement Programs: Protocol for an Exploratory Case Study. Robertson C, Hibberd C, Shepherd A, Johnston G. Robertson C, et al. JMIR Res Protoc. 2024 Apr 19;13:e51779. doi: 10.2196/51779. JMIR Res Protoc. 2024. PMID: 38640479 Free PMC article.
• Implementation of an office-based addiction treatment model for Medicaid enrollees: A mixed methods study. Treitler P, Enich M, Bowden C, Mahone A, Lloyd J, Crystal S. Treitler P, et al. J Subst Use Addict Treat. 2024 Jan;156:209212. doi: 10.1016/j.josat.2023.209212. Epub 2023 Nov 5. J Subst Use Addict Treat. 2024. PMID: 37935350
• Using the quadruple aim to understand the impact of virtual delivery of care within Ontario community health centres: a qualitative study. Bhatti S, Dahrouge S, Muldoon L, Rayner J. Bhatti S, et al. BJGP Open. 2022 Dec 20;6(4):BJGPO.2022.0031. doi: 10.3399/BJGPO.2022.0031. Print 2022 Dec. BJGP Open. 2022. PMID: 36109022 Free PMC article.
• The components of diabetes educator's competence in diabetes self-management education in Iran: A qualitative study. Kashani F, Abazari P, Haghani F. Kashani F, et al. J Educ Health Promot. 2021 Mar 31;10:111. doi: 10.4103/jehp.jehp_912_20. eCollection 2021. J Educ Health Promot. 2021. PMID: 34084858 Free PMC article.
• Search in MeSH
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

Was this resource helpful?

• << Previous: Thematic Data Analysis in Qualitative Design
• Next: Journal Article Reporting Standards (JARS) >>
• Last Updated: Jul 22, 2024 8:15 PM
• URL: https://resources.nu.edu/researchtools

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• 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.

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 5 August 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

Is this article helpful?

Shona McCombes

Other students also liked, correlational research | guide, design & examples, a quick guide to experimental design | 5 steps & examples, descriptive research design | definition, methods & examples.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Qual Stud Health Well-being

Methodology or method? A critical review of qualitative case study reports

Despite on-going debate about credibility, and reported limitations in comparison to other approaches, case study is an increasingly popular approach among qualitative researchers. We critically analysed the methodological descriptions of published case studies. Three high-impact qualitative methods journals were searched to locate case studies published in the past 5 years; 34 were selected for analysis. Articles were categorized as health and health services ( n= 12), social sciences and anthropology ( n= 7), or methods ( n= 15) case studies. The articles were reviewed using an adapted version of established criteria to determine whether adequate methodological justification was present, and if study aims, methods, and reported findings were consistent with a qualitative case study approach. Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported. Improved reporting of case studies by qualitative researchers will advance the methodology for the benefit of researchers and practitioners.

Case study research is an increasingly popular approach among qualitative researchers (Thomas, 2011 ). Several prominent authors have contributed to methodological developments, which has increased the popularity of case study approaches across disciplines (Creswell, 2013b ; Denzin & Lincoln, 2011b ; Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Current qualitative case study approaches are shaped by paradigm, study design, and selection of methods, and, as a result, case studies in the published literature vary. Differences between published case studies can make it difficult for researchers to define and understand case study as a methodology.

Experienced qualitative researchers have identified case study research as a stand-alone qualitative approach (Denzin & Lincoln, 2011b ). Case study research has a level of flexibility that is not readily offered by other qualitative approaches such as grounded theory or phenomenology. Case studies are designed to suit the case and research question and published case studies demonstrate wide diversity in study design. There are two popular case study approaches in qualitative research. The first, proposed by Stake ( 1995 ) and Merriam ( 2009 ), is situated in a social constructivist paradigm, whereas the second, by Yin ( 2012 ), Flyvbjerg ( 2011 ), and Eisenhardt ( 1989 ), approaches case study from a post-positivist viewpoint. Scholarship from both schools of inquiry has contributed to the popularity of case study and development of theoretical frameworks and principles that characterize the methodology.

The diversity of case studies reported in the published literature, and on-going debates about credibility and the use of case study in qualitative research practice, suggests that differences in perspectives on case study methodology may prevent researchers from developing a mutual understanding of practice and rigour. In addition, discussion about case study limitations has led some authors to query whether case study is indeed a methodology (Luck, Jackson, & Usher, 2006 ; Meyer, 2001 ; Thomas, 2010 ; Tight, 2010 ). Methodological discussion of qualitative case study research is timely, and a review is required to analyse and understand how this methodology is applied in the qualitative research literature. The aims of this study were to review methodological descriptions of published qualitative case studies, to review how the case study methodological approach was applied, and to identify issues that need to be addressed by researchers, editors, and reviewers. An outline of the current definitions of case study and an overview of the issues proposed in the qualitative methodological literature are provided to set the scene for the review.

Definitions of qualitative case study research

Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, 1995 ). Qualitative case study research, as described by Stake ( 1995 ), draws together “naturalistic, holistic, ethnographic, phenomenological, and biographic research methods” in a bricoleur design, or in his words, “a palette of methods” (Stake, 1995 , pp. xi–xii). Case study methodology maintains deep connections to core values and intentions and is “particularistic, descriptive and heuristic” (Merriam, 2009 , p. 46).

As a study design, case study is defined by interest in individual cases rather than the methods of inquiry used. The selection of methods is informed by researcher and case intuition and makes use of naturally occurring sources of knowledge, such as people or observations of interactions that occur in the physical space (Stake, 1998 ). Thomas ( 2011 ) suggested that “analytical eclecticism” is a defining factor (p. 512). Multiple data collection and analysis methods are adopted to further develop and understand the case, shaped by context and emergent data (Stake, 1995 ). This qualitative approach “explores a real-life, contemporary bounded system (a case ) or multiple bounded systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information … and reports a case description and case themes ” (Creswell, 2013b , p. 97). Case study research has been defined by the unit of analysis, the process of study, and the outcome or end product, all essentially the case (Merriam, 2009 ).

The case is an object to be studied for an identified reason that is peculiar or particular. Classification of the case and case selection procedures informs development of the study design and clarifies the research question. Stake ( 1995 ) proposed three types of cases and study design frameworks. These include the intrinsic case, the instrumental case, and the collective instrumental case. The intrinsic case is used to understand the particulars of a single case, rather than what it represents. An instrumental case study provides insight on an issue or is used to refine theory. The case is selected to advance understanding of the object of interest. A collective refers to an instrumental case which is studied as multiple, nested cases, observed in unison, parallel, or sequential order. More than one case can be simultaneously studied; however, each case study is a concentrated, single inquiry, studied holistically in its own entirety (Stake, 1995 , 1998 ).

Researchers who use case study are urged to seek out what is common and what is particular about the case. This involves careful and in-depth consideration of the nature of the case, historical background, physical setting, and other institutional and political contextual factors (Stake, 1998 ). An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case. The case is developed in a relationship between the researcher and informants, and presented to engage the reader, inviting them to join in this interaction and in case discovery (Stake, 1995 ). A postpositivist approach to case study involves developing a clear case study protocol with careful consideration of validity and potential bias, which might involve an exploratory or pilot phase, and ensures that all elements of the case are measured and adequately described (Yin, 2009 , 2012 ).

Current methodological issues in qualitative case study research

The future of qualitative research will be influenced and constructed by the way research is conducted, and by what is reviewed and published in academic journals (Morse, 2011 ). If case study research is to further develop as a principal qualitative methodological approach, and make a valued contribution to the field of qualitative inquiry, issues related to methodological credibility must be considered. Researchers are required to demonstrate rigour through adequate descriptions of methodological foundations. Case studies published without sufficient detail for the reader to understand the study design, and without rationale for key methodological decisions, may lead to research being interpreted as lacking in quality or credibility (Hallberg, 2013 ; Morse, 2011 ).

There is a level of artistic license that is embraced by qualitative researchers and distinguishes practice, which nurtures creativity, innovation, and reflexivity (Denzin & Lincoln, 2011b ; Morse, 2009 ). Qualitative research is “inherently multimethod” (Denzin & Lincoln, 2011a , p. 5); however, with this creative freedom, it is important for researchers to provide adequate description for methodological justification (Meyer, 2001 ). This includes paradigm and theoretical perspectives that have influenced study design. Without adequate description, study design might not be understood by the reader, and can appear to be dishonest or inaccurate. Reviewers and readers might be confused by the inconsistent or inappropriate terms used to describe case study research approach and methods, and be distracted from important study findings (Sandelowski, 2000 ). This issue extends beyond case study research, and others have noted inconsistencies in reporting of methodology and method by qualitative researchers. Sandelowski ( 2000 , 2010 ) argued for accurate identification of qualitative description as a research approach. She recommended that the selected methodology should be harmonious with the study design, and be reflected in methods and analysis techniques. Similarly, Webb and Kevern ( 2000 ) uncovered inconsistencies in qualitative nursing research with focus group methods, recommending that methodological procedures must cite seminal authors and be applied with respect to the selected theoretical framework. Incorrect labelling using case study might stem from the flexibility in case study design and non-directional character relative to other approaches (Rosenberg & Yates, 2007 ). Methodological integrity is required in design of qualitative studies, including case study, to ensure study rigour and to enhance credibility of the field (Morse, 2011 ).

Case study has been unnecessarily devalued by comparisons with statistical methods (Eisenhardt, 1989 ; Flyvbjerg, 2006 , 2011 ; Jensen & Rodgers, 2001 ; Piekkari, Welch, & Paavilainen, 2009 ; Tight, 2010 ; Yin, 1999 ). It is reputed to be the “the weak sibling” in comparison to other, more rigorous, approaches (Yin, 2009 , p. xiii). Case study is not an inherently comparative approach to research. The objective is not statistical research, and the aim is not to produce outcomes that are generalizable to all populations (Thomas, 2011 ). Comparisons between case study and statistical research do little to advance this qualitative approach, and fail to recognize its inherent value, which can be better understood from the interpretive or social constructionist viewpoint of other authors (Merriam, 2009 ; Stake, 1995 ). Building on discussions relating to “fuzzy” (Bassey, 2001 ), or naturalistic generalizations (Stake, 1978 ), or transference of concepts and theories (Ayres, Kavanaugh, & Knafl, 2003 ; Morse et al., 2011 ) would have more relevance.

Case study research has been used as a catch-all design to justify or add weight to fundamental qualitative descriptive studies that do not fit with other traditional frameworks (Merriam, 2009 ). A case study has been a “convenient label for our research—when we ‘can't think of anything ‘better”—in an attempt to give it [qualitative methodology] some added respectability” (Tight, 2010 , p. 337). Qualitative case study research is a pliable approach (Merriam, 2009 ; Meyer, 2001 ; Stake, 1995 ), and has been likened to a “curious methodological limbo” (Gerring, 2004 , p. 341) or “paradigmatic bridge” (Luck et al., 2006 , p. 104), that is on the borderline between postpositivist and constructionist interpretations. This has resulted in inconsistency in application, which indicates that flexibility comes with limitations (Meyer, 2001 ), and the open nature of case study research might be off-putting to novice researchers (Thomas, 2011 ). The development of a well-(in)formed theoretical framework to guide a case study should improve consistency, rigour, and trust in studies published in qualitative research journals (Meyer, 2001 ).

Assessment of rigour

The purpose of this study was to analyse the methodological descriptions of case studies published in qualitative methods journals. To do this we needed to develop a suitable framework, which used existing, established criteria for appraising qualitative case study research rigour (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ). A number of qualitative authors have developed concepts and criteria that are used to determine whether a study is rigorous (Denzin & Lincoln, 2011b ; Lincoln, 1995 ; Sandelowski & Barroso, 2002 ). The criteria proposed by Stake ( 1995 ) provide a framework for readers and reviewers to make judgements regarding case study quality, and identify key characteristics essential for good methodological rigour. Although each of the factors listed in Stake's criteria could enhance the quality of a qualitative research report, in Table I we present an adapted criteria used in this study, which integrates more recent work by Merriam ( 2009 ) and Creswell ( 2013b ). Stake's ( 1995 ) original criteria were separated into two categories. The first list of general criteria is “relevant for all qualitative research.” The second list, “high relevance to qualitative case study research,” was the criteria that we decided had higher relevance to case study research. This second list was the main criteria used to assess the methodological descriptions of the case studies reviewed. The complete table has been preserved so that the reader can determine how the original criteria were adapted.

Framework for assessing quality in qualitative case study research.

Adapted from Stake ( 1995 , p. 131).

Study design

The critical review method described by Grant and Booth ( 2009 ) was used, which is appropriate for the assessment of research quality, and is used for literature analysis to inform research and practice. This type of review goes beyond the mapping and description of scoping or rapid reviews, to include “analysis and conceptual innovation” (Grant & Booth, 2009 , p. 93). A critical review is used to develop existing, or produce new, hypotheses or models. This is different to systematic reviews that answer clinical questions. It is used to evaluate existing research and competing ideas, to provide a “launch pad” for conceptual development and “subsequent testing” (Grant & Booth, 2009 , p. 93).

Qualitative methods journals were located by a search of the 2011 ISI Journal Citation Reports in Social Science, via the database Web of Knowledge (see m.webofknowledge.com). No “qualitative research methods” category existed in the citation reports; therefore, a search of all categories was performed using the term “qualitative.” In Table II , we present the qualitative methods journals located, ranked by impact factor. The highest ranked journals were selected for searching. We acknowledge that the impact factor ranking system might not be the best measure of journal quality (Cheek, Garnham, & Quan, 2006 ); however, this was the most appropriate and accessible method available.

International Journal of Qualitative Studies on Health and Well-being.

Search strategy

In March 2013, searches of the journals, Qualitative Health Research , Qualitative Research , and Qualitative Inquiry were completed to retrieve studies with “case study” in the abstract field. The search was limited to the past 5 years (1 January 2008 to 1 March 2013). The objective was to locate published qualitative case studies suitable for assessment using the adapted criterion. Viewpoints, commentaries, and other article types were excluded from review. Title and abstracts of the 45 retrieved articles were read by the first author, who identified 34 empirical case studies for review. All authors reviewed the 34 studies to confirm selection and categorization. In Table III , we present the 34 case studies grouped by journal, and categorized by research topic, including health sciences, social sciences and anthropology, and methods research. There was a discrepancy in categorization of one article on pedagogy and a new teaching method published in Qualitative Inquiry (Jorrín-Abellán, Rubia-Avi, Anguita-Martínez, Gómez-Sánchez, & Martínez-Mones, 2008 ). Consensus was to allocate to the methods category.

Outcomes of search of qualitative methods journals.

In Table III , the number of studies located, and final numbers selected for review have been reported. Qualitative Health Research published the most empirical case studies ( n= 16). In the health category, there were 12 case studies of health conditions, health services, and health policy issues, all published in Qualitative Health Research . Seven case studies were categorized as social sciences and anthropology research, which combined case study with biography and ethnography methodologies. All three journals published case studies on methods research to illustrate a data collection or analysis technique, methodological procedure, or related issue.

The methodological descriptions of 34 case studies were critically reviewed using the adapted criteria. All articles reviewed contained a description of study methods; however, the length, amount of detail, and position of the description in the article varied. Few studies provided an accurate description and rationale for using a qualitative case study approach. In the 34 case studies reviewed, three described a theoretical framework informed by Stake ( 1995 ), two by Yin ( 2009 ), and three provided a mixed framework informed by various authors, which might have included both Yin and Stake. Few studies described their case study design, or included a rationale that explained why they excluded or added further procedures, and whether this was to enhance the study design, or to better suit the research question. In 26 of the studies no reference was provided to principal case study authors. From reviewing the description of methods, few authors provided a description or justification of case study methodology that demonstrated how their study was informed by the methodological literature that exists on this approach.

The methodological descriptions of each study were reviewed using the adapted criteria, and the following issues were identified: case study methodology or method; case of something particular and case selection; contextually bound case study; researcher and case interactions and triangulation; and, study design inconsistent with methodology. An outline of how the issues were developed from the critical review is provided, followed by a discussion of how these relate to the current methodological literature.

