case study vs cross sectional

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

Describes specific characteristics in a population of interest
The most common forms are case reports and case series
In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes. On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

This design is transverse where we take a specific sample at a specific time without any follow-up
It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
This design is easy to conduct
For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
Prospective : we follow the individuals in the future to know who will develop the disease
Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
This design is the strongest among the observational studies
For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
This design is always retrospective
We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
Relatively easy to conduct
For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

Also known as interventional studies
Can involve animals and humans
Pre-clinical trials involve animals
Clinical trials are experimental studies involving humans
In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I: We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

A visual diagram describing the following. Two types of epidemiological studies are descriptive and analytical. Types of descriptive studies are case reports, case series, descriptive surveys. Types of analytical studies are observational or experimental. Observational studies can be cross-sectional, case-control or cohort studies. Types of experimental studies can be lab trials or field trials.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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

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Cross-Sectional Study: Definition, Designs & Examples

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A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.

This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time.

It can be used to assess the prevalence of outcomes and exposures, determine relationships among variables, and generate hypotheses about causal connections between factors to be explored in experimental designs.

Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population.

In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment.

Cross-sectional studies aim to describe a variable , not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time.

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

For example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity.

While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating.

Cross-sectional studies can be categorized based on the nature of the data collection and the type of data being sought.

Cross-Sectional Study	Purpose	Example
	To describe the characteristics of a population.	Examining the dietary habits of high school students.
	To investigate associations between variables.	Studying the correlation between smoking and lung disease in adults.
	To gather information on a population or a subset.	Conducting a survey on the use of public transportation in a city.
	To determine the proportion of a population with a specific characteristic, condition, or disease.	Assessing the prevalence of obesity in a country.
	To examine the effects of certain occupational or environmental exposures.	Studying the impact of air pollution on respiratory health in industrial workers.
	To generate hypotheses for future research.	Investigating relationships between various lifestyle factors and mental health conditions.

Analytical Studies

In analytical cross-sectional studies, researchers investigate an association between two parameters. They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population.

The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.

Descriptive Studies

Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population.
They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.
This is the most common type of cross-sectional study.
Evaluating the COVID-19 positivity rates among vaccinated and unvaccinated adolescents
Investigating the prevalence of dysfunctional breathing in patients treated for asthma in primary care (Wang & Cheng, 2020)
Analyzing whether individuals in a community have any history of mental illness and whether they have used therapy to help with their mental health
Comparing grades of elementary school students whose parents come from different income levels
Determining the association between gender and HIV status (Setia, 2016)
Investigating suicide rates among individuals who have at least one parent with chronic depression
Assessing the prevalence of HIV and risk behaviors in male sex workers (Shinde et al., 2009)
Examining sleep quality and its demographic and psychological correlates among university students in Ethiopia (Lemma et al., 2012)
Calculating what proportion of people served by a health clinic in a particular year have high cholesterol
Analyzing college students’ distress levels with regard to their year level (Leahy et al., 2010)

Simple and Inexpensive

These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.

Minimal room for error

Because all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.

Multiple variables and outcomes can be researched and compared at once

Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.

The data can be a starting point for future research

The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Limitations

Does not help determine cause and effect.

Cross-sectional studies can be influenced by an antecedent consequent bias which occurs when it cannot be determined whether exposure preceded disease. (Alexander et al.)

Report bias is probable

Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented.

The timing of the snapshot is not always representative

Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.

It cannot be used to analyze behavior over a period of time

Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods.

Cross-Sectional vs. Longitudinal

Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment.

However, cross-sectional studies differ from longitudinal studies in that cross-sectional studies look at a characteristic of a population at a specific point in time, while longitudinal studies involve studying a population over an extended period.

Longitudinal studies require more time and resources and can be less valid as participants might quit the study before the data has been fully collected.

Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects.

Cross-sectional studies can be done much quicker than longitudinal studies and are a good starting point to establish any associations between variables, while longitudinal studies are more timely but are necessary for studying cause and effect.

Alexander, L. K., Lopez, B., Ricchetti-Masterson, K., & Yeatts, K. B. (n.d.). Cross-sectional Studies. Eric Notebook. Retrieved from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf

Cherry, K. (2019, October 10). How Does the Cross-Sectional Research Method Work? Verywell Mind. Retrieved from https://www.verywellmind.com/what-is-a-cross-sectional-study-2794978

Cross-sectional vs. longitudinal studies. Institute for Work & Health. (2015, August). Retrieved from https://www.iwh.on.ca/what-researchers-mean-by/cross-sectional-vs-longitudinal-studies

Leahy, C. M., Peterson, R. F., Wilson, I. G., Newbury, J. W., Tonkin, A. L., & Turnbull, D. (2010). Distress levels and self-reported treatment rates for medicine, law, psychology and mechanical engineering tertiary students: cross-sectional study. The Australian and New Zealand journal of psychiatry, 44(7), 608–615.

Lemma, S., Gelaye, B., Berhane, Y. et al. Sleep quality and its psychological correlates among university students in Ethiopia: a cross-sectional study. BMC Psychiatry 12, 237 (2012).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.

Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61 (3), 261–264.

Shinde S, Setia MS, Row-Kavi A, Anand V, Jerajani H. Male sex workers: Are we ignoring a risk group in Mumbai, India? Indian J Dermatol Venereol Leprol. 2009;75:41–6.

Further Information

Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian journal of dermatology, 61(3), 261.
Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. Bmj, 348.

1. Are cross-sectional studies qualitative or quantitative?

Cross-sectional studies can be either qualitative or quantitative , depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

2. What’s the difference between cross-sectional and cohort studies?

A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas cohort studies follow individuals over extended periods.

Another difference between these two types of studies is the subject pool. In cross-sectional studies, researchers select a sample population and gather data to determine the prevalence of a problem.

Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.

3. What’s the difference between cross-sectional and case-control studies?

Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk.

In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies are used to determine what factors might be associated with the condition and help researchers form hypotheses about a population.

4. Does a cross-sectional study have a control group?

A cross-sectional study does not need to have a control group , as the population studied is not selected based on exposure.

In a cross-sectional study, data are collected from a sample of the target population at a specific point in time, and everyone in the sample is assessed in the same way. There isn’t a manipulation of variables or a control group as there would be in an experimental study design.

5. Is a cross-sectional study prospective or retrospective?

A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time.

Cross-sectional studies are not designed to follow individuals forward in time ( prospective ) or look back at historical data ( retrospective ), as they analyze data from a specific point in time.

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How to choose your study design

Affiliation.

1 Department of Medicine, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
PMID: 32479703
DOI: 10.1111/jpc.14929

Research designs are broadly divided into observational studies (i.e. cross-sectional; case-control and cohort studies) and experimental studies (randomised control trials, RCTs). Each design has a specific role, and each has both advantages and disadvantages. Moreover, while the typical RCT is a parallel group design, there are now many variants to consider. It is important that both researchers and paediatricians are aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design. While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient numbers and research expertise. All these factors will severely limit the choice. While paediatricians would like to see more RCTs, these require a huge amount of resources, and in many situations will be unethical (e.g. potentially harmful intervention) or impractical (e.g. rare diseases). This paper gives a brief overview of the common study types, and for those embarking on such studies you will need far more comprehensive, detailed sources of information.

Keywords: experimental studies; observational studies; research method.

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Cross-Sectional Study | Definitions, Uses & Examples

Cross-Sectional Study | Definitions, Uses & Examples

Published on 5 May 2022 by Lauren Thomas .

