What Is A Case Control Study?

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A case-control study is a research method where two groups of people are compared – those with the condition (cases) and those without (controls). By looking at their past, researchers try to identify what factors might have contributed to the condition in the ‘case’ group.

Explanation

A case-control study looks at people who already have a certain condition (cases) and people who don’t (controls). By comparing these two groups, researchers try to figure out what might have caused the condition. They look into the past to find clues, like habits or experiences, that are different between the two groups.

The “cases” are the individuals with the disease or condition under study, and the “controls” are similar individuals without the disease or condition of interest.

The controls should have similar characteristics (i.e., age, sex, demographic, health status) to the cases to mitigate the effects of confounding variables .

Case-control studies identify any associations between an exposure and an outcome and help researchers form hypotheses about a particular population.

Researchers will first identify the two groups, and then look back in time to investigate which subjects in each group were exposed to the condition.

If the exposure is found more commonly in the cases than the controls, the researcher can hypothesize that the exposure may be linked to the outcome of interest.

Case Control Study

Figure: Schematic diagram of case-control study design. Kenneth F. Schulz and David A. Grimes (2002) Case-control studies: research in reverse . The Lancet Volume 359, Issue 9304, 431 – 434

Quick, inexpensive, and simple

Because these studies use already existing data and do not require any follow-up with subjects, they tend to be quicker and cheaper than other types of research. Case-control studies also do not require large sample sizes.

Beneficial for studying rare diseases

Researchers in case-control studies start with a population of people known to have the target disease instead of following a population and waiting to see who develops it. This enables researchers to identify current cases and enroll a sufficient number of patients with a particular rare disease.

Useful for preliminary research

Case-control studies are beneficial for an initial investigation of a suspected risk factor for a condition. The information obtained from cross-sectional studies then enables researchers to conduct further data analyses to explore any relationships in more depth.

Limitations

Subject to recall bias.

Participants might be unable to remember when they were exposed or omit other details that are important for the study. In addition, those with the outcome are more likely to recall and report exposures more clearly than those without the outcome.

Difficulty finding a suitable control group

It is important that the case group and the control group have almost the same characteristics, such as age, gender, demographics, and health status.

Forming an accurate control group can be challenging, so sometimes researchers enroll multiple control groups to bolster the strength of the case-control study.

Do not demonstrate causation

Case-control studies may prove an association between exposures and outcomes, but they can not demonstrate causation.

A case-control study is an observational study where researchers analyzed two groups of people (cases and controls) to look at factors associated with particular diseases or outcomes.

Below are some examples of case-control studies:
  • Investigating the impact of exposure to daylight on the health of office workers (Boubekri et al., 2014).
  • Comparing serum vitamin D levels in individuals who experience migraine headaches with their matched controls (Togha et al., 2018).
  • Analyzing correlations between parental smoking and childhood asthma (Strachan and Cook, 1998).
  • Studying the relationship between elevated concentrations of homocysteine and an increased risk of vascular diseases (Ford et al., 2002).
  • Assessing the magnitude of the association between Helicobacter pylori and the incidence of gastric cancer (Helicobacter and Cancer Collaborative Group, 2001).
  • Evaluating the association between breast cancer risk and saturated fat intake in postmenopausal women (Howe et al., 1990).

Frequently asked questions

1. what’s the difference between a case-control study and a cross-sectional study.

Case-control studies are different from cross-sectional studies in that case-control studies compare groups retrospectively while cross-sectional studies analyze information about a population at a specific point in time.

In  cross-sectional studies , researchers are simply examining a group of participants and depicting what already exists in the population.

2. What’s the difference between a case-control study and a longitudinal study?

Case-control studies compare groups retrospectively, while longitudinal studies can compare groups either retrospectively or prospectively.

In a  longitudinal study , researchers monitor a population over an extended period of time, and they can be used to study developmental shifts and understand how certain things change as we age.

In addition, case-control studies look at a single subject or a single case, whereas longitudinal studies can be conducted on a large group of subjects.

3. What’s the difference between a case-control study and a retrospective cohort study?

Case-control studies are retrospective as researchers begin with an outcome and trace backward to investigate exposure; however, they differ from retrospective cohort studies.

In a  retrospective cohort study , researchers examine a group before any of the subjects have developed the disease, then examine any factors that differed between the individuals who developed the condition and those who did not.

Thus, the outcome is measured after exposure in retrospective cohort studies, whereas the outcome is measured before the exposure in case-control studies.

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

Ford, E. S., Smith, S. J., Stroup, D. F., Steinberg, K. K., Mueller, P. W., & Thacker, S. B. (2002). Homocyst (e) ine and cardiovascular disease: a systematic review of the evidence with special emphasis on case-control studies and nested case-control studies. International journal of epidemiology, 31 (1), 59-70.