Case study methodology or method

A third of the case studies reviewed appeared to use a case report method, not case study methodology as described by principal authors (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). Case studies were identified as a case report because of missing methodological detail and by review of the study aims and purpose. These reports presented data for small samples of no more than three people, places or phenomenon. Four studies, or “case reports” were single cases selected retrospectively from larger studies (Bronken, Kirkevold, Martinsen, & Kvigne, 2012 ; Coltart & Henwood, 2012 ; Hooghe, Neimeyer, & Rober, 2012 ; Roscigno et al., 2012 ). Case reports were not a case of something, instead were a case demonstration or an example presented in a report. These reports presented outcomes, and reported on how the case could be generalized. Descriptions focussed on the phenomena, rather than the case itself, and did not appear to study the case in its entirety.

Case reports had minimal in-text references to case study methodology, and were informed by other qualitative traditions or secondary sources (Adamson & Holloway, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nagar-Ron & Motzafi-Haller, 2011 ). This does not suggest that case study methodology cannot be multimethod, however, methodology should be consistent in design, be clearly described (Meyer, 2001 ; Stake, 1995 ), and maintain focus on the case (Creswell, 2013b ).

To demonstrate how case reports were identified, three examples are provided. The first, Yeh ( 2013 ) described their study as, “the examination of the emergence of vegetarianism in Victorian England serves as a case study to reveal the relationships between boundaries and entities” (p. 306). The findings were a historical case report, which resulted from an ethnographic study of vegetarianism. Cunsolo Willox, Harper, Edge, ‘My Word’: Storytelling and Digital Media Lab, and Rigolet Inuit Community Government (2013) used “a case study that illustrates the usage of digital storytelling within an Inuit community” (p. 130). This case study reported how digital storytelling can be used with indigenous communities as a participatory method to illuminate the benefits of this method for other studies. This “case study was conducted in the Inuit community” but did not include the Inuit community in case analysis (Cunsolo Willox et al., 2013 , p. 130). Bronken et al. ( 2012 ) provided a single case report to demonstrate issues observed in a larger clinical study of aphasia and stroke, without adequate case description or analysis.

Case study of something particular and case selection

Case selection is a precursor to case analysis, which needs to be presented as a convincing argument (Merriam, 2009 ). Descriptions of the case were often not adequate to ascertain why the case was selected, or whether it was a particular exemplar or outlier (Thomas, 2011 ). In a number of case studies in the health and social science categories, it was not explicit whether the case was of something particular, or peculiar to their discipline or field (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson, Botelho, Welch, Joseph, & Tennstedt, 2012 ; Mawn et al., 2010 ; Snyder-Young, 2011 ). There were exceptions in the methods category ( Table III ), where cases were selected by researchers to report on a new or innovative method. The cases emerged through heuristic study, and were reported to be particular, relative to the existing methods literature (Ajodhia-Andrews & Berman, 2009 ; Buckley & Waring, 2013 ; Cunsolo Willox et al., 2013 ; De Haene, Grietens, & Verschueren, 2010 ; Gratton & O'Donnell, 2011 ; Sumsion, 2013 ; Wimpenny & Savin-Baden, 2012 ).

Case selection processes were sometimes insufficient to understand why the case was selected from the global population of cases, or what study of this case would contribute to knowledge as compared with other possible cases (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson et al., 2012 ; Mawn et al., 2010 ). In two studies, local cases were selected (Barone, 2010 ; Fourie & Theron, 2012 ) because the researcher was familiar with and had access to the case. Possible limitations of a convenience sample were not acknowledged. Purposeful sampling was used to recruit participants within the case of one study, but not of the case itself (Gallagher et al., 2013 ). Random sampling was completed for case selection in two studies (Colón-Emeric et al., 2010 ; Jackson et al., 2012 ), which has limited meaning in interpretive qualitative research.

To demonstrate how researchers provided a good justification for the selection of case study approaches, four examples are provided. The first, cases of residential care homes, were selected because of reported occurrences of mistreatment, which included residents being locked in rooms at night (Rytterström, Unosson, & Arman, 2013 ). Roscigno et al. ( 2012 ) selected cases of parents who were admitted for early hospitalization in neonatal intensive care with a threatened preterm delivery before 26 weeks. Hooghe et al. ( 2012 ) used random sampling to select 20 couples that had experienced the death of a child; however, the case study was of one couple and a particular metaphor described only by them. The final example, Coltart and Henwood ( 2012 ), provided a detailed account of how they selected two cases from a sample of 46 fathers based on personal characteristics and beliefs. They described how the analysis of the two cases would contribute to their larger study on first time fathers and parenting.

Contextually bound case study

The limits or boundaries of the case are a defining factor of case study methodology (Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Adequate contextual description is required to understand the setting or context in which the case is revealed. In the health category, case studies were used to illustrate a clinical phenomenon or issue such as compliance and health behaviour (Colón-Emeric et al., 2010 ; D'Enbeau, Buzzanell, & Duckworth, 2010 ; Gallagher et al., 2013 ; Hooghe et al., 2012 ; Jackson et al., 2012 ; Roscigno et al., 2012 ). In these case studies, contextual boundaries, such as physical and institutional descriptions, were not sufficient to understand the case as a holistic system, for example, the general practitioner (GP) clinic in Gallagher et al. ( 2013 ), or the nursing home in Colón-Emeric et al. ( 2010 ). Similarly, in the social science and methods categories, attention was paid to some components of the case context, but not others, missing important information required to understand the case as a holistic system (Alexander, Moreira, & Kumar, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nairn & Panelli, 2009 ; Wimpenny & Savin-Baden, 2012 ).

In two studies, vicarious experience or vignettes (Nairn & Panelli, 2009 ) and images (Jorrín-Abellán et al., 2008 ) were effective to support description of context, and might have been a useful addition for other case studies. Missing contextual boundaries suggests that the case might not be adequately defined. Additional information, such as the physical, institutional, political, and community context, would improve understanding of the case (Stake, 1998 ). In Boxes 1 and 2 , we present brief synopses of two studies that were reviewed, which demonstrated a well bounded case. In Box 1 , Ledderer ( 2011 ) used a qualitative case study design informed by Stake's tradition. In Box 2 , Gillard, Witt, and Watts ( 2011 ) were informed by Yin's tradition. By providing a brief outline of the case studies in Boxes 1 and 2 , we demonstrate how effective case boundaries can be constructed and reported, which may be of particular interest to prospective case study researchers.

Article synopsis of case study research using Stake's tradition

Ledderer ( 2011 ) used a qualitative case study research design, informed by modern ethnography. The study is bounded to 10 general practice clinics in Denmark, who had received federal funding to implement preventative care services based on a Motivational Interviewing intervention. The researcher question focussed on “why is it so difficult to create change in medical practice?” (Ledderer, 2011 , p. 27). The study context was adequately described, providing detail on the general practitioner (GP) clinics and relevant political and economic influences. Methodological decisions are described in first person narrative, providing insight on researcher perspectives and interaction with the case. Forty-four interviews were conducted, which focussed on how GPs conducted consultations, and the form, nature and content, rather than asking their opinion or experience (Ledderer, 2011 , p. 30). The duration and intensity of researcher immersion in the case enhanced depth of description and trustworthiness of study findings. Analysis was consistent with Stake's tradition, and the researcher provided examples of inquiry techniques used to challenge assumptions about emerging themes. Several other seminal qualitative works were cited. The themes and typology constructed are rich in narrative data and storytelling by clinic staff, demonstrating individual clinic experiences as well as shared meanings and understandings about changing from a biomedical to psychological approach to preventative health intervention. Conclusions make note of social and cultural meanings and lessons learned, which might not have been uncovered using a different methodology.

Article synopsis of case study research using Yin's tradition

Gillard et al. ( 2011 ) study of camps for adolescents living with HIV/AIDs provided a good example of Yin's interpretive case study approach. The context of the case is bounded by the three summer camps of which the researchers had prior professional involvement. A case study protocol was developed that used multiple methods to gather information at three data collection points coinciding with three youth camps (Teen Forum, Discover Camp, and Camp Strong). Gillard and colleagues followed Yin's ( 2009 ) principles, using a consistent data protocol that enhanced cross-case analysis. Data described the young people, the camp physical environment, camp schedule, objectives and outcomes, and the staff of three youth camps. The findings provided a detailed description of the context, with less detail of individual participants, including insight into researcher's interpretations and methodological decisions throughout the data collection and analysis process. Findings provided the reader with a sense of “being there,” and are discovered through constant comparison of the case with the research issues; the case is the unit of analysis. There is evidence of researcher immersion in the case, and Gillard reports spending significant time in the field in a naturalistic and integrated youth mentor role.

This case study is not intended to have a significant impact on broader health policy, although does have implications for health professionals working with adolescents. Study conclusions will inform future camps for young people with chronic disease, and practitioners are able to compare similarities between this case and their own practice (for knowledge translation). No limitations of this article were reported. Limitations related to publication of this case study were that it was 20 pages long and used three tables to provide sufficient description of the camp and program components, and relationships with the research issue.

Researcher and case interactions and triangulation

Researcher and case interactions and transactions are a defining feature of case study methodology (Stake, 1995 ). Narrative stories, vignettes, and thick description are used to provoke vicarious experience and a sense of being there with the researcher in their interaction with the case. Few of the case studies reviewed provided details of the researcher's relationship with the case, researcher–case interactions, and how these influenced the development of the case study (Buzzanell & D'Enbeau, 2009 ; D'Enbeau et al., 2010 ; Gallagher et al., 2013 ; Gillard et al., 2011 ; Ledderer, 2011 ; Nagar-Ron & Motzafi-Haller, 2011 ). The role and position of the researcher needed to be self-examined and understood by readers, to understand how this influenced interactions with participants, and to determine what triangulation is needed (Merriam, 2009 ; Stake, 1995 ).

Gillard et al. ( 2011 ) provided a good example of triangulation, comparing data sources in a table (p. 1513). Triangulation of sources was used to reveal as much depth as possible in the study by Nagar-Ron and Motzafi-Haller ( 2011 ), while also enhancing confirmation validity. There were several case studies that would have benefited from improved range and use of data sources, and descriptions of researcher–case interactions (Ajodhia-Andrews & Berman, 2009 ; Bronken et al., 2012 ; Fincham, Scourfield, & Langer, 2008 ; Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Snyder-Young, 2011 ; Yeh, 2013 ).

Study design inconsistent with methodology

Good, rigorous case studies require a strong methodological justification (Meyer, 2001 ) and a logical and coherent argument that defines paradigm, methodological position, and selection of study methods (Denzin & Lincoln, 2011b ). Methodological justification was insufficient in several of the studies reviewed (Barone, 2010 ; Bronken et al., 2012 ; Hooghe et al., 2012 ; Mawn et al., 2010 ; Roscigno et al., 2012 ; Yeh, 2013 ). This was judged by the absence, or inadequate or inconsistent reference to case study methodology in-text.

In six studies, the methodological justification provided did not relate to case study. There were common issues identified. Secondary sources were used as primary methodological references indicating that study design might not have been theoretically sound (Colón-Emeric et al., 2010 ; Coltart & Henwood, 2012 ; Roscigno et al., 2012 ; Snyder-Young, 2011 ). Authors and sources cited in methodological descriptions were inconsistent with the actual study design and practices used (Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Jorrín-Abellán et al., 2008 ; Mawn et al., 2010 ; Rytterström et al., 2013 ; Wimpenny & Savin-Baden, 2012 ). This occurred when researchers cited Stake or Yin, or both (Mawn et al., 2010 ; Rytterström et al., 2013 ), although did not follow their paradigmatic or methodological approach. In 26 studies there were no citations for a case study methodological approach.

The findings of this study have highlighted a number of issues for researchers. A considerable number of case studies reviewed were missing key elements that define qualitative case study methodology and the tradition cited. A significant number of studies did not provide a clear methodological description or justification relevant to case study. Case studies in health and social sciences did not provide sufficient information for the reader to understand case selection, and why this case was chosen above others. The context of the cases were not described in adequate detail to understand all relevant elements of the case context, which indicated that cases may have not been contextually bounded. There were inconsistencies between reported methodology, study design, and paradigmatic approach in case studies reviewed, which made it difficult to understand the study methodology and theoretical foundations. These issues have implications for methodological integrity and honesty when reporting study design, which are values of the qualitative research tradition and are ethical requirements (Wager & Kleinert, 2010a ). Poorly described methodological descriptions may lead the reader to misinterpret or discredit study findings, which limits the impact of the study, and, as a collective, hinders advancements in the broader qualitative research field.

The issues highlighted in our review build on current debates in the case study literature, and queries about the value of this methodology. Case study research can be situated within different paradigms or designed with an array of methods. In order to maintain the creativity and flexibility that is valued in this methodology, clearer descriptions of paradigm and theoretical position and methods should be provided so that study findings are not undervalued or discredited. Case study research is an interdisciplinary practice, which means that clear methodological descriptions might be more important for this approach than other methodologies that are predominantly driven by fewer disciplines (Creswell, 2013b ).

Authors frequently omit elements of methodologies and include others to strengthen study design, and we do not propose a rigid or purist ideology in this paper. On the contrary, we encourage new ideas about using case study, together with adequate reporting, which will advance the value and practice of case study. The implications of unclear methodological descriptions in the studies reviewed were that study design appeared to be inconsistent with reported methodology, and key elements required for making judgements of rigour were missing. It was not clear whether the deviations from methodological tradition were made by researchers to strengthen the study design, or because of misinterpretations. Morse ( 2011 ) recommended that innovations and deviations from practice are best made by experienced researchers, and that a novice might be unaware of the issues involved with making these changes. To perpetuate the tradition of case study research, applications in the published literature should have consistencies with traditional methodological constructions, and deviations should be described with a rationale that is inherent in study conduct and findings. Providing methodological descriptions that demonstrate a strong theoretical foundation and coherent study design will add credibility to the study, while ensuring the intrinsic meaning of case study is maintained.

The value of this review is that it contributes to discussion of whether case study is a methodology or method. We propose possible reasons why researchers might make this misinterpretation. Researchers may interchange the terms methods and methodology, and conduct research without adequate attention to epistemology and historical tradition (Carter & Little, 2007 ; Sandelowski, 2010 ). If the rich meaning that naming a qualitative methodology brings to the study is not recognized, a case study might appear to be inconsistent with the traditional approaches described by principal authors (Creswell, 2013a ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). If case studies are not methodologically and theoretically situated, then they might appear to be a case report.

Case reports are promoted by university and medical journals as a method of reporting on medical or scientific cases; guidelines for case reports are publicly available on websites ( http://www.hopkinsmedicine.org/institutional_review_board/guidelines_policies/guidelines/case_report.html ). The various case report guidelines provide a general criteria for case reports, which describes that this form of report does not meet the criteria of research, is used for retrospective analysis of up to three clinical cases, and is primarily illustrative and for educational purposes. Case reports can be published in academic journals, but do not require approval from a human research ethics committee. Traditionally, case reports describe a single case, to explain how and what occurred in a selected setting, for example, to illustrate a new phenomenon that has emerged from a larger study. A case report is not necessarily particular or the study of a case in its entirety, and the larger study would usually be guided by a different research methodology.

This description of a case report is similar to what was provided in some studies reviewed. This form of report lacks methodological grounding and qualities of research rigour. The case report has publication value in demonstrating an example and for dissemination of knowledge (Flanagan, 1999 ). However, case reports have different meaning and purpose to case study, which needs to be distinguished. Findings of our review suggest that the medical understanding of a case report has been confused with qualitative case study approaches.

In this review, a number of case studies did not have methodological descriptions that included key characteristics of case study listed in the adapted criteria, and several issues have been discussed. There have been calls for improvements in publication quality of qualitative research (Morse, 2011 ), and for improvements in peer review of submitted manuscripts (Carter & Little, 2007 ; Jasper, Vaismoradi, Bondas, & Turunen, 2013 ). The challenging nature of editor and reviewers responsibilities are acknowledged in the literature (Hames, 2013 ; Wager & Kleinert, 2010b ); however, review of case study methodology should be prioritized because of disputes on methodological value.

Authors using case study approaches are recommended to describe their theoretical framework and methods clearly, and to seek and follow specialist methodological advice when needed (Wager & Kleinert, 2010a ). Adequate page space for case study description would contribute to better publications (Gillard et al., 2011 ). Capitalizing on the ability to publish complementary resources should be considered.