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyse the relevant data.

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, frequently asked questions about cross-sectional studies.

The opposite of a cross-sectional study is a longitudinal study . While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals connected by a common trait.

Both types are useful for answering different kinds of research questions . A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study.

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When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

Sometimes a cross-sectional study is the best choice for practical reasons – for instance, if you only have the time or money to collect cross-sectional data, or if the only data you can find to answer your research question were gathered at a single point in time.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

An analytical study tries to answer how or why a certain outcome might occur.
A descriptive study only summarises said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organisations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

However, these datasets are often aggregated to a regional level, which may prevent the investigation of certain research questions. You will also be restricted to whichever variables the original researchers decided to study.

If you want to choose the variables in your study and analyse your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
Since cross-sectional studies only study a single moment in time, they cannot be used to analyse behavior over a period of time or establish long-term trends.
The timing of the cross-sectional snapshot may be unrepresentative of behaviour of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a ‘cross-section’) in the population
Follows in participants over time	Provides of society at a given point

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

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Cohort vs Cross-Sectional Study: Similarities and Differences

In a cohort study , the researcher selects a group of exposed and another group of unexposed individuals and follows them over time to determine whether or not a particular outcome of interest will occur.

The objective is to find out which group is more likely to develop the outcome (eg. disease) by comparing its incidence (i.e. the number of individuals who developed this disease) in both groups over that period of time.

In a cross-sectional study , the researcher collects data simultaneously on both exposure and outcome at one given point in time.

The objective is to find out if the exposure is related to the outcome by comparing the prevalence of the outcome (i.e. the proportion of people who have the disease) in exposed and unexposed individuals.

graphical representation of the cross-sectional study design

Similarities between cohort and cross-sectional designs

1. both are observational studies.

In experiments (a.k.a. Randomized Controlled Trials), the investigator actively determines (in general via random allocation) who gets exposed to the risk factor (or treatment) and who doesn’t.

In observational studies, the investigator is an observer and does not intervene. So the participants are naturally divided into 2 groups: the exposed and the unexposed.

2. Both designs aim to study the association between an exposure and an outcome

So before conducting a cross-sectional or a cohort study, we need to have at least 1 hypothesis on which exposure or risk factor we think may cause the outcome.

We can of course examine multiple hypotheses by testing the association of more than 1 exposure with the outcome.

However, we should keep the number of statistical tests at minimum, as with multiple testing we will be at risk of p-hacking — in simple terms, by doing multiple tests, some associations will appear statistically significant just by chance.

3. Both are subject to information bias

Both cohort and cross-sectional studies are subject to bias in collection of information, errors in measurement of exposure and outcome, misclassification of participants, and bias in data analysis.

4. Both are subject to selection bias

People may refuse to participate in any type of study. The problem is when those who refuse to participate are not a random group people, but instead have higher or lower chance of being exposed (or having the disease) therefore biasing the study results.

5. Both are subject to confounding

Confounding happens when some variable or factor confuses the association between exposure and outcome, tricking us into believing, for example, that there is a statistically significant association between exposure and outcome, when, in reality there isn’t.

What this means is that, if we find an association between exposure and outcome in a cohort or a cross-sectional study, we cannot be 100% sure that it is causal in nature.

Differences between cohort and cross-sectional designs

Where a cohort design is better, 1. a cohort is better for assessing causality.

When trying to determine whether an exposure causes a particular outcome, it is very important that at least the exposure precedes the outcome.

In a cohort design, because we start with exposed and unexposed participants and follow them in time, we can be sure that the exposure occurred before the disease.

In a cross-sectional study, the exposure and the outcome are measured at the same time, so it is harder to determine which comes first.

2. Unlike a cross-sectional study, a cohort is not prone to survival bias

Suppose we have a risk factor that shortens the life of people who are exposed to it.

So if take a snapshot of people who are alive at a certain point in time (i.e. conduct a cross-sectional study), then we are by definition measuring the survivors excluding those who died of the disease caused by the exposure.

This will bias the study toward falsely concluding that the exposure is not related to the disease.

Where a cross-sectional design is better

1. in general, a cross-sectional study is less expensive and less time-consuming.

In a cohort study we need to wait for the outcome to occur. In case of rare outcomes, the follow-up period may be very long (sometimes we will be waiting years for the outcome to develop in enough numbers so that the exposed and unexposed groups can be compared).

A cross-sectional design will be, in general, cheaper and faster to execute.

Here’s an example of how this translates in practice:

Suppose we have a new hypothesis about a causative association between 2 variables. A smart decision might be to start with a cross-sectional design (as it is faster and cheaper), then if the results are positive, replicate the results using a cohort or a randomized controlled trial if possible.

Because it is fast and cheap, a cross-sectional study is useful for assessing the disease burden in society — it is good for examining a change in the prevalence of a disease or an exposure, for instance, for studying the trend in cancer, heart disease, etc.

2. In a cross-sectional study you won’t have to deal with participants follow-up

A cohort design requires following people over a period of time, so participants may be lost to follow-up. This may happen for a variety of reasons, but the problem occurs when loss to follow-up does not happen at random.

For instance, if participants who are more (or less) likely than others to develop the outcome are lost to follow-up the study will be biased.

A cross-sectional study does not suffer from such bias as it does not follow participants in time.

How Do Cross-Sectional Studies Work?

Gathering Data From a Single Point in Time

Defining Characteristics

Advantages of Cross-Sectional Studies

Challenges of cross-sectional studies, cross-sectional vs. longitudinal studies.

A cross-sectional study looks at data at a single point in time. The participants in this type of study are selected based on particular variables. Cross-sectional studies are typically used in developmental psychology , but they are useful in many other areas as well, including social science and education.

Cross-sectional studies are observational and are known as descriptive research, not causal or relational—meaning you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables .

This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.

Example: Researchers studying developmental psychology might select groups of people who are different ages but investigate them at one point in time. By doing this, any differences among the age groups can be attributed to age differences rather than something that happened over time.

Defining Characteristics of Cross-Sectional Studies

Some of the key characteristics of a cross-sectional study include:

The study takes place at a single point in time
It does not involve manipulating variables
It allows researchers to look at numerous characteristics at once (age, income, gender, etc.)
It's often used to look at the prevailing characteristics in a given population
It can provide information about what is happening in a current population

Verywell / Jessica Olah

Think of a cross-sectional study as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.This type of research is frequently used to determine the prevailing characteristics in a population at a certain point in time. For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes.

A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example. While this type of study cannot demonstrate cause and effect, it can provide a quick look at correlations that may exist at a particular point.

For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further.

Cross-sectional studies are popular because they offer many benefits for researchers.

Inexpensive and Fast

Cross-sectional studies typically allow researchers to collect a great deal of information quickly. Data is often obtained inexpensively using self-report surveys . Researchers are then able to amass large amounts of information from a large pool of participants.

For example, a university might post a short online survey about library usage habits among biology majors, and the responses would be recorded in a database automatically for later analysis. This is a simple, inexpensive way to encourage participation and gather data across a wide swath of individuals who fit certain criteria.

Can Assess Multiple Variables

Researchers can collect data on a few different variables to see how they affect a certain condition. For example, differences in sex, age, educational status, and income might correlate with voting tendencies or give market researchers clues about purchasing habits.

