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What Is a Case-Control Study? | Definition & Examples

What Is a Case-Control Study? | Definition & Examples

Published on February 4, 2023 by Tegan George . Revised on June 22, 2023.

A case-control study is an experimental design that compares a group of participants possessing a condition of interest to a very similar group lacking that condition. Here, the participants possessing the attribute of study, such as a disease, are called the “case,” and those without it are the “control.”

It’s important to remember that the case group is chosen because they already possess the attribute of interest. The point of the control group is to facilitate investigation, e.g., studying whether the case group systematically exhibits that attribute more than the control group does.

When to use a case-control study, examples of case-control studies, advantages and disadvantages of case-control studies, other interesting articles, frequently asked questions.

Case-control studies are a type of observational study often used in fields like medical research, environmental health, or epidemiology. While most observational studies are qualitative in nature, case-control studies can also be quantitative , and they often are in healthcare settings. Case-control studies can be used for both exploratory and explanatory research , and they are a good choice for studying research topics like disease exposure and health outcomes.

A case-control study may be a good fit for your research if it meets the following criteria.

Data on exposure (e.g., to a chemical or a pesticide) are difficult to obtain or expensive.
The disease associated with the exposure you’re studying has a long incubation period or is rare or under-studied (e.g., AIDS in the early 1980s).
The population you are studying is difficult to contact for follow-up questions (e.g., asylum seekers).

Retrospective cohort studies use existing secondary research data, such as medical records or databases, to identify a group of people with a common exposure or risk factor and to observe their outcomes over time. Case-control studies conduct primary research , comparing a group of participants possessing a condition of interest to a very similar group lacking that condition in real time.

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Case-control studies are common in fields like epidemiology, healthcare, and psychology.

You would then collect data on your participants’ exposure to contaminated drinking water, focusing on variables such as the source of said water and the duration of exposure, for both groups. You could then compare the two to determine if there is a relationship between drinking water contamination and the risk of developing a gastrointestinal illness. Example: Healthcare case-control study You are interested in the relationship between the dietary intake of a particular vitamin (e.g., vitamin D) and the risk of developing osteoporosis later in life. Here, the case group would be individuals who have been diagnosed with osteoporosis, while the control group would be individuals without osteoporosis.

You would then collect information on dietary intake of vitamin D for both the cases and controls and compare the two groups to determine if there is a relationship between vitamin D intake and the risk of developing osteoporosis. Example: Psychology case-control study You are studying the relationship between early-childhood stress and the likelihood of later developing post-traumatic stress disorder (PTSD). Here, the case group would be individuals who have been diagnosed with PTSD, while the control group would be individuals without PTSD.

Case-control studies are a solid research method choice, but they come with distinct advantages and disadvantages.

Advantages of case-control studies

Case-control studies are a great choice if you have any ethical considerations about your participants that could preclude you from using a traditional experimental design .
Case-control studies are time efficient and fairly inexpensive to conduct because they require fewer subjects than other research methods .
If there were multiple exposures leading to a single outcome, case-control studies can incorporate that. As such, they truly shine when used to study rare outcomes or outbreaks of a particular disease .

Disadvantages of case-control studies

Case-control studies, similarly to observational studies, run a high risk of research biases . They are particularly susceptible to observer bias , recall bias , and interviewer bias.
In the case of very rare exposures of the outcome studied, attempting to conduct a case-control study can be very time consuming and inefficient .
Case-control studies in general have low internal validity and are not always credible.

Case-control studies by design focus on one singular outcome. This makes them very rigid and not generalizable , as no extrapolation can be made about other outcomes like risk recurrence or future exposure threat. This leads to less satisfying results than other methodological choices.

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

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

A case-control study differs from a cohort study because cohort studies are more longitudinal in nature and do not necessarily require a control group .

While one may be added if the investigator so chooses, members of the cohort are primarily selected because of a shared characteristic among them. In particular, retrospective cohort studies are designed to follow a group of people with a common exposure or risk factor over time and observe their outcomes.

Case-control studies, in contrast, require both a case group and a control group, as suggested by their name, and usually are used to identify risk factors for a disease by comparing cases and controls.

A case-control study differs from a cross-sectional study because case-control studies are naturally retrospective in nature, looking backward in time to identify exposures that may have occurred before the development of the disease.

On the other hand, cross-sectional studies collect data on a population at a single point in time. The goal here is to describe the characteristics of the population, such as their age, gender identity, or health status, and understand the distribution and relationships of these characteristics.

Cases and controls are selected for a case-control study based on their inherent characteristics. Participants already possessing the condition of interest form the “case,” while those without form the “control.”

Keep in mind that by definition the case group is chosen because they already possess the attribute of interest. The point of the control group is to facilitate investigation, e.g., studying whether the case group systematically exhibits that attribute more than the control group does.

The strength of the association between an exposure and a disease in a case-control study can be measured using a few different statistical measures , such as odds ratios (ORs) and relative risk (RR).

No, case-control studies cannot establish causality as a standalone measure.

As observational studies , they can suggest associations between an exposure and a disease, but they cannot prove without a doubt that the exposure causes the disease. In particular, issues arising from timing, research biases like recall bias , and the selection of variables lead to low internal validity and the inability to determine causality.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2023, June 22). What Is a Case-Control Study? | Definition & Examples. Scribbr. Retrieved September 27, 2024, from https://www.scribbr.com/methodology/case-control-study/

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis (Monographs in Epidemiology and Biostatistics, 2) (Illustrated). Oxford University Press.

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Case Study vs. Experiment

What's the difference.

Case studies and experiments are both research methods used in various fields to gather data and draw conclusions. However, they differ in their approach and purpose. A case study involves in-depth analysis of a particular individual, group, or situation, aiming to provide a detailed understanding of a specific phenomenon. On the other hand, an experiment involves manipulating variables and observing the effects on a sample population, aiming to establish cause-and-effect relationships. While case studies provide rich qualitative data, experiments provide quantitative data that can be statistically analyzed. Ultimately, the choice between these methods depends on the research question and the desired outcomes.

Attribute	Case Study	Experiment
Research Method	Qualitative	Quantitative
Objective	Descriptive	Causal
Sample Size	Small	Large
Controlled Variables	Less controlled	Highly controlled
Manipulation of Variables	Not manipulated	Manipulated
Data Collection	Observations, interviews, surveys	Measurements, surveys, experiments
Data Analysis	Qualitative analysis	Statistical analysis
Generalizability	Less generalizable	More generalizable
Timeframe	Longer	Shorter

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze phenomena. Two commonly used approaches are case study and experiment. While both methods aim to provide insights and answers to research questions, they differ in their design, implementation, and the type of data they generate. In this article, we will explore the attributes of case study and experiment, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting and analyzing detailed information from multiple sources, such as interviews, observations, documents, and archival records. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex and unique situations.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather a wide range of information, allowing for a comprehensive analysis of the case. This depth of data enables researchers to explore complex relationships, identify patterns, and generate new hypotheses.

Furthermore, case studies are particularly useful when studying rare or unique phenomena. Since they focus on specific cases, they can provide valuable insights into situations that are not easily replicated or observed in controlled experiments. This attribute makes case studies highly relevant in fields where generalizability is not the primary goal.

However, it is important to note that case studies have limitations. Due to their qualitative nature, the findings may lack generalizability to broader populations or contexts. The small sample size and the subjective interpretation of data can also introduce bias. Additionally, case studies are time-consuming and resource-intensive, requiring extensive data collection and analysis.

An experiment is a research method that involves manipulating variables and measuring their effects on outcomes. It aims to establish cause-and-effect relationships by controlling and manipulating independent variables while keeping other factors constant. Experiments are commonly used in natural sciences, psychology, and medicine to test hypotheses and determine the impact of specific interventions or treatments.

One of the key attributes of an experiment is its ability to establish causal relationships. By controlling variables and randomly assigning participants to different conditions, researchers can confidently attribute any observed effects to the manipulated variables. This attribute allows for strong internal validity, making experiments a powerful tool for drawing causal conclusions.

Moreover, experiments often provide quantitative data, allowing for statistical analysis and objective comparisons. This attribute enhances the precision and replicability of findings, enabling researchers to draw more robust conclusions. The ability to replicate experiments also contributes to the cumulative nature of scientific knowledge.

However, experiments also have limitations. They are often conducted in controlled laboratory settings, which may limit the generalizability of findings to real-world contexts. Ethical considerations may also restrict the manipulation of certain variables or the use of certain interventions. Additionally, experiments can be time-consuming and costly, especially when involving large sample sizes or long-term follow-ups.

While case studies and experiments have distinct attributes, they can complement each other in research. Case studies provide in-depth insights and a rich understanding of complex phenomena, while experiments offer controlled conditions and the ability to establish causal relationships. By combining these methods, researchers can gain a more comprehensive understanding of the research question at hand.

When deciding between case study and experiment, researchers should consider the nature of their research question, the available resources, and the desired level of control and generalizability. Case studies are particularly suitable when exploring unique or rare phenomena, aiming for depth rather than breadth, and when resources allow for extensive data collection and analysis. On the other hand, experiments are ideal for establishing causal relationships, testing specific hypotheses, and when control over variables is crucial.

In conclusion, case study and experiment are two valuable research methods with their own attributes and limitations. Both approaches contribute to the advancement of knowledge in various fields, and their selection depends on the research question, available resources, and desired outcomes. By understanding the strengths and weaknesses of each method, researchers can make informed decisions and conduct rigorous and impactful research.

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Observational vs. Experimental Study: A Comprehensive Guide

Explore the fundamental disparities between experimental and observational studies in this comprehensive guide by Santos Research Center, Corp. Uncover concepts such as control group, random sample, cohort studies, response variable, and explanatory variable that shape the foundation of these methodologies. Discover the significance of randomized controlled trials and case control studies, examining causal relationships and the role of dependent variables and independent variables in research designs.

This enlightening exploration also delves into the meticulous scientific study process, involving survey members, systematic reviews, and statistical analyses. Investigate the careful balance of control group and treatment group dynamics, highlighting how researchers meticulously assign variables and analyze statistical patterns to discern meaningful insights. From dissecting issues like lung cancer to understanding sleep patterns, this guide emphasizes the precision of controlled experiments and controlled trials, where variables are isolated and scrutinized, paving the way for a deeper comprehension of the world through empirical research.

Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies: A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies: Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies: Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials: Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups: Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding: Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages: These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations: However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence . These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

Case-control studies

Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design. This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is odds ratio (OR) .

Figure 1. Case-control study design.

Cases should be selected based on objective inclusion and exclusion criteria from a reliable source such as a disease registry. An inherent issue with selecting cases is that a certain proportion of those with the disease would not have a formal diagnosis, may not present for medical care, may be misdiagnosed or may have died before getting a diagnosis. Regardless of how the cases are selected, they should be representative of the broader disease population that you are investigating to ensure generalisability.

Case-control studies should include two groups that are identical EXCEPT for their outcome / disease status.

As such, controls should also be selected carefully. It is possible to match controls to the cases selected on the basis of various factors (e.g. age, sex) to ensure these do not confound the study results. It may even increase statistical power and study precision by choosing up to three or four controls per case (2).

Case-controls can provide fast results and they are cheaper to perform than most other studies. The fact that the analysis is retrospective, allows rare diseases or diseases with long latency periods to be investigated. Furthermore, you can assess multiple exposures to get a better understanding of possible risk factors for the defined outcome / disease.

Nevertheless, as case-controls are retrospective, they are more prone to bias. One of the main examples is recall bias. Often case-control studies require the participants to self-report their exposure to a certain factor. Recall bias is the systematic difference in how the two groups may recall past events e.g. in a study investigating stillbirth, a mother who experienced this may recall the possible contributing factors a lot more vividly than a mother who had a healthy birth.

A summary of the pros and cons of case-control studies are provided in Table 1.

Table 1. Advantages and disadvantages of case-control studies.

Cohort studies

Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.

In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes.

The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. See Figure 2 for a pictorial representation of a cohort study design. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).

Figure 2. Cohort study design.

Cohort studies should include two groups that are identical EXCEPT for their exposure status.

As a result, both exposed and unexposed groups should be recruited from the same source population. Another important consideration is attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study.

Cohort studies can assess a range of outcomes allowing an exposure to be rigorously assessed for its impact in developing disease. Additionally, they are good for rare exposures, e.g. contact with a chemical radiation blast.

Whilst cohort studies are useful, they can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency.

A summary of the pros and cons of cohort studies are provided in Table 2.

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

STROBE provides a checklist of important steps for conducting these types of studies, as well as acting as best-practice reporting guidelines (3). Both case-control and cohort studies are observational, with varying advantages and disadvantages. However, the most important factor to the quality of evidence these studies provide, is their methodological quality.

Song, J. and Chung, K. Observational Studies: Cohort and Case-Control Studies .Â Plastic and Reconstructive Surgery. Â 2010 Dec;126(6):2234-2242.
Ury HK. Efficiency of case-control studies with multiple controls per case: Continuous or dichotomous data .Â Biometrics .Â 1975 Sep;31(3):643â€“649.
von Elm E, Altman DG, Egger M, Pocock SJ, GÃ¸tzsche PC, Vandenbroucke JP; STROBE Initiative.Â The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Â Lancet 2007 Oct;370(9596):1453-14577. PMID: 18064739.

Saul Crandon

No Comments on Case-control and Cohort studies: A brief overview

Very well presented, excellent clarifications. Has put me right back into class, literally!

Very clear and informative! Thank you.

very informative article.

Thank you for the easy to understand blog in cohort studies. I want to follow a group of people with and without a disease to see what health outcomes occurs to them in future such as hospitalisations, diagnoses, procedures etc, as I have many health outcomes to consider, my questions is how to make sure these outcomes has not occurred before the “exposure disease”. As, in cohort studies we are looking at incidence (new) cases, so if an outcome have occurred before the exposure, I can leave them out of the analysis. But because I am not looking at a single outcome which can be checked easily and if happened before exposure can be left out. I have EHR data, so all the exposure and outcome have occurred. my aim is to check the rates of different health outcomes between the exposed)dementia) and unexposed(non-dementia) individuals.

Very helpful information

Thanks for making this subject student friendly and easier to understand. A great help.

Thanks a lot. It really helped me to understand the topic. I am taking epidemiology class this winter, and your paper really saved me.

Happy new year.