Limitations of the review

There is a level of subjectivity involved in this type of review and this should be considered when interpreting study findings. Qualitative methods journals were selected because the aims and scope of these journals are to publish studies that contribute to methodological discussion and development of qualitative research. Generalist health and social science journals were excluded that might have contained good quality case studies. Journals in business or education were also excluded, although a review of case studies in international business journals has been published elsewhere (Piekkari et al., 2009 ).

The criteria used to assess the quality of the case studies were a set of qualitative indicators. A numerical or ranking system might have resulted in different results. Stake's ( 1995 ) criteria have been referenced elsewhere, and was deemed the best available (Creswell, 2013b ; Crowe et al., 2011 ). Not all qualitative studies are reported in a consistent way and some authors choose to report findings in a narrative form in comparison to a typical biomedical report style (Sandelowski & Barroso, 2002 ), if misinterpretations were made this may have affected the review.

Case study research is an increasingly popular approach among qualitative researchers, which provides methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods. However, whereas flexibility can be an advantage, a myriad of different interpretations has resulted in critics questioning the use of case study as a methodology. Using an adaptation of established criteria, we aimed to identify and assess the methodological descriptions of case studies in high impact, qualitative methods journals. Few articles were identified that applied qualitative case study approaches as described by experts in case study design. There were inconsistencies in methodology and study design, which indicated that researchers were confused whether case study was a methodology or a method. Commonly, there appeared to be confusion between case studies and case reports. Without clear understanding and application of the principles and key elements of case study methodology, there is a risk that the flexibility of the approach will result in haphazard reporting, and will limit its global application as a valuable, theoretically supported methodology that can be rigorously applied across disciplines and fields.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

• Adamson S, Holloway M. Negotiating sensitivities and grappling with intangibles: Experiences from a study of spirituality and funerals. Qualitative Research. 2012; 12 (6):735–752. doi: 10.1177/1468794112439008. [ CrossRef ] [ Google Scholar ]
• Ajodhia-Andrews A, Berman R. Exploring school life from the lens of a child who does not use speech to communicate. Qualitative Inquiry. 2009; 15 (5):931–951. doi: 10.1177/1077800408322789. [ CrossRef ] [ Google Scholar ]
• Alexander B. K, Moreira C, Kumar H. S. Resisting (resistance) stories: A tri-autoethnographic exploration of father narratives across shades of difference. Qualitative Inquiry. 2012; 18 (2):121–133. doi: 10.1177/1077800411429087. [ CrossRef ] [ Google Scholar ]
• Austin W, Park C, Goble E. From interdisciplinary to transdisciplinary research: A case study. Qualitative Health Research. 2008; 18 (4):557–564. doi: 10.1177/1049732307308514. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ayres L, Kavanaugh K, Knafl K. A. Within-case and across-case approaches to qualitative data analysis. Qualitative Health Research. 2003; 13 (6):871–883. doi: 10.1177/1049732303013006008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barone T. L. Culturally sensitive care 1969–2000: The Indian Chicano Health Center. Qualitative Health Research. 2010; 20 (4):453–464. doi: 10.1177/1049732310361893. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bassey M. A solution to the problem of generalisation in educational research: Fuzzy prediction. Oxford Review of Education. 2001; 27 (1):5–22. doi: 10.1080/03054980123773. [ CrossRef ] [ Google Scholar ]
• Bronken B. A, Kirkevold M, Martinsen R, Kvigne K. The aphasic storyteller: Coconstructing stories to promote psychosocial well-being after stroke. Qualitative Health Research. 2012; 22 (10):1303–1316. doi: 10.1177/1049732312450366. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Broyles L. M, Rodriguez K. L, Price P. A, Bayliss N. K, Sevick M. A. Overcoming barriers to the recruitment of nurses as participants in health care research. Qualitative Health Research. 2011; 21 (12):1705–1718. doi: 10.1177/1049732311417727. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buckley C. A, Waring M. J. Using diagrams to support the research process: Examples from grounded theory. Qualitative Research. 2013; 13 (2):148–172. doi: 10.1177/1468794112472280. [ CrossRef ] [ Google Scholar ]
• Buzzanell P. M, D'Enbeau S. Stories of caregiving: Intersections of academic research and women's everyday experiences. Qualitative Inquiry. 2009; 15 (7):1199–1224. doi: 10.1177/1077800409338025. [ CrossRef ] [ Google Scholar ]
• Carter S. M, Little M. Justifying knowledge, justifying method, taking action: Epistemologies, methodologies, and methods in qualitative research. Qualitative Health Research. 2007; 17 (10):1316–1328. doi: 10.1177/1049732307306927. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheek J, Garnham B, Quan J. What's in a number? Issues in providing evidence of impact and quality of research(ers) Qualitative Health Research. 2006; 16 (3):423–435. doi: 10.1177/1049732305285701. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Colón-Emeric C. S, Plowman D, Bailey D, Corazzini K, Utley-Smith Q, Ammarell N, et al. Regulation and mindful resident care in nursing homes. Qualitative Health Research. 2010; 20 (9):1283–1294. doi: 10.1177/1049732310369337. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coltart C, Henwood K. On paternal subjectivity: A qualitative longitudinal and psychosocial case analysis of men's classed positions and transitions to first-time fatherhood. Qualitative Research. 2012; 12 (1):35–52. doi: 10.1177/1468794111426224. [ CrossRef ] [ Google Scholar ]
• Creswell J. W. Five qualitative approaches to inquiry. In: Creswell J. W, editor. Qualitative inquiry and research design: Choosing among five approaches. 3rd ed. Thousand Oaks, CA: Sage; 2013a. pp. 53–84. [ Google Scholar ]
• Creswell J. W. Qualitative inquiry and research design: Choosing among five approaches. 3rd ed. Thousand Oaks, CA: Sage; 2013b. [ Google Scholar ]
• Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Medical Research Methodology. 2011; 11 (1):1–9. doi: 10.1186/1471-2288-11-100. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cunsolo Willox A, Harper S. L, Edge V. L, ‘My Word’: Storytelling and Digital Media Lab, & Rigolet Inuit Community Government Storytelling in a digital age: Digital storytelling as an emerging narrative method for preserving and promoting indigenous oral wisdom. Qualitative Research. 2013; 13 (2):127–147. doi: 10.1177/1468794112446105. [ CrossRef ] [ Google Scholar ]
• De Haene L, Grietens H, Verschueren K. Holding harm: Narrative methods in mental health research on refugee trauma. Qualitative Health Research. 2010; 20 (12):1664–1676. doi: 10.1177/1049732310376521. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• D'Enbeau S, Buzzanell P. M, Duckworth J. Problematizing classed identities in fatherhood: Development of integrative case studies for analysis and praxis. Qualitative Inquiry. 2010; 16 (9):709–720. doi: 10.1177/1077800410374183. [ CrossRef ] [ Google Scholar ]
• Denzin N. K, Lincoln Y. S. Introduction: Disciplining the practice of qualitative research. In: Denzin N. K, Lincoln Y. S, editors. The SAGE handbook of qualitative research. 4th ed. Thousand Oaks, CA: Sage; 2011a. pp. 1–6. [ Google Scholar ]
• Denzin N. K, Lincoln Y. S, editors. The SAGE handbook of qualitative research. 4th ed. Thousand Oaks, CA: Sage; 2011b. [ Google Scholar ]
• Edwards R, Weller S. Shifting analytic ontology: Using I-poems in qualitative longitudinal research. Qualitative Research. 2012; 12 (2):202–217. doi: 10.1177/1468794111422040. [ CrossRef ] [ Google Scholar ]
• Eisenhardt K. M. Building theories from case study research. The Academy of Management Review. 1989; 14 (4):532–550. doi: 10.2307/258557. [ CrossRef ] [ Google Scholar ]
• Fincham B, Scourfield J, Langer S. The impact of working with disturbing secondary data: Reading suicide files in a coroner's office. Qualitative Health Research. 2008; 18 (6):853–862. doi: 10.1177/1049732307308945. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Flanagan J. Public participation in the design of educational programmes for cancer nurses: A case report. European Journal of Cancer Care. 1999; 8 (2):107–112. doi: 10.1046/j.1365-2354.1999.00141.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Flyvbjerg B. Five misunderstandings about case-study research. Qualitative Inquiry. 2006; 12 (2):219–245. doi: 10.1177/1077800405284.363. [ CrossRef ] [ Google Scholar ]
• Flyvbjerg B. Case study. In: Denzin N. K, Lincoln Y. S, editors. The SAGE handbook of qualitative research. 4th ed. Thousand Oaks, CA: Sage; 2011. pp. 301–316. [ Google Scholar ]
• Fourie C. L, Theron L. C. Resilience in the face of fragile X syndrome. Qualitative Health Research. 2012; 22 (10):1355–1368. doi: 10.1177/1049732312451871. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gallagher N, MacFarlane A, Murphy A. W, Freeman G. K, Glynn L. G, Bradley C. P. Service users’ and caregivers’ perspectives on continuity of care in out-of-hours primary care. Qualitative Health Research. 2013; 23 (3):407–421. doi: 10.1177/1049732312470521. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gerring J. What is a case study and what is it good for? American Political Science Review. 2004; 98 (2):341–354. doi: 10.1017/S0003055404001182. [ CrossRef ] [ Google Scholar ]
• Gillard A, Witt P. A, Watts C. E. Outcomes and processes at a camp for youth with HIV/AIDS. Qualitative Health Research. 2011; 21 (11):1508–1526. doi: 10.1177/1049732311413907. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grant M, Booth A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal. 2009; 26 :91–108. doi: 10.1111/j.1471-1842.2009.00848.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gratton M.-F, O'Donnell S. Communication technologies for focus groups with remote communities: A case study of research with First Nations in Canada. Qualitative Research. 2011; 11 (2):159–175. doi: 10.1177/1468794110394068. [ CrossRef ] [ Google Scholar ]
• Hallberg L. Quality criteria and generalization of results from qualitative studies. International Journal of Qualitative Studies on Health and Wellbeing. 2013; 8 :1. doi: 10.3402/qhw.v8i0.20647. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hames I. Committee on Publication Ethics, 1. 2013, March. COPE Ethical guidelines for peer reviewers. Retrieved April 7, 2013, from http://publicationethics.org/resources/guidelines . [ Google Scholar ]
• Hooghe A, Neimeyer R. A, Rober P. “Cycling around an emotional core of sadness”: Emotion regulation in a couple after the loss of a child. Qualitative Health Research. 2012; 22 (9):1220–1231. doi: 10.1177/1049732312449209. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jackson C. B, Botelho E. M, Welch L. C, Joseph J, Tennstedt S. L. Talking with others about stigmatized health conditions: Implications for managing symptoms. Qualitative Health Research. 2012; 22 (11):1468–1475. doi: 10.1177/1049732312450323. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jasper M, Vaismoradi M, Bondas T, Turunen H. Validity and reliability of the scientific review process in nursing journals—time for a rethink? Nursing Inquiry. 2013 doi: 10.1111/nin.12030. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jensen J. L, Rodgers R. Cumulating the intellectual gold of case study research. Public Administration Review. 2001; 61 (2):235–246. doi: 10.1111/0033-3352.00025. [ CrossRef ] [ Google Scholar ]
• Jorrín-Abellán I. M, Rubia-Avi B, Anguita-Martínez R, Gómez-Sánchez E, Martínez-Mones A. Bouncing between the dark and bright sides: Can technology help qualitative research? Qualitative Inquiry. 2008; 14 (7):1187–1204. doi: 10.1177/1077800408318435. [ CrossRef ] [ Google Scholar ]
• Ledderer L. Understanding change in medical practice: The role of shared meaning in preventive treatment. Qualitative Health Research. 2011; 21 (1):27–40. doi: 10.1177/1049732310377451. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lincoln Y. S. Emerging criteria for quality in qualitative and interpretive research. Qualitative Inquiry. 1995; 1 (3):275–289. doi: 10.1177/107780049500100301. [ CrossRef ] [ Google Scholar ]
• Luck L, Jackson D, Usher K. Case study: A bridge across the paradigms. Nursing Inquiry. 2006; 13 (2):103–109. doi: 10.1111/j.1440-1800.2006.00309.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mawn B, Siqueira E, Koren A, Slatin C, Devereaux Melillo K, Pearce C, et al. Health disparities among health care workers. Qualitative Health Research. 2010; 20 (1):68–80. doi: 10.1177/1049732309355590. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Merriam S. B. Qualitative research: A guide to design and implementation. 3rd ed. San Francisco, CA: Jossey-Bass; 2009. [ Google Scholar ]
• Meyer C. B. A case in case study methodology. Field Methods. 2001; 13 (4):329–352. doi: 10.1177/1525822x0101300402. [ CrossRef ] [ Google Scholar ]
• Morse J. M. Mixing qualitative methods. Qualitative Health Research. 2009; 19 (11):1523–1524. doi: 10.1177/1049732309349360. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morse J. M. Molding qualitative health research. Qualitative Health Research. 2011; 21 (8):1019–1021. doi: 10.1177/1049732311404706. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morse J. M, Dimitroff L. J, Harper R, Koontz A, Kumra S, Matthew-Maich N, et al. Considering the qualitative–quantitative language divide. Qualitative Health Research. 2011; 21 (9):1302–1303. doi: 10.1177/1049732310392386. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nagar-Ron S, Motzafi-Haller P. “My life? There is not much to tell”: On voice, silence and agency in interviews with first-generation Mizrahi Jewish women immigrants to Israel. Qualitative Inquiry. 2011; 17 (7):653–663. doi: 10.1177/1077800411414007. [ CrossRef ] [ Google Scholar ]
• Nairn K, Panelli R. Using fiction to make meaning in research with young people in rural New Zealand. Qualitative Inquiry. 2009; 15 (1):96–112. doi: 10.1177/1077800408318314. [ CrossRef ] [ Google Scholar ]
• Nespor J. The afterlife of “teachers’ beliefs”: Qualitative methodology and the textline. Qualitative Inquiry. 2012; 18 (5):449–460. doi: 10.1177/1077800412439530. [ CrossRef ] [ Google Scholar ]
• Piekkari R, Welch C, Paavilainen E. The case study as disciplinary convention: Evidence from international business journals. Organizational Research Methods. 2009; 12 (3):567–589. doi: 10.1177/1094428108319905. [ CrossRef ] [ Google Scholar ]
• Ragin C. C, Becker H. S. What is a case?: Exploring the foundations of social inquiry. Cambridge: Cambridge University Press; 1992. [ Google Scholar ]
• Roscigno C. I, Savage T. A, Kavanaugh K, Moro T. T, Kilpatrick S. J, Strassner H. T, et al. Divergent views of hope influencing communications between parents and hospital providers. Qualitative Health Research. 2012; 22 (9):1232–1246. doi: 10.1177/1049732312449210. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosenberg J. P, Yates P. M. Schematic representation of case study research designs. Journal of Advanced Nursing. 2007; 60 (4):447–452. doi: 10.1111/j.1365-2648.2007.04385.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rytterström P, Unosson M, Arman M. Care culture as a meaning- making process: A study of a mistreatment investigation. Qualitative Health Research. 2013; 23 :1179–1187. doi: 10.1177/1049732312470760. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M. Whatever happened to qualitative description? Research in Nursing & Health. 2000; 23 (4):334–340. doi: 10.1002/1098-240X. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M. What's in a name? Qualitative description revisited. Research in Nursing & Health. 2010; 33 (1):77–84. doi: 10.1002/nur.20362. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M, Barroso J. Reading qualitative studies. International Journal of Qualitative Methods. 2002; 1 (1):74–108. [ Google Scholar ]
• Snyder-Young D. “Here to tell her story”: Analyzing the autoethnographic performances of others. Qualitative Inquiry. 2011; 17 (10):943–951. doi: 10.1177/1077800411425149. [ CrossRef ] [ Google Scholar ]
• Stake R. E. The case study method in social inquiry. Educational Researcher. 1978; 7 (2):5–8. [ Google Scholar ]
• Stake R. E. The art of case study research. Thousand Oaks, CA: Sage; 1995. [ Google Scholar ]
• Stake R. E. Case studies. In: Denzin N. K, Lincoln Y. S, editors. Strategies of qualitative inquiry. Thousand Oaks, CA: Sage; 1998. pp. 86–109. [ Google Scholar ]
• Sumsion J. Opening up possibilities through team research: Investigating infants’ experiences of early childhood education and care. Qualitative Research. 2013; 14 (2):149–165. doi: 10.1177/1468794112468471.. [ CrossRef ] [ Google Scholar ]
• Thomas G. Doing case study: Abduction not induction, phronesis not theory. Qualitative Inquiry. 2010; 16 (7):575–582. doi: 10.1177/1077800410372601. [ CrossRef ] [ Google Scholar ]
• Thomas G. A typology for the case study in social science following a review of definition, discourse, and structure. Qualitative Inquiry. 2011; 17 (6):511–521. doi: 10.1177/1077800411409884. [ CrossRef ] [ Google Scholar ]
• Tight M. The curious case of case study: A viewpoint. International Journal of Social Research Methodology. 2010; 13 (4):329–339. doi: 10.1080/13645570903187181. [ CrossRef ] [ Google Scholar ]
• Wager E, Kleinert S. Responsible research publication: International standards for authors. A position statement developed at the 2nd World Conference on Research Integrity, Singapore, July 22–24, 2010. In: Mayer T, Steneck N, editors. Promoting research integrity in a global environment. Singapore: Imperial College Press/World Scientific; 2010a. pp. 309–316. [ Google Scholar ]
• Wager E, Kleinert S. Responsible research publication: International standards for editors. A position statement developed at the 2nd World Conference on Research Integrity, Singapore, July 22–24, 2010. In: Mayer T, Steneck N, editors. Promoting research integrity in a global environment. Singapore: Imperial College Press/World Scientific; 2010b. pp. 317–328. [ Google Scholar ]
• Webb C, Kevern J. Focus groups as a research method: A critique of some aspects of their use in nursing research. Journal of Advanced Nursing. 2000; 33 (6):798–805. doi: 10.1046/j.1365-2648.2001.01720.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wimpenny K, Savin-Baden M. Exploring and implementing participatory action synthesis. Qualitative Inquiry. 2012; 18 (8):689–698. doi: 10.1177/1077800412452854. [ CrossRef ] [ Google Scholar ]
• Yeh H.-Y. Boundaries, entities, and modern vegetarianism: Examining the emergence of the first vegetarian organization. Qualitative Inquiry. 2013; 19 (4):298–309. doi: 10.1177/1077800412471516. [ CrossRef ] [ Google Scholar ]
• Yin R. K. Enhancing the quality of case studies in health services research. Health Services Research. 1999; 34 (5 Pt 2):1209–1224. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yin R. K. Case study research: Design and methods. 4th ed. Thousand Oaks, CA: Sage; 2009. [ Google Scholar ]
• Yin R. K. Applications of case study research. 3rd ed. Thousand Oaks, CA: Sage; 2012. [ Google Scholar ]

• PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
• EDIT Edit this Article
• EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Happiness Hub Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
• Browse Articles
• Learn Something New
• Quizzes Hot
• Happiness Hub
• This Or That Game
• Train Your Brain
• Explore More
• Support wikiHow
• About wikiHow
• Log in / Sign up
• Education and Communications

How to Analyse a Case Study

Last Updated: April 13, 2024 Fact Checked

This article was co-authored by Sarah Evans . Sarah Evans is a Public Relations & Social Media Expert based in Las Vegas, Nevada. With over 14 years of industry experience, Sarah is the Founder & CEO of Sevans PR. Her team offers strategic communications services to help clients across industries including tech, finance, medical, real estate, law, and startups. The agency is renowned for its development of the "reputation+" methodology, a data-driven and AI-powered approach designed to elevate brand credibility, trust, awareness, and authority in a competitive marketplace. Sarah’s thought leadership has led to regular appearances on The Doctors TV show, CBS Las Vegas Now, and as an Adobe influencer. She is a respected contributor at Entrepreneur magazine, Hackernoon, Grit Daily, and KLAS Las Vegas. Sarah has been featured in PR Daily and PR Newswire and is a member of the Forbes Agency Council. She received her B.A. in Communications and Public Relations from Millikin University. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 413,384 times.

Case studies are used in many professional education programs, primarily in business school, to present real-world situations to students and to assess their ability to parse out the important aspects of a given dilemma. In general, a case study should include, in order: background on the business environment, description of the given business, identification of a key problem or issue, steps taken to address the issue, your assessment of that response, and suggestions for better business strategy. The steps below will guide you through the process of analyzing a business case study in this way.

• Describe the nature of the organization under consideration and its competitors. Provide general information about the market and customer base. Indicate any significant changes in the business environment or any new endeavors upon which the business is embarking.

• Analyze its management structure, employee base, and financial history. Describe annual revenues and profit. Provide figures on employment. Include details about private ownership, public ownership, and investment holdings. Provide a brief overview of the business's leaders and command chain.

• In all likelihood, there will be several different factors at play. Decide which is the main concern of the case study by examining what most of the data talks about, the main problems facing the business, and the conclusions at the end of the study. Examples might include expansion into a new market, response to a competitor's marketing campaign, or a changing customer base. [3] X Research source

• Draw on the information you gathered and trace a chronological progression of steps taken (or not taken). Cite data included in the case study, such as increased marketing spending, purchasing of new property, changed revenue streams, etc.

• Indicate whether or not each aspect of the response met its goal and whether the response overall was well-crafted. Use numerical benchmarks, like a desired customer share, to show whether goals were met; analyze broader issues, like employee management policies, to talk about the response as a whole. [4] X Research source

• Suggest alternative or improved measures that could have been taken by the business, using specific examples and backing up your suggestions with data and calculations.

Community Q&A

• Always read a case study several times. At first, you should read just for the basic details. On each subsequent reading, look for details about a specific topic: competitors, business strategy, management structure, financial loss. Highlight phrases and sections relating to these topics and take notes. Thanks Helpful 0 Not Helpful 0
• In the preliminary stages of analyzing a case study, no detail is insignificant. The biggest numbers can often be misleading, and the point of an analysis is often to dig deeper and find otherwise unnoticed variables that drive a situation. Thanks Helpful 0 Not Helpful 0
• If you are analyzing a case study for a consulting company interview, be sure to direct your comments towards the matters handled by the company. For example, if the company deals with marketing strategy, focus on the business's successes and failures in marketing; if you are interviewing for a financial consulting job, analyze how well the business keeps their books and their investment strategy. Thanks Helpful 0 Not Helpful 0

• Do not use impassioned or emphatic language in your analysis. Business case studies are a tool for gauging your business acumen, not your personal beliefs. When assigning blame or identifying flaws in strategy, use a detached, disinterested tone. Thanks Helpful 16 Not Helpful 4

Things You'll Need

You might also like.

Expert Interview

Thanks for reading our article! If you’d like to learn more about business writing, check out our in-depth interview with Sarah Evans .

• ↑ https://www.gvsu.edu/cms4/asset/CC3BFEEB-C364-E1A1-A5390F221AC0FD2D/business_case_analysis_gg_final.pdf
• ↑ https://bizfluent.com/12741914/how-to-analyze-a-business-case-study
• ↑ http://www.business-fundas.com/2009/how-to-analyze-business-case-studies/
• ↑ https://writingcenter.uagc.edu/writing-case-study-analysis
• http://college.cengage.com/business/resources/casestudies/students/analyzing.htm

About This Article

• Send fan mail to authors

Reader Success Stories

Lisa Upshur

Jun 15, 2019

Did this article help you?

Tejiri Aren

Jul 21, 2016

Jul 15, 2017

Jenn M.T. Tseka

Jul 3, 2016

Devanand Sbuurayan

Dec 6, 2020

Featured Articles

Trending Articles

Watch Articles

• Terms of Use
• Privacy Policy
• Do Not Sell or Share My Info
• Not Selling Info

Get all the best how-tos!

Sign up for wikiHow's weekly email newsletter

Data Analytics Case Study: Complete Guide in 2024

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

• What metrics should I propose?
• How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

TRY CHECKING: Marketing Analytics Case Study Guide

Data Analytics Case Study Question: Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

• If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
• If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

• Who uses this social network? How do they interact with each other?
• Has there been any performance issues that might be causing the problem?
• What are the goals of this particular launch?
• Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

• The decreasing comments issue isn’t a result of a declining user base.
• The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

• January: 10000 users, 30000 comments, 3 comments/user
• February: 20000 users, 50000 comments, 2.5 comments/user
• March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

Related Posts

How To Choose the Right Tool for the Task – Power BI, Python, R or SQL?

Data Analytics

A step-by-step guide to understanding when and why to use Power BI, Python, R, and SQL for business analysis.

Choosing the Right Visual for Your Data

Data Analytics , Data Visualization

Explore the crucial role of appropriate visual selection for various types of data including categorical, numerical, temporal, and spatial data.

4 Types of Data Analytics: Explained

In a world full of data, data analytics is the heart and soul of an operation. It's what transforms raw...

Data Analytics Outsourcing: Pros and Cons Explained

In today's data-driven world, businesses are constantly swimming in a sea of information, seeking the...

Ultimate Guide to Mastering Color in Data Visualization

Color plays a vital role in the success of data visualization. When used effectively, it can help guide...

Beginner’s Guide to Choosing the Right Data Visualization

As a beginner in data visualization, you’ll need to learn the various chart types to effectively...

Simple To Use Best Practises For Data Visualization

So you’ve got a bunch of data and you want to make it look pretty. Or maybe you’ve heard about this...

Exploring The Benefits Of Geospatial Data Visualization Techniques

Data visualization has come a long way from simple bar charts and line graphs. As the volume and...

What Does a Data Analyst Do on a Daily Basis?

In the digital age, data plays a significant role in helping organizations make informed decisions and...

• Directory Global directory
• Logins Product logins
• Support Support & training
• Contact Contact us

Thomson Reuters ESG Solutions

Trusted ESG resources that lead to better business decisions and expert tools to navigate environmental, social, and governance challenges.

Featured event

Sep 26, 2024

The 20th Annual Southeastern M&A/Private Equity Forum

The Southeastern M&A/Private Equity Forum is the premier gathering of mid-market deals professionals throughout the Southeastern US. Our program imparts …

Related posts

ESG Case Study: How Mejuri is prioritizing Scope 3 emission reductions to maximize sustainability impact

Majority of climate-washing litigation succeeds in producing positive outcomes but prompts companies to proceed cautiously

GenAI promises to deliver productivity gains, but at what environmental cost?

More insights.

Guidance for in-house lawyers navigating the pro- and anti-ESG legal landscape

Thomson Reuters launches new Human Trafficking Resource Center

Forum: Practice Makes Perfect for Generative AI — An Interview with Northwestern’s Daniel Linna

The Use of Floating Car Data for Traffic Analysis: A Case Study of the City of Olomouc

Description.

This data serves as supplementary material to the article The Use of Floating Car Data for Traffic Analysis: A Case Study of the City of Olomouc. The excel files contain input data downloaded from the HERE API, after an aggreagation process by the road sections can by linked to the provided spatial data (here_roads.gpkg).

Institutions

• Skip to main content
• Skip to search
• Skip to footer

Products and Services

Cisco Security

Master your goals. innovate. we'll tackle threats..

Get powerful security across all your networks, cloud, endpoints, and email to protect everything that matters, from anywhere.

If it's connected, you're protected

Cisco Security “The Hacker”

More connected users and devices creates more complexity. Cisco Security Cloud makes security easier for IT and safer for everyone anywhere security meets the network.

Deliver smarter, stronger security

Protect your organization across a multicloud environment, while simplifying security operations, improving scalability, and driving data-informed outcomes, powered by Cisco Talos.

Unlock better user experiences

Create a seamless experience that frustrates attackers, not users, by granting access from any device, anywhere, and adding more proactive security controls.

Deliver cost-effective defenses

Improve ROI by consolidating vendors, reducing complexity and integrating your security.

Strengthen security resilience

Unified, end-to-end protection maximizes value, minimizes risk, and closes security gaps everywhere to defend against evolving threats. Protect access, apps, and innovation across your network to secure your future.

Cisco Secure Firewall

Better visibility and actionable insights across networks, clouds, endpoints, and email allows users to respond confidently to the most sophisticated threats at machine scale.

Featured security products

Cisco hypershield.

A new groundbreaking security architecture that makes hyperscaler technology accessible to enterprises of all sizes and delivers AI-native security for modern data centers and cloud.

Cisco Secure Access (SSE)

A converged cybersecurity solution, grounded in zero trust, that radically reduces risk and delights both end users and IT staff by safely connecting anything to anywhere.

Detect the most sophisticated threats sooner across all vectors and prioritize by impact for faster responses.

Cisco Multicloud Defense

Gain multidirectional protection across clouds to stop inbound attacks, data exfiltration, and lateral movement.

Secure applications and enable frictionless access with strong MFA and more. Establish user and device trust, gain visibility into devices, and enable secure access to all apps.

Cisco Identity Services Engine (ISE)

Simplify highly secure network access control with software-defined access and automation.

Security Suites delivered by Cisco Security Cloud

Cisco User Protection Suite

Get secure access to any application, on any device, from anywhere. Defend against threats targeting users and deliver seamless access for hybrid work.

Cisco Cloud Protection Suite

Secure your apps and data with a powerful, flexible framework for a hybrid and multicloud world.

Cisco Breach Protection Suite

Secure your business by investigating, prioritizing, and resolving incidents through unified defense and contextual insights from data-backed, AI-powered security.

Customer stories and insights

Global partnerships fight to end child exploitation together.

"Marriott has long championed human rights and human trafficking awareness. Combating CSAM is an important extension of that work. The IWF provided the level of rigor we needed in a URL list, and Cisco's security technology provided the means to easily apply it."

Abbe Horswill, Director, Human Rights and Social Impact

Company: Marriott International

The NFL relies on Cisco

"From securing stadiums, broadcasts, and fans to protecting the largest live sporting event in America, the right tools and the right team are key in making sure things run smoothly, avoiding disruptions to the game, and safeguarding the data and devices that make mission-critical gameday operations possible."

Add value to security solutions

Cisco Security Enterprise Agreement

Instant savings

Experience security software buying flexibility with one easy-to-manage agreement.

Services for security

Let the experts secure your business

Get more from your investments and enable constant vigilance to protect your organization.

Sharpen your security insights

Cisco Cybersecurity Viewpoints

Set your vision to a more secure future with Cisco Cybersecurity Viewpoints. With specialized content from podcasts to industry news, you'll walk away with a deeper understanding of the trends, research, and topics in our rapidly changing world.

• eLibrary Home
• eLibrary Login
• < Previous

Home > CHAPTERS > ACIS > ACIS 2002 > 38

ACIS 2002 Proceedings

Four steps to analyse data from a case study method.

John Atkinson , Charles Sturt University

Four steps are proposed to assist the novice researcher analyse their data that has been collected using a case study method. The first step proposes the creation of a data repository using basic relational database theory. The second step involves creating codes to identify the respective ‘chunks’ of data. These resulting codes are then analysed and rationalised. The third step involves analysing the case study data by generating a variety of reports. The fourth step generates the final propositions by linking the rationalised codes back to the initial propositions and where appropriate new propositions are generated. The outcome of these steps is a series of propositions that reflect the nature of the data associated with the case studies data.

Recommended Citation

Atkinson, John, "Four Steps to Analyse Data from a Case Study Method" (2002). ACIS 2002 Proceedings . 38. https://aisel.aisnet.org/acis2002/38

Since August 21, 2008

Advanced Search

• Notify me via email or RSS
• ACIS 2002 Website
• All Content

Author Corner

• eLibrary FAQ

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

An equity evaluation of healthcare accessibility across age strata using the g2sfca method: a case study in karamay district, china, 1. introduction, 2. literature review, 2.1. measuring healthcare accessibility, 2.2. the equity of healthcare accessibility, 3. study area and data, 3.1. study area, 3.2.1. road network and healthcare facility data, 3.2.2. demographic indicators, 4.1. measuring accessibility, 4.2. spatial autocorrelation, 4.3. equity analysis, 5.1. accessibility of two types of healthcare facilities, 5.2. spatial agglomeration characteristics, 5.3. equity evaluation, 5.3.1. equality of healthcare accessibility, 5.3.2. equity across age strata, 6. discussion, 6.1. interpreting results, 6.2. planning implications, 6.3. assumptions and limitations, 7. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest.