Might Prompt Further Study

Although researchers can't use cross-sectional studies to determine causal relationships, these studies can provide useful springboards to further research. For example, when looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that can spur further experimental studies.

For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Conducting such a study can give researchers clues about the types of exercise that might be most beneficial to the elderly and inspire further experimental research on the subject.

No method of research is perfect. Cross-sectional studies also have potential drawbacks.

Difficulties in Determining Causal Effects

Researchers can't always be sure that the conditions a cross-sectional study measures are the result of a particular factor's influence. In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. This type of research simply doesn't allow for conclusions about causation.

For example, a study conducted some 20 years ago queried thousands of women about their consumption of diet soft drinks. The results of the study, published in the medical journal Stroke , associated diet soft drink intake with stroke risk that was greater than that of those who did not consume such beverages. In other words, those who drank lots of diet soda were more prone to strokes. However, correlation does not equal causation. The increased stroke risk might arise from any number of factors that tend to occur among those who drink diet beverages. For example, people who consume sugar-free drinks might be more likely to be overweight or diabetic than those who drink the regular versions. Therefore, they might be at greater risk of stroke—regardless of what they drink.

Cohort Differences

Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events.

Report Biases

Surveys and questionnaires about certain aspects of people's lives might not always result in accurate reporting. For example, respondents might not disclose certain behaviors or beliefs out of embarrassment, fear, or other limiting perception. Typically, no mechanism for verifying this information exists.

Cross-sectional research differs from longitudinal studies in several important ways. The key difference is that a cross-sectional study is designed to look at a variable at a particular point in time. A longitudinal study evaluates multiple measures over an extended period to detect trends and changes.

Evaluates variable at single point in time

Participants less likely to drop out

Uses new participant(s) with each study

Measures variable over time

Requires more resources

More expensive

Subject to selective attrition

Follows same participants over time

Longitudinal studies tend to require more resources; these are often more expensive than those used by cross-sectional studies. They are also more likely to be influenced by what is known as selective attrition , which means that some individuals are more likely to drop out of a study than others. Because a longitudinal study occurs over a span of time, researchers can lose track of subjects. Individuals might lose interest, move to another city, change their minds about participating, etc. This can influence the validity of the study.

One of the advantages of cross-sectional studies is that data is collected all at once, so participants are less likely to quit the study before data is fully collected.

A Word From Verywell

Cross-sectional studies can be useful research tools in many areas of health research. By learning about what is going on in a specific population, researchers can improve their understanding of relationships among certain variables and develop additional studies that explore these conditions in greater depth.

Levin KA. Study design III: Cross-sectional studies . Evid Based Dent . 2006;7(1):24-5. doi:10.1038/sj.ebd.6400375

Morin JF, Olsson C, Atikcan EO, eds. Research Methods in the Social Sciences: An A-Z of Key Concepts . Oxford University Press; 2021.

Abbasi J. Unpacking a recent study linking diet soda with stroke risks . JAMA . 2019;321(16):1554-1555. doi:10.1001/jama.2019.2123

Setia MS. Methodology series module 3: Cross-sectional studies . Indian J Dermatol . 2016;61(3):261-4. doi:10.4103/0019-5154.182410

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Knowledge Base

Methodology

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 August 12, 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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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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Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the condition to be studied is rare. Cohort studies are used to study incidence, causes, and prognosis. Because they measure events in chronological order they can be used to distinguish between cause and effect. Cross sectional studies are used to determine prevalence. They are relatively quick and easy but do not permit distinction between cause and effect. Case controlled studies compare groups retrospectively. They seek to identify possible predictors of outcome and are useful for studying rare diseases or outcomes. They are often used to generate hypotheses that can then be studied via prospective cohort or other studies.

research methods
cohort study
case-control study
cross sectional study

https://doi.org/10.1136/emj.20.1.54

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Open access
Published: 10 August 2024

Performance of calf circumference in identifying sarcopenia in older patients with chronic low back pain: a retrospective cross-sectional study

Hee Jung Kim 1 ,
Ji Young Kim 1 &
Shin Hyung Kim ORCID: orcid.org/0000-0003-4058-7697 1

BMC Geriatrics volume 24 , Article number: 674 ( 2024 ) Cite this article

26 Accesses

Metrics details

Calf circumference is currently recommended as a case-finding marker for sarcopenia, but its usefulness has not been determined in chronic pain conditions. Therefore, the present study aimed to evaluate the predictive performance of calf circumference in diagnosing sarcopenia in older patients with chronic low back pain.

Ambulatory adult patients aged ≥ 65 years with chronic low back pain were enrolled. A diagnosis of sarcopenia was established based on the criteria outlined by the Asian Working Group for Sarcopenia in 2019. Patient demographics, pain-related factors, clinical factors, and sarcopenia-related measurements were compared between non-sarcopenic and sarcopenic patients. Linear regression analysis was used to evaluate the correlation of calf circumference with muscle mass, strength, and physical performance. Also, a receiver operating characteristic curve analysis for calf circumference in predicting sarcopenia was conducted; and area under the curve (AUC) values, along with their corresponding 95% confidence intervals (CI), were calculated.

Data from 592 patients were included in the analysis. Eighty-five patients were diagnosed with sarcopenia (14.3%), 71 of whom had severe sarcopenia (11.9%). A higher prevalence of sarcopenia was observed in female patients (9.0% vs. 16.7%, p = 0.016). After adjusting for age, BMI, and comorbidities, calf circumference correlated positively with muscle mass but not with muscle strength and physical performance. The AUC values for sarcopenia were 0.754 (95% CI = 0.636–0.871, p = 0.001) in males and 0.721 (95% CI = 0.657–0.786, p < 0.001) in females. The cut-offs for calf circumference in predicting sarcopenia were 34 cm (sensitivity 67.1%, specificity 70.6%) in males, and 31 cm (sensitivity 82.5%, specificity 51.5%) in females.

Conclusions

Even though sex differences in its predictive value for sarcopenia should be considered, our findings suggest that calf circumference can be used as an indicator for predicting muscle mass and may serve as a potential marker for identifying sarcopenia in older patients with chronic low back pain.

Peer Review reports

Sarcopenia is currently defined as the decline in skeletal muscle mass and strength that occurs with advancing age and is often accompanied by diminished physical performance in its severe form [ 1 , 2 ]. Sarcopenia is associated with adverse health outcomes, including increased risk of falls and fractures, higher rates of hospitalization, and elevated mortality risk [ 1 , 3 ]. This condition is an increasing problem in our aging society; thus, sarcopenia prevention, treatment, and rehabilitation have become significant public health concerns when considering the economic and societal burden of sarcopenia. [ 3 , 4 ].

Chronic low back pain (CLBP) is one of the most common and major disabling health conditions among older adult populations [ 5 ]. The prevalence of sarcopenia among older patients with CLBP seems to be somewhat higher than in patients without pain [ 6 ]. Also, sarcopenia is associated with poor CLBP treatment outcomes [ 6 , 7 ]. Therefore, early identification of older patients at risk of sarcopenia is important for those with CLBP.