Helicobacter and Cancer Collaborative Group. (2001). Gastric cancer and Helicobacter pylori: a combined analysis of 12 case control studies nested within prospective cohorts. Gut, 49 (3), 347-353.

Howe, G. R., Hirohata, T., Hislop, T. G., Iscovich, J. M., Yuan, J. M., Katsouyanni, K., … & Shunzhang, Y. (1990). Dietary factors and risk of breast cancer: combined analysis of 12 case—control studies. JNCI: Journal of the National Cancer Institute, 82 (7), 561-569.

Lewallen, S., & Courtright, P. (1998). Epidemiology in practice: case-control studies. Community eye health, 11 (28), 57–58.

Strachan, D. P., & Cook, D. G. (1998). Parental smoking and childhood asthma: longitudinal and case-control studies. Thorax, 53 (3), 204-212.

Tenny, S., Kerndt, C. C., & Hoffman, M. R. (2021). Case Control Studies. In StatPearls . StatPearls Publishing.

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

Further Information

  • Schulz, K. F., & Grimes, D. A. (2002). Case-control studies: research in reverse. The Lancet, 359(9304), 431-434.
  • What is a case-control study?

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Man in suit with binoculars

Introduction

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

Case-control studies

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

case control study groups

Figure 1. Case-control study design.

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

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

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

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

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

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

case control study groups

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

Cohort studies

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

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

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

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

case control study groups

Figure 2. Cohort study design.

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

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

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

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

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

case control study groups

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

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

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

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

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Very clear and informative! Thank you.

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very informative article.

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

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Very helpful information

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

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

Happy new year.

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

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Saul you absolute melt! Really good work man

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

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

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

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Apreciated the information provided above.

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

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Great to hear, thank you AJ!

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

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

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Very much helpful

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Study Design 101: Case Control Study

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

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

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

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

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

Disadvantages

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

Design pitfalls to look out for

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

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

Fictitious Example

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

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

Real-life Examples

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

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

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

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

Related Formulas

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

Related Terms

A patient with the disease or outcome of interest.

Confounding

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

A patient who does not have the disease or outcome.

Matched Design

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

Observed Assignment

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

Unmatched Design

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

Now test yourself!

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

a) True b) False

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

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

Evidence Pyramid - Navigation

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Quantitative study designs: Case Control

Quantitative study designs.

  • Introduction
  • Cohort Studies
  • Randomised Controlled Trial

Case Control

  • Cross-Sectional Studies
  • Study Designs Home

In a Case-Control study there are two groups of people: one has a health issue (Case group), and this group is “matched” to a Control group without the health issue based on characteristics like age, gender, occupation. In this study type, we can look back in the patient’s histories to look for exposure to risk factors that are common to the Case group, but not the Control group. It was a case-control study that demonstrated a link between carcinoma of the lung and smoking tobacco . These studies estimate the odds between the exposure and the health outcome, however they cannot prove causality. Case-Control studies might also be referred to as retrospective or case-referent studies. 

Stages of a Case-Control study

This diagram represents taking both the case (disease) and the control (no disease) groups and looking back at their histories to determine their exposure to possible contributing factors.  The researchers then determine the likelihood of those factors contributing to the disease.

case control study groups

(FOR ACCESSIBILITY: A case control study is likely to show that most, but not all exposed people end up with the health issue, and some unexposed people may also develop the health issue)

Which Clinical Questions does Case-Control best answer?

Case-Control studies are best used for Prognosis questions.

For example: Do anticholinergic drugs increase the risk of dementia in later life? (See BMJ Case-Control study Anticholinergic drugs and risk of dementia: case-control study )

What are the advantages and disadvantages to consider when using Case-Control?

* Confounding occurs when the elements of the study design invalidate the result. It is usually unintentional. It is important to avoid confounding, which can happen in a few ways within Case-Control studies. This explains why it is lower in the hierarchy of evidence, superior only to Case Studies.

What does a strong Case-Control study look like?

A strong study will have:

  • Well-matched controls, similar background without being so similar that they are likely to end up with the same health issue (this can be easier said than done since the risk factors are unknown). 
  • Detailed medical histories are available, reducing the emphasis on a patient’s unreliable recall of their potential exposures. 

What are the pitfalls to look for?

  • Poorly matched or over-matched controls.  Poorly matched means that not enough factors are similar between the Case and Control. E.g. age, gender, geography. Over-matched conversely means that so many things match (age, occupation, geography, health habits) that in all likelihood the Control group will also end up with the same health issue! Either of these situations could cause the study to become ineffective. 
  • Selection bias: Selection of Controls is biased. E.g. All Controls are in the hospital, so they’re likely already sick, they’re not a true sample of the wider population. 
  • Cases include persons showing early symptoms who never ended up having the illness. 