Wow its amazing n simple way of briefing ,which i was enjoyed to learn this.its very easy n quick to pick ideas .. Thanks n stay connected

Saul you absolute melt! Really good work man

am a student of public health. This information is simple and well presented to the point. Thank you so much.

very helpful information provided here

really thanks for wonderful information because i doing my bachelor degree research by survival model

Quite informative thank you so much for the info please continue posting. An mph student with Africa university Zimbabwe.

Thank you this was so helpful amazing

Apreciated the information provided above.

So clear and perfect. The language is simple and superb.I am recommending this to all budding epidemiology students. Thanks a lot.

Great to hear, thank you AJ!

I have recently completed an investigational study where evidence of phlebitis was determined in a control cohort by data mining from electronic medical records. We then introduced an intervention in an attempt to reduce incidence of phlebitis in a second cohort. Again, results were determined by data mining. This was an expedited study, so there subjects were enrolled in a specific cohort based on date(s) of the drug infused. How do I define this study? Thanks so much.

thanks for the information and knowledge about observational studies. am a masters student in public health/epidemilogy of the faculty of medicines and pharmaceutical sciences , University of Dschang. this information is very explicit and straight to the point

Very much helpful

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Cluster Randomized Trials: Concepts

This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.

Expertise-based Randomized Controlled Trials

This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.

An introduction to different types of study design

Conducting successful research requires choosing the appropriate study design. This article describes the most common types of designs conducted by researchers.

Study Design 101: Case Control Study

Case Report
Case Control Study
Cohort Study
Randomized Controlled Trial
Practice Guideline
Systematic Review
Meta-Analysis
Helpful Formulas
Finding Specific Study Types

A study that compares patients who have a disease or outcome of interest (cases) with patients who do not have the disease or outcome (controls), and looks back retrospectively to compare how frequently the exposure to a risk factor is present in each group to determine the relationship between the risk factor and the disease.

Case control studies are observational because no intervention is attempted and no attempt is made to alter the course of the disease. The goal is to retrospectively determine the exposure to the risk factor of interest from each of the two groups of individuals: cases and controls. These studies are designed to estimate odds.

Case control studies are also known as "retrospective studies" and "case-referent studies."

Good for studying rare conditions or diseases
Less time needed to conduct the study because the condition or disease has already occurred
Lets you simultaneously look at multiple risk factors
Useful as initial studies to establish an association
Can answer questions that could not be answered through other study designs

Disadvantages

Retrospective studies have more problems with data quality because they rely on memory and people with a condition will be more motivated to recall risk factors (also called recall bias).
Not good for evaluating diagnostic tests because it's already clear that the cases have the condition and the controls do not
It can be difficult to find a suitable control group

Design pitfalls to look out for

Care should be taken to avoid confounding, which arises when an exposure and an outcome are both strongly associated with a third variable. Controls should be subjects who might have been cases in the study but are selected independent of the exposure. Cases and controls should also not be "over-matched."

Is the control group appropriate for the population? Does the study use matching or pairing appropriately to avoid the effects of a confounding variable? Does it use appropriate inclusion and exclusion criteria?

Fictitious Example

There is a suspicion that zinc oxide, the white non-absorbent sunscreen traditionally worn by lifeguards is more effective at preventing sunburns that lead to skin cancer than absorbent sunscreen lotions. A case-control study was conducted to investigate if exposure to zinc oxide is a more effective skin cancer prevention measure. The study involved comparing a group of former lifeguards that had developed cancer on their cheeks and noses (cases) to a group of lifeguards without this type of cancer (controls) and assess their prior exposure to zinc oxide or absorbent sunscreen lotions.

This study would be retrospective in that the former lifeguards would be asked to recall which type of sunscreen they used on their face and approximately how often. This could be either a matched or unmatched study, but efforts would need to be made to ensure that the former lifeguards are of the same average age, and lifeguarded for a similar number of seasons and amount of time per season.

Real-life Examples

Boubekri, M., Cheung, I., Reid, K., Wang, C., & Zee, P. (2014). Impact of windows and daylight exposure on overall health and sleep quality of office workers: a case-control pilot study. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 10 (6), 603-611. https://doi.org/10.5664/jcsm.3780

This pilot study explored the impact of exposure to daylight on the health of office workers (measuring well-being and sleep quality subjectively, and light exposure, activity level and sleep-wake patterns via actigraphy). Individuals with windows in their workplaces had more light exposure, longer sleep duration, and more physical activity. They also reported a better scores in the areas of vitality and role limitations due to physical problems, better sleep quality and less sleep disturbances.

Togha, M., Razeghi Jahromi, S., Ghorbani, Z., Martami, F., & Seifishahpar, M. (2018). Serum Vitamin D Status in a Group of Migraine Patients Compared With Healthy Controls: A Case-Control Study. Headache, 58 (10), 1530-1540. https://doi.org/10.1111/head.13423

This case-control study compared serum vitamin D levels in individuals who experience migraine headaches with their matched controls. Studied over a period of thirty days, individuals with higher levels of serum Vitamin D was associated with lower odds of migraine headache.

Related Formulas

Odds ratio in an unmatched study
Odds ratio in a matched study

Related Terms

A patient with the disease or outcome of interest.

Confounding

When an exposure and an outcome are both strongly associated with a third variable.

A patient who does not have the disease or outcome.

Matched Design

Each case is matched individually with a control according to certain characteristics such as age and gender. It is important to remember that the concordant pairs (pairs in which the case and control are either both exposed or both not exposed) tell us nothing about the risk of exposure separately for cases or controls.

Observed Assignment

The method of assignment of individuals to study and control groups in observational studies when the investigator does not intervene to perform the assignment.

Unmatched Design

The controls are a sample from a suitable non-affected population.

Now test yourself!

1. Case Control Studies are prospective in that they follow the cases and controls over time and observe what occurs.

a) True b) False

2. Which of the following is an advantage of Case Control Studies?

a) They can simultaneously look at multiple risk factors. b) They are useful to initially establish an association between a risk factor and a disease or outcome. c) They take less time to complete because the condition or disease has already occurred. d) b and c only e) a, b, and c

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What Is a Case-Control Study? | Definition & Examples

What Is a Case-Control Study? | Definition & Examples

Published on 4 February 2023 by Tegan George .

A case-control study is an experimental design that compares a group of participants possessing a condition of interest to a very similar group lacking that condition. Here, the participants possessing the attribute of study, such as a disease, are called the ‘case’, and those without it are the ‘control’.

When to use a case-control study, examples of case-control studies, advantages and disadvantages of case-control studies, frequently asked questions.

A case-control study may be a good fit for your research if it meets the following criteria.

Data on exposure (e.g., to a chemical or a pesticide) are difficult to obtain or expensive.
The disease associated with the exposure you’re studying has a long incubation period or is rare or under-studied (e.g., AIDS in the early 1980s).
The population you are studying is difficult to contact for follow-up questions (e.g., asylum seekers).

Prevent plagiarism, run a free check.

Case-control studies are common in fields like epidemiology, healthcare, and psychology.

Case-control studies are a solid research method choice, but they come with distinct advantages and disadvantages.

Advantages of case-control studies

Case-control studies are a great choice if you have any ethical considerations about your participants that could preclude you from using a traditional experimental design .
Case-control studies are time efficient and fairly inexpensive to conduct because they require fewer subjects than other research methods .
If there were multiple exposures leading to a single outcome, case-control studies can incorporate that. As such, they truly shine when used to study rare outcomes or outbreaks of a particular disease .

Disadvantages of case-control studies

Case-control studies, similarly to observational studies, run a high risk of research biases . They are particularly susceptible to observer bias , recall bias , and interviewer bias.
In the case of very rare exposures of the outcome studied, attempting to conduct a case-control study can be very time consuming and inefficient .
Case-control studies in general have low internal validity and are not always credible.

Case-control studies by design focus on one singular outcome. This makes them very rigid and not generalisable , as no extrapolation can be made about other outcomes like risk recurrence or future exposure threat. This leads to less satisfying results than other methodological choices.

A case-control study differs from a cohort study because cohort studies are more longitudinal in nature and do not necessarily require a control group .

No, case-control studies cannot establish causality as a standalone measure.

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2023, February 04). What Is a Case-Control Study? | Definition & Examples. Scribbr. Retrieved 28 September 2024, from https://www.scribbr.co.uk/research-methods/case-control-studies/

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis (Monographs in Epidemiology and Biostatistics, 2) (Illustrated). Oxford University Press.

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Making statistics intuitive

Case Control Study: Definition, Benefits & Examples

By Jim Frost 2 Comments

What is a Case Control Study?

A case control study is a retrospective, observational study that compares two existing groups. Researchers form these groups based on the existence of a condition in the case group and the lack of that condition in the control group. They evaluate the differences in the histories between these two groups looking for factors that might cause a disease.

Photograph of medical scientist at work.

By evaluating differences in exposure to risk factors between the case and control groups, researchers can learn which factors are associated with the medical condition.

For example, medical researchers study disease X and use a case-control study design to identify risk factors. They create two groups using available medical records from hospitals. Individuals with disease X are in the case group, while those without it are in the control group. If the case group has more exposure to a risk factor than the control group, that exposure is a potential cause for disease X. However, case-control studies establish only correlation and not causation. Be aware of spurious correlations!

Case-control studies are observational studies because researchers do not control the risk factors—they only observe them. They are retrospective studies because the scientists create the case and control groups after the outcomes for the subjects (e.g., disease vs. no disease) are known.

This post explains the benefits and limitations of case-control studies, controlling confounders, and analyzing and interpreting the results. I close with an example case control study showing how to calculate and interpret the results.

Learn more about Experimental Design: Definition, Types, and Examples .

Related posts : Observational Studies Explained and Control Groups in Experiments

Benefits of a Case Control Study

A case control study is a relatively quick and simple design. They frequently use existing patient data, and the experimenters form the groups after the outcomes are known. Researchers do not conduct an experiment. Instead, they look for differences between the case and control groups that are potential risk factors for the condition. Small groups and individual facilities can conduct case-control studies, unlike other more intensive types of experiments.

Case-control studies are perfect for evaluating outbreaks and rare conditions. Researchers simply need to let a sufficient number of known cases accumulate in an established database. The alternative would be to select a large random sample and hope that the condition afflicts it eventually.

A case control study can provide rapid results during outbreaks where the researchers need quick answers. They are ideal for the preliminary investigation phase, where scientists screen potential risk factors. As such, they can point the way for more thorough, time-consuming, and expensive studies. They are especially beneficial when the current state of science knows little about the connection between risk factors and the medical condition. And when you need to identify potential risk factors quickly!

Cohort studies are another type of observational study that are similar to case-control studies, but there are some important differences. To learn more, read my post about Cohort Studies .

Limitations of a Case Control Study

Because case-control studies are observational, they cannot establish causality and provide lower quality evidence than other experimental designs, such as randomized controlled trials . Additionally, as you’ll see in the next section, this type of study is susceptible to confounding variables unless experimenters correctly match traits between the two groups.

A case-control study typically depends on health records. If the necessary data exist in sources available to the researchers, all is good. However, the investigation becomes more complicated if the data are not readily available.

Case-control studies can incorporate biases from the underlying data sources. For example, researchers frequently obtain patient data from hospital records. The population of hospital patients is likely to differ from the general population. Even the control patients are in the hospital for some reason—they likely have serious health problems. Consequently, the subjects in case-control studies are likely to differ from the general population, which reduces the generalizability of the results.

A case-control study cannot estimate incidence or prevalence rates for the disease. The data from these studies do not allow you to calculate the probability of a new person contracting the condition in a given period nor how common it is in the population. This limitation occurs because case-control studies do not use a representative sample.

Case-control studies cannot determine the time between exposure and onset of the medical condition. In fact, case-control studies cannot reliably assess each subject’s exposure to risk factors over time. Longitudinal studies, such as prospective cohort studies, can better make those types of assessment.

Related post : Causation versus Correlation in Statistics

Use Matching to Control Confounders

Because case-control studies are observational studies, they are particularly vulnerable to confounding variables and spurious correlations . A confounder correlates with both the risk factor and the outcome variable. Because observational studies don’t use random assignment to equalize confounders between the case and control groups, they can become unbalanced and affect the results.

Unfortunately, confounders can be the actual cause of the medical condition rather than the risk factor that the researchers identify. If a case-control study does not account for confounding variables, it can bias the results and make them untrustworthy.

Case-control studies typically use trait matching to control confounders. This technique involves selecting study participants for the case and control groups with similar characteristics, which helps equalize the groups for potential confounders. Equalizing confounders limits their impact on the results.

Ultimately, the goal is to create case and control groups that have equal risks for developing the condition/disease outside the risk factors the researchers are explicitly assessing. Matching facilitates valid comparisons between the two groups because the controls are similar to cases. The researchers use subject-area knowledge to identify characteristics that are critical to match.

Note that you cannot assess matching variables as potential risk factors. You’ve intentionally equalized them across the case and control groups and, consequently, they do not correlate with the condition. Hence, do not use the risk factors you want to evaluate as trait matching variables.

Learn more about confounding variables .

Statistical Analysis of a Case Control Study

Researchers frequently include two controls for each case to increase statistical power for a case-control study. Adding even more controls per case provides few statistical benefits, so studies usually do not use more than a 2:1 control to case ratio.

For statistical results, case-control studies typically produce an odds ratio for each potential risk factor. The equation below shows how to calculate an odds ratio for a case-control study.

Equation for an odds ratio in a case-control study.

Notice how this ratio takes the exposure odds in the case group and divides it by the exposure odds in the control group. Consequently, it quantifies how much higher the odds of exposure are among cases than the controls.

In general, odds ratios greater than one flag potential risk factors because they indicate that exposure was higher in the case group than in the control group. Furthermore, higher ratios signify stronger associations between exposure and the medical condition.

An odds ratio of one indicates that exposure was the same in the case and control groups. Nothing to see here!

Ratios less than one might identify protective factors.

Learn more about Understanding Ratios .

Now, let’s bring this to life with an example!

Example Odds Ratio in a Case-Control Study

The Kent County Health Department in Michigan conducted a case-control study in 2005 for a company lunch that produced an outbreak of vomiting and diarrhea. Out of multiple lunch ingredients, researchers found the following exposure rates for lettuce consumption.


	53	33
	1	7

By plugging these numbers into the equation, we can calculate the odds ratio for lettuce in this case-control study.