In the Asian Working Group for Sarcopenia 2019 (AWGS 2019) guidelines, calf circumference is recommended as an anthropometric measurement for identifying sarcopenia, facilitating early detection in older adults [ 1 ]. The role of calf circumference in the diagnosis algorithm for sarcopenia is supported by validation reports [ 8 , 9 , 10 , 11 , 12 , 13 ]. Calf circumference demonstrated a positive correlation with skeletal muscle mass assessed through dual-energy X-ray absorptiometry [ 8 , 9 , 10 , 11 , 12 ] and was also significantly associated with both muscle strength and physical performance [ 12 ]. However, these results were obtained from samples of the community-dwelling older population [ 12 ]. In recent reports, calf circumference showed promise for the screening for sarcopenia in subgroups with several comorbidities such as stroke, Parkinson’s disease, and diabetes [ 14 , 15 , 16 ]. However, the usefulness of calf circumference as a screening marker for sarcopenia has not been investigated in older patients with symptomatic degenerative lumbar spinal disease.

Accordingly, the aims of this study were to determine calf circumference cut-off values for sarcopenia prediction in older patients with CLBP and to evaluate its diagnostic performance using AWGS 2019 criteria. Also, the relationship between calf circumference and skeletal muscle mass, muscle strength, and physical performance was investigated in this population.

Study population

This study received approval from the Institutional Review Board of Yonsei University Health System, Seoul, Republic of Korea (IRB No. 4-2024-0094). In our previous studies, we have observed that low handgrip strength and high fat infiltration of paraspinal muscles resulted in poor treatment outcomes in older patients with CLBP [ 17 , 18 ]. Therefore, in 2022, we began sarcopenia screening and diagnosis for older patients with chronic pain at their initial visit to our pain clinic. The present study employs a retrospective cross-sectional observational design. Specifically, it is a retrospective audit of CLBP patients who completed sarcopenia assessment based on the AWGS 2019 diagnostic protocol. Patients who visited our clinic seeking treatment for low back pain from January to December 2022 were enrolled in the study. Based on the patho-anatomical approach of CLBP used for confirmation [ 19 ], adult patients aged 65 years and above diagnosed with degenerative lumbar spinal disease, such as spondylolisthesis, herniated disc, spinal/foraminal stenosis, and facet joint arthropathy, confirmed by radiological evaluation within one year from the date of initial visit were included. Pain duration of three months or longer was used to define chronicity. Non-ambulatory patients or patients with severe cognitive impairment that precluded completion of the sarcopenia assessment protocol were excluded. Patients with abnormal calf asymmetry with a difference in circumference greater than 2.0 cm between calves [ 20 ] or pitting edema of the lower limbs were excluded. To assess lower limb pitting edema, visual inspection for swelling or skin changes, gentle palpation to assess skin indentation, and observation for persistence of indentation after pressure release were conducted. In addition, patients with incomplete medical records for this study were excluded.

Sarcopenia assessment

All measurements followed standard protocols for each measurement based on AWGS 2019 recommendations [ 1 ]. An independent nurse practitioner experienced in comprehensive geriatric assessment conducted all measurements throughout the study period. Calf circumference was measured at the widest part of both calves using a non-elastic tape to capture the maximum value. Patients were instructed to stand with their feet shoulder-width apart to ensure equal distribution of body weight. The tape was applied snugly but without compressing the calf and was positioned flat on the skin and parallel to the floor. After measuring each calf twice, an average circumference was recorded. Handgrip strength (HGS) was assessed by conducting three measurements on each hand using a Smedley-type dynamometer (EH101; CAMRY, Guangdong, China). Patients were instructed to stand with their elbows fully extended and to exert a maximum-effort isometric contraction while squeezing the dynamometer. The highest reading obtained from three measurements on each hand was recorded, and the maximum value from either hand was utilized for analysis. Appendicular skeletal muscle mass (ASM) was measured using a bioelectrical impedance analysis (BIA) device (Inbody H20N, InBody Co., Ltd., Seoul, Korea). Participants were instructed to undergo BIA measurements in the morning on an empty stomach to standardize body water distribution, ensuring they emptied their bladder and bowels and refrained from physical activities, showering, sauna use, or any activities affecting body moisture beforehand. Skeletal muscle mass index (SMI) was calculated by dividing ASM by the square of the patient’s height. A short physical performance battery (SPPB) was conducted, and its subtest scores and timings were determined. The SPPB consists of three subsets including static balance, gait speed, and chair sit-to-stand test [ 21 ]. To evaluate static balance, patients were instructed to maintain three standing postures of increasing difficulty, feet-together, semi-tandem, and full-tandem stance, for up to 10 s each. Patients were timed until movement or until 10 s had elapsed. For the gait speed test, patients walked at their comfortable pace across a 4-meter distance, and the average time for two trials was recorded. To assess chair sit-to-stand time, patients crossed their arms over their chests and, as quickly as possible, performed five stands from a standard chair. The time taken to complete the five sit-to-stand tasks was recorded. Each of the three subtests was scored on a scale from 0 to 4; the total score, ranging from 0 to 12, was the sum of these subtest scores.

Definition of Sarcopenia

In this study, cut-off values recommended by AWGS 2019 were utilized for identifying low calf circumference (males: < 34 cm and females: < 33 cm), low SMI (males: < 7.0 kg/m 2 and females: < 5.7 kg/m 2 ), low HGS (males: < 28 kg and females: < 18 kg), and low SPPB score (total score ≤ 9 for both sexes) [ 1 ]. Calf circumference cut-off values were used for screening or case-finding of sarcopenia. Sarcopenia was defined as cases with both low muscle mass and strength (low SMI + low HGS), irrespective of the SPPB score, and cases with poor physical performance were classified into severe sarcopenia (low SMI + low HGS + low SPPB score) [ 1 ].

Patient demographics and clinical data

Demographic information, pain-related data, and clinical data were extracted from the institutional electronic medical record database system. Patient characteristics encompassed age, sex, and body mass index (BMI). Patient history of diagnosed comorbid conditions and current medications was obtained. Conditions assessed included fall history, cerebro-cardiovascular diseases, diabetes mellitus, osteoporosis, and urinary incontinence. The presence of leg pain (a sciatica symptom), pain duration, and average pain intensity score using a 0 to 10 numeric rating scale (NRS) for the preceding week were identified as pain-related variables.

Statistical analysis

Descriptive statistics were utilized to summarize continuous variables and are presented as mean values along with standard deviations (SD) and ranges. Categorical variables are expressed as counts and percentages. For data not conforming to normal distribution, median values and interquartile ranges (IQR) are reported with the Shapiro-Wilk test normality assessment results. To compare patient characteristics between the non-sarcopenia and sarcopenia groups, various statistical tests were employed. Independent Student’s t-tests compared means for continuous variables with normal distributions, while the Mann–Whitney U test compared medians for continuous variables with non-normal distributions. Chi-squared tests or Fisher’s exact tests were used for categorical variables. To explore the relationship between calf circumference and SMI, HGS, and SPPB score, linear regression analysis was performed with adjustments for age, BMI and comorbidities that showed significant differences between sarcopenia and non-sarcopenia groups. Specifically, calf circumference was adjusted based on BMI categories (< 25 kg/m² [normal], 25–29 kg/m² [overweight], and ≥ 30 kg/m² [obese]), as recommended by Gonzalez et al. [ 22 ], to address potential underestimation in individuals with excess weight who could otherwise show falsely normal calf circumference values. Receiver operating characteristic (ROC) curve analysis was utilized to assess the predictive ability of calf circumference, and corresponding area under the curve (AUC) values and confidence intervals were calculated. Sex-specific calf circumference cut-off values for predicting low SMI, sarcopenia, and severe sarcopenia were determined using ROC analysis and the Youden index. Statistical analyses were conducted using IBM SPSS Statistics, version 25.0 (IBM Corp, Armonk, NY), and statistical significance was set at a p -value less than 0.05.