• Rocco, R.; Bracken, G.; Newton, C.; Dabrowski, M. Teaching, Learning & Researching: Spatial Planning ; TU Delft OPEN Books: Delft, The Netherlands, 2022. [ Google Scholar ] [ CrossRef ]
• Sen, A. The Idea of Justice ; Harvard University Press: Cambridge, MA, USA, 2011. [ Google Scholar ]
• Soja, E.W. Seeking Spatial Justice ; University of Minnesota Press: Minneapolis, MN, USA, 2013. [ Google Scholar ]
• Guagliardo, M.F. Spatial accessibility of primary care: Concepts, methods and challenges. Int. J. Health Geogr. 2004 , 3 , 3. Available online: https://www.webofscience.com/wos/alldb/full-record/MEDLINE:14987337 (accessed on 20 May 2024). [ CrossRef ] [ PubMed ]
• Luo, W.; Wang, F. Measures of Spatial Accessibility to Health Care in a GIS Environment: Synthesis and a Case Study in the Chicago Region. Environ. Plan. B Plan. Des. 2003 , 30 , 865–884. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guida, C.; Carpentieri, G. Quality of life in the urban environment and primary health services for the elderly during the COVID-19 pandemic: An application to the city of Milan (Italy). Cities 2020 , 110 , 103038. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Omer, I. Evaluating accessibility using house-level data: A spatial equity perspective. Comput. Environ. Urban Syst. 2006 , 30 , 254–274. [ Google Scholar ] [ CrossRef ]
• Plachkinova, M.; Vo, A.; Bhaskar, R.; Hilton, B. A conceptual framework for quality healthcare accessibility: A scalable approach for big data technologies. Inf. Syst. Front. 2018 , 20 , 289–302. [ Google Scholar ] [ CrossRef ]
• Boisjoly, G.; Deboosere, R.; Wasfi, R.; Orpana, H.; Manaugh, K.; Buliung, R.; El-Geneidy, A. Measuring accessibility to hospitals by public transport: An assessment of eight Canadian metropolitan regions. J. Transp. Health 2020 , 18 , 100916. [ Google Scholar ] [ CrossRef ]
• Zhang, Y. Access to Healthcare Facilities and Women’s Healthcare Requirements in Urban Areas: A Case Study of Beijing. Int. J. Environ. Res. Public Health 2022 , 19 , 3709. [ Google Scholar ] [ CrossRef ]
• Shao, H.; Jin, C.; Xu, J.; Zhong, Y.; Xu, B. Supply-demand matching of medical services at a city level under the background of hierarchical diagnosis and treatment-based on Didi Chuxing Data in Haikou, China. BMC Health Serv. Res. 2022 , 22 , 354. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Falchetta, G.; Hammad, A.T.; Shayegh, S. Planning universal accessibility to public health care in sub-Saharan Africa. Proc. Natl. Acad. Sci. USA 2020 , 117 , 31760–31769. [ Google Scholar ] [ CrossRef ]
• Bodhisane, S.; Pongpanich, S. The impact of National Health Insurance upon accessibility of health services and financial protection from catastrophic health expenditure: A case study of Savannakhet province, the Lao People’s Democratic Republic. Health Res. Policy Syst. 2019 , 17 , 99. [ Google Scholar ] [ CrossRef ]
• Huotari, T.; Antikainen, H.; Keistinen, T.; Rusanen, J. Accessibility of tertiary hospitals in Finland: A comparison of administrative and normative catchment areas. Soc. Sci. Med. 2017 , 182 , 60–67. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Agha, L.; Ericson, K.M.; Zhao, X. The impact of organizational boundaries on health care coordination and utilization. Am. Econ. J. Econ. Policy 2023 , 15 , 184–214. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jin, T.; Cheng, L.; Wang, K.; Cao, J.; Huang, H.; Witlox, F. Examining equity in accessibility to multi-tier healthcare services across different income households using estimated travel time. Transp. Policy 2022 , 121 , 1–13. [ Google Scholar ] [ CrossRef ]
• Surendra, H.; Salama, N.; Lestari, K.D.; Adrian, V.; Widyastuti, W.; Oktavia, D.; Lina, R.N.; A Djaafara, B.; Fadilah, I.; Sagara, R.; et al. Pandemic inequity in a megacity: A multilevel analysis of individual, community and healthcare vulnerability risks for COVID-19 mortality in Jakarta, Indonesia. BMJ Glob. Health 2022 , 7 , e008329. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liu, J.; Cao, Y.; Wang, Y.; Ren, F.; Du, Q. Evaluating the Accessibility of Medical Services in the 15 min Life Circle Using Internet Pan-Map Resources: A Case Study in Shanghai. Geomat. Inf. Sci. Wuhan Univ. 2022 , 47 , 2054–2063. [ Google Scholar ] [ CrossRef ]
• Bokhari, A.; Sharifi, F. Simultaneous inequity of elderly residents in Melbourne metropolitan. Metropolitan. Sustainability 2023 , 15 , 2189. [ Google Scholar ] [ CrossRef ]
• Cardoso, R.V. City-regional demographic composition and the fortunes of regional second cities. Urban Geogr. 2023 , 44 , 1541–1563. [ Google Scholar ] [ CrossRef ]
• Wang, L.; Liu, L. The Present Condition of China’s Medical and Health Facilities in the Epidemic Situation of COVID-19, and a Comparison with Other Countries. Sci. Technol. Rev. 2020 , 38 , 29–38. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=KJDB202004006&v= (accessed on 24 May 2024).
• Shen, Y.; Shi, Y.W.; Wang, H.X.; Sun, B.Y. The Impacts of Medical Facility Accessibility on Patients’ Medical Treatment Space: A Case Study in Shanghai. Urban Stud. 2019 , 26 , 46–52+61. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=CSFY201912007&v= (accessed on 24 May 2024).
• Zhang, Q.; Li, T.; Ren, X. Study on the Accessibility of Urban Community Health Institutions under Grading Treatment System: A Case Study of Xi’an Downtown. J. Shaanxi Norm. Univ. (Nat. Sci. Ed.) 2016 , 44 , 87–93. [ Google Scholar ] [ CrossRef ]
• Wei, Z.; Bai, J. Measuring spatial accessibility of residents’ medical treatment under hierarchical diagnosis and treatment system: Multi-scenario simulation in China. PLoS ONE 2023 , 18 , e0282713. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shen, Y.; Li, L. Progress of Research on Medical Resource Accessibility and Residents’ Health Seeking Behavior. Sci. Technol. Rev. 2020 , 38 , 85–92. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=KJDB202007013&v= (accessed on 24 May 2024).
• Wang, F. Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review. Ann. Assoc. Am. Geogr. 2012 , 102 , 1104–1112. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hansen, W.G. How Accessibility Shapes Land Use. J. Am. Inst. Plan. 1959 , 25 , 73–76. [ Google Scholar ] [ CrossRef ]
• Ben-Akiva, M.; Lerman, S.R. Disaggregate Travel and Mobility-Choice Models and Measures of Accessibility. In Behavioural Travel Modelling ; Routledge: London, UK, 1979. [ Google Scholar ]
• Lenntorp, B. Paths in Space-Time Environments. A Time-Geographic Study of Movement Possibilities of Individuals. Lund Stud. Geogr. Hum. Geogr. 1976 . [ Google Scholar ]
• Khan, A.A. An integrated approach to measuring potential spatial access to health care services. Socio-Econ. Plan. Sci. 1992 , 26 , 275–287. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Khan, A.; Bhardwaj, S. Access to Health-Care—A Conceptual-Framework and Its Relevance to Health-Care Planning. Eval. Health Prof. 1994 , 17 , 60–76. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kwan, M.P. Space-time and integral measures of individual accessibility: A comparative analysis using a point-based framework. Geogr. Anal. 1998 , 30 , 191–216. [ Google Scholar ] [ CrossRef ]
• Joseph, A.E.; Phillips, D.R. Accessibility and Utilization: Geographical Perspectives on Health Care Delivery ; Harper & Row: London, UK, 1984. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1959857/ (accessed on 19 June 2024).
• Thouez, J.-P.M.; Bodson, P.; Joseph, A.E. Some Methods for Measuring the Geographic Accessibility of Medical Services in Rural Regions. Med. Care 1988 , 26 , 34. Available online: https://journals.lww.com/lww-medicalcare/abstract/1988/01000/some_methods_for_measuring_the_geographic.4.aspx (accessed on 15 April 2024). [ CrossRef ] [ PubMed ]
• Xu, R.; Yue, W.; Wei, F.; Yang, G.; Chen, Y.; Pan, K. Inequality of public facilities between urban and rural areas and its driving factors in ten cities of China. Sci. Rep. 2022 , 12 , 13244. [ Google Scholar ] [ CrossRef ]
• Yin, C.; He, Q.; Liu, Y.; Chen, W.; Gao, Y. Inequality of public health and its role in spatial accessibility to medical facilities in China. Appl. Geogr. 2018 , 92 , 50–62. [ Google Scholar ] [ CrossRef ]
• Du, M.; Zhao, S. An Equity Evaluation on Accessibility of Primary Healthcare Facilities by Using V2SFCA Method: Taking Fukuoka City, Japan, as a Case Study. Land 2022 , 11 , 640. [ Google Scholar ] [ CrossRef ]
• Guida, C.; Carpentieri, G.; Masoumi, H. Measuring spatial accessibility to urban services for older adults: An application to healthcare facilities in Milan. Eur. Transp. Res. Rev. 2022 , 14 , 23. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Song, Z.; Chen, W.; Che, Q.; Zhang, L. Measurement of Spatial Accessibility to Health Care Facilities and Defining Health Professional Shortage Areas Based on Improved Potential Model—A Case Study of Rudong County in Jiangsu Province. Sci. Geogr. Sin. 2010 , 30 , 213–219. [ Google Scholar ] [ CrossRef ]
• Cheng, M.; Lian, Y. Spatial Accessibility of Urban Medical Facilities Based on Improved Potential Model: A Case Study of Yangpu District in Shanghai. Prog. Geogr. 2018 , 37 , 266–275. [ Google Scholar ] [ CrossRef ]
• Shen, Q. Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environ. Plan. B Plan. Des. 1998 , 25 , 345–365. [ Google Scholar ] [ CrossRef ]
• Radke, J.; Mu, L. Spatial Decompositions, Modeling and Mapping Service Regions to Predict Access to Social Programs. Ann. GIS 2000 , 6 , 105–112. [ Google Scholar ] [ CrossRef ]
• McGrail, M.R. Spatial accessibility of primary health care utilizing the two step floating catchment area method: An assessment of recent improvements. Int. J. Health Geogr. 2012 , 11 , 50. [ Google Scholar ] [ CrossRef ]
• Tao, Z.; Cheng, Y. Research Progress of the Two-step Floating Catchment Area Method and Extensions. Prog. Geogr. 2016 , 35 , 589–599. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2016&filename=DLKJ201605006&v= (accessed on 2 June 2024).
• Dai, D. Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health Place 2010 , 16 , 1038–1052. [ Google Scholar ] [ CrossRef ]
• Wan, N.; Zou, B.; Sternberg, T. A three-step floating catchment area method for analyzing spatial access to health services. Int. J. Geogr. Inf. Sci. 2012 , 26 , 1073–1089. [ Google Scholar ] [ CrossRef ]
• Wang, F. Inverted Two-Step Floating Catchment Area Method for Measuring Facility Crowdedness. Prof. Geogr. 2018 , 70 , 251–260. [ Google Scholar ] [ CrossRef ]
• Yang, D.-H.; Goerge, R.; Mullner, R. Comparing GIS-Based Methods of Measuring Spatial Accessibility to Health Services. J. Med. Syst. 2006 , 30 , 23–32. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brondeel, R.; Weill, A.; Thomas, F.; Chaix, B. Use of healthcare services in the residence and workplace neighbourhood: The effect of spatial accessibility to healthcare services. Health Place 2014 , 30 , 127–133. [ Google Scholar ] [ CrossRef ]
• Zhong, S.; Yang, X.; Chen, R. The Accessibility Measurement of Hierarchy Public Service Facilities Based on Multi-mode Network Dataset and the Two-step 2SFCA: A Case Study of Beijing’s Medical Facilities. Geogr. Res. 2016 , 35 , 731–744. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2016&filename=DLYJ201604012&v= (accessed on 2 June 2024).
• Wang, S.; Sadahiro, Y. Horizontal and vertical inequity of multi-modal healthcare accessibility in the aging Japan in the post-COVID era: A GIS-based approach. Int. J. Digit. Earth 2024 , 17 , 2310731. [ Google Scholar ] [ CrossRef ]
• McGrail, M.R.; Humphreys, J.S. Measuring spatial accessibility to primary care in rural areas: Improving the effectiveness of the two-step floating catchment area method. Appl. Geogr. 2009 , 29 , 533–541. [ Google Scholar ] [ CrossRef ]
• Blumenberg, E.; Yao, Z.; Wander, M. Variation in child care access across neighborhood types: A two-step floating catchment area (2SFCA) approach. Appl. Geogr. 2023 , 158 , 103054. [ Google Scholar ] [ CrossRef ]
• Hawthorne, T.L.; Kwan, M.-P. Using GIS and perceived distance to understand the unequal geographies of healthcare in lower-income urban neighbourhoods. Geogr. J. 2012 , 178 , 18–30. [ Google Scholar ] [ CrossRef ]
• Zhang, Z.; Sun, S.; Wang, X.; Xiao, Y.; Gao, J. Spatial Pattern of Medical Public Services Accessibility in Megacities and Its Influencing Factors: A Case Study of Shanghai. Sci. Geogr. Sin. 2022 , 42 , 622–630. [ Google Scholar ] [ CrossRef ]
• Jin, M.; Liu, L.; Tong, D.; Gong, Y.; Liu, Y. Evaluating the Spatial Accessibility and Distribution Balance of Multi-Level Medical Service Facilities. Int. J. Environ. Res. Public Health 2019 , 16 , 1150. [ Google Scholar ] [ CrossRef ]
• Guo, C.; Liang, J. Accessibility Analysis of Medical Facilities Based on Multiple Transportation Modes of Network Map. J. Geo-Inf. Sci. 2022 , 24 , 483–494. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2022&filename=DQXX202203006&v= (accessed on 20 May 2024).
• Xu, H.; Qiao, Q.; Li, Y.; Chen, G.; Liu, J.; Gan, L. Analysis on Spatio-Temporal Accessibility of Medical Services Supported by Real-Time Traffic Data. Bull. Surv. Mapp. 2023 , 01 , 113–119. [ Google Scholar ] [ CrossRef ]
• Ni, J.; Liang, M.; Lin, Y.; Wu, Y.; Wang, C. Multi-Mode Two-Step Floating Catchment Area (2SFCA) Method to Measure the Potential Spatial Accessibility of Healthcare Services. ISPRS Int. J. Geo-Inf. 2019 , 8 , 236. [ Google Scholar ] [ CrossRef ]
• Ma, L.; Luo, N.; Wan, T.; Hu, C.; Peng, M. An Improved Healthcare Accessibility Measure Considering the Temporal Dimension and Population Demand of Different Ages. Int. J. Environ. Res. Public Health 2018 , 15 , 2421. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Whitehead, J.; Pearson, A.L.; Lawrenson, R.; Atatoa-Carr, P. How can the spatial equity of health services be defined and measured? A systematic review of spatial equity definitions and methods. J. Health Serv. Res. Policy 2019 , 24 , 270–278. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Davies, B. Social Needs and Resources in Local Services: A Study of Variations in Standards of Provision of Personal Social Services between Local Authority Areas. [Preprint]. 1968. Available online: https://cir.nii.ac.jp/crid/1130000795923445248 (accessed on 30 December 2023).
• Rich, R.C. Neglected Issues in the Study of Urban Service Distributions: A Research Agenda. Urban Stud. 1979 , 16 , 143–156. [ Google Scholar ] [ CrossRef ]
• Lucy, W. Equity and Planning for Local Services. J. Am. Plan. Assoc. 1981 , 47 , 447–457. [ Google Scholar ] [ CrossRef ]
• Truelove, M. Measurement of Spatial Equity. Environ. Plan. C Gov. Policy 1993 , 11 , 19–34. [ Google Scholar ] [ CrossRef ]
• Dadashpoor, H.; Rostami, F.; Alizadeh, B. Is inequality in the distribution of urban facilities inequitable? Exploring a method for identifying spatial inequity in an Iranian city. Cities 2016 , 52 , 159–172. [ Google Scholar ] [ CrossRef ]
• Neutens, T.; Schwanen, T.; Witlox, F.; De Maeyer, P. Equity of Urban Service Delivery: A Comparison of Different Accessibility Measures. Environ. Plan. A Econ. Space 2010 , 42 , 1613–1635. [ Google Scholar ] [ CrossRef ]
• Rong, P.; Zheng, Z.; Kwan, M.-P.; Qin, Y. Evaluation of the spatial equity of medical facilities based on improved potential model and map service API: A case study in Zhengzhou, China. Appl. Geogr. 2020 , 119 , 102192. [ Google Scholar ] [ CrossRef ]
• Talen, E.; Anselin, L. Assessing Spatial Equity: An Evaluation of Measures of Accessibility to Public Playgrounds. Environ. Plan. A Econ. Space 1998 , 30 , 595–613. [ Google Scholar ] [ CrossRef ]
• Wang, L.; Zhou, K.; Wang, Z. Spatial Distribution of Community Pension Facilities from the Perspective of Health Equity: A Case Study of the Central City of Shanghai. Hum. Geogr. 2021 , 36 , 48–55. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Luan, H.; Zhen, F.; Kong, Y.; Xi, G. Does online food delivery improve the equity of food accessibility? A case study of Nanjing, China. J. Transp. Geogr. 2023 , 107 , 103516. [ Google Scholar ] [ CrossRef ]
• Zenk, S.N.; Tarlov, E.; Sun, J. Spatial Equity in Facilities Providing Low- or No-Fee Screening Mammography in Chicago Neighborhoods. J. Urban Health 2006 , 83 , 195–210. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Neutens, T. Accessibility, equity and health care: Review and research directions for transport geographers. J. Transp. Geogr. 2015 , 43 , 14–27. [ Google Scholar ] [ CrossRef ]
• Gao, J.; Han, Y.; Yu, C.; Zhang, Y.; Yan, J. Research on the Medical Service Consumption Space and Its Social Differentiation of Urban Residents in Small and Middle-sized Cities Based on Individual Behaviors: A Comparative Study with Mega Cities. Hum. Geogr. 2018 , 33 , 28–34+86. [ Google Scholar ] [ CrossRef ]
• Zhang, D.; Zhang, G.; Zhou, C. Differences in Accessibility of Public Health Facilities in Hierarchical Municipalities and the Spatial Pattern Characteristics of Their Services in Doumen District, China. Land 2021 , 10 , 1249. [ Google Scholar ] [ CrossRef ]
• Epperson, B.K.; Li, T.Q. Measurement of genetic structure within populations using Moran’s spatial autocorrelation statistics. Proc. Natl. Acad. Sci. USA 1996 , 93 , 10528–10532. [ Google Scholar ] [ CrossRef ]
• Anselin, L.; Syabri, I.; Kho, Y. GeoDa: An Introduction to Spatial Data Analysis. Geogr. Anal. 2006 , 38 , 5–22. [ Google Scholar ] [ CrossRef ]
• Moran, P.A.P. The Interpretation of Statistical Maps. J. R. Stat. Soc. Ser. B (Methodol.) 1948 , 10 , 243–251. Available online: https://www.jstor.org/stable/2983777 (accessed on 3 June 2024). [ CrossRef ]
• Cliff, A.D.; Ord, J.K. Spatial Autocorrelation ; Pion: London, UK, 1973. [ Google Scholar ]
• Anselin, L. Local Indicators of Spatial Association-LISA. Geogr. Anal. 1995 , 27 , 93–115. [ Google Scholar ] [ CrossRef ]
• Asl, I.M.; Abolhallaje, M.; Raadabadi, M.; Nazari, H.; Nazari, A.; Salimi, M.; Javani, A. Distribution of hospital beds in Tehran Province based on Gini coefficient and Lorenz curve from 2010 to 2012. Electron. Physician 2015 , 7 , 1653. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jin, J.; Wang, J.; Ma, X.; Wang, Y.; Li, R. Equality of medical health resource allocation in China based on the Gini coefficient method. Iran. J. Public Health 2015 , 44 , 445. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4441957/ (accessed on 25 July 2024). [ PubMed ]
• World Bank. World Development Report 1997: The State in a Changing World ; Oxford University Press: Oxford, UK, 1997; Available online: https://elibrary.worldbank.org/doi/abs/10.1596/978-0-1952-1114-6 (accessed on 20 June 2024).
• Yu, H.; Yu, S.; He, D.; Lu, Y. Equity analysis of Chinese physician allocation based on Gini coefficient and Theil index. BMC Health Serv. Res. 2021 , 21 , 455. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hu, S.; Lu, Y.; Hu, G.; Sun, J. Measuring Accessibility and Equity of Medical Resources in Shenzhen Based on Multi-source Big Data. Econ. Geogr. 2021 , 41 , 87–96. [ Google Scholar ] [ CrossRef ]
• Khakh, A.K.; Fast, V.; Shahid, R. Spatial Accessibility to Primary Healthcare Services by Multimodal Means of Travel: Synthesis and Case Study in the City of Calgary. Int. J. Environ. Res. Public Health 2019 , 16 , 170. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schläpfer, M.; Dong, L.; O’keeffe, K.; Santi, P.; Szell, M.; Salat, H.; Anklesaria, S.; Vazifeh, M.; Ratti, C.; West, G.B. The universal visitation law of human mobility. Nature 2021 , 593 , 522–527. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shen, Y.; Wang, D. Interdisciplinary application of theories and methods of behavioral geography. Prog. Geogr. 2022 , 41 , 40–52. Available online: https://kns.cnki.net/kcms2/article/abstract?v=4wkQyjAcIEd1l285kfTQBXgw-BVVLKQYwbWkj2EKfkbEtir95XuL4TzBkPxxfZYJy2lvSAspLw_0TMSxDCDQItg6Dzi_SE7w3Rgutc9M4FSIYMyXXr54oQgpkQbnwJyNBCe2CLVQIbKQwikoMs0k6B5v6ABYRFIQxqjeIXltYe6-uS9UE0KS1rwRYmPgSNQ8fhV06A1Pmhs=&uniplatform=NZKPT&language=CHS (accessed on 11 May 2024). [ CrossRef ]
• Delbosc, A.; Currie, G. The spatial context of transport disadvantage, social exclusion and well-being. J. Transp. Geogr. 2011 , 19 , 1130–1137. [ Google Scholar ] [ CrossRef ]
• Camporeale, R.; Caggiani, L.; Ottomanelli, M. Modeling horizontal and vertical equity in the public transport design problem: A case study. Transp. Res. Part A Policy Pract. 2019 , 125 , 184–206. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Liu, L.; Gao, R.; Zhang, L. An Equity Evaluation of Healthcare Accessibility across Age Strata Using the G2SFCA Method: A Case Study in Karamay District, China. Land 2024 , 13 , 1259. https://doi.org/10.3390/land13081259