Critical appraisal tools 

To assist with critically appraising case control studies there are some tools / checklists you can use.

CASP - Case Control Checklist

JBI – Critical appraisal checklist for case control studies

CEBMA – Centre for Evidence Based Management  – Critical appraisal questions (focus on leadership and management)

STROBE - Observational Studies checklists includes Case control

SIGN - Case-Control Studies Checklist

NCCEH - Critical Appraisal of a Case Control Study for environmental health

Real World Examples

Smoking and carcinoma of the lung; preliminary report

  • Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report.  British Medical Journal ,  2 (4682), 739–748. Retrieved from  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/
  • Key Case-Control study linking tobacco smoking with lung cancer
  • Notes a marked increase in incidence of Lung Cancer disproportionate to population growth.
  • 20 London Hospitals contributed current Cases of lung, stomach, colon and rectum cancer via admissions, house-physician and radiotherapy diagnosis, non-cancer Controls were selected at each hospital of the same-sex and within 5 year age group of each.
  • 1732 Cases and 743 Controls were interviewed for social class, gender, age, exposure to urban pollution, occupation and smoking habits.
  • It was found that continued smoking from a younger age and smoking a greater number of cigarettes correlated with incidence of lung cancer.

Anticholinergic drugs and risk of dementia: case-control study

  • Richardson, K., Fox, C., Maidment, I., Steel, N., Loke, Y. K., Arthur, A., . . . Savva, G. M. (2018). Anticholinergic drugs and risk of dementia: case-control study. BMJ , 361, k1315. Retrieved from  http://www.bmj.com/content/361/bmj.k1315.abstract .
  • A recent study linking the duration and level of exposure to Anticholinergic drugs and subsequent onset of dementia.
  • Anticholinergic Cognitive Burden (ACB) was estimated in various drugs, the higher the exposure (measured as the ACB score) the greater likeliness of onset of dementia later in life.
  • Antidepressant, urological, and antiparkinson drugs with an ACB score of 3 increased the risk of dementia. Gastrointestinal drugs with an ACB score of 3 were not strongly linked with onset of dementia.
  • Tricyclic antidepressants such as Amitriptyline have an ACB score of 3 and are an example of a common area of concern.

Omega-3 deficiency associated with perinatal depression: Case-Control study 

  • Rees, A.-M., Austin, M.-P., Owen, C., & Parker, G. (2009). Omega-3 deficiency associated with perinatal depression: Case control study. Psychiatry Research , 166(2), 254-259. Retrieved from  http://www.sciencedirect.com/science/article/pii/S0165178107004398 .
  • During pregnancy women lose Omega-3 polyunsaturated fatty acids to the developing foetus.
  • There is a known link between Omgea-3 depletion and depression
  • Sixteen depressed and 22 non-depressed women were recruited during their third trimester
  • High levels of Omega-3 were associated with significantly lower levels of depression.
  • Women with low levels of Omega-3 were six times more likely to be depressed during pregnancy.

References and Further Reading

Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report. British Medical Journal, 2(4682), 739–748. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/

Greenhalgh, Trisha. How to Read a Paper: the Basics of Evidence-Based Medicine, John Wiley & Sons, Incorporated, 2014. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/deakin/detail.action?docID=1642418 .

Himmelfarb Health Sciences Library. (2019). Study Design 101: Case-Control Study. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casecontrols.cfm   

Hoffmann, T., Bennett, S., & Del Mar, C. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier. 

Lewallen, S., & Courtright, P. (1998). Epidemiology in practice: case-control studies. Community Eye Health, 11(28), 57.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/  

Pelham, B. W. a., & Blanton, H. (2013). Conducting research in psychology : measuring the weight of smoke /Brett W. Pelham, Hart Blanton (Fourth edition. ed.): Wadsworth Cengage Learning. 

Rees, A.-M., Austin, M.-P., Owen, C., & Parker, G. (2009). Omega-3 deficiency associated with perinatal depression: Case control study. Psychiatry Research, 166(2), 254-259. Retrieved from http://www.sciencedirect.com/science/article/pii/S0165178107004398

Richardson, K., Fox, C., Maidment, I., Steel, N., Loke, Y. K., Arthur, A., … Savva, G. M. (2018). Anticholinergic drugs and risk of dementia: case-control study. BMJ, 361, k1315. Retrieved from http://www.bmj.com/content/361/bmj.k1315.abstract

Statistics How To. (2019). Case-Control Study: Definition, Real Life Examples. Retrieved from https://www.statisticshowto.com/case-control-study/  

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Selecting & Defining Cases and Controls

The "case" definition, sources of cases, selection of the controls.