Example odds ratio calculations for a case-control study.

The study determined that the odds ratio for lettuce is 11.2.

This ratio indicates that those with symptoms were 11.2 times more likely to have eaten lettuce than those without symptoms. These results raise a big red flag for contaminated lettuce being the culprit!

Learn more about Odds Ratios.

Epidemiology in Practice: Case-Control Studies (NIH)

Interpreting Results of Case-Control Studies (CDC)

Reader Interactions

January 18, 2022 at 7:56 am

Great post, thanks for writing it!

Is it possible to test an odds ration for statistical significance?

January 18, 2022 at 7:41 pm

Hi Michael,

Thanks! And yes, you can test for significance. To learn more about that, read my post about odds ratios , where I discuss p-values and confidence intervals.

Comments and Questions Cancel reply

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2 Spectrum Health/Michigan State University College of Human Medicine
PMID: 28846237
Bookshelf ID: NBK448143

A case-control study is a type of observational study commonly used to look at factors associated with diseases or outcomes. The case-control study starts with a group of cases, which are the individuals who have the outcome of interest. The researcher then tries to construct a second group of individuals called the controls, who are similar to the case individuals but do not have the outcome of interest. The researcher then looks at historical factors to identify if some exposure(s) is/are found more commonly in the cases than the controls. If the exposure is found more commonly in the cases than in the controls, the researcher can hypothesize that the exposure may be linked to the outcome of interest.

For example, a researcher may want to look at the rare cancer Kaposi's sarcoma. The researcher would find a group of individuals with Kaposi's sarcoma (the cases) and compare them to a group of patients who are similar to the cases in most ways but do not have Kaposi's sarcoma (controls). The researcher could then ask about various exposures to see if any exposure is more common in those with Kaposi's sarcoma (the cases) than those without Kaposi's sarcoma (the controls). The researcher might find that those with Kaposi's sarcoma are more likely to have HIV, and thus conclude that HIV may be a risk factor for the development of Kaposi's sarcoma.

There are many advantages to case-control studies. First, the case-control approach allows for the study of rare diseases. If a disease occurs very infrequently, one would have to follow a large group of people for a long period of time to accrue enough incident cases to study. Such use of resources may be impractical, so a case-control study can be useful for identifying current cases and evaluating historical associated factors. For example, if a disease developed in 1 in 1000 people per year (0.001/year) then in ten years one would expect about 10 cases of a disease to exist in a group of 1000 people. If the disease is much rarer, say 1 in 1,000,0000 per year (0.0000001/year) this would require either having to follow 1,000,0000 people for ten years or 1000 people for 1000 years to accrue ten total cases. As it may be impractical to follow 1,000,000 for ten years or to wait 1000 years for recruitment, a case-control study allows for a more feasible approach.

Second, the case-control study design makes it possible to look at multiple risk factors at once. In the example above about Kaposi's sarcoma, the researcher could ask both the cases and controls about exposures to HIV, asbestos, smoking, lead, sunburns, aniline dye, alcohol, herpes, human papillomavirus, or any number of possible exposures to identify those most likely associated with Kaposi's sarcoma.

Case-control studies can also be very helpful when disease outbreaks occur, and potential links and exposures need to be identified. This study mechanism can be commonly seen in food-related disease outbreaks associated with contaminated products, or when rare diseases start to increase in frequency, as has been seen with measles in recent years.

Because of these advantages, case-control studies are commonly used as one of the first studies to build evidence of an association between exposure and an event or disease.

In a case-control study, the investigator can include unequal numbers of cases with controls such as 2:1 or 4:1 to increase the power of the study.

Disadvantages and Limitations

The most commonly cited disadvantage in case-control studies is the potential for recall bias. Recall bias in a case-control study is the increased likelihood that those with the outcome will recall and report exposures compared to those without the outcome. In other words, even if both groups had exactly the same exposures, the participants in the cases group may report the exposure more often than the controls do. Recall bias may lead to concluding that there are associations between exposure and disease that do not, in fact, exist. It is due to subjects' imperfect memories of past exposures. If people with Kaposi's sarcoma are asked about exposure and history (e.g., HIV, asbestos, smoking, lead, sunburn, aniline dye, alcohol, herpes, human papillomavirus), the individuals with the disease are more likely to think harder about these exposures and recall having some of the exposures that the healthy controls.

Case-control studies, due to their typically retrospective nature, can be used to establish a correlation between exposures and outcomes, but cannot establish causation . These studies simply attempt to find correlations between past events and the current state.

When designing a case-control study, the researcher must find an appropriate control group. Ideally, the case group (those with the outcome) and the control group (those without the outcome) will have almost the same characteristics, such as age, gender, overall health status, and other factors. The two groups should have similar histories and live in similar environments. If, for example, our cases of Kaposi's sarcoma came from across the country but our controls were only chosen from a small community in northern latitudes where people rarely go outside or get sunburns, asking about sunburn may not be a valid exposure to investigate. Similarly, if all of the cases of Kaposi's sarcoma were found to come from a small community outside a battery factory with high levels of lead in the environment, then controls from across the country with minimal lead exposure would not provide an appropriate control group. The investigator must put a great deal of effort into creating a proper control group to bolster the strength of the case-control study as well as enhance their ability to find true and valid potential correlations between exposures and disease states.

Similarly, the researcher must recognize the potential for failing to identify confounding variables or exposures, introducing the possibility of confounding bias, which occurs when a variable that is not being accounted for that has a relationship with both the exposure and outcome. This can cause us to accidentally be studying something we are not accounting for but that may be systematically different between the groups.

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Connor Kerndt declares no relevant financial relationships with ineligible companies.

Disclosure: Mary Hoffman declares no relevant financial relationships with ineligible companies.

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Chapter 8. Case-control and cross sectional studies

Case-control studies

Selection of cases, selection of controls, ascertainment of exposure, cross sectional studies.

Chapter 1. What is epidemiology?
Chapter 2. Quantifying disease in populations
Chapter 3. Comparing disease rates
Chapter 4. Measurement error and bias
Chapter 5. Planning and conducting a survey
Chapter 6. Ecological studies
Chapter 7. Longitudinal studies
Chapter 9. Experimental studies
Chapter 10. Screening
Chapter 11. Outbreaks of disease
Chapter 12. Reading epidemiological reports
Chapter 13. Further reading

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Designing and Conducting Analytic Studies in the Field

Brendan R. Jackson And Patricia M. Griffin

Analytic studies can be a key component of field investigations, but beware of an impulse to begin one too quickly. Studies can be time- and resource-intensive, and a hastily constructed study might not answer the correct questions. For example, in a foodborne disease outbreak investigation, if the culprit food is not on your study’s questionnaire, you probably will not be able to implicate it. Analytic studies typically should be used to test hypotheses, not generate them. However, in certain situations, collecting data quickly about patients and a comparison group can be a way to explore multiple hypotheses. In almost all situations, generating hypotheses before designing a study will help you clarify your study objectives and ask better questions.

Generating Hypotheses
Study Designs for Testing Hypotheses
Types of Observational Studies for Testing Hypotheses
Selection of Controls in Case–Control Studies
Matching in Case–Control Studies
Example: Using an Analytic Study to Solve an Outbreak at a Church Potluck Dinner (But Not That Church Potluck)
Outbreaks with Universal Exposure

The initial steps of an investigation, described in previous chapters, are some of your best sources of hypotheses. Key activities include the following:

By examining the sex distribution among persons in outbreaks, US enteric disease investigators have learned to suspect a vegetable as the source when most patients are women. (Of course, generalizations do not always hold true!)
In an outbreak of bloodstream infections caused by Serratia marcescens among patients receiving parenteral nutrition (food administered through an intravenous catheter), investigators had a difficult time finding the source until they noted that none of the 19 cases were among children. Further investigation of the parenteral nutrition administered to adults but not children in that hospital identified contaminated amino acid solution as the source ( 1 ).
Focus on outliers. Give extra attention to the earliest and latest cases on an epidemic curve and to persons who recently visited the neighborhood where the outbreak is occurring. Interviews with these patients can yield important clues (e.g., by identifying the index case, secondary case, or a narrowed list of common exposures).
Determine sources of similar outbreaks. Consult health department records, review the literature, and consult experts to learn about previous sources. Be mindful that new sources frequently occur, given ever-changing social, behavioral, and commercial trends.
Conduct a small number of in-depth, open-ended interviews. When a likely source is not quickly evident, conducting in-depth (often >1 hour), open-ended interviews with a subset of patients (usually 5 to 10) or their caregivers can be the best way to identify possible sources. It helps to begin with a semistructured list of questions designed to help the patient recall the events and exposures of every day during the incubation period. The interview can end with a “shotgun” questionnaire (see activity 6) ( Box 7.1 ). A key component of this technique is that one investigator ideally conducts, or at least participates in, as many interviews as possible (five or more) because reading notes from others’ interviews is no substitute for soliciting and hearing the information first-hand. For example, in a 2009 Escherichia coli O157 outbreak, investigators were initially unable to find the source through general and targeted questionnaires. During open-ended interviews with five patients, the interviewer noted that most reported having eaten strawberries, a particular type of candy, and uncooked prepackaged cookie dough. An analytic study was then conducted that included questions about these exposures; it confirmed cookie dough as the source ( 3 ).
Ask patients what they think. Patients can have helpful thoughts about the source of their illness. However, be aware that patients often associate their most recent food exposure (e.g., a meal) with illness, whereas the inciting exposure might have been long before.
Consider administering a shotgun questionnaire. Such questionnaires, which typically ask about hundreds of possible exposures, are best used on a limited number of patients as part of hypothesis-generating interviews. After generating hypotheses, investigators can create a questionnaire targeted to that investigation. Although not an ideal method, shotgun questionnaires can be used by multiple interviewers to obtain data about large numbers of patients ( Box 7.1 ).

In November 2014, a US surveillance system for foodborne diseases (PulseNet) detected a cluster (i.e., a possible outbreak) of listeriosis cases based on similar-appearing Listeria monocytogenes isolates by pulsed-field gel electrophoresis of the isolates. No suspected foods were identified through routine patient interviews by using a Listeria -specific questionnaire with approximately 40 common food sources of listeriosis (e.g., soft cheese and deli meat). The outbreak’s descriptive epidemiology offered no clear leads: the sex distribution was nearly even, the age spectrum was wide, and the case-fatality rate of approximately 20% was typical. Notably, however, 3 of the 35 cases occurred among previously healthy school-aged children, which is highly unusual for listeriosis. Most cases occurred during late October and early November.

Investigators began reinterviewing patients by using a hypothesis-generating shotgun questionnaire with more than 500 foods, but it did not include caramel apples. By comparing the first nine patient responses with data from a published survey of food consumption, strawberries and ice cream emerged as hypotheses. However, several interviewed patients denied having eaten these foods during the month before illness. An investigator then conducted lengthy, open-ended interviews with patients and their family members. During one interview, he asked about special foods eaten during recent holidays, and the patient’s wife replied that her husband had eaten prepackaged caramel apples around Halloween. Although produce items had been implicated in past listeriosis outbreaks, caramel apples seemed an unlikely source. However, the interviewer took note of this connection because he had previously interviewed another patient who reported having eaten caramel apples. This event underscores the importance of one person conducting multiple interviews because that person might make subtle mental connections that may be missed when reviewing other interviewers’ notes. In fact, several other investigators listening to the interview noted this exposure—among hundreds of others—but thought little of it.

In this investigation, the finding of high strawberry and ice cream consumption among patients, coupled with the timing of the outbreak during a holiday period, helped make a sweet food (i.e., caramel apples) seem more plausible as the possible source.

To explore the caramel apple hypothesis, investigators asked five other patients about this exposure, and four reported having eaten them. On the basis of these initial results, investigators designed and administered a targeted questionnaire to patients involved in the outbreak, as well as to patients infected with unrelated strains of L. monocytogenes (i.e., a case–case study). This study, combined with testing of apples and the apple packing facility, confirmed that caramel apples were the source (2). Had a single interviewer performed multiple open-ended interviews to generate hypotheses before the shotgun questionnaire, the outbreak might have been solved sooner.

As evident in public health and clinical guidelines, randomized controlled trials (e.g., trials of drugs, vaccines, and community-level interventions) are the reference standard for epidemiology, providing the highest level of evidence. However, such studies are not possible in certain situations, including outbreak investigations. Instead, investigators must rely on observational studies, which can provide sufficient evidence for public health action. In observational studies, the epidemiologist documents rather than determines the exposures, quantifying the statistical association between exposure and disease. Here again, the key when designing such studies is to obtain a relevant comparison group for the patients ( Box 7.2 ).

Because field analytic studies are used to quantify the association between exposure and disease, defining what is meant by exposure and disease is essential. Exposure is used broadly, meaning demographic characteristics, genetic or immunologic makeup, behaviors, environmental exposures, and other factors that might influence a person’s risk for disease. Because precise information can help accurately estimate an exposure’s effect on disease, exposure measures should be as objective and standard as possible. Developing a measure of exposure can be conceptually straightforward for an exposure that is a relatively discrete event or characteristic—for example, whether a person received a spinal injection with steroid medication compounded at a specific pharmacy or whether a person received a typhoid vaccination during the year before international travel. Although these exposures might be straightforward in theory, they can be subject to interpretation in practice. Should a patient injected with a medication from an unknown pharmacy be considered exposed? Whatever decision is made should be documented and applied consistently.

Additionally, exposures often are subject to the whims of memory. Memory aids (e.g., restaurant menus, vaccination cards, credit card receipts, and shopper cards) can be helpful. More than just a binary yes or no, the dose of an exposure can also be enlightening. For example, in an outbreak of fungal bloodstream infections linked to contaminated intravenous saline flushes administered at an oncology clinic, affected patients had received a greater number of flushes than unaffected patients ( 4 ). Similarly, in an outbreak of Listeria monocytogenes infections, the association with deli meat became apparent only when the exposure evaluated was consumption of deli meat more than twice a week ( 5 ).

Defining disease (e.g., does a person have botulism?) might sound simple, but often it is not; read more about making and applying disease case definitions in Chapter 3 .

Three types of observational studies are commonly used in the field. All are best performed by using a standard questionnaire specific for that investigation, developed on the basis of hypothesis-generating interviews.