Within the study period, 988 patients presented with low back pain as their chief complaint at our clinic. After excluding 396 patients based on the study’s exclusion criteria, 592 patients aged 65–90 years (mean age 71.77 ± 6.24 years) were included in the analysis. The sample consisted of 187 males and 405 females. All participants underwent sarcopenia assessment according to the AWGS 2019 criteria, with 507 patients classified as non-sarcopenic and 85 patients (14.3%) classified as sarcopenic (Fig. 1 ). There was a notable difference in the prevalence of sarcopenia between male and female patients; prevalence was 9.0% among males and 16.7% among females ( p = 0.016). The number of patients diagnosed as having severe sarcopenia was 71 out of 592 patients (11.9%).

Study flowchart. MRI, magnetic resonance imaging; AWGS, Asian Working Group for Sarcopenia

A comparison of patient demographics, comorbid medical conditions, sarcopenia-related measurements, and pain-related data between patients with and without sarcopenia is presented in Table 1 . For both sexes, older patients and patients with lower BMIs were more frequently diagnosed with sarcopenia. In the sarcopenia group, more patients of both sexes had a history of falling. The prevalence of osteoporosis was higher in women with sarcopenia than in those without sarcopenia. Smaller calf circumference, lower muscle mass, lower HGS, and lower SPBB scores were observed in the sarcopenia group. Between the two groups in both sexes, there were no significant differences in pain-related variables. Also, after adjusting for age, BMI, and comorbidities, calf circumference showed a positive correlation with SMI but not with HGS and SPPB score in both male and female patients (Table 2 ).

The results of ROC analysis for predicting low muscle mass and sarcopenia using calf circumference values are illustrated in Fig. 2 . The AUC values for low SMI and sarcopenia were 0.776 (95% CI = 0.698–0.854, p < 0.001) and 0.754 (95% CI = 0.636–0.871, p = 0.001), respectively, in males, and 0.717 (95% CI = 0.663–0.771, p < 0.001) and 0.721 (95% CI = 0.657–0.786, p < 0.001), respectively, in females. The cut-off values of calf circumference for predicting low SMI and sarcopenia were 34 cm (sensitivity 71.8%, specificity 68.4%) and 34 cm (sensitivity 67.1%, specificity 70.6%), respectively, in males, and 32 cm (sensitivity 74.9%, specificity 57.1%) and 31 cm (sensitivity 82.5%, specificity 51.5%) respectively, in females. When applying the AWGS 2019 cut-off of calf circumference, < 33 cm, for predicting sarcopenia in female patients, sensitivity and specificity were 57.3% and 75.0%, respectively.

Receiver operating characteristic curves for calf circumference in predicting low muscle mass and sarcopenia. Receiver operating characteristic curves for calf circumference in the prediction of low muscle mass (solid line) and sarcopenia (dotted line) in males (A) and females (B) The area under the curve values with 95% confidence intervals for low muscle mass and sarcopenia were 0.776 (0.698–0.854) and 0.754 (0.636–0.871), respectively in males, and 0.717 (0.663–0.771) and 0.721 (0.657–0.786), respectively, in females

In this study, we observed that calf circumference cut-off values for predicting low muscle mass and sarcopenia were determined to be 34 cm in males, while in females, these values were 32 cm and 31 cm, respectively, which diverged from the AWGS 2019 recommendations. Furthermore, our findings indicated a significant positive correlation between calf circumference and muscle mass, though no such association was observed with muscle strength and physical performance measures.

Previously reported cut-offs for calf circumference were 32 to 34 cm in men and 32 to 33 cm in women among the older Asian population [ 8 , 9 , 10 , 11 , 12 ]. These values were developed in consideration of the increase in sensitivity and were consistent with AWGS 2019 recommendations of < 34 cm for men and < 33 cm for women during sarcopenia screening or case-finding [ 1 ]. The AUC value of calf circumference cut-offs suggested by AWGS 2019 for predicting sarcopenia (defined by low SMI and low HGS) was 0.647 in 2123 adults aged 70 to 84 years [ 11 ]. In 657 adults with mean age of 76.2 years, the AUC values of calf circumference for predicting sarcopenia met AWGS 2019 criteria, 0.82 for men and 0.72 for women [ 12 ]. Thus, the predictive performance of calf circumference for sarcopenia in the study population, AUC = 0.754 in males and AUC = 0.721 in females, was similar to previous results from the older population data according to AWGS 2019 criteria. These data are clinically acceptable; however, the predictive power of calf circumference for muscle mass and sarcopenia was lower in women than in men in this study. This observation was consistent with previous results [ 7 , 12 ]. As higher fat mass in the legs is generally observed in women compared to men [ 23 ], this factor could potentially affect the predictive power of calf circumference regarding muscle mass and sarcopenia in female patients in this study.

The pattern of changes in calf circumference in patients with symptomatic degenerative lumbar spinal disease has not been widely studied. Peripheral nerves originating from the lumbar spinal nerves are distributed to the muscles of the lower limbs. In this anatomical context, muscle denervation as the result of neural compression following degenerative change of lumbar spine structures causes a reduction in muscle size in the affected area of the lower limbs [ 24 ]. In older patients with CLBP, leg pain and neurogenic claudication can precipitate a detrimental cycle in which reduced physical activity contributes to muscle atrophy and exacerbates deconditioning and disability [ 25 ]. Furthermore, electromyographical evidence suggests that reinnervation of muscle fibers in the older population with sarcopenia to compensate for the loss of innervating motor neurons and denervation of muscle fibers was observed significantly less frequently than in healthy controls [ 26 ]. These potential changes of calf circumference in our study population might affect relatively lower sensitivity values of calf circumference for identifying low muscle mass and sarcopenia when compared with those from general older papulation data [ 12 ].

Also, the clinical features discussed occur more among women with CLBP than among men; in addition, among patients with degenerative lumbar spinal disease, female patients have higher pain scores and more frequent functional impairment and lower quality of life than male patients [ 27 ]. In this study, the prevalence of sarcopenia was almost twice as high in female patients compared to male patients. The difference in the prevalence of sarcopenia between sexes varies depending on which guidelines are applied. In recent European and Asian guideline reports, sarcopenia was more prevalent in men than in women [ 11 , 28 ]. Although the causal relationship between sarcopenia and pain cannot be determined from this study, female patients seem to be more vulnerable to the risk of sarcopenia in chronic pain conditions.

Our results showed that the proposed AWGS 2019 calf circumference cut-off values were valid for predicting sarcopenia in male patients with CLBP. However, in female CLBP patients, the sensitivity of calf circumference for predicting sarcopenia was 82.5% when applying a cut-off of < 31 cm; however, when applying the AWGS 2019 recommended value of < 33 cm, a 30% reduction in sensitivity resulted. Therefore, when using calf circumference as a case-finding marker for sarcopenia among patients with CLBP, sex difference in predictive value for sarcopenia should be considered.

Notably, severe sarcopenia was more prevalent in the study population than in the general older population. In a previous study using AWGS 2019 criteria, the prevalence of severe sarcopenia was 3.3% [ 11 ], but our prevalence was 11.9%, almost four times higher. In this study, physical performance was measured using SPPB, a tool designed to evaluate lower limb function encompassing balance, strength, and mobility [ 21 ]. We found that the presence or absence of sarcopenia did not correlate with differences in reported pain levels or pain-related characteristics among our study participants. However, it is important to note that patients with CLBP often experience leg or foot pain and may exhibit difficulties in walking, which could adversely impact their SPPB scores. This suggests that while sarcopenia may not directly correlate with reported pain levels, the functional implications of CLBP are significant considerations in this patient population.