Liu L, Gao R, Zhang L. An Equity Evaluation of Healthcare Accessibility across Age Strata Using the G2SFCA Method: A Case Study in Karamay District, China. Land . 2024; 13(8):1259. https://doi.org/10.3390/land13081259

Liu, Lu, Runyi Gao, and Li Zhang. 2024. "An Equity Evaluation of Healthcare Accessibility across Age Strata Using the G2SFCA Method: A Case Study in Karamay District, China" Land 13, no. 8: 1259. https://doi.org/10.3390/land13081259

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 1783 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Open access
• Published: 07 August 2024

Impact of neonatal sepsis on neurocognitive outcomes: a systematic review and meta-analysis

• Wei Jie Ong   ORCID: orcid.org/0000-0001-8244-2977 1   na1 ,
• Jun Jie Benjamin Seng   ORCID: orcid.org/0000-0002-3039-3816 1 , 2 , 3   na1 ,
• Beijun Yap 1 ,
• George He 4 ,
• Nooriyah Aliasgar Moochhala 4 ,
• Chen Lin Ng 1 ,
• Rehena Ganguly   ORCID: orcid.org/0000-0001-9347-5571 5 ,
• Jan Hau Lee   ORCID: orcid.org/0000-0002-8430-4217 6 &
• Shu-Ling Chong   ORCID: orcid.org/0000-0003-4647-0019 7  

BMC Pediatrics volume  24 , Article number:  505 ( 2024 ) Cite this article

150 Accesses

1 Altmetric

Metrics details

Introduction

Sepsis is associated with neurocognitive impairment among preterm neonates but less is known about term neonates with sepsis. This systematic review and meta-analysis aims to provide an update of neurocognitive outcomes including cognitive delay, visual impairment, auditory impairment, and cerebral palsy, among neonates with sepsis.

We performed a systematic review of PubMed, Embase, CENTRAL and Web of Science for eligible studies published between January 2011 and March 2023. We included case–control, cohort studies and cross-sectional studies. Case reports and articles not in English language were excluded. Using the adjusted estimates, we performed random effects model meta-analysis to evaluate the risk of developing neurocognitive impairment among neonates with sepsis.

Of 7,909 studies, 24 studies ( n  = 121,645) were included. Majority of studies were conducted in the United States ( n  = 7, 29.2%), and all studies were performed among neonates. 17 (70.8%) studies provided follow-up till 30 months. Sepsis was associated with increased risk of cognitive delay [adjusted odds ratio, aOR 1.14 (95% CI: 1.01—1.28)], visual impairment [aOR 2.57 (95%CI: 1.14- 5.82)], hearing impairment [aOR 1.70 (95% CI: 1.02–2.81)] and cerebral palsy [aOR 2.48 (95% CI: 1.03–5.99)].

Neonates surviving sepsis are at a higher risk of poorer neurodevelopment. Current evidence is limited by significant heterogeneity across studies, lack of data related to long-term neurodevelopmental outcomes and term infants.

Peer Review reports

Sepsis is a major cause of mortality and morbidity among neonates [ 1 , 2 , 3 , 4 ]. Young infants especially neonates, defined by age < 28 days old, have a relatively immature immune system and are susceptible to sepsis [ 5 , 6 ]. Annually, there are an estimated 1.3 to 3.9 million cases of infantile sepsis worldwide and up to 700,000 deaths [ 7 ]. Low-income and middle-income countries bear a disproportionate burden of neonatal sepsis cases and deaths [ 7 , 8 ]. While advances in medical care over the past decade have reduced mortality, neonates who survive sepsis are at risk of developing neurocognitive complications, which affect the quality of life for these children and their caregivers [ 9 ].

Previous reviews evaluating neurocognitive outcomes in neonates with infections or sepsis have focused on specific types of pathogens (e.g., Group B streptococcus or nosocomial infections [ 10 ]), or are limited to specific populations such as very low birth weight or very preterm neonates [ 11 ], and there remains paucity of data regarding neurocognitive outcomes among term and post-term neonates. There remains a gap for an updated comprehensive review which is not limited by type of pathogen or gestation. In this systematic review, we aim to provide a comprehensive update to the current literature on the association between sepsis and the following adverse neurocognitive outcomes (1) mental and psychomotor delay (cognitive delay (CD)), (2) visual impairment, (3) auditory impairment and (4) cerebral palsy (CP) among neonates [ 11 ].

We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [ 12 ]. This study protocol was registered with Open Science Framework ( https://doi.org/10.17605/OSF.IO/B54SE ).

Eligibility criteria

We identified studies which evaluated neurocognitive outcomes in neonates less than 90 days old (regardless of gestational age) with sepsis. While the neonatal period is traditionally defined to be either the first 28 days postnatally for term and post-term infants, or 27 days after the expected date of delivery for preterm infants [ 13 ], serious late onset infections in the young infant population can present beyond the neonatal period [ 14 ], hence we defined the upper age limit as 90 days old to obtain a more complete picture of the burden of young infantile sepsis [ 15 ]. Post-term neonates was defined as a neonate delivered at >  = 42 weeks of gestational age in this study [ 16 ]. We included studies that either follow international sepsis definitions such as Surviving Sepsis Campaign guidelines definitions [ 17 ], or if they fulfilled clinical, microbiological and/or biochemical criteria for sepsis as defined by study authors. The primary outcome of interest was impaired neurocognitive outcome defined by the following domains of neurodevelopmental impairment (NDI) [ 11 ]: (1) CD, (2) visual impairment, (3) auditory impairment and (4) CP. We selected these domains because they were highlighted as key neurocognitive sequelae after intrauterine insults in a landmark review by Mwaniki et al. [ 18 ]. The authors’ definitions of these outcomes and their assessment tools were captured, including the use of common validated instruments (e.g., a common scale used for CD is the Bayley Scales of Infant Development (BSID) [ 19 ] while a common instrument used for CP was the Gross Motor Function Classification System (GMFCS) [ 20 ]. Specifically for BSID, its two summative indices score – Mental Development Index (MDI) and Psychomotor Development Index (PDI) were collected. The MDI assesses both the non-verbal cognitive and language skills, while PDI assess the combination of fine and gross motor skills. The cut-off points for mild, moderate and severe delay for MDI and PDI were < 85 or < 80, < 70 and < 55 respectively [ 21 ]. There were no restrictions on duration of follow-up or time of assessment of neurocognitive outcomes to allow capturing of both short- and long-term neurocognitive outcomes.

Case–control, cohort studies and cross-sectional studies published between January 2011 and March 2023 were included. Because the definition and management of sepsis has evolved over the years [ 22 ], we chose to include studies published from 2011 onwards. Case reports, animal studies, laboratory studies and publications that were not in English language were excluded. Hand-searching of previous systematic reviews were performed to ensure all relevant articles were included. To avoid small study effects, we also excluded studies with a sample size of less than 50 [ 23 ].

Information sources and search strategy

Four databases (PubMed, Cochrane Central, Embase and Web of Science) were used to identify eligible studies. The search strategy was developed in consultation with a research librarian. The first search was conducted on 4 December 2021 and an updated search was conducted on 3 April 2023. The detailed search strategy can be found in Supplementary Tables 1A and B.

Study selection process

Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) [ 24 ] was utilized during this review. Five reviewers (WJO, BJY, NM, CLN and GH) independently conducted the database search and screened the title and abstracts for relevance. Following training on inclusion and exclusion eligibility, 4 reviewers (WJO, NM, CLN and GH) subsequently assessed the full text of shortlisted articles for eligibility. All full texts were independently assessed by at least 2 reviewers. Any conflict related to study eligibility were resolved in discussion with the senior author (S-LC). We recorded the reason(s) for exclusion of each non-eligible article.

Data collection process and data items

Four reviewers (WJO, NM, CLN and GH) independently carried out the data extraction using a standardized data collection form, and any conflict was resolved by discussion, or with input from the senior author (S-LC). A pilot search was performed for the first 200 citations to evaluate concordance among reviewers and showed good concordance among reviewers of 94%. For studies with missing data required for data collection or meta-analyses, we contacted the corresponding authors of articles to seek related information. If there was no reply from the authors, the data were labelled as missing.

Study risk of bias assessment

Three reviewers (BJY, GH and WJO) independently carried out the assessment of risk of bias using the Newcastle–Ottawa Scale (NOS) for all observational studies [ 25 ]. Studies were graded based on three domains namely, selection, comparability and outcomes. Studies were assigned as low, moderate and high risk of bias if they were rated 0–2 points, 3–5 points and 6–9 points respectively. Any conflict was resolved by discussion or with input from the senior author (S-LC).

Statistical analysis

All outcomes (i.e. CD, visual impairment, auditory impairment and CP) were analysed as categorical data. Analyses were done for each NDI domain separately. To ensure comparability across scales, results from different studies were only pooled if the same measurement tools were used to assess the outcomes and hence sub-group analyses were based on different scales and/or different definitions of neurocognitive outcomes used by authors. Both unadjusted and adjusted odds ratios (aOR) and/or relative risk (RR) for each NDI domain were recorded. Where source data were present, we calculated the unadjusted OR if the authors did not report one, together with the 95% confidence interval (CI). For adjusted odds ratio, these were extracted from individual studies and variables used for adjustment were determined at the individual study level.