Observational Study Type 1: Cohort

In concept, a cohort study, like an experimental study, begins with a group of persons without the disease under study, but with different exposure experiences, and follows them over time to find out whether they experience the disease or health condition of interest. However, in a cohort study, each person’s exposure is merely recorded rather than assigned randomly by the investigator. Then the occurrence of disease among persons with different exposures is compared to assess whether the exposures are associated with increased risk for disease. Cohort studies can be prospective or retrospective.

Prospective Cohort Studies

A prospective cohort study enrolls participants before they experience the disease or condition of interest. The enrollees are then followed over time for occurrence of the disease or condition. The unexposed or lowest exposure group serves as the comparison group, providing an estimate of the baseline or expected amount of disease. An example of a prospective cohort study is the Framingham Heart Study. By assessing the exposures of an original cohort of more than 5,000 adults without cardiovascular disease (CVD), beginning in 1948 and following them over time, the study was the first to identify common CVD risk factors ( 6 ). Each case of CVD identified after enrollment was counted as an incident case. Incidence was then quantified as the number of cases divided by the sum of time that each person was followed (incidence rate) or as the number of cases divided by the number of participants being followed (attack rate or risk or i ncidence proportion). In field epidemiology, prospective cohort studies also often involve a group of persons who have had a known exposure (e.g., survived the World Trade Center attack on September 11, 2001 [ 7 ]) and who are then followed to examine the risk for subsequent illnesses with long incubation or latency periods.

Retrospective Cohort Studies

A retrospective cohort study enrolls a defined participant group after the disease or condition of interest has occurred. In field epidemiology, these studies are more common than prospective studies. The population affected is often well-defined (e.g., banquet attendees, a particular school’s students, or workers in a certain industry). Investigators elicit exposure histories and compare disease incidence among persons with different exposures or exposure levels.

Observational Study Type 2: Case–Control

In a case–control study, the investigator must identify a comparison group of control persons who have had similar opportunities for exposure as the case-patients. Case–control studies are commonly performed in field epidemiology when a cohort study is impractical (e.g., no defined cohort or too many non-ill persons in the group to interview). Whereas a cohort study proceeds conceptually from exposure to disease or condition, a case–control study begins conceptually with the disease or condition and looks backward at exposures. Excluding controls by symptoms alone might not guarantee that they do not have mild cases of the illness under investigation. Table 7.1 presents selected key differences between a case–control and retrospective cohort study.

Benefits and drawbacks of three observational study types commonly used in field investigations
Feature	Retrospective cohort study	Case–control study	Case–case study
Sample size	Larger	Smaller	Smaller
Costs	More (because of size)	Less	Less
Study time	Short	Short	Short
If disease is rare	Inefficient	Efficient	Efficient (if comparison cases already identified)
If exposure is rare	Efficient	Inefficient	Inefficient
If multiple exposures are relevant	Often can examine	Can examine	Can examine
If patients have multiple outcomes	Can examine	Cannot examine	Cannot examine
Natural history	Can ascertain	Cannot ascertain	Cannot ascertain
Disease risk	Can measure	Cannot measure	Cannot measure
Recall bias	Potential problem	Potential problem	Generally less of a problem
Selection bias	Potential problem	Potential problem	Potential problem
If population is not well-defined	Difficult	Advantageous	Advantageous

Observational Study Type 3: Case–Case

In case–case studies, a group of patients with the same or similar disease serve as a comparison group (8). This method might require molecular subtyping of the suspected pathogen to distinguish outbreak-associated cases from other cases and is especially useful when relevant controls are difficult to identify. For example, controls for an investigation of Listeria illnesses typically are patients with immunocompromising conditions (e.g., cancer or corticosteroid use) who might be difficult to identify among the general population. Patients with Listeria isolates of a different subtype than the outbreak strain can serve as comparisons to help reduce bias when comparing food exposures. However, patients with similar illnesses can have similar exposures, which can introduce a bias, making identifying the source more difficult. Moreover, other considerations should influence the choice of a comparison group. If most outbreak-associated case-patients are from a single neighborhood or are of a certain race/ethnicity, other patients with listeriosis from across the country will serve as an inadequate comparison group.

Considerations for Selecting Controls

Selecting relevant controls is one of the most important considerations when designing a case–control study. Several key considerations are presented here; consult other resources for in-depth discussion ( 9,10 ). Ideally, controls should

Thoroughly reflect the source population from which case-patients arose, and
Provide a good estimate of the level of exposure one would expect from that population. Sometimes the source population is not so obvious, and a case–control study using controls from the general population might be needed to implicate a general exposure (e.g., visiting a specific clinic, restaurant, or fair). The investigation can then focus on specific exposures among persons with the general exposure (see also next section).

Controls should be chosen independently of any specific exposure under evaluation. If you select controls on the basis of lack of exposure, you are likely to find an association between illness and that exposure regardless of whether one exists. Also important is selecting controls from a source population in a way that minimizes confounding (see Chapter 8 ), which is the existence of a factor (e.g., annual income) that, by being associated with both exposure and disease, can affect the associations you are trying to examine.

When trying to enroll controls who reflect the source population, try to avoid overmatching (i.e., enrolling controls who are too similar to case-patients, resulting in fewer differences among case-patients and controls than ought to exist and decreased ability to identify exposure–disease associations). When conducting case–control studies in hospitals and other healthcare settings, ensure that controls do not have other diseases linked to the exposure under study.

Commonly Used Control Selection Methods

When an outbreak does not affect a defined population (e.g., potluck dinner attendees) but rather the community at large, a range of options can be used to determine how to select controls from a large group of persons.

Random-digit dialing . This method, which involves selecting controls by using a system that randomly selects telephone numbers from a directory, has been a staple of US outbreak investigations. In recent years, however, declining response rates because of increasing use of caller identification and cellular phones and lack of readily available directory listings of cellular phone numbers by geographic area have made this method increasingly difficult. Even when this method was most useful, often 50 or more numbers needed to be dialed to reach one household or person who both answered and provided a usable match for the case-patient. Commercial databases that include cellular phone numbers have been used successfully to partially address this problem, but the method remains time-consuming ( 11 ).
Random or systematic sampling from a list . For investigations in settings where a roster is available (e.g., attendees at a resort on certain dates), controls can be selected by either random or systematic sampling. Government records (e.g., motor vehicle, voter, or tax records) can provide lists of possible controls, but they might not be representative of the population being studied ( 11 ). For random sampling, a table or computer-generated list of random numbers can be used to select every n th persons to contact (e.g., every 12th or 13th).
Neighborhood . Recruiting controls from the same neighborhood as case-patients (i.e., neighborhood matching) has commonly been used during case–control studies, particularly in low-and middle-income countries. For example, during an outbreak of typhoid fever in Tajikistan ( 12 ), investigators recruited controls by going door-to-door down a street, starting at a case-patient’s house; a study of cholera in Haiti used a similar method ( 13 ). Typically, the immediately neighboring households are skipped to prevent overmatching.
Patients’ friends or relatives . Using friends and relatives as controls can be an effective technique when the characteristics of case-patients (e.g., very young children) make finding controls by a random method difficult. Typically, the investigator interviews a patient or his or her parent, then asks for the names and contact information for more friends or relatives who are needed as controls. One advantage is that the friends of an ill person are usually willing to participate, knowing their cooperation can help solve the puzzle. However, because they can have similar personal habits and preferences as patients, their exposures might be similar. Such overmatching can decrease the likelihood of finding the source of the illness or condition.
Databases of persons with exposure information . Sources of data on persons with exposure information include survey data (e.g., FoodNet Population Survey [ 14 ]), public health databases of patients with other illnesses or a different subtype of the same illness, and previous studies. ( Chapter 4 describes additional sources.)

When considering outside data sources, investigators must determine whether those data provide an appropriate comparison group. For example, persons in surveys might differ from case-patients in ways that are impossible to determine. Other patients might be so similar to case-patients that risky exposures are unidentifiable, or they might be so different that exposures identified as risks are not true risks.

To help control for confounding, controls can be matched to case-patients on characteristics specified by investigators, including age group, sex, race/ethnicity, and neighborhood. Such matching does not itself reduce confounding, but it enables greater efficiency when matched analyses are performed that do ( 15 ). When deciding to match, however, be judicious. Matching on too many characteristics can make controls difficult to find (making a tough process even harder). Imagine calling hundreds of random telephone numbers trying to find a man of a particular ethnicity aged 50–54 years who is then willing to answer your questions. Also, remember not to match on the exposure of interest or on any other characteristic you wish to examine. Matched case–control study data typically necessitate a matched analysis (e.g., conditional logistic regression) ( 15 ).

Matching Types

The two main types of matching are pair matching and frequency matching.

Pair Matching

In pair matching, each control is matched to a specific case-patient. This method can be helpful logistically because it allows matching by friends or relatives, neighborhood, or telephone exchange, but finding controls who meet specific criteria can be burdensome.

Frequency Matching

In frequency matching, also called category matching , controls are matched to case-patients in proportion to the distribution of a characteristic among case-patients. For example, if 20% of case-patients are children aged 5–18 years, 50% are adults aged 19–49 years, and 30% are adults 50 years or older, controls should be enrolled in similar proportions. This method works best when most case-patients have been identified before control selection begins. It is more efficient than pair matching because a person identified as a possible control who might not meet the criteria for matching a particular case-patient might meet criteria for one of the case-patient groups.

Number of Controls

Most field case–control studies use control-to-case-patient ratios of 1:1, 2:1, or 3:1. Enrolling more than one control per case-patient can increase study power, which might be needed to detect a statistically significant difference in exposure between case-patients and controls, particularly when an outbreak involves a limited number of cases. The incremental gain of adding more controls beyond three or four is small because study power begins to plateau. Note that not all case-patients need to have the same number of controls. Sample size calculations can help in estimating a target number of controls to enroll, although sample sizes in certain field investigations are limited more by time and resource constraints. Still, estimating study power under a range of scenarios is wise because an analytic study might not be worth doing if you have little chance of detecting a statistically significant association. Sample size calculators for unmatched case–control studies are available at http://www.openepi.com and in the StatCalc function of Epi Info ( https://www.cdc.gov/epiinfo ).

More than One Control Group

Sometimes the choice of a control group is so vexing that investigators decide to use more than one type of control group (e.g., a hospital-based group and a community group). If the two control groups provide similar results and conclusions about risk factors for disease, the credibility of the findings is increased. In contrast, if the two control groups yield conflicting results, interpretation becomes more difficult.

Since the 1940s, field epidemiology students have studied a classic outbreak of gastrointestinal illness at a church potluck dinner in Oswego, New York ( 16 ). However, the case study presented here, used to illustrate study designs, is a different potluck dinner.

In April 2015, an astute neurologist in Lancaster, Ohio, contacted the local health department about a patient in the emergency department with a suspected case of botulism. Within 2 hours, four more patients arrived with similar symptoms, including blurred vision and shortness of breath. Health officials immediately recognized this as a botulism outbreak.

If the source is a widely distributed commercial product, then the population to study is persons across the United States and possibly abroad.
If the source is airborne, then the population to study is residents of a single city or area.
If the source is food from a restaurant, then the population to study is predominantly local residents and some travelers.
If the source is a meal at a workplace or social setting, then the population to study is meal attendees.
If the source is a meal at home, then the population to study is household members and any guests.

Descriptive epidemiology and questioning of the case-patients revealed that all had eaten at the same church potluck dinner and had no other common exposures, making the potluck the likely exposure site and attendees the likely source population. Thus, an analytic study would be targeted at potluck attendees, although investigators must remain alert to case-patients among nonattendees. As initial interviews were conducted, more cases of botulism were being diagnosed, quickly increasing to more than 25. The source of the outbreak needed to be identified rapidly to halt further exposure and illness.

List of foods served at the potluck.
Approximate number of attendees.
A case definition.
Information from 5–10 hypothesis-generating interviews with a few case-patients or their family members.
A cohort study would be a reasonable option because a defined group exists (i.e., a cohort) of exposed persons who could be interviewed in a reasonable amount of time. The study would be retrospective because the outcome (i.e., botulism) has already occurred, and investigators could assess exposures retrospectively (i.e., foods eaten at the potluck) by interviewing attendees.
In a cohort study, investigators can calculate the attack rate for botulism among potluck attendees who reported having eaten each food and for those who had not. For example, if 20 of the 30 attendees who had eaten a particular food (e.g., potato salad) had botulism, you would calculate the attack rate by dividing 20 (corresponding to cell a in Handout 7.1 ) by 30 (total exposed, or a + b), yielding approximately 67%. If 5 of the 45 attendees who had not eaten potato salad had botulism, the attack rate among the unexposed—5 / 45, corresponding to c/ (c + d)—would be approximately 11%. The risk ratio would be 6, which is calculated by dividing the attack rate among the exposed (67%) by the attack rate among the unexposed (11%).
A case–control study would be the most feasible option because the entire cohort could not be identified and because the large number of attendees could make interviewing them all difficult. Rather than interview all non-ill persons, a subset could be interviewed as control subjects.
The method of control subject selection should be considered carefully. If all attendees are not interviewed, determining the risk for botulism among the exposed and unexposed is impossible because investigators would not know the exposures for all non-ill attendees. Instead of risk, investigators calculate the odds of exposure, which can approximate risk. For example, if 20 (80%) of 25 case-patients had eaten potato salad, the odds of potato salad exposure among case-patients would be 20/ 5 = 4 (exposed/ unexposed, or a/ c in Handout 7.2 ). If 10 (20%) of 50 selected controls had eaten potato salad, the odds of exposure among control subjects would be 10/ 40 = 0.25 (or b/ d in Handout 7.2). Dividing the odds of exposure among the case-patients (a/ c) by the odds of exposure among control subjects (b / d) yields an odds ratio of 16 (4/ 0.25). The odds ratio is not a true measure of risk, but it can be used to implicate a food. An odds ratio can approximate a risk ratio when the outcome or disease is rare (e.g., roughly <5% of a population). In such cases, a/ b is similar to a/ (a + b). The odds ratio is typically higher than the risk ratio when >5% of exposed persons in the analysis have the illness.

In the actual outbreak, 29 (38%) of 77 potluck attendees had botulism. The investigators performed a cohort study, interviewing 75 of the 77 attendees about 52 foods served ( 17 ). The attack rate among persons who had eaten potato salad was significantly and substantially higher than the attack rate among those who had not, with a risk ratio of 14 (95% confidence interval 5–42). One of the potato salads served was made with incorrectly home-canned potatoes (a known source of botulinum toxin), and samples of discarded potato salad tested positive for botulinum toxin, supporting the findings of the analytic study. (Of note, persons often blame potato salad for causing illness when, in fact, it rarely is a source. This outbreak was a notable exception.)