Anthropometric measurements do not reflect body composition including intramuscular and subcutaneous fat. Therefore, calf circumference does not fully reflect muscle quality which is closely related to muscle strength and physical function [ 29 ]. Indeed, calf circumference did not significantly correlate with muscle strength and physical performance in this study, which contrasts with the results from the general older population [ 12 ]. Recent research has indicated that age-related declines in skeletal muscle strength, muscle mass, and muscle quality vary between the upper limbs and lower limbs, leading to potential differences in clinical interpretations for diagnosing sarcopenia [ 30 , 31 ]. Therefore, when diagnosing sarcopenia and evaluating the severity of sarcopenia for this population, it is crucial to employ a multidimensional assessment approach that considers not only anthropometric measurements and functional assessments but also integrates the clinical characteristics of the chronic pain condition and specific muscle group impairments.

This study has some limitations. The study was conducted at a single tertiary care hospital and included patients of a homogeneous racial and ethnic background, potentially limiting the generalizability of our results to other clinical settings and populations. Our study specifically included patients with confirmed degenerative lumbar spinal diseases identified through radiological evaluation, excluding those with idiopathic low back pain, which is the most prevalent type. This selection criterion may restrict the external validity of our findings. The sample size, particularly for male participants, was small. This not only increases the possibility of sample bias but also limits the statistical power to detect differences and associations accurately within the study cohort. This retrospective analysis only included patients with complete clinical data; the presence of selection biases in the findings cannot be entirely ruled out. The ROC curve can be influenced by class imbalance, where the number of non-sarcopenic cases outweighs the number of sarcopenic cases. This imbalance can lead to misleading optimism about the diagnostic performance of calf circumference as a predictor for sarcopenia. As this study adopts a cross-sectional design, a causal relationship between calf circumference and sarcopenia could not be established. Consequently, longitudinal studies are necessary to validate our findings and elucidate any potential causal associations. BIA is not considered the gold standard for body composition measurement. Also, we did not exclude patients taking diuretic and corticosteroid medications from the analyses, which could affect body water distribution and potentially influence BIA results. However, BIA measurements with multifrequency devices have shown closer correlation with ASM measured by dual-energy X-ray absorptiometry and its adequate performance across multiple domains [ 32 ]. Additionally, while there is no worldwide consensus on the exact list of geriatric syndromes, we collected data on several important factors leading to geriatric syndromes, including falls, urinary incontinence, functional decline, and sarcopenia. Although polypharmacy was not explicitly investigated, the comorbidities we examined are based on current medication diagnoses and thus reflect drug administration to some extent. Specific malnutrition and cognitive impairment statuses were not measured with dedicated tools for each individual; however, we excluded patients who were non-ambulatory or unable to complete the sarcopenia assessment due to severe cognitive impairment. Future studies should include a broader range of factors to provide a more comprehensive assessment and to better inform clinical interventions.

In conclusion, calf circumference appears to be a proxy marker for muscle mass estimated by BIA measurements and may serve as a potential case-finding marker for sarcopenia in older patients with CLBP. Also, although the predictive characteristics differed between the sexes, the predictive performance of calf circumference for sarcopenia in the study population was similar to the results from the older, community-dwelling population data. Therefore, our results suggest that calf circumference is a clinical indicator for predicting muscle mass and may serve as a case-finding marker for sarcopenia in older patients with CLBP.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21(3):300–e3072.

Article PubMed Google Scholar

Kirk B, Cawthon PM, Arai H, Ávila-Funes JA, Barazzoni R, Bhasin S, et al. The conceptual definition of Sarcopenia: Delphi Consensus from the Global Leadership Initiative in Sarcopenia (GLIS). Age Ageing. 2024;53(3):afae052.

Article PubMed PubMed Central Google Scholar

Cruz-Jentoft AJ, Sayer AA, Sarcopenia. Lancet. 2019;393(10191):2636–46.

Beaudart C, Rizzoli R, Bruyère O, Reginster JY, Biver E. Sarcopenia: burden and challenges for public health. Arch Public Health. 2014;72(1):45.

Rudy TE, Weiner DK, Lieber SJ, Slaboda J, Boston RJ. The impact of chronic low back pain on older adults: a comparative study of patients and controls. Pain. 2007;131(3):293–301.

Park S, Kim HJ, Ko BG, Chung JW, Kim SH, Park SH, et al. The prevalence and impact of Sarcopenia on degenerative lumbar spinal stenosis. Bone Joint J. 2016;98–B(8):1093–8.

Wu WT, Lee TM, Han DS, Chang KV. The prevalence of Sarcopenia and its impact on clinical outcomes in lumbar degenerative spine Disease-A systematic review and Meta-analysis. J Clin Med. 2021;10(4):773.

Kawakami R, Murakami H, Sanada K, Tanaka N, Sawada SS, Tabata I, et al. Calf circumference as a surrogate marker of muscle mass for diagnosing Sarcopenia in Japanese men and women. Geriatr Gerontol Int. 2015;15(8):969–76.

Kusaka S, Takahashi T, Hiyama Y, Kusumoto Y, Tsuchiya J, Umeda M. Large calf circumference indicates non-sarcopenia despite body mass. J Phys Ther Sci. 2017;29(11):1925–8.

Hwang AC, Liu LK, Lee WJ, Peng LN, Chen LK. Calf circumference as a screening instrument for appendicular muscle Mass Measurement. J Am Med Dir Assoc. 2018;19(2):182–4.

Kim M, Won CW. Sarcopenia in Korean Community-Dwelling adults aged 70 years and older: application of Screening and Diagnostic Tools from the Asian Working Group for Sarcopenia 2019 Update. J Am Med Dir Assoc. 2020;21(6):752–8.

Kim S, Kim M, Lee Y, Kim B, Yoon TY, Won CW. Calf circumference as a simple screening marker for diagnosing Sarcopenia in older Korean adults: the Korean Frailty and Aging Cohort Study (KFACS). J Korean Med Sci. 2018;33(20):e151.

Kim GM, Song S, Park JH, Tak YJ, Wang IJ, Huh U, et al. Diagnostic significance of calf circumference in Sarcopenia of healthy Korean adult males. Front Physiol. 2022;13:973265.

Inoue T, Maeda K, Shimizu A, Nagano A, Ueshima J, Sato K, et al. Calf circumference value for Sarcopenia screening among older adults with stroke. Arch Gerontol Geriatr. 2021;93:104290.

de Luna JRG, Lima DP, Gomes VC, de Almeida SB, Monteiro PA, Viana-Júnior AB, et al. Screening tools for Sarcopenia in mild to moderate Parkinson’s Disease: assessing the Accuracy of SARC-F and Calf Circumference. J Parkinsons Dis. 2023;13(6):947–59.

Xu Z, Zhang P, Chen Y, Jiang J, Zhou Z, Zhu H. Comparing SARC-CalF with SARC-F for Screening Sarcopenia in adults with type 2 diabetes Mellitus. Front Nutr. 2022;9:803924.