Meta-analysis was conducted for all outcomes that were reported by at least 2 independent studies or cohorts. Studies were included in the meta-analysis only if they reported outcomes for individual NDI domains within 30 months from sepsis occurrence. For each domain, all selected studies were pooled using DerSimonian-Laird random effects model due to expected heterogeneity. Studies were pooled based on adjusted and unadjusted analyses. Case–control and cohort studies were pooled separately. The pooled results were expressed as unadjusted odds ratio (OR) or adjusted odds ratio (aOR) with corresponding 95% confidence interval (95% CI). If there was more than 1 study that utilized the same population, we only analysed data from the most recent publication or from the larger sample size, to avoid double counting. Standard error (SE) from studies with multiple arms with same control group were adjusted using SE = √(K/2), where K refers to number of treatment arms including control [ 26 ]. Heterogeneity across studies was evaluated using the I^2 statistic, for which ≥ 50% is indicative of significant heterogeneity. With regards to publication bias, this was performed using Egger’s test and funnel plots only if the number of studies pooled were 10 or more for each outcome.

For neurocognitive related outcomes, subgroup analyses were performed based on the severity of the NDI domain outcomes and distinct, non-overlapping populations of septic infants (such as late onset vs early onset sepsis, culture positive sepsis vs clinically diagnosed sepsis, term and post term patients).

All analyses were done using ‘meta’ library from R software (version 4.2.2) [ 27 ]. The statistical significance threshold was a two tailed P- value < 0.05.

Certainty of evidence

The certainty of evidence for outcomes in this review was performed during the GRADE criteria [ 28 ] which is centred on the study design, risk of bias, inconsistency, indirectness, imprecision, and other considerations.

Study selection

From 7,909 studies identified, a total of 24 articles were included (Fig.  1 ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. A total of 101,657 and 19,988 preterm and term infants were included in this review.

PRISMA flowchart of the study selection process for search

Study characteristics

There were 2 case–control studies and 22 cohort studies, with a total of 121,645 infants (Table  1 ). Studies were conducted in 16 different countries (Fig.  2 ), with the most studies conducted in the United States of America (USA) (7 studies, n  = 92,358 patients) [ 30 , 33 , 37 , 41 , 42 , 47 , 52 ]. There were no studies that were conducted solely on term infants. 5 studies reported data specifically on ELBW infants (27,078 infants) and 6 studies on VLBW infants (3,322 infants). All studies were performed among neonates.

World map depicting distribution of studies that evaluate neurocognitive outcomes in infantile and neonatal sepsis

Risk of bias 

Overall, all 24 studies were classified as low risk (Supplementary Table 2). 5 papers scored high risk for outcome bias for having greater than 10% of initial population being lost to follow-up [ 29 , 32 , 40 , 41 , 42 ].

Outcome measures reported by domain

As the number of studies pooled for each outcome was less than 10, publication bias was not analysed in the meta-analyses.

Cognitive delay (CD)

Among 24 studies that assessed for CD, 16 studies reported either the incidence of CD among young infants with sepsis compared to those without, and/or the odds ratio (adjusted and/or unadjusted) comparing the two populations [ 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 48 , 49 ]. The scales used, authors’ definition of CD, incidence of CD among those with sepsis and those without are described in Table  2 . The most common tools used for assessment of CD were the Bayley Scales of Infant Development (BSID) ( n  = 13) and Denver Development Screening Test II ( n  = 2).

Infantile sepsis was associated with increased risk of overall CD delays [aOR 1.14 (95%CI: 1.01, 1.28)], overall PDI delay (aOR 1.73 (95%CI: 1.16, 2.58)) and moderate PDI delay [aOR 1.85 (95%CI: 1.01, 3.36)]. Conversely, infantile sepsis was not associated with increased risk for severe PDI delay nor overall MDI delay [aOR 1.30 (95%CI: 0.99, 1.71)] or its subgroups. There were no significant differences in outcomes between different subgroups of infections as well as culture-proven or clinically defined sepsis for either MDI or PDI (Table  8 , Fig.  3 A and B).

A Forest plot on adjusted odds ratios for neurocognitive outcomes related to MDI, PDI, visual impairment, hearing impairment and cerebral palsy. B Forest plot on unadjusted odds ratios for neurocognitive outcomes related to MDI, PDI, visual impairment, hearing impairment and cerebral palsy. Legend: MDI: Mental Developmental Index; PDI: Psychomotor Developmental Index. Foot note: Mild MDI or PDI: < 85 or < 80; Moderate MDI or PDI < 70; Severe MDI or PDI < 55

Visual impairment

Seven studies reported data on visual impairment (Table  3 ) [ 31 , 33 , 41 , 42 , 47 , 49 ]. The most common definition of visual impairment utilized was “visual acuity of < 20/200” ( n  = 4, 66.7%).

In the meta-analysis, infantile sepsis was associated with significantly increased risk of visual impairment [aOR 2.57 (95%CI: 1.14, 5.82)] but there were no statistically significant differences in visual impairment between subgroups of early or late onset sepsis, and blood culture negative conditions as compared to the non-septic population (Table  8 , Fig.  3 A and B).

Hearing impairment

Seven studies reported data on hearing impairment (Table  4 ) [ 31 , 33 , 41 , 42 , 47 , 49 ]. Two studies defined hearing impairment as permanent hearing loss affecting communication with or without amplification [ 42 , 47 ]. Other definitions included “sensorineural hearing loss requiring amplification” ( n  = 1), “bilateral hearing impairment with no functional hearing (with or without amplification)” ( n  = 1), “clinical hearing loss” ( n  = 1).

In the meta-analysis, sepsis was associated with increased risk of hearing impairment [aOR 1.70 (95% CI: 1.02–2.81)]. However, in the subgroup analyses, there were no differences in risk of hearing impairment between patients with late onset sepsis as compared to the non-septic population (Table  8 , Fig.  3 A and B).

Cerebral palsy

Nine studies [ 29 , 32 , 33 , 41 , 42 , 47 , 48 , 49 , 50 ] reported data on CP (Table  5 ), of which 5 studies [ 41 , 42 , 45 , 49 , 50 ] used the GMFCS scale. In the meta-analysis, infantile sepsis was associated with significantly increased risk of CP [aOR 2.48 (95%CI: 1.03; 5.99)]. There was no difference in rates of CP among patients with proven or suspected sepsis, as compared with infants with no sepsis (Table  8 , Fig.  3 A and B).

Differences in neurocognitive outcomes between neonates with culture-proven or clinically diagnosed sepsis as well as early or late onset sepsis

Tables 6 and 7 showed data related to differences in neurocognitive outcomes between neonates with culture-proven or clinically diagnosed sepsis as well as early or late onset sepsis. Meta-analyses were not be performed due to significant heterogeneity in definitions of sepsis, time of assessment of outcomes.

Differences in neurocognitive outcomes between term and post-term neonates

There were no studies which evaluated neurocognitive outcomes between term and post-term neonates and infants.

We found that the certainty of evidence to be very low to low for the four main neurocognitive outcomes selected. (Supplementary File 3).

In this review involving more than 121,000 infants, we provide an update to the literature regarding young infant sepsis and neurocognitive impairment. Current collective evidence demonstrate that young infant sepsis was associated with increased risk of developing neurocognitive impairment in all domains of CD, visual impairment, auditory impairment and cerebral palsy.

Cognitive delay

In this review, higher rates of cognitive delay were noted among infants with sepsis [ 29 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 48 , 49 , 52 ]. We found that infants with sepsis reported lower PDI scores (Table  8 ), which measures mainly neuromotor development. On the other hand, young infant sepsis was not associated with lower MDI scores (Table  8 ), which assesses cognitive and language development. The pathophysiological mechanism of young infant sepsis and its preferential impact on PDI remains unclear. Postulated mechanisms include development of white matter lesions which may arise from the susceptibility of oligodendrocyte precursors to inflammatory processes such as hypoxia and ischemia [ 53 ]. Future studies should look into evaluating the causes of the above findings. A majority of included studies focused on early CD outcomes while no studies evaluated long-term outcomes into adulthood. CD is known to involve complex genetic and experiential interactions [ 54 ] and may evolve overtime with brain maturation. Delays in speech and language, intellectual delay and borderline intellectual functioning are shown to be associated with poorer academic or employment outcomes in adulthood [ 55 , 56 ], and early assessment of CD may not fully reveal the extent of delays. The only study with follow-up to the adolescent phase showed a progressive increase in NDI rate as the participants aged, which provides evidence of incremental long-term negative outcomes associated with infantile sepsis [ 44 ]. Moving forward, studies with longer follow-up may allow for further examination of the long-term effects of neonatal sepsis on CD.

There were different versions of the BSID instrument (BSID-II and BSID-III) [ 19 , 57 , 58 ]. BSID-II lacked subscales in PDI and MDI scores, leading to the development of BSID-III with the segregation of PDI into fine and gross motor scales and MDI into cognitive, receptive language, and expressive language scales [ 59 ]. Although we pooled results of both BSID-II and BSID-III in our study, we recognize that comparisons between BSID-II and BSID-III are technically challenging due to differences in standardised scores [ 59 , 60 ]. In addition, the BSID-IV was created in 2019 which has fewer items, However, none of our studies utilized this instrument. Future studies should consider this instrument, as well as standardising the timepoints for assessment of CD.

Young infant sepsis was associated with increased risk of developing visual impairment. This was similar to results noted by a previous systematic review published in 2014 [ 61 ] and 2019 [ 62 ] which showed that neonatal sepsis was associated with twofold risk of developing retinopathy of prematurity in preterm infants. Specifically, meningitis was associated with a greater risk of visual impairment compared to just sepsis alone [ 47 ]. The mechanism of visual impairment has not been fully described although various theories have been suggested, including sepsis mediated vascular endothelial damage, increased body oxidative stress response as well as involvement of inflammatory cytokines and mediators [ 63 , 64 ].

Our meta-analysis showed an increased risk of hearing impairment for young infants with young infants with sepsis. This is consistent with a previous report that found an association between neonatal meningitis and sensorineural hearing loss [ 65 ]. One potential confounder which we were unable to account for may have been the use of ototoxic antimicrobial agents such as aminoglycosides. Additional confounders include very low birth weight, patient’s clinical states (e.g. hyperbilirubinemia requiring exchange transfusion) and use of mechanical ventilation or extracorporeal membrane support. To allow for meaningful comparisons of results across different study populations, it is imperative that a standardised definition of hearing impairment post neonatal sepsis be established for future studies.

Our meta-analysis found an association between neonatal sepsis and an increased risk of developing CP. This is also consistent with previous systematic reviews which had found a significant association of sepsis and CP in VLBW and early preterm infants [ 11 ]. One study found that infants born at full term and who experienced neonatal infections were at a higher risk of developing a spastic triplegia or quadriplegia phenotype of CP [ 66 ]. The pathophysiology and mechanism of injury to white matter resulting in increased motor dysfunction remains unclear and more research is required in this area.

Limitations and recommendations for future research

The main limitation of this review lies in the heterogeneity in the definitions of sepsis, exposures and assessment of outcomes across studies. This is likely attributed to the varying definition of sepsis used in different countries as well as lack of gold standard definitions or instruments for assessment of each component of NDI. A recent review of RCTs [ 67 ] also reported similar limitations where 128 different varying definitions of neonatal sepsis were used in literature. Notably, there is a critical need for developing international standardized guidelines for defining neonatal sepsis as well as assessment of NDI such as hearing and visual impairment. Another important limitation relates to the inability to assess quality of neonatal care delivered as well as temporal changes in medical practices which could have affected neurocognitive outcomes for neonates with sepsis. Improving quality of neonatal care has been shown to significantly reduce mortality risk among neonates with sepsis, especially in resource-poor countries [ 68 ]. We performed a comprehensive search strategy (PubMed, Embase, Web of Science and CENTRAL) coupled with hand searching of references within included systematic reviews, but did not evaluate grey literature. Future studies should include additional literature databases and grey literature. Another area of research gap lies in the paucity of data related to differences in neurocognitive outcomes between term and post-term neonates with sepsis and future research is required to bridge this area of research gap. Likewise, there are few studies which evaluated differences in neurocognitive outcomes between early or late onset sepsis and outcomes assessed were significantly heterogenous which limits meaningful meta-analyses. Similarly, there was significant heterogeneity in study outcomes, causative organisms and severity of disease.

We found a lack of long-term outcomes and recommend that future prospective cohorts include a longer follow-up duration as part of the study design. This is important given the implication of NDI on development into adulthood. Most data were reported for preterm infants with low birth weight, and there was a paucity of data for term infants in our literature review. Since prematurity itself is a significant cause of NDI [ 69 ], future studies should consider how gestational age and/or birth weight can be adequately adjusted for in the analysis.

Apart from the domains of NDI we chose to focus on in this review, there are other cognitive domains classified by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) [ 70 ] and/or recommended by the Common Data Elements (CDE) workgroup [ 71 ]. Future studies may wish to look into the implications of sepsis on other neuro-cognitive domains related to executive function, complex attention and societal cognition which are studied for other types of acquired brain injury [ 71 , 72 ].

Our systematic review and meta-analysis found that neonates surviving sepsis are at a higher risk of poorer neurodevelopment. However, the evidence is limited by significant heterogeneity and selection bias due to differing definitions used for NDI and for sepsis. There is also a lack of long-term follow-up data, as well as data specific for term and post-term infants. Future prospective studies should be conducted with long-term follow-up to assess the impact of neurodevelopmental impairment among all populations of neonates with sepsis.

Availability of data and materials

All data generated or analyzed in the study are found in the tables and supplementary materials.

Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn JE, et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet. 2012;379:2151–61.

Article   PubMed   Google Scholar  

WHO. Newborns: improving survival and well-being. Geneve: World Health Organisation; 2020.

Chiesa C, Panero A, Osborn JF, Simonetti AF, Pacifico L. Diagnosis of neonatal sepsis: a clinical and laboratory challenge. Clin Chem. 2004;50:279–87.

Article   CAS   PubMed   Google Scholar  

Ramaswamy VV, Abiramalatha T, Bandyopadhyay T, Shaik NB, Bandiya P, Nanda D, et al. ELBW and ELGAN outcomes in developing nations-Systematic review and meta-analysis. PLoS ONE. 2021;16:e0255352.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zhang X, Zhivaki D, Lo-Man R. Unique aspects of the perinatal immune system. Nat Rev Immunol. 2017;17:495–507.

Prabhudas M, Adkins B, Gans H, King C, Levy O, Ramilo O, et al. Challenges in infant immunity: Implications for responses to infection and vaccines. Nat Immunol. 2011;12:189–94.

World Health Organization. Global report on the epidemiology and burden of sepsis. 2020. Available from: https://www.who.int/publications/i/item/9789240010789 .

Milton R, Gillespie D, Dyer C, Taiyari K, Carvalho MJ, Thomson K, et al. Neonatal sepsis and mortality in low-income and middle-income countries from a facility-based birth cohort: an international multisite prospective observational study. Lancet Glob Health. 2022May 1;10(5):e661-72. https://doi.org/10.1016/S2214-109X(22)00043-2 .

Li Y, Ji M, Yang J. Current understanding of long-term cognitive impairment after sepsis. Front Immunol. 2022;13:855006.

Haller S, Deindl P, Cassini A, Suetens C, Zingg W, Abu Sin M, et al. Neurological sequelae of healthcare-associated sepsis in very-low-birthweight infants: Umbrella review and evidence-based outcome tree. Euro Surveill. 2016;21:30143.

Alshaikh B, Yusuf K, Sauve R. Neurodevelopmental outcomes of very low birth weight infants with neonatal sepsis: Systematic review and meta-analysis. J Perinatol. 2013;33:558–64.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71 .

U.S. Department of Health and Human Services F and D, Administration C for DE and R (CDER), (CBER) C for BE and R. General Clinical Pharmacology Considerations for Neonatal Studies for Drugs and Biological Products Guidance for Industry. 2019. Available from: https://www.fda.gov/media/129532/download . [cited 2022 Aug 9].