In field epidemiology, the link between exposure and illness is often so strong that it is evident despite such inherent study limitations as small sample size and exposure misclassification. In this outbreak, a few of the patients with botulism reported not having eaten potato salad, and some of the attendees without botulism reported having eaten it. In epidemiologic studies, you rarely find 100% concordance between exposure and outcome for various reasons, including incomplete or erroneous recall because remembering everything eaten is difficult. Here, cross-contamination of potato salad with other foods might have helped explain cases among patients who had not eaten potato salad because only a small amount of botulinum toxin is needed to produce illness.

Two-by-Two Table to Calculate the Relative Risk, or Risk Ratio, in Cohort Studies

Two- by- two tables are covered in more detail in Chapter 8 .

Cohort Study Approach
	Ill	Not Ill
Exposed	a	b
Unexposed	c	d

Risk Ratio = Incidence in exposed over Incidence in unexposed = a over a+b over c over c+d

Two-by-Two Table to Calculate the Odds Ratio in Case–Control Studies

A risk ratio cannot be calculated from a case–control study because true attack rates cannot be calculated.

Case-Control Study Approach
	III (Cases)	Not III (Controls)
Exposed	a	b
Unexposed	c	d

Odds ratio = Odds of exposure in cases over Odds of exposure in controls = a/c over b/d = ad over bc

What kind of study would you design if your hypothesis-generating interviews lead you to believe that everyone, or nearly everyone, was exposed to the same suspected infection source? How would you test hypotheses if all barbecue attendees, ill and non-ill, had eaten the chicken or if all town residents had drunk municipal tap water, and no unexposed group exists for comparison? A few factors that might be of help are the exposure timing (e.g., a particularly undercooked batch of barbeque), the exposure place (e.g., a section of the water system more contaminated than others), and the exposure dose (e.g., number of chicken pieces eaten or glasses of water drunk). Including questions about the time, place, and frequency of highly suspected exposures in a questionnaire can improve the chances of detecting a difference ( 18 ).

Cohort, case–control, and case–case studies are the types of analytic studies that field epidemiologists use most often. They are best used as mechanisms for evaluating—quantifying and testing—hypotheses identified in earlier phases of the investigation. Cohort studies, which are oriented conceptually from exposure to disease, are appropriate in settings in which an entire population is well-defined and available for enrollment (e.g., guests at a wedding reception). Cohort studies are also appropriate when well-defined groups can be enrolled by exposure status (e.g., employees working in different parts of a manufacturing plant). Case–control studies, in contrast, are useful when the population is less clearly defined. Case–control studies, oriented from disease to exposure, identify persons with disease and a comparable group of persons without disease (controls). Then the exposure experiences of the two groups are compared. Case–case studies are similar to case–control studies, except that controls have an illness not linked to the outbreak. Case–control studies are probably the type most often appropriate for field investigations. Although conceptually straightforward, the design of an effective epidemiologic study requires many careful decisions. Taking the time needed to develop good hypotheses can result in a questionnaire that is useful for identifying risk factors. The choice of an appropriate comparison group, how many controls per case-patient to enroll, whether to match, and how best to avoid potential biases are all crucial decisions for a successful study.

This chapter relies heavily on the work of Richard C. Dicker, who authored this chapter in the previous edition.

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Open access
Published: 27 September 2024

Effectiveness of comprehensive geriatric assessment adapted to primary care when provided by a nurse or a general practitioner: the CEpiA cluster-randomised trial

Veronique Orcel 1 , 2 ,
Leon Banh 1 ,
Sylvie Bastuji-Garin 2 ,
Vincent Renard 1 , 2 ,
Emmanuelle Boutin 2 , 4 ,
Amel Gouja 4 ,
Philippe Caillet 2 , 3 ,
Elena Paillaud 2 , 3 ,
Etienne Audureau 2 , 4 &
Emilie Ferrat 1 , 2

BMC Medicine volume 22 , Article number: 414 ( 2024 ) Cite this article

Metrics details

The benefits of comprehensive geriatric assessment (CGA) are well established for hospital care but less so for primary care. Our primary objective was to assess the effect of two multifaceted interventions based on a CGA adapted for primary care on a composite criterion combining all-cause mortality, emergency department visits, unplanned hospital admissions, and institutionalisation.

This open-label, pragmatic, three-arm, cluster-randomised controlled trial involved 39 general practices in France. It included 634 patients aged 70 years or over with chronic health conditions and/or an unplanned hospital admission in the past 3 months, between 05/2016 and 08/2018. Interventions were in arm 1: a systematic nurse-led CGA; arm 2: a GP-led CGA, at the GP’s discretion; arm 3: standard care. The primary composite endpoint was assessed at 12 months. The secondary endpoints included: components of the composite endpoint, health-related quality of life (Duke Health Profile), functional status (Katz Activities of Daily Living Index) and medications (number) at 12 months. Pairwise comparisons between the experimental groups and the control were tested. The main analysis was performed on the intention-to-treat (ITT) population, after imputing missing information and adjusting for baseline imbalances by mixed effects regressions.

For the primary composite outcome, no statistically significant difference was found between arm 1 and the control (adjusted odds ratio [aOR] = 0.81 [95%CI 0.54–1.21], P = 0.31), whereas arm 2 and the control differed significantly (aOR = 0.60 [0.39–0.93], P = 0.022). A statistically lower risk of unplanned hospital admission in arm 2 vs control (aOR = 0.57 [0.36–0.92], P = 0.020)) was observed, while no statistically significant differences were found for the other components and between arm 1 and the control. None of the other secondary endpoints differed between arms.

Conclusions

Our study led in community-dwelling older patients with chronic conditions found no significant effect of a CGA adapted for primary care on mortality, functional independence and quality of life, but suggests that a GP-led CGA may reduce the risk of unplanned hospital admission. Our study demonstrates the feasibility of incorporating CGA into clinical practice and highlights its potential benefits when applied on a case-by-case basis, guided by the GPs who develop the resulting PCP.

Trial registration

NCT02664454.

Peer Review reports

Worldwide, the population is ageing: according to the United Nations, the number of people aged 65 and over will rise from 700 million in 2019 to 1.5 billion in 2050 [ 1 ]. In the European Union, the proportion of over-65 s is expected to increase from 18.5% in 2014 to 30% in 2080 [ 2 ]. Population ageing will raise crucial challenges for healthcare systems and primary care settings for the coming decades [ 3 , 4 , 5 , 6 ]. The assessment and management of ageing has to change [ 7 , 8 ]. The World Health Organization (WHO) defined the concept of “healthy ageing” as helping older people to develop and maintain the functional ability that enables well-being [ 9 ]. The prevalences of multimorbidity, frailty and functional decline increase with age and thus lead to a greater risk of falls, hospital admission, disability, long-term care and death [ 10 ]. Thus, strategies for preventing, slowing or reverse the declines in older people’s abilities are essential for healthy ageing [ 11 ].

Multidimensional comprehensive geriatric assessments (CGAs) were developed to better define the sometimes complex care required by older adults and to improve patient-centred care [ 12 ]. This is a holistic diagnostic process for determining overall health status, with a focus on the person’s physical, functional, psychological and social capabilities [ 13 ]. A CGA is not limited to assessment because it prompts the formulation of a coordinated, integrated, multicomponent, personalised care plan (PCP) for treatment and follow-up [ 12 , 14 ]. The PCP takes account of the patients’ preferences and priorities with regard to managing the identified health problems [ 12 , 15 , 16 ].

The benefits of performing a CGA have been well established in hospital care [ 17 ] but less so in primary care [ 18 , 19 ]. A recent Cochrane Collaboration review (2022) found that a CGA in a primary care setting might not have an impact on death or institutionalisation among community-dwelling older patients but might (according to low-certainty evidence) reduce the risk of unplanned hospital admission [ 18 ]. The researchers concluded that further studies (using validate scales) were required to examine the effects of primary care CGAs on emergency department visits, functional independence and quality of life [ 18 ].

To contribute to provide primary care data, we conducted the Clinical Epidemiology and Ageing (CEpiA) three-arm, cluster-randomised trial. We hypothesised that relative to standard care (arm 3), a complex intervention including a specific training seminar, a dedicated helpline for GPs, and a CGA modified for use in primary care and led by a nurse (arm 1) or a GP (arm 2) would be associated with lower morbidity and mortality rates in over-70 patients with chronic health conditions. We chose not to focus on vulnerable older patients with acute care needs because the literature suggests that frail older adults living in the community would benefit the most from CGA [ 18 ]. With increasing global longevity and imminent demographic shifts, it is crucial to find ways to reduce the anticipated surge in hospitalisations. Furthermore, interventions targeting community-dwelling older patients could positively influence the course of their chronic diseases by reducing functional decline at an earlier stage. Therefore, we chose to focus on regular older patients with chronic conditions. This choice was intended to reflect the type of older patients GPs usually encounter in their daily practice, making the results of this study more relevant to their clinical practice.

We also hypothesised that the effect of the intervention would be greatest in arm 1 (i.e., with a nurse-led CGA). We assumed that the design for arm 1, with a systematic CGA provided by a trained nurses (unlike in arm 2, where it is left to the GP’s discretion), along with the delegation of tasks to nurses by GPs, would enable the full deployment of the intervention and increase its likelihood of effectiveness. The implementation of CGA in primary care is challenging, because GPs are already facing a workload and this new task may be seen as burdensome [ 4 ], thus leading to the design of delegation of this time-consuming task to nurses in arm 1, and the hypothesis of arm 2 (with GPs only) could potentially be less effective. Multidomain assessments are core nursing competencies [ 20 ], and nurse-led care reportedly gives similar biomedical outcomes, and even slightly better results for the patient’s quality of life, relative to GP-led care [ 21 , 22 , 23 ]. Few large randomised controlled trials (RCTs) found benefits of a nurse-led CGA (geriatric nurses or intensively trained practice nurses) among the community-dwelling older patients with multiple comorbidities on their daily functioning (24) and mental well-being [ 24 ]. Also, the WHO has recommended that older patients receive integrated interventions from a multidisciplinary primary care team to improve healthy ageing [ 7 ] and the British Geriatrics Society has advised to delegate a part of CGA in primary care to nurses [ 14 ].

The primary objective of the present study was to assess the impact on 12-month morbidity and mortality rates of nurse-led or GP-led CGAs, compared with standard care with a composite endpoint. The secondary objectives were to assess these interventions’ effects on the individual components of the composite endpoint (all-cause mortality, unplanned hospital admissions, emergency department visits, and institutionalisation), together with quality of life, functional independence, and polypharmacy.

Trial design

The CEpiA multicentre, open-label, three-arm, parallel-group, pragmatic, cluster-randomised controlled trial involved 39 general practices in France and was conducted between May 2016 and August 2018. The detailed protocol has been published elsewhere [ 10 ] and preliminary (partial) results of this study were presented in scientific meetings [ 25 , 26 , 27 ]. The study was approved by an independent ethics committee ( CPP Ile-de-France IV , Paris, France; reference: 2015/48SC). The study was conducted in accordance with the Declaration of Helsinki. All followed procedures were in accordance with relevant guidelines and French regulations. The study database was registered with the French National Data Protection Commission ( Commission nationale de l'informatique et des libertés , Paris, France). All the study participants provided their verbal, informed consent. This study was registered at ClinicalTrials.gov (NCT02664454) before the first recruitment. The results were reported in accordance with the CONSORT guidelines.

Participants

The inclusion criteria were age 70 or over, a long-term health condition (see reference 10) or an unplanned hospital admission in the previous 3 months, and consultation with their usual GP or another GP in the same investigating centre. The main exclusion criteria were an estimated life expectancy below 12 months, inability to speak or understand French, residence in an institutional, and the absence of health insurance coverage. Once GP practices were randomised to one of the three arms of the trial, patients seen consecutively during consultations or home visits by GPs and in compliance with eligibility criteria were invited to participate to the study. Patients then received oral and written information about the trial by the GP investigator. Eligible patients were included after giving verbal informed consent (the date of given verbal consent was written in the Case Report Form).

Interventions

The two interventions combined three components. In arm 1: firstly, a 1-day multidisciplinary seminar on CGA in primary care was organised for the GPs and nurses. Secondly, a CGA was systematically performed by a nurse. Lastly, a dedicated hotline (staffed by geriatricians) was made available for the GPs all along the follow-up. In arm 2: the same 1-day multidisciplinary seminar on CGA in primary care was organised for the GPs. Secondly, a CGA was performed by the GP if he deemed it necessary. Lastly, the dedicated hotline was made available for the GPs all along the follow-up. In Arm 3: there were no specific interventions (usual care). A figure describing the two interventions is available in Additional file 1: Fig. S1. The detailed description of each component of the intervention was published elsewhere [ 10 ]. The CGAs in arms 1 and 2 had to be performed within a month of the patient’s inclusion and could be performed at the patient’s home or in the GP’s office. CGA tools used in arms 1 and 2 were identical (Additional file 2: Table S1).

The CGA led to the formulation of a PCP including shared objectives and care actions planned in the short term (within 3 months) and medium term (within 6 months) [ 10 ] (Additional file 3: Table S1). The combination of tailored care actions initiated with the PCP and an optimised and regular follow-up were expected to improve geriatric outcomes, notably by allowing medicines optimisation and earlier detection and management of nutritional, functional or cognitive deficits. This hypothesis was in line with a recent review article [ 28 ] on complex community-based interventions to sustain independence in older patients, whose findings indicate that multifactorial action from individualised care planning and regular follow-up reviews, tailored to the needs of older patients, may contribute to their health and wellbeing and maximise their independence.

Collaboration between nurses and GPs in Arm 1

Nurse-led GCAs were performed by registered nurses (community or practices nurses) with no specific specialisation in geriatrics, apart from the training provided in the 1-day seminar. The collaboration modalities between GPs and nurses were not imposed by the study and were left to the discretion of the study actors. GPs were responsible for proposing the inclusion in the study to patients meeting the eligibility criteria. Then, nurses performed the CGA within a month after inclusion. Finally, GPs were expected to establish the PCP based on the results of the nurse-led CGA.