Kim SH, Park SJ, Yoon KB, Jun EK, Cho J, Kim HJ. Influence of Handgrip Strength and Psoas muscle index on Analgesic Efficacy of Epidural Steroid Injection in patients with degenerative lumbar spinal disease. Pain Physician. 2022;25(7):E1105–13.

PubMed Google Scholar

Kim HJ, Rho M, Yoon KB, Jo M, Lee DW, Kim SH. Influence of cross-sectional area and fat infiltration of paraspinal muscles on analgesic efficacy of epidural steroid injection in elderly patients. Pain Pract. 2022;22(7):621–30.

O’Sullivan P. Diagnosis and classification of chronic low back pain disorders: maladaptive movement and motor control impairments as underlying mechanism. Man Ther. 2005;10(4):242–55.

Suh IS, Jung MS, Lee BH, Kim JH, Tak KS, Ahn DK. Classification and surgical correction of asymmetric calves in asians. Plast Reconstr Surg. 2014;134(5):e707–16.

Article Google Scholar

Lee SY, Choo PL, Pang BWJ, Lau LK, Jabbar KA, Seah WT, et al. SPPB reference values and performance in assessing Sarcopenia in community-dwelling singaporeans - Yishun study. BMC Geriatr. 2021;21(1):213.

Gonzalez MC, Mehrnezhad A, Razaviarab N, Barbosa-Silva TG, Heymsfield SB. Calf circumference: cutoff values from the NHANES 1999–2006. Am J Clin Nutr. 2021;113(6):1679–87.

Article PubMed PubMed Central CAS Google Scholar

Nindl BC, Scoville CR, Sheehan KM, Leone CD, Mello RP. Gender differences in regional body composition and somatotrophic influences of IGF-I and leptin. J Appl Physiol (1985). 2002;92(4):1611–8.

Article PubMed CAS Google Scholar

Chong F, Fan W, Liu L, Zhang Y, Lin H, Huang B. Correlation between atrophy of the Gluteus Medius muscle and symptoms of lumbar spinal stenosis. World Neurosurg. 2023;172:e177–84.

Costandi S, Chopko B, Mekhail M, Dews T, Mekhail N. Lumbar spinal stenosis: therapeutic options review. Pain Pract. 2015;15(1):68–81.

Piasecki M, Ireland A, Piasecki J, Stashuk DW, Swiecicka A, Rutter MK, et al. Failure to expand the motor unit size to compensate for declining motor unit numbers distinguishes sarcopenic from non-sarcopenic older men. J Physiol. 2018;596(9):1627–37.

Gautschi OP, Corniola MV, Smoll NR, Joswig H, Schaller K, Hildebrandt G, et al. Sex differences in subjective and objective measures of pain, functional impairment, and health-related quality of life in patients with lumbar degenerative disc disease. Pain. 2016;157(5):1065–71.

Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, et al. Global prevalence of Sarcopenia and severe Sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):86–99.

Kuyumcu ME, Halil M, Kara Ö, Çuni B, Çağlayan G, Güven S, et al. Ultrasonographic evaluation of the calf muscle mass and architecture in elderly patients with and without Sarcopenia. Arch Gerontol Geriatr. 2016;65:218–24.

Phillipe de Lucena Alves C, Câmara M, Dantas Macêdo GA, Freire YA, de Melo Silva R, Paulo-Pereira R, et al. Agreement between upper and lower limb measures to identify older adults with low skeletal muscle strength, muscle mass and muscle quality. PLoS ONE. 2022;17(1):e0262732.

da Costa Pereira JP, Freire YA, da Silva AMB, de Lucena Alves CP, de Melo Silva R, Câmara M, et al. Associations of upper- and lower-limb muscle strength, mass, and quality with health-related quality of life in community-dwelling older adults. Geriatr Gerontol Int. 2024;24(7):683–92.

Siedler MR, Rodriguez C, Stratton MT, Harty PS, Keith DS, Green JJ, et al. Assessing the reliability and cross-sectional and longitudinal validity of fifteen bioelectrical impedance analysis devices. Br J Nutr. 2023;130(5):827–40.

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Acknowledgements

The authors thank C.H. Hwang, BS, for helping with the data analysis for this study.

This work was supported by the National Research Foundation Korea grant funded by the Korea government (No.RS-2023-00245723).

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HJK: literature review, data collection, data analysis, manuscript writing, and manuscript editing, JYK: literature review and data collection, SHK: conceptualization, study design, data analysis, manuscript writing, and manuscript editing. All authors approved the final manuscript.

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Kim, H., Kim, J. & Kim, S. Performance of calf circumference in identifying sarcopenia in older patients with chronic low back pain: a retrospective cross-sectional study. BMC Geriatr 24 , 674 (2024). https://doi.org/10.1186/s12877-024-05263-z

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Indian J Psychol Med
v.44(3); 2022 May

Research Design: Case-Control Studies

Chittaranjan andrade.

1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India.

Case-control studies are observational studies in which cases are subjects who have a characteristic of interest, such as a clinical diagnosis, and controls are (usually) matched subjects who do not have that characteristic. After cases and controls are identified, researchers “look back” to determine what past events (exposures), if any, are significantly associated with caseness. For “looking back,” data may be obtained by clinical history-taking or from medical records such as case files or large electronic health care databases. The data are analyzed using logistic regression, which adjusts for confounding variables and yields an odds ratio and a probability value for the association between the exposure of interest (independent variable) and caseness (dependent variable). Because case-control studies are not randomized controlled studies, cause–effect relationships do not necessarily explain significant associations detected in the regressions; unexplored confounding may be responsible. These concepts are explained with the help of examples.

Earlier articles in this series described classifications in research design, 1 prospective and retrospective studies, cross-sectional and longitudinal studies, 2 and cohort studies. 3 This article considers a research design that is often used in present-day research in medicine and psychiatry: the case-control study.

Case-Control Study: General Description

A case-control study is one in which cases are compared with controls to identify historical exposures that are significantly associated with a current state or, stated in different words, variables that are significantly associated with caseness. In case-control studies, cases are subjects with a particular characteristic. The characteristic that defines caseness may be a clinical diagnosis (e.g., schizophrenia [Sz]), a treatment outcome (e.g., treatment-resistance), a side effect (e.g., tardive dyskinesia), or any other characteristic that is the subject of interest. Controls are subjects who do not have the characteristic that defines caseness. For Sz, controls may be healthy controls; for treatment-resistance, controls would be subjects with the same diagnosis and who are treatment-responsive; for tardive dyskinesia, controls would be subjects who received the same treatment but did not develop this adverse outcome. Controls are commonly selected based on matching with cases for variables such as age, sex, site of recruitment, and other variables. Matching may be 1:1, but when data are drawn from large electronic databases, it is often possible to match five or even 10 controls with each case. In such studies, there may be thousands of cases and tens or even hundreds of thousands of controls.

As an actual example of a case-control study, children with autism spectrum disorder (ASD) may be compared with normally developing children to determine whether a history of maternal antidepressant use during pregnancy is more frequent among cases than among controls; if it is, and if the association remains statistically significant after adjusting for confounding variables, one may speculate that gestational exposure to antidepressants predisposes to autism spectrum disorder. 4 Here, readers may note that there is only one exposure of interest: gestational exposure to antidepressant drugs.