Bizzarro MJ, Shabanova V, Baltimore RS, Dembry LM, Ehrenkranz RA, Gallagher PG. Neonatal sepsis 2004–2013: the rise and fall of coagulase-negative staphylococci. J Pediatr. 2015;166:1193–9.

Article   PubMed   PubMed Central   Google Scholar  

Goddard B, Chang J, Sarkar IN. Using self organizing maps to compare sepsis patients from the neonatal and adult intensive care unit. AMIA Jt Summits Transl Sci Proc. 2019;2019:127–35.

PubMed   PubMed Central   Google Scholar  

Galal M, Symonds I, Murray H, Petraglia F, Smith R. Postterm pregnancy. Facts Views Vis Obgyn. 2012;4(3):175–87. Available from: https://www.ncbi.nlm.nih.gov/pubmed/24753906 .

Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47:1181–247.

Mwaniki MK, Atieno M, Lawn JE, Newton CRJC. Long-term neurodevelopmental outcomes after intrauterine and neonatal insults: a systematic review. Lancet. 2012;379:445–52.

Bayley N. Bayley scales of infant and toddler development, Third edition: screening test manual. San Antonio, Texas: Pearson Clinical Assessment PsychCorp; 2006.

Google Scholar  

Palisano R, Rosenbaum P, Walter S, Russell D, Wood E, Galuppi B. Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev Med Child Neurol. 1997;39:214–23.

Spencer-Smith MM, Spittle AJ, Lee KJ, Doyle LW, Anderson PJ. Bayley-III cognitive and language scales in preterm children. Pediatrics. 2015;135(5):e1258-65.

Survival Sepsis Campaign. https://www.sccm.org/SurvivingSepsisCampaign/About-SSC/History . History of Surviving Sepsis Campaign | SCCM.

Tan B, Wong JJM, Sultana R, Koh JCJW, Jit M, Mok YH, et al. Global Case-Fatality Rates in Pediatric Severe Sepsis and Septic Shock: A Systematic Review and Meta-analysis. JAMA Pediatr. 2019;173:352–62.

Covidence systematic review software. Melbourne, Australia: Veritas Health Innovation; Available from: www.covidence.org .

GA Wells, B Shea, D O’Connell, J Peterson, V Welch, M Losos PT. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Available from: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp . [cited 2022 Aug 9].

Rücker G, Cates CJ, Schwarzer G. Methods for including information from multi-arm trials in pairwise meta-analysis. Res Synth Methods. 2017;8:392–403.

R Core Team. R core team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org . 2021.

Schünemann H, Oxman JBGGA. GRADE Handbook. עלון הנוטע. 2013;66(1997).

Schlapbach LJ, Aebischer M, Adams M, Natalucci G, Bonhoeffer J, Latzin P, et al. Impact of sepsis on neurodevelopmental outcome in a swiss national cohort of extremely premature infants. Pediatrics. 2011;128:e348-57.

Adams-Chapman I, Bann CM, Das A, Goldberg RN, Stoll BJ, Walsh MC, et al. Neurodevelopmental outcome of extremely low birth weight infants with Candida infection. J Pediatr. 2013;163(4):961-7.e3.

de Haan TR, Beckers L, de Jonge RCJ, Spanjaard L, van Toledo L, Pajkrt D, et al. Neonatal gram negative and candida sepsis survival and neurodevelopmental outcome at the corrected age of 24 months. PLoS One. 2013;8:e59214.

Mitha A, Foix-L’Hélias L, Arnaud C, Marret S, Vieux R, Aujard Y, et al. Neonatal infection and 5-year neurodevelopmental outcome of very preterm infants. Pediatrics. 2013;132:e372-80.

Alshaikh B, Yee W, Lodha A, Henderson E, Yusuf K, Sauve R. Coagulase-negative staphylococcus sepsis in preterm infants and long-term neurodevelopmental outcome. J Perinatol. 2014;34:125–9.

Ferreira RC, Mello RR, Silva KS. Neonatal sepsis as a risk factor for neurodevelopmental changes in preterm infants with very low birth weight. J Pediatr (Rio J). 2014;90:293–9.

Hentges CR, Silveira RC, Procianoy RS, Carvalho CG, Filipouski GR, Fuentefria RN, et al. Association of late-onset neonatal sepsis with late neurodevelopment in the first two years of life of preterm infants with very low birth weight. J Pediatr (Rio J). 2014;90:50–7.

Dangor Z, Lala SG, Cutland CL, Koen A, Jose L, Nakwa F, et al. Burden of invasive group B Streptococcus disease and early neurological sequelae in South African infants. PLoS One. 2015;10:e0123014.

Savioli K, Rouse C, Susi A, Gorman G, Hisle-Gorman E. Suspected or known neonatal sepsis and neurodevelopmental delay by 5 years. J Perinatol. 2018;38:1573–80.

Singh L, Das S, Bhat VB, Plakkal N. Early neurodevelopmental outcome of very low birthweight neonates with culture-positive blood stream infection: a prospective cohort study. Cureus. 2018;10:e3492.

Zonnenberg IA, van Dijk-Lokkart EM, van den Dungen FAM, Vermeulen RJ, van Weissenbruch MM. Eur J Pediatr. 2019;178:673–80.

Nakwa FL, Lala SG, Madhi SA, Dangor Z. Neurodevelopmental impairment at 1 year of age in infants with previous invasive group B streptococcal sepsis and meningitis. Pediatric Infect Dis J. 2020;39:794–8.

Article   Google Scholar  

Mukhopadhyay S, Puopolo KM, Hansen NI, Lorch SA, Demauro SB, Greenberg RG, et al. Neurodevelopmental outcomes following neonatal late-onset sepsis and blood culture-negative conditions. Arch Dis Child Fetal Neonatal Ed. 2021;106:467–73.

Mukhopadhyay S, Puopolo KM, Hansen NI, Lorch SA, DeMauro SB, Greenberg RG, et al. Impact of early-onset sepsis and antibiotic use on death or survival with neurodevelopmental impairment at 2 years of age among extremely preterm infants. J Pediatr. 2020;221:39-46.e5.

Horváth-Puhó E, Snoek L, van Kassel MN, Gonçalves BP, Chandna J, Procter SR, et al. Prematurity modifies the risk of long-term neurodevelopmental impairments after invasive group B streptococcus infections during infancy in Denmark and the Netherlands. Clin Infect Dis. 2021;74:S44–53.

Article   PubMed Central   Google Scholar  

Horváth-Puhó E, van Kassel MN, Gonçalves BP, de Gier B, Procter SR, Paul P, et al. Mortality, neurodevelopmental impairments, and economic outcomes after invasive group B streptococcal disease in early infancy in Denmark and the Netherlands: a national matched cohort study. Lancet Child Adolesc Health. 2021;5:398–407.

Ortgies T, Rullmann M, Ziegelhöfer D, Bläser A, Thome UH. The role of early-onset-sepsis in the neurodevelopment of very low birth weight infants. BMC Pediatr. 2021;21:289.

Shim SY, Cho SJ, Park EA. Neurodevelopmental outcomes at 18–24 months of corrected age in very low birth weight infants with late-onset sepsis. J Korean Med Sci. 2021;36:e205.

Brumbaugh JE, Bell EF, Do BT, Greenberg RG, Stoll BJ, Demauro SB, et al. Incidence of and neurodevelopmental outcomes after late-onset meningitis among children born extremely preterm. JAMA Netw Open. 2022;5(12):e2245826.

Golin MO, Souza FIS, Paiva L da S, Sarni ROS. The value of clinical examination in preterm newborns after neonatal sepsis: a cross-sectional observational study. Dev Neurorehabil. 2022;25(2):80–6.

Humberg A, Fortmann MI, Spiegler J, Rausch TK, Siller B, Silwedel C, et al. Recurrent late-onset sepsis in extremely low birth weight infants is associated with motor deficits in early school age. Neonatology. 2022;119(6):695–702.

Kartam M, Embaireeg A, Albalool S, Almesafer A, Hammoud M, Al-Hathal M, et al. Late-onset sepsis in preterm neonates is associated with higher risks of cerebellar hemorrhage and lower motor scores at three years of age. Oman Med J. 2022;37(2):e368.

Paul P, Chandna J, Procter SR, Dangor Z, Leahy S, Santhanam S, et al. Neurodevelopmental and growth outcomes after invasive Group B Streptococcus in early infancy: a multi-country matched cohort study in South Africa, Mozambique, India, Kenya, and Argentina. EClinicalMedicine. 2022;47:101358.

Bright HR, Babata K, Allred EN, Erdei C, Kuban KCK, Joseph RM, et al. Neurocognitive outcomes at 10 years of age in extremely preterm newborns with late-onset bacteremia. Journal of Pediatrics. 2017;187:43-49. e1.

Romanelli RMC, Anchieta LM, Mourão MVA, Campos FA, Loyola FC, Mourão PHO, et al. Fatores de risco e letalidade de infecção da corrente sanguínea laboratorialmente confirmada, causada por patógenos não contaminantes da pele em recém-nascidos. J pediatr (Rio J). 2013;89(2):189–96.

Burgaleta M, Johnson W, Waber DP, Colom R, Karama S. Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. Neuroimage. 2014;84:810–9.

Peltopuro M, Ahonen T, Kaartinen J, Seppälä H, Närhi V. Borderline intellectual functioning: a systematic literature review. Intellect Dev Disabil. 2014;52:419–43.

Conti-Ramsden G, Durkin K, Toseeb U, Botting N, Pickles A. Education and employment outcomes of young adults with a history of developmental language disorder. Int J Lang Commun Disord. 2018;53:237–55.

Czeizel AE, Dudas I;, Murphy MM, Fernandez-Ballart JD, Arija V. Bayley scales of infant development-administration manual. Paediatr Perinat Epidemiol. 2019.

Bayley N. Manual for the Bayley Scales of Infant Development (2nd ed.). San Antonio: TX: The Psychological Corporation; 1993.

Bos AF. Bayley-Ii Or Bayley-Iii: what do the scores tell us? Dev Med Child Neurol. 2013;55:978–9.

Johnson S, Marlow N. Developmental screen or developmental testing? Early Hum Dev. 2006;82(3):173–83.

Bakhuizen SE, De Haan TR, Teune MJ, Van Wassenaer-Leemhuis AG, Van Der Heyden JL, Van Der Ham DP, et al. Meta-analysis shows that infants who have suffered neonatal sepsis face an increased risk of mortality and severe complications. Acta Paediatr Int J Paediatr. 2014;103:1211–8.

Cai S, Thompson DK, Yang JYM, Anderson PJ. Short-and long-term neurodevelopmental outcomes of very preterm infants with neonatal sepsis: a systematic review and meta-analysis. Children. 2019;6:131.

Joussen AM, Poulaki V, Le ML, Koizumi K, Esser C, Janicki H, Schraermeyer U, Kociok N, Fauser S, Kirchhof B, Kern TS, Adamis AP. A central role for inflammation in the pathogenesis of diabetic retinopathy. FASEB J. 2004;18(12):1450–2.

Ushio-Fukai M. VEGF signaling through NADPH oxidase-derived ROS. In: Antioxidants and Redox Signaling. 2007.

Sharma A, Leaf JM, Thomas S, Cane C, Stuart C, Tremlett C, et al. Sensorineural hearing loss after neonatal meningitis: a single-centre retrospective study. BMJ Paediatr Open. 2022;6(1):e001601.

Smilga AS, Garfinkle J, Ng P, Andersen J, Buckley D, Fehlings D, et al. Neonatal infection in children with cerebral palsy: a registry-based cohort study. Pediatr Neurol. 2018;80:77–83.

Hayes R, Hartnett J, Semova G, Murray C, Murphy K, Carroll L, et al. Neonatal sepsis definitions from randomised clinical trials. Pediatr Res. 2023;93:1141–8.

Rahman AE, Iqbal A, Hoque DME, Moinuddin M, Zaman SB, Rahman QSU, et al. Managing neonatal and early childhood syndromic sepsis in sub-district hospitals in resource poor settings: Improvement in quality of care through introduction of a package of interventions in rural Bangladesh. PLoS One. 2017;12(1):e0170267.

Singh M, Alsaleem M GCP. StatPearls. Treasure Island (FL): StatPearls Publishing. 2022. Neonatal Sepsis. [Updated 2022 Sep 29]. Available from: https://www-ncbi-nlm-nih-gov.libproxy1.nus.edu.sg/books/NBK531478/ .

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. Diagnostic and Statistical Manual of Mental Disorders. 2022.

Book   Google Scholar  

McCauley SR, Wilde EA, Anderson VA, Bedell G, Beers SR, Campbell TF, et al. Recommendations for the Use of Common Outcome measures in pediatric traumatic brain injury research. J Neurotrauma. 2012;29:678–705.

Goh MSL, Looi DSH, Goh JL, Sultana R, Goh SSM, Lee JH, Chong SL. The Impact of Traumatic Brain Injury on Neurocognitive Outcomes in Children: a Systematic Review and Meta-Analysis. J Neurol Neurosurg Psychiatry. 2021:jnnp-2020-325066.

Download references

Acknowledgements

We would like to thank Ms. Wong Suei Nee, senior librarian from the National University of Singapore for helping us with the search strategy. We will also like to thank Dr Ming Ying Gan, Dr Shu Ting Tammie Seethor, Dr Jen Heng Pek, Dr Rachel Greenberg, Dr Christoph Hornik and Dr Bobby Tan, for their inputs in the initial design of this study.

Conflict of interest

No financial or non-financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article.

Author information

Wei Jie Ong and Jun Jie Benjamin Seng are co-first authors.

Authors and Affiliations

MOH Holdings, Singapore, 1 Maritime Square, Singapore, 099253, Singapore

Wei Jie Ong, Jun Jie Benjamin Seng, Beijun Yap & Chen Lin Ng

SingHealth Regional Health System PULSES Centre, Singapore Health Services, Outram Rd, Singapore, 169608, Singapore

Jun Jie Benjamin Seng

SingHealth Duke-NUS Family Medicine Academic Clinical Programme, Singapore, Singapore

Yong Loo Lin School of Medicine, 10 Medical Dr, Yong Loo Lin School of Medicine, Singapore, Singapore

George He & Nooriyah Aliasgar Moochhala

Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore

Rehena Ganguly

Children’s Intensive Care Unit, KK Women’s and Children’s Hospital, SingHealth Paediatrics Academic Clinical Programme, 100 Bukit Timah Rd, Singapore, 229899, Singapore

Jan Hau Lee

Department of Emergency Medicine, KK Women’s and Children’s Hospital, SingHealth Paediatrics Academic Clinical Programme, SingHealth Emergency Medicine Academic Clinical Programme, 100 Bukit Timah Rd, Singapore, 229899, Singapore

Shu-Ling Chong

You can also search for this author in PubMed   Google Scholar

Contributions

SLC and JHL were the study’s principal investigators and were responsible for the conception and design of the study. WJO, JJBS, BY, GE, NAM and CLN were the co-investigators. WJO, JJBS, BY, GE, NAM and CLN were responsible for the screening and inclusion of articles and data extraction. All authors contributed to the data analyses and interpretation of data. WJO, JJBS, BY, GE, NAM and CLN prepared the initial draft of the manuscript. All authors revised the draft critically for important intellectual content and agreed to the final submission. All authors had access to all study data, revised the draft critically for important intellectual content and agreed to the final submission.

Corresponding author

Correspondence to Jun Jie Benjamin Seng .

Ethics declarations

Ethics approval and consent to participate.

As this was a systematic review with no access to patient data, ethical approval from the institutional review board was exempted.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Ong, W.J., Seng, J.J.B., Yap, B. et al. Impact of neonatal sepsis on neurocognitive outcomes: a systematic review and meta-analysis. BMC Pediatr 24 , 505 (2024). https://doi.org/10.1186/s12887-024-04977-8

Download citation

Received : 11 May 2024

Accepted : 26 July 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s12887-024-04977-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Neonatal sepsis
• Infantile sepsis
• Neurocognitive outcomes
• Systematic review

BMC Pediatrics

ISSN: 1471-2431

• Submission enquiries: [email protected]
• General enquiries: [email protected]