Outcomes and follow-up

Patients were evaluated three times by the GPs: at baseline and after 6 and 12 months. Printed Case Report Forms (CRFs) were to be completed by GPs based on consultation findings and medical records. CRFs were collected and monitored for consistency and missing data by clinical research technicians at M6 and M12 based on available hospital stay reports.

The primary outcome (a composite of all-cause mortality, unplanned hospital admissions, emergency department visits and institutionalisation) was assessed at 12 months. The secondary endpoints included an individual measurement of each component of the composite outcome at 12 months, as well as the change from baseline to 12 months in the number of medications, health-related quality of life (assessed with the validated French-language version of the Duke Health Profile) and functional independence (on the Katz Index of Independence in Activities of Daily Living scale). The Duke Health Profile comprises 17 items that can be combined into six health measures (physical, mental, social, general and perceived health, and self-esteem) and four dysfunction measurements (anxiety, depression, pain, and disability) on 0-to-100 scales (a higher score = better health).

Process indicators were used to measure intervention coverage in the two interventional arms, up to 12 months post-inclusion. They included the number of CGAs performed and PCPs drafted, the number of calls to the helpline, and number of care actions delivered. The feasibility and the GPs’ and nurses’ levels of satisfaction were assessed qualitatively and will be published elsewhere.

Randomisation

Cluster-randomisation was applied at the practice level because of potential organisational changes in participating practices and in order to avoid contamination bias between control and experimental groups. The computerised randomisation used an allocation list prepared by an independent statistician who was not involved in patient enrolment or the final analyses. To limit potential differences between arms, we applied a “best balance” allocation procedure to the GP centres [ 29 , 30 , 31 ] based on the following prespecified characteristics: rural/urban setting, proportion of over-70 s in the past year, the number of GPs, and the presence/absence of a nurse in the practice. All units were enrolled before randomisation, allowing for collecting this information beforehand. In a nutshell, the procedure is based on the calculation of all possible allocations with estimation of a balanced statistic for each one. A subset of all allocations with the highest level of balance (i.e., 1% lowest measures of imbalance) is then identified, from which the final allocation is randomly selected. Because of the extremely large total number of possible allocations—more than 1*10^17— too computationally intensive to allow direct calculations, randomisation was performed in three blocks, with block sizes of 14, 13 and 13 units, respectively. Allocation involved 5 v 4 v 4 units for the two arms with 13-unit blocks and 5 v 5 v 4 units for the arm with the 14-unit block. Because of the nature of the intervention, the trial is an open-label study, but primary and secondary outcomes were analysed with blinding of the trial statistician masked to arm allocation.

Sample size

Based on French national health insurance databases, the primary endpoint was expected to be 35% in the control group (standard care). The greatest intervention effect was expected in arm 1 (a systematic nurse-led CGA). With a two-sided type 1 error of 5%, a maximal intraclass correlation coefficient of 0.01, and 5% loss-to-follow-up rate, we calculated that a total of 750 patients (250 per group) in 40 clusters was required to achieve a power of 80% for detecting an absolute difference of − 15% (i.e. 35% [control] vs 20% [intervention]) between the interventional arms and the control group.

Statistical analysis

Standard descriptive statistics were used to evaluate baseline characteristics and process indicators. Quantitative data were expressed as the mean (standard deviation) or the median (interquartile range (IQR)), and categorical data were expressed as the frequency (percentage). The groups’ baseline variables and care actions at 12 months were compared in a Kruskal–Wallis test (for continuous variables) and Pearson’s chi-squared test or Fisher exact test (for categorical variables).

We evaluated the primary and secondary outcomes in the intention-to-treat (ITT) population. The analyses featured mixed-effects logistic regression models for categorical variables and mixed-effects linear regression models for quantitative variables, using the GP’s practice as a random effect. Pairwise comparisons between the interventional groups and the control group (i.e. arm 1 vs arm 3, arm 2 vs arm 3) were tested. Data that were missing at 12 months in the ITT population were imputed using the missForest nonparametric machine learning imputation method [ 32 ], assuming data to be missing at random conditional on other predictors and on the outcome. A comparison of patients with complete information on the primary endpoint to those with incomplete information is shown in Additionalfile 4: Table S1, finding no significant differences in patients’ main characteristics.

To account for potential inter-arm imbalances in important prognostic factors after randomisation, we performed multivariable analyses and adjusted for potential confounders (sex, age, depression and loss of functional independence). To verify the robustness of the results, additional supporting analyses without missing data imputation were conducted on the complete-case population.

All tests were two-sided and the threshold for statistical significance was set to P < 0.05. Odds ratios were calculated and their 95% confidence intervals (CIs) were obtained with the Wald estimation. All analyses were performed with STATA software v14.2 (StataCorp, TX, USA) and R software v4.03 (R Foundation, Austria).

Study population

Forty general practices (comprising 90 GPs) in three French regions were initially enrolled and 39 (comprising 89 physicians) were randomised (Table 1 ). Most practices were in urban areas (74.4%). A total of 634 patients were recruited. Complete data on the primary outcome at 12 months were available for 586 (92%) patients ( Fig. 1 ).

Study flow chart

The median [IQR] age of the included patients was 82 [77.1–86.5] (Table 2 ). At baseline, the three arms did not differ significantly with regard to marital status, living arrangements, comorbidity, polypharmacy, emergency department visits and hospital admissions in the previous 3 months, and quality of life (Table 2 ). However, there were significant inter-arm differences at baseline in age, sex ratio, depression and functional independence. The patients in arm 3 (standard care) were notably younger and less likely to lose functional independence. The patients in arm 1 (systematic nurse-led CGA) were more likely to be depressed, with a higher proportion of women.

Primary outcome

For the primary composite endpoint main analysis led in the ITT population after missing data imputation and adjusting for baseline imbalances (Table 3 , right lower section), no statistically significant difference was found between arm 1 and the control (adjusted odds ratio [aOR] 0.81 [0.54–1.21], P = 0.31), whereas arm 2 and the control differed significantly (aOR 0.60 [95%CI 0.39–0.93); P = 0.022). Analyses of the components of the primary endpoint revealed a statistically lower risk of unplanned hospital admission in arm 2 vs control (aOR 0.57 (0.36–0.92); P = 0.020)), while no statistically significant differences were found for the other components (all-cause mortality, emergency department visits, institutionalisation) and between arm 1 and the control. Unadjusted analyses (Table 3 , left sections) and/or led on complete cases without missing information (Table 3 , upper section) found no statistically significant differences between the two interventional arms and the control arm with regard to the primary composite endpoint and its components.

Secondary outcomes and process indicators

There were no statistical differences between the two interventional arms and the control arm regarding changes from baseline in the number of medications, functional independence and health-related quality of life (Additional file 5: Table S1). Sensitivity analyses on the final 12-month follow-up value adjusting for baseline information were performed and yielded largely similar results (Additional file6: Table S1).

At 12 months, 211 CGAs (93.4%) had been performed by nurses (arm 1) and 158 (85.0%) had been performed by GPs (arm 2) (Additional file 7: Table S1). There were no CGAs in the control arm 3. The median (IQR) duration of a CGA was significantly ( P < 0.001) longer in arm 1 (50 min (45–60) than in arm 2 (40 min (30–46)). The GPs in arm 2 were more likely to divide the CGA into two sessions ( P < 0.001).

There were 192 (85.3%) PCPs in arm 1 and 141 (78.8%) in arm 2 (Additional file 7: Table S1). The median time spent drawing up a PCP was longer in arm 1 (15 min (10–20)) than in arm 2 (10 min (10–20); P = 0.007). In arm 1, the PCPs were provided mainly by nurses (52.7%) but also by GPs (23.4%), a GP-nurse collaboration (11.2%) and by teams of other healthcare professionals (12.2%).

None of the GPs in arms 1 and 2 called the helpline during the study period.

Fewer care actions at 12 months were delivered in arm 2 ( P < 0.001) (Table 4 ). Laboratory tests ( P < 0.001) and medical imaging ( P < 0.001) were prescribed more frequently in the control group. Nutritional care was prescribed more frequently ( P < 0.001) in arm 2.

Our results suggest that GP-led CGAs of community-dwelling older patients with chronic conditions treated in primary care may reduce unplanned hospital admissions. We did not observe significant associations between GP-led CGAs and the other secondary endpoints: death, emergency department visits, institutionalisation, quality of life, functional independence and polypharmacy. There were no significant differences in any of the endpoints between arm 1 (nurse-led CGAs) and arm 3 (control).

Most of our results are consistent with the literature data. A recent Cochrane review [ 18 ] suggested that a CGA of community-dwelling older patients in primary care might not have an impact on death or institutionalisation, as we also found in our analysis of secondary outcomes. There is low-certainty evidence of an association between CGAs and fewer unplanned hospital admissions [ 18 , 33 ]; we also observed this association but only for GP-led CGAs. Our study suggests that a CGA adapted for use in primary care does not reduce the risk of emergency department visits and does not influence the patient’s functional. These findings are in line with those of the Cochrane review. Of the nine studies reviewed, only one [ 34 ] used the same scale as we did (the Katz Index) to evaluate the change in functional status, and it also found no significant differences. Our results suggest that a primary care CGA may have no impact on health-related quality of life; this finding differs from that of the Cochrane review, where CGA was associated with small changes in quality of life in the few included studies. However, the level of evidence was estimated to be very low and the impact was ultimately considered to be uncertain by the investigators. Furthermore, none of the six reviewed studies used the scale that we did; this might well explain the disparity. It is noteworthy that we used a generic, health-related quality of life instrument (i.e. the Duke Health Profile) in our CGA. Since some domains specific for older patients are not addressed by this instrument, some subtle changes in quality of life might have been overlooked. To the best of our knowledge, our study is the first to have looked at the impact of CGA on polypharmacy and care actions delivered in primary care settings, and so a comparison with the literature data is not possible.

The results in arm 2 suggest that GP-led CGA was associated with fewer unplanned hospital admissions at 12 months. Care actions were less frequent in arm 2, although nutritional support was more frequent. However, patients in arm 2 were similar to those in arm 1 in terms of malnutrition (data not shown). We suspect that the care actions delivered in arm 2 were probably better targeted, with fewer prescriptions overall but with more appropriate prescriptions in some domains (e.g. nutrition). We also suspect that nutritional assessments might often be neglected during a consultation with a GP, compared with higher-priority concerns in other domains; hence, the CGA might have made GPs more aware of this topic. Improving the prevention or management of malnutrition by GPs via a CGA might explain the reduction in unplanned hospital admissions in this arm. It has been reported in the literature that unidentified or untreated malnutrition is associated with a higher risk of hospital admission [ 35 , 36 ]. It is also well established that a strengthened follow-up in general practice is associated with fewer hospital admissions for ambulatory patients with chronic health issues [ 37 , 38 ].

We did not find any significant benefits in Arm 1 where CGA was systematically performed by a trained nurse. Yet, when the CEpiA study was designed, arm 1 was expected to have the greatest intervention effect because all the assigned patients would have a CGA. The lack of an effect in arm 1 might be due to the delegation of tasks to nurses by GPs, rather than a formalised and effective collaboration, notably involving common objectives and shared decisions-making. Approximately 53% of the PCPs in arm 1 were provided solely by nurses, revealing issues in the collaboration process with GPs. This raises questions about the GPs’ involvement in the establishment of PCPs and their participation in implementing the interventions in arm 1, when CGA was not guided by GPs but systematically performed by nurses. This is highlighted by the increased number of interventions conducted in arm 2, as shown in Table 4 . The first potential explanation for the collaboration issues is that the 1-day training seminar focused on the CGA tool but not on teamwork; it did not explore how to work together. Secondly, the nurses and GPs in the two-person teams did not necessarily know each other before the study. Thirdly, some nurses did not work in the same practice as their partner GP; organisational issues might have occurred in some teams. Fourthly, 53% of the PCPs in arm 1 were developed by a nurse without supervision by a GP; some of these prescriptions might not have been relevant. Lastly, the CGAs and PCPs in arm 1 were often conducted or drafted a long time after study inclusion (median time interval: 21 days and 46 days, respectively). A complementary analysis using qualitative methodology is currently underway, and its results will be presented in a separate dedicated article specifically studying the collaboration modalities between GPs and nurses, and their issues. Even though the associations in arm 1 were not statistically significant, there were nevertheless trends towards fewer emergency department visits ( P = 0.105) and institutionalisations ( P = 0.109). Our results are mostly consistent with literature data. Few large RCTs found benefits of a nurse-led CGA (geriatric nurses or intensively trained practice nurses) among the community-dwelling older patients with multiple comorbidities on their daily functioning [ 39 ] and mental well-being [ 24 ]. However, and similarly to our study, some other RCTs found no benefits of a CGA performed by an interdisciplinary collaborative team (involving advanced practice nurses or trained practice nurses) among primary care older adults on health-related quality of life and physical function outcomes [ 40 , 41 ].

Strengths and limitations

One of the study’s main strengths was the large number of patients in primary care (over 600) and the provision of high-quality data. Few published studies have examined the effect of a CGA on emergency department visits or have used using standardised scales to gauge a change in functional independence or quality of life [ 18 ]. Our data on secondary outcomes were collected with validated instruments, such as the Katz Index for functional independence and the Duke Health Profile for health-related quality of life. The trial was designed rigorously to minimise bias [ 10 ] and incorporated a number of sensitivity analyses. Lastly, the trial’s pragmatic design and relatively broad inclusion criteria provided results of relevance to routine clinical practice.

Our study had several limitations. Firstly, given that the GPs knew to which arm they (or their centre) had been assigned, they might have tended to choose the most fragile or seriously ill patients for inclusion in the interventional arms. To limit this risk and facilitate enrollment, investigators were invited to establish a pre-screening list of patients potentially eligible prior to the study inception, and all analyses were further adjusted for baseline imbalances between randomised groups. Secondly, the arms differed significantly with regard to some covariates (despite the cluster randomisation), and some data of the primary and secondary endpoints were missing. However, the proportion of missing data was low (7.6% on the primary endpoint), and all analyses were performed after adjusting for covariate imbalances and imputing missing data in the ITT population. It should also be noticed that difficulties in interpretation may arise from the use of the proposed composite criteria, as its individual components do not have equal clinical weight (dying being obviously worse than an emergency department visit). The choice of this criterion was based on the clinical relevance of its components to reflect frailty outcomes and to assess the efficacy of the CGA-based intervention on pejorative geriatric events, but recently developed alternative methodological approaches such as win ratios based on hierarchical endpoints could have been valuable to deal with this issue. Lastly, our calculation of the sample size was based on the hypothesis that the highest intervention effect would be observed in arm 1, with a prespecified fixed sequence of pairwise comparisons between groups starting with 1 v 3, followed by 1 v 2 and 2 v 3, where each comparison is made if the previous one in the sequence was statistically significant to keep an overall global alpha risk at the 5% level. The highest effect was actually observed in arm 2. As a result, this fixed sequence was not applied and our results should not be interpreted as confirmatory and should be confirmed in further research.