As a hypothetical example of a case-control study, patients with Sz may be compared with healthy controls to determine whether a family history of Sz, viral infection during pregnancy, season of birth, obstetric complications during pregnancy, brain insults in early childhood, and other variables are associated with Sz in the sample. Here, readers may note that all the variables listed are exposures of interest and corrections are desirable to protect against the risk of Type 1 statistical error associated with multiple hypothesis testing. 5

In summary, in case-control studies, there are cases and there are controls that are matched with cases. Researchers then “look back” to ascertain what past events (exposures) are associated with caseness. The exposures of interest may be one or many.

Analysis of Case-Control Studies

Case-control studies are analyzed using logistic regression. The dependent variable is the (dichotomous) grouping variable: case vs. control. The independent variables are the exposure(s) of interest plus the confounding variables whose effects must be adjusted for in the regression to understand the unique effect of the exposure variable(s). The logistic regression yields an odds ratio and a statistical significance (P) value for each independent variable; this allows us to understand whether or not the independent variables are significantly associated with caseness, and, if they are, what the effect sizes are, as exemplified by the odds ratios. Readers may note that whether a significant association is a marker of risk or a cause of the risk cannot be determined from an observational study; this was explained in an earlier article. 3

As a special note, when cases and controls are well matched on many important variables, a procedure known as conditional logistic regression analysis may be employed. 6

Characteristics of Case-Control Studies

How do case-control studies fit into classifications of research design described in an earlier article? 1 Case-control studies are empirical studies that are based on samples, not individual cases or case series. They are cross-sectional because cases and controls are identified and evaluated for caseness, historical exposures, and confounding variables at a single point in time. They are observational; there is no intervention. They are prospective when cases and controls are identified and interviewed in real time, such as in an outpatient department, and retrospective when they are identified in and studied from medical records or electronic health care databases. Strengths and limitations of prospectively vs. retrospectively ascertained data were described in an earlier article. 3

The nested case-control study is a special situation in which cases and controls are both identified from within a cohort. So, instead of studying the entire cohort, which would be time- and labor- intensive, the researchers study only cases and matched controls within that cohort. 7 To explain with the help of an actual example, Gronich et al. 8 examined the electronic database of the largest health care provider in Israel and identified a cohort of 1,762,164 adults who did not have a diagnosis of Parkinson’s disease (PD). During follow-up, 11,314 patients were newly diagnosed with PD. Each patient (case) was matched with 10 randomly selected controls based on age, sex, ethnicity, and duration of follow-up. Thus, rather than extracting data for 11,314 cases and the rest of the 1,762,164 adults who did not develop PD and who were therefore noncases, the authors carved out a smaller sample of controls from within the cohort. Thus, the final sample of 11,314 cases and 113,140 controls was “nested” within the original cohort; studying this smaller sample took less time and was less labor-intensive than studying the entire cohort.

Parting Notes

There are two reasons why, in case-control studies, large samples are desirable, and why many controls may be matched to a single case. One reason is that patients are not randomized to be cases or controls. In such circumstances, as in quasi-controlled studies, 9 there is bound to be confounding. With larger samples, statistical power to adjust for confounding will improve. The other reason is that, in case-control studies, data are usually drawn from medical records or databases. Information extracted from such sources is very unlikely to have been collected and recorded with the expectation of use in future research. So, there are bound to be inaccuracies. When data are blurred (inaccurate), there is statistical noise. When the sample size is large, it becomes easier to see a signal through the noise.

Cohort and case-control study designs are not “opposites” as are prospective vs. retrospective, or cross-sectional vs. longitudinal, or controlled vs. uncontrolled research designs. Rather, like the randomized controlled and quasi-controlled designs, these designs are special kinds of research design in the controlled vs. uncontrolled classification. Note that whereas a case-control study is always a special kind of controlled study, a cohort study can be classified under controlled or uncontrolled, depending on whether or not there is a comparison group for the group of interest.

Case-control studies in India tend to be poor in quality because they are based on small sample sizes. Small samples do not have sufficient statistical power to adjust for the multitude of confounding variables that bedevil research in psychiatry. Large samples cannot be identified because India does not as yet have large electronic health care databases as a source of data.

Finally, case-control studies, like cohort studies, are observational in nature, and authors who conduct and report such studies should follow the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.

COMMENTS

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Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Observational studies are often the only practicable method of answering questions of aetiology, the natural history and treatment of rare conditions and instances where a randomised controlled trial might be unethical.
Chapter 8. Case-control and cross sectional studies
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.
Types of Studies
Cross-Sectional vs Longitudinal. Cross-sectional study. A cross-sectional study is an observational one. This means that researchers record information about their subjects without manipulating the study environment. In our study, we would simply measure the cholesterol levels of daily walkers and non-walkers along with any other ...
Observational research methods. Research design II: cohort, cross
Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the condition to be studied is rare. Cohort studies are used to study incidence, causes, and prognosis ...
Cross-sectional study
In medical research, cross-sectional studies differ from case-control studies in that they aim to provide data on the entire population under study, whereas case-control studies typically include only individuals who have developed a specific condition and compare them with a matched sample, often a tiny minority, of the rest of the population ...
Observational research methods. Research design II: cohort, cross
Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the condition to be studied is rare.
Performance of calf circumference in identifying sarcopenia in older
As this study adopts a cross-sectional design, a causal relationship between calf circumference and sarcopenia could not be established. Consequently, longitudinal studies are necessary to validate our findings and elucidate any potential causal associations. BIA is not considered the gold standard for body composition measurement.
Research Design: Case-Control Studies
The nested case-control study is a special situation in which cases and controls are both identified from within a cohort. ... Cohort and case-control study designs are not "opposites" as are prospective vs. retrospective, or cross-sectional vs. longitudinal, or controlled vs. uncontrolled research designs. Rather, like the randomized ...

An introduction to different types of study design

Descriptive studies

Analytical Studies

Observational studies

Cross-sectional study:

Cohort study:

Case-Control Study:

Experimental Studies

References (pdf)

Cohort studies: prospective and retrospective designs

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Descriptive Studies

Simple and Inexpensive

Minimal room for error

Multiple variables and outcomes can be researched and compared at once

The data can be a starting point for future research

Limitations

Report bias is probable

The timing of the snapshot is not always representative

It cannot be used to analyze behavior over a period of time

Cross-Sectional vs. Longitudinal

Further Information

1. Are cross-sectional studies qualitative or quantitative?

2. What’s the difference between cross-sectional and cohort studies?

3. What’s the difference between cross-sectional and case-control studies?

4. Does a cross-sectional study have a control group?

5. Is a cross-sectional study prospective or retrospective?

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Cross-Sectional Study | Definitions, Uses & Examples

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Descriptive vs analytical studies

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Differences between cross-sectional, case-control, and cohort study designs.

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Cohort vs Cross-Sectional Study: Similarities and Differences

Similarities between cohort and cross-sectional designs

2. Both designs aim to study the association between an exposure and an outcome

3. Both are subject to information bias

4. Both are subject to selection bias

5. Both are subject to confounding

Differences between cohort and cross-sectional designs

2. Unlike a cross-sectional study, a cohort is not prone to survival bias

Where a cross-sectional design is better

2. In a cross-sectional study you won’t have to deal with participants follow-up

Further reading

How Do Cross-Sectional Studies Work?

Advantages of Cross-Sectional Studies

Defining Characteristics of Cross-Sectional Studies

Inexpensive and Fast

Can Assess Multiple Variables

Might Prompt Further Study

Difficulties in Determining Causal Effects

Cohort Differences

Report Biases

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

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Advantages of case-control studies

Disadvantages of case-control studies

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