Implications for research and practice

Our results indicate that GPs can integrate the use of CGA adapted to primary care into their daily routines, as evidenced by 83.2% of the patients in arm 2 receiving a GP-led CGA, albeit on a case-by-case basis. Furthermore, the absence of calls to the geriatric helpline in both arms suggests a good appropriation of the CGA tool for their daily practice, with no perceived need for help or supervision from the study’s dedicated geriatricians. Our findings suggest that GP-led CGA may reduce the number of unplanned hospitalisations among this population. We hypothesise that the underlying mechanisms explaining this are likely GPs being less involved in the establishment of PCPs in arm 1 with nurse-led CGA, which results in lower implementation of the interventions. Our results also suggest that GP-led CGA may improve nutritional care among this population, with probably better targeted prescriptions. The GPs likely had a more holistic view of the patient’s situation and were able to prioritise their actions more pragmatically (implementing more targeted interventions). About clinical implications, these results suggest that the person conducting the CGA and the one establishing the PCP should be the same. They also represent a significant contribution to primary care research in context of imminent demographic shifts and an anticipated surge in hospitalisations. The study conditions were very similar to current GPs’ practices, suggesting that these results could be generalised to the broader population of community-dwelling older patients with chronic conditions, although further evaluation is warranted.

Further studies are needed to examine the perceived utility of CGA-based interventions in primary care. It will notably be important to (i) identify barriers to and facilitators of CGAs in routine practice in primary care and (ii) understand the lack of effectiveness of nurse-led CGAs.

Our study led in community-dwelling older patients with chronic conditions found no significant effect of a CGA adapted for use in primary care on mortality, functional independence and quality of life, but suggests that a GP-led CGA may reduce the risk of unplanned hospital admission. It also suggests that a GP-led CGA may improve nutritional care with better-targeted actions. GPs could integrate the use of CGA adapted to primary care into their daily routines for their registered older patients with chronic conditions. Our study demonstrates the feasibility of incorporating CGA into clinical practice and highlights its potential benefits when applied on a case-by-case basis, guided by the GPs who develop the resulting PCP. Further research using qualitative methods is needed to better understand the CGA’s perceived utility in routine practice and the lack of effectiveness observed for nurse-led CGAs.

Availability of data and materials

The data that support the findings of this study are not openly available but are available from the corresponding author upon reasonable request.

Abbreviations

Adjusted odds ratio

Clinical Epidemiology and Ageing

Comprehensive geriatric assessment

Confidence interval

Case Report Forms

General practitioner

Health-related quality of life

Interquartile range

Intention-to-treat

Personalised care plan

Randomised controlled trial

Standard deviation

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Acknowledgements

The authors want to thank all the professionals and patients who participated and made this study possible. The authors are indebted to David Fraser (Biotech Communication, France) for his language review of the manuscript. They warmly thank Julie Fabre for her useful discussions.

The CEpiA trial was supported by public funding from the French Ministry of Health (Programme de Recherche sur la Performance du Système des soins, PREPS14370_K140707).

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Veronique Orcel, Leon Banh, Vincent Renard & Emilie Ferrat

IMRB (CEpiA Group), Univ Paris Est Creteil, INSERM U955, Creteil, 94010, France

Veronique Orcel, Sylvie Bastuji-Garin, Vincent Renard, Emmanuelle Boutin, Philippe Caillet, Elena Paillaud, Etienne Audureau & Emilie Ferrat

Department of Geriatrics, Paris Cancer Institute CARPEM, Georges-Pompidou European Hospital, AP-HP, Paris, 75015, France

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Contributions

VO did the literature search, conducted statistical analyses, designed the figures, interpretated data, and wrote the first draft of the manuscript. LB aided in interpreting the results, critically revised the manuscript. SBG, VR, PC, and EP designed the study and critically revised the manuscript. EB conducted statistical analyses and critically revised the manuscript. AG helped collect the data. EA and EF supervised the project, designed the study and the analyses, contributed analysis tools, aided in performing the analyses, and critically revised the manuscript (directed). All authors read and approved the final manuscript.

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Correspondence to Emilie Ferrat .

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The study was approved by an independent ethics committee ( CPP Ile-de-France IV , Paris, France; reference: 2015/48SC) in October 2015. The study was conducted in accordance with the Declaration of Helsinki. All followed procedures were in accordance with relevant guidelines and French regulations. The study database was registered with the French National Data Protection Commission ( Commission nationale de l'informatique et des libertés , Paris, France). The protocol was in conformity with the French legislation applied to usual care. All the study participants provided their verbal, informed consent. Patients giving their verbal consent had previously received oral and written information about the trial by the GP investigator. The date the patients gave their verbal consent was reported in the Case Report Form (CRF).

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Additional file 1. figure s1. intervention components., additional file 2. table s1. comprehensive geriatric assessment tool adapted to primary care., additional file 3. table s1. personalised care plan template., 12916_2024_3613_moesm4_esm.docx.

Additional file 4. Table S1. Comparison of patients with or without complete information on the primary composite endpoint ( N =634).

12916_2024_3613_MOESM5_ESM.docx

Additional file 5. Table S1. Results from secondary outcome analyses in patients alive at 12 months: changes from baseline to 12-month follow-up ( N =598).

12916_2024_3613_MOESM6_ESM.docx

Additional file 6. Table S1. Results from secondary outcome analyses in patients alive at 12 months: 12-month follow-up values ( N =598).

Additional file 7. Table S1. Process indicators.

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Orcel, V., Banh, L., Bastuji-Garin, S. et al. Effectiveness of comprehensive geriatric assessment adapted to primary care when provided by a nurse or a general practitioner: the CEpiA cluster-randomised trial. BMC Med 22 , 414 (2024). https://doi.org/10.1186/s12916-024-03613-7

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Observational Studies: Cohort and Case-Control Studies

Jae w. song.

1 Research Fellow, Section of Plastic Surgery, Department of Surgery The University of Michigan Health System; Ann Arbor, MI

Kevin C. Chung

2 Professor of Surgery, Section of Plastic Surgery, Department of Surgery The University of Michigan Health System; Ann Arbor, MI

Observational studies are an important category of study designs. To address some investigative questions in plastic surgery, randomized controlled trials are not always indicated or ethical to conduct. Instead, observational studies may be the next best method to address these types of questions. Well-designed observational studies have been shown to provide results similar to randomized controlled trials, challenging the belief that observational studies are second-rate. Cohort studies and case-control studies are two primary types of observational studies that aid in evaluating associations between diseases and exposures. In this review article, we describe these study designs, methodological issues, and provide examples from the plastic surgery literature.

Because of the innovative nature of the specialty, plastic surgeons are frequently confronted with a spectrum of clinical questions by patients who inquire about “best practices.” It is thus essential that plastic surgeons know how to critically appraise the literature to understand and practice evidence-based medicine (EBM) and also contribute to the effort by carrying out high-quality investigations. 1 Well-designed randomized controlled trials (RCTs) have held the pre-eminent position in the hierarchy of EBM as level I evidence ( Table 1 ). However, RCT methodology, which was first developed for drug trials, can be difficult to conduct for surgical investigations. 3 Instead, well-designed observational studies, recognized as level II or III evidence, can play an important role in deriving evidence for plastic surgery. Results from observational studies are often criticized for being vulnerable to influences by unpredictable confounding factors. However, recent work has challenged this notion, showing comparable results between observational studies and RCTs. 4 , 5 Observational studies can also complement RCTs in hypothesis generation, establishing questions for future RCTs, and defining clinical conditions.

Levels of Evidence Based Medicine

Level of Evidence	Qualifying Studies
I	High-quality, multicenter or single-center, randomized controlled trial with adequate power; or systematic review of these studies
II	Lesser quality, randomized controlled trial; prospective cohort study; or systematic review of these studies
III	Retrospective comparative study; case-control study; or systematic review of these studies
IV	Case-series
V	Expert opinion; case report or clinical example; or evidence based on physiology, bench research, or “first principles”

From REF 1 .

Observational studies fall under the category of analytic study designs and are further sub-classified as observational or experimental study designs ( Figure 1 ). The goal of analytic studies is to identify and evaluate causes or risk factors of diseases or health-related events. The differentiating characteristic between observational and experimental study designs is that in the latter, the presence or absence of undergoing an intervention defines the groups. By contrast, in an observational study, the investigator does not intervene and rather simply “observes” and assesses the strength of the relationship between an exposure and disease variable. 6 Three types of observational studies include cohort studies, case-control studies, and cross-sectional studies ( Figure 1 ). Case-control and cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a temporal dimension (i.e. prospective or retrospective study design). Cross-sectional studies, also known as prevalence studies, examine the data on disease and exposure at one particular time point ( Figure 2 ). 6 Because the temporal relationship between disease occurrence and exposure cannot be established, cross-sectional studies cannot assess the cause and effect relationship. In this review, we will primarily discuss cohort and case-control study designs and related methodologic issues.

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Analytic Study Designs. Adapted with permission from Joseph Eisenberg, Ph.D.

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Temporal Design of Observational Studies: Cross-sectional studies are known as prevalence studies and do not have an inherent temporal dimension. These studies evaluate subjects at one point in time, the present time. By contrast, cohort studies can be either retrospective (latin derived prefix, “retro” meaning “back, behind”) or prospective (greek derived prefix, “pro” meaning “before, in front of”). Retrospective studies “look back” in time contrasting with prospective studies, which “look ahead” to examine causal associations. Case-control study designs are also retrospective and assess the history of the subject for the presence or absence of an exposure.

COHORT STUDY

The term “cohort” is derived from the Latin word cohors . Roman legions were composed of ten cohorts. During battle each cohort, or military unit, consisting of a specific number of warriors and commanding centurions, were traceable. The word “cohort” has been adopted into epidemiology to define a set of people followed over a period of time. W.H. Frost, an epidemiologist from the early 1900s, was the first to use the word “cohort” in his 1935 publication assessing age-specific mortality rates and tuberculosis. 7 The modern epidemiological definition of the word now means a “group of people with defined characteristics who are followed up to determine incidence of, or mortality from, some specific disease, all causes of death, or some other outcome.” 7

Study Design

A well-designed cohort study can provide powerful results. In a cohort study, an outcome or disease-free study population is first identified by the exposure or event of interest and followed in time until the disease or outcome of interest occurs ( Figure 3A ). Because exposure is identified before the outcome, cohort studies have a temporal framework to assess causality and thus have the potential to provide the strongest scientific evidence. 8 Advantages and disadvantages of a cohort study are listed in Table 2 . 2 , 9 Cohort studies are particularly advantageous for examining rare exposures because subjects are selected by their exposure status. Additionally, the investigator can examine multiple outcomes simultaneously. Disadvantages include the need for a large sample size and the potentially long follow-up duration of the study design resulting in a costly endeavor.

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Cohort and Case-Control Study Designs

Advantages and Disadvantages of the Cohort Study

Gather data regarding sequence of events; can assess causality

Examine multiple outcomes for a given exposure

Good for investigating rare exposures

Can calculate rates of disease in exposed and unexposed individuals over time (e.g. incidence, relative risk)

Large numbers of subjects are required to study rare exposures

Susceptible to selection bias

May be expensive to conduct

May require long durations for follow-up

Maintaining follow-up may be difficult

Susceptible to loss to follow-up or withdrawals

Susceptible to recall bias or information bias

Less control over variables

Cohort studies can be prospective or retrospective ( Figure 2 ). Prospective studies are carried out from the present time into the future. Because prospective studies are designed with specific data collection methods, it has the advantage of being tailored to collect specific exposure data and may be more complete. The disadvantage of a prospective cohort study may be the long follow-up period while waiting for events or diseases to occur. Thus, this study design is inefficient for investigating diseases with long latency periods and is vulnerable to a high loss to follow-up rate. Although prospective cohort studies are invaluable as exemplified by the landmark Framingham Heart Study, started in 1948 and still ongoing, 10 in the plastic surgery literature this study design is generally seen to be inefficient and impractical. Instead, retrospective cohort studies are better indicated given the timeliness and inexpensive nature of the study design.

Retrospective cohort studies, also known as historical cohort studies, are carried out at the present time and look to the past to examine medical events or outcomes. In other words, a cohort of subjects selected based on exposure status is chosen at the present time, and outcome data (i.e. disease status, event status), which was measured in the past, are reconstructed for analysis. The primary disadvantage of this study design is the limited control the investigator has over data collection. The existing data may be incomplete, inaccurate, or inconsistently measured between subjects. 2 However, because of the immediate availability of the data, this study design is comparatively less costly and shorter than prospective cohort studies. For example, Spear and colleagues examined the effect of obesity and complication rates after undergoing the pedicled TRAM flap reconstruction by retrospectively reviewing 224 pedicled TRAM flaps in 200 patients over a 10-year period. 11 In this example, subjects who underwent the pedicled TRAM flap reconstruction were selected and categorized into cohorts by their exposure status: normal/underweight, overweight, or obese. The outcomes of interest were various flap and donor site complications. The findings revealed that obese patients had a significantly higher incidence of donor site complications, multiple flap complications, and partial flap necrosis than normal or overweight patients. An advantage of the retrospective study design analysis is the immediate access to the data. A disadvantage is the limited control over the data collection because data was gathered retrospectively over 10-years; for example, a limitation reported by the authors is that mastectomy flap necrosis was not uniformly recorded for all subjects. 11

An important distinction lies between cohort studies and case-series. The distinguishing feature between these two types of studies is the presence of a control, or unexposed, group. Contrasting with epidemiological cohort studies, case-series are descriptive studies following one small group of subjects. In essence, they are extensions of case reports. Usually the cases are obtained from the authors' experiences, generally involve a small number of patients, and more importantly, lack a control group. 12 There is often confusion in designating studies as “cohort studies” when only one group of subjects is examined. Yet, unless a second comparative group serving as a control is present, these studies are defined as case-series. The next step in strengthening an observation from a case-series is selecting appropriate control groups to conduct a cohort or case-control study, the latter which is discussed in the following section about case-control studies. 9

Methodological Issues

Selection of subjects in cohort studies.

The hallmark of a cohort study is defining the selected group of subjects by exposure status at the start of the investigation. A critical characteristic of subject selection is to have both the exposed and unexposed groups be selected from the same source population ( Figure 4 ). 9 Subjects who are not at risk for developing the outcome should be excluded from the study. The source population is determined by practical considerations, such as sampling. Subjects may be effectively sampled from the hospital, be members of a community, or from a doctor's individual practice. A subset of these subjects will be eligible for the study.

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Levels of Subject Selection. Adapted from Ref 9 .

Attrition Bias (Loss to follow-up)

Because prospective cohort studies may require long follow-up periods, it is important to minimize loss to follow-up. Loss to follow-up is a situation in which the investigator loses contact with the subject, resulting in missing data. If too many subjects are loss to follow-up, the internal validity of the study is reduced. A general rule of thumb requires that the loss to follow-up rate not exceed 20% of the sample. 6 Any systematic differences related to the outcome or exposure of risk factors between those who drop out and those who stay in the study must be examined, if possible, by comparing individuals who remain in the study and those who were loss to follow-up or dropped out. It is therefore important to select subjects who can be followed for the entire duration of the cohort study. Methods to minimize loss to follow-up are listed in Table 3 .

Methods to Minimize Loss to Follow-Up

Exclude subjects likely to be lost

Planning to move

Non-committal

Obtain information to allow future tracking

Collect subject's contact information (e.g. mailing addresses, telephone numbers, and email addresses)

Collect social security and/or Medicare numbers

Maintain periodic contact

By telephone: may require calls during the weekends and/or evenings

By mail: repeated mailings by e-mail or with stamped, self-addressed return envelopes

Other: newsletters or token gifts with study logo

Adapted from REF 2 .

CASE-CONTROL STUDIES

Case-control studies were historically borne out of interest in disease etiology. The conceptual basis of the case-control study is similar to taking a history and physical; the diseased patient is questioned and examined, and elements from this history taking are knitted together to reveal characteristics or factors that predisposed the patient to the disease. In fact, the practice of interviewing patients about behaviors and conditions preceding illness dates back to the Hippocratic writings of the 4 th century B.C. 7

Reasons of practicality and feasibility inherent in the study design typically dictate whether a cohort study or case-control study is appropriate. This study design was first recognized in Janet Lane-Claypon's study of breast cancer in 1926, revealing the finding that low fertility rate raises the risk of breast cancer. 13 , 14 In the ensuing decades, case-control study methodology crystallized with the landmark publication linking smoking and lung cancer in the 1950s. 15 Since that time, retrospective case-control studies have become more prominent in the biomedical literature with more rigorous methodological advances in design, execution, and analysis.

Case-control studies identify subjects by outcome status at the outset of the investigation. Outcomes of interest may be whether the subject has undergone a specific type of surgery, experienced a complication, or is diagnosed with a disease ( Figure 3B ). Once outcome status is identified and subjects are categorized as cases, controls (subjects without the outcome but from the same source population) are selected. Data about exposure to a risk factor or several risk factors are then collected retrospectively, typically by interview, abstraction from records, or survey. Case-control studies are well suited to investigate rare outcomes or outcomes with a long latency period because subjects are selected from the outset by their outcome status. Thus in comparison to cohort studies, case-control studies are quick, relatively inexpensive to implement, require comparatively fewer subjects, and allow for multiple exposures or risk factors to be assessed for one outcome ( Table 4 ). 2 , 9

Advantages and Disadvantages of the Case-Control Study

Good for examining rare outcomes or outcomes with long latency

Relatively quick to conduct

Relatively inexpensive

Requires comparatively few subjects

Existing records can be used

Multiple exposures or risk factors can be examined

Susceptible to recall bias or information bias

Difficult to validate information

Control of extraneous variables may be incomplete

Selection of an appropriate comparison group may be difficult

Rates of disease in exposed and unexposed individuals cannot be determined

An example of a case-control investigation is by Zhang and colleagues who examined the association of environmental and genetic factors associated with rare congenital microtia, 16 which has an estimated prevalence of 0.83 to 17.4 in 10,000. 17 They selected 121 congenital microtia cases based on clinical phenotype, and 152 unaffected controls, matched by age and sex in the same hospital and same period. Controls were of Hans Chinese origin from Jiangsu, China, the same area from where the cases were selected. This allowed both the controls and cases to have the same genetic background, important to note given the investigated association between genetic factors and congenital microtia. To examine environmental factors, a questionnaire was administered to the mothers of both cases and controls. The authors concluded that adverse maternal health was among the main risk factors for congenital microtia, specifically maternal disease during pregnancy (OR 5.89, 95% CI 2.36-14.72), maternal toxicity exposure during pregnancy (OR 4.76, 95% CI 1.66-13.68), and resident area, such as living near industries associated with air pollution (OR 7.00, 95% CI 2.09-23.47). 16 A case-control study design is most efficient for this investigation, given the rarity of the disease outcome. Because congenital microtia is thought to have multifactorial causes, an additional advantage of the case-control study design in this example is the ability to examine multiple exposures and risk factors.

Selection of Cases

Sampling in a case-control study design begins with selecting the cases. In a case-control study, it is imperative that the investigator has explicitly defined inclusion and exclusion criteria prior to the selection of cases. For example, if the outcome is having a disease, specific diagnostic criteria, disease subtype, stage of disease, or degree of severity should be defined. Such criteria ensure that all the cases are homogenous. Second, cases may be selected from a variety of sources, including hospital patients, clinic patients, or community subjects. Many communities maintain registries of patients with certain diseases and can serve as a valuable source of cases. However, despite the methodologic convenience of this method, validity issues may arise. For example, if cases are selected from one hospital, identified risk factors may be unique to that single hospital. This methodological choice may weaken the generalizability of the study findings. Another example is choosing cases from the hospital versus the community; most likely cases from the hospital sample will represent a more severe form of the disease than those in the community. 2 Finally, it is also important to select cases that are representative of cases in the target population to strengthen the study's external validity ( Figure 4 ). Potential reasons why cases from the original target population eventually filter through and are available as cases (study participants) for a case-control study are illustrated in Figure 5 .

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Levels of Case Selection. Adapted from Ref 2 .

Selection of Controls

Selecting the appropriate group of controls can be one of the most demanding aspects of a case-control study. An important principle is that the distribution of exposure should be the same among cases and controls; in other words, both cases and controls should stem from the same source population. The investigator may also consider the control group to be an at-risk population, with the potential to develop the outcome. Because the validity of the study depends upon the comparability of these two groups, cases and controls should otherwise meet the same inclusion criteria in the study.

A case-control study design that exemplifies this methodological feature is by Chung and colleagues, who examined maternal cigarette smoking during pregnancy and the risk of newborns developing cleft lip/palate. 18 A salient feature of this study is the use of the 1996 U.S. Natality database, a population database, from which both cases and controls were selected. This database provides a large sample size to assess newborn development of cleft lip/palate (outcome), which has a reported incidence of 1 in 1000 live births, 19 and also enabled the investigators to choose controls (i.e., healthy newborns) that were generalizable to the general population to strengthen the study's external validity. A significant relationship with maternal cigarette smoking and cleft lip/palate in the newborn was reported in this study (adjusted OR 1.34, 95% CI 1.36-1.76). 18

Matching is a method used in an attempt to ensure comparability between cases and controls and reduces variability and systematic differences due to background variables that are not of interest to the investigator. 8 Each case is typically individually paired with a control subject with respect to the background variables. The exposure to the risk factor of interest is then compared between the cases and the controls. This matching strategy is called individual matching. Age, sex, and race are often used to match cases and controls because they are typically strong confounders of disease. 20 Confounders are variables associated with the risk factor and may potentially be a cause of the outcome. 8 Table 5 lists several advantages and disadvantages with a matching design.

Advantages and Disadvantages for Using a Matching Strategy

Advantages	Disadvantages
Eliminate influence of measurable confounders (e.g. age, sex)	May be time-consuming and expensive
Eliminate influence of confounders that are difficult to measure	Decision to match and confounding variables to match upon are decided at the outset of the study
May be a sampling convenience, making it easier to select the controls in a case-control study	Matched variables cannot be examined in the study
May improve study efficiency (i.e. smaller sample size)	Requires a matched analysis
	Vulnerable to overmatching: when matching variable has some relationship with the outcome

Multiple Controls

Investigations examining rare outcomes may have a limited number of cases to select from, whereas the source population from which controls can be selected is much larger. In such scenarios, the study may be able to provide more information if multiple controls per case are selected. This method increases the “statistical power” of the investigation by increasing the sample size. The precision of the findings may improve by having up to about three or four controls per case. 21 - 23

Bias in Case-Control Studies

Evaluating exposure status can be the Achilles heel of case-control studies. Because information about exposure is typically collected by self-report, interview, or from recorded information, it is susceptible to recall bias, interviewer bias, or will rely on the completeness or accuracy of recorded information, respectively. These biases decrease the internal validity of the investigation and should be carefully addressed and reduced in the study design. Recall bias occurs when a differential response between cases and controls occurs. The common scenario is when a subject with disease (case) will unconsciously recall and report an exposure with better clarity due to the disease experience. Interviewer bias occurs when the interviewer asks leading questions or has an inconsistent interview approach between cases and controls. A good study design will implement a standardized interview in a non-judgemental atmosphere with well-trained interviewers to reduce interviewer bias. 9

The STROBE Statement: The Strengthening the Reporting of Observational Studies in Epidemiology Statement

In 2004, the first meeting of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) group took place in Bristol, UK. 24 The aim of the group was to establish guidelines on reporting observational research to improve the transparency of the methods, thereby facilitating the critical appraisal of a study's findings. A well-designed but poorly reported study is disadvantaged in contributing to the literature because the results and generalizability of the findings may be difficult to assess. Thus a 22-item checklist was generated to enhance the reporting of observational studies across disciplines. 25 , 26 This checklist is also located at the following website: www.strobe-statement.org . This statement is applicable to cohort studies, case-control studies, and cross-sectional studies. In fact, 18 of the checklist items are common to all three types of observational studies, and 4 items are specific to each of the 3 specific study designs. In an effort to provide specific guidance to go along with this checklist, an “explanation and elaboration” article was published for users to better appreciate each item on the checklist. 27 Plastic surgery investigators should peruse this checklist prior to designing their study and when they are writing up the report for publication. In fact, some journals now require authors to follow the STROBE Statement. A list of participating journals can be found on this website: http://www.strobe-statement.org./index.php?id=strobe-endorsement .

Due to the limitations in carrying out RCTs in surgical investigations, observational studies are becoming more popular to investigate the relationship between exposures, such as risk factors or surgical interventions, and outcomes, such as disease states or complications. Recognizing that well-designed observational studies can provide valid results is important among the plastic surgery community, so that investigators can both critically appraise and appropriately design observational studies to address important clinical research questions. The investigator planning an observational study can certainly use the STROBE statement as a tool to outline key features of a study as well as coming back to it again at the end to enhance transparency in methodology reporting.

Acknowledgments

Supported in part by a Midcareer Investigator Award in Patient-Oriented Research (K24 AR053120) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (to Dr. Kevin C. Chung).

None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this manuscript.

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Cross sectional studies. A cross sectional study measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. Such information can be used to explore aetiology - for example, the relation between cataract and vitamin status has been examined in cross sectional surveys.
An Introduction to the Fundamentals of Cohort and Case-Control Studies
Design. In a case-control study, a number of cases and noncases (controls) are identified, and the occurrence of one or more prior exposures is compared between groups to evaluate drug-outcome associations (Figure 1). A case-control study runs in reverse relative to a cohort study. 21 As such, study inception occurs when a patient ...
Designing and Conducting Analytic Studies in the Field
Most field case-control studies use control-to-case-patient ratios of 1:1, 2:1, or 3:1. Enrolling more than one control per case-patient can increase study power, which might be needed to detect a statistically significant difference in exposure between case-patients and controls, particularly when an outbreak involves a limited number of cases.
In brief: What types of studies are there?
A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards. The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.
Effectiveness of comprehensive geriatric assessment adapted to primary
Background The benefits of comprehensive geriatric assessment (CGA) are well established for hospital care but less so for primary care. Our primary objective was to assess the effect of two multifaceted interventions based on a CGA adapted for primary care on a composite criterion combining all-cause mortality, emergency department visits, unplanned hospital admissions, and ...
Observational Studies: Cohort and Case-Control Studies
Cohort studies and case-control studies are two primary types of observational studies that aid in evaluating associations between diseases and exposures. In this review article, we describe these study designs, methodological issues, and provide examples from the plastic surgery literature. Keywords: observational studies, case-control study ...

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What Is a Case-Control Study? | Definition & Examples

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Advantages of case-control studies

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Tegan George

Case Study vs. Experiment

Further Detail

Observational vs. Experimental Study: A Comprehensive Guide

Introduction to Observational and Experimental Studies

Observational Studies: A Closer Look

What is an Observational Study?

Types of Observational Studies

Advantages and Limitations of Observational Studies

Experimental Studies: Delving Deeper

What is an Experimental Study?

Key Features of Experimental Studies

Advantages and Limitations of Experimental Studies

Observational vs Experimental: A Side-by-Side Comparison

Key Differences and Notable Similarities

When to Use Which: Practical Applications

Conclusion: The Synergy of Experimental and Observational Studies in Research

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Case-control and Cohort studies: A brief overview

Introduction

Case-control studies

Cohort studies

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

Saul Crandon

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What Is a Case-Control Study? | Definition & Examples

Table of contents

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Advantages of case-control studies

Disadvantages of case-control studies

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Tegan George

Case Control Study: Definition, Benefits & Examples

What is a Case Control Study?

Benefits of a Case Control Study

Limitations of a Case Control Study

Use Matching to Control Confounders

Statistical Analysis of a Case Control Study

Example Odds Ratio in a Case-Control Study

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Designing and Conducting Analytic Studies in the Field