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Careful thought should be given to the case definition to be used. If the definition is too broad or vague, it is easier to capture people with the outcome of interest, but a loose case definition will also capture people who do not have the disease. On the other hand, an overly restrictive case definition is employed, fewer cases will be captured, and the sample size may be limited. Investigators frequently wrestle with this problem during outbreak investigations. Initially, they will often use a somewhat broad definition in order to identify potential cases. However, as an outbreak investigation progresses, there is a tendency to narrow the case definition to make it more precise and specific, for example by requiring confirmation of the diagnosis by laboratory testing. In general, investigators conducting case-control studies should thoughtfully construct a definition that is as clear and specific as possible without being overly restrictive.

Investigators studying chronic diseases generally prefer newly diagnosed cases, because they tend to be more motivated to participate, may remember relevant exposures more accurately, and because it avoids complicating factors related to selection of longer duration (i.e., prevalent) cases. However, it is sometimes impossible to have an adequate sample size if only recent cases are enrolled.

Typical sources for cases include:

  • Patient rosters at medical facilities
  • Death certificates
  • Disease registries (e.g., cancer or birth defect registries; the SEER Program [Surveillance, Epidemiology and End Results] is a federally funded program that identifies newly diagnosed cases of cancer in population-based registries across the US )
  • Cross-sectional surveys (e.g., NHANES, the National Health and Nutrition Examination Survey)

As noted above, it is always useful to think of a case-control study as being nested within some sort of a cohort, i.e., a source population that produced the cases that were identified and enrolled. In view of this there are two key principles that should be followed in selecting controls:

  • The comparison group ("controls") should be representative of the source population that produced the cases.
  • The "controls" must be sampled in a way that is independent of the exposure, meaning that their selection should not be more (or less) likely if they have the exposure of interest.

If either of these principles are not adhered to, selection bias can result (as discussed in detail in the module on Bias ).

case control study groups

Note that in the earlier example of a case-control study conducted in the Massachusetts population, we specified that our sampling method was random so that exposed and unexposed members of the population had an equal chance of being selected. Therefore, we would expect that about 1,000 would be exposed and 5,000 unexposed (the same ratio as in the whole population), and came up with an odds ratio that was same as the hypothetical risk ratio we would have had if we had collected exposure information from the whole population of six million:

What if we had instead been more likely to sample those who were exposed, so that we instead found 1,500 exposed and 4,500 unexposed among the 6,000 controls?   Then the odds ratio would have been:

This odds ratio is biased because it differs from the true odds ratio.   In this case, the bias stemmed from the fact that we violated the second principle in selection of controls. Depending on which category is over or under-sampled, this type of bias can result in either an underestimate or an overestimate of the true association.

A hypothetical case-control study was conducted to determine whether lower socioeconomic status (the exposure) is associated with a higher risk of cervical cancer (the outcome). The "cases" consisted of 250 women with cervical cancer who were referred to Massachusetts General Hospital for treatment for cervical cancer. They were referred from all over the state. The cases were asked a series of questions relating to socioeconomic status (household income, employment, education, etc.). The investigators identified control subjects by going door-to-door in the community around MGH from 9:00 AM to 5:00  PM. Many residents are not home, but they persist and eventually enroll enough controls. The problem is that the controls were selected by a different mechanism than the cases, AND the selection mechanism may have tended to select individuals of different socioeconomic status, since women who were at home may have been somewhat more likely to be unemployed. In other words, the controls were more likely to be enrolled (selected) if they had the exposure of interest (lower socioeconomic status). 

Toggle open/close quiz question

What is the purpose of the control group in a case-control study?

a.  To provide information on the disease distribution in the population that gave rise to the cases.

b.  To provide information on the exposure distribution in the population that gave rise to the cases.

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Content ©2016. All Rights Reserved. Date last modified: June 7, 2016. Wayne W. LaMorte, MD, PhD, MPH

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case control study groups

  • Health and social care
  • Public health
  • Health improvement

Case-control study: comparative studies

How to use a case-control study to evaluate your digital health product.

This page is part of a collection of guidance on evaluating digital health products .

A case-control study is a type of observational study. It looks at 2 sets of participants. One group has the condition you are interested in (the cases) and one group does not have it (the controls).

In other respects, the participants in both groups are similar. You can then look at a particular factor that might have caused the condition, such as your digital product, and compare participants from the 2 groups in relation to that.

A case-control study is an observational study because you observe the effects on existing groups rather than designing an experiment where participants are allocated into different groups.

What to use it for

A case-control study can help you to find out if your digital product or service achieves its aims, so it can be useful when you have developed your product (summative evaluation).

It can be a useful method when it would be difficult or impossible to randomise participants, for example, if your product aims to help people with rare health conditions.

Case-control studies have many benefits.

  • help to estimate the effects of your digital product when randomisation is not possible
  • use existing data, which could be cheaper and easier
  • operate with fewer participants compared to other designs

There can also be drawbacks of a case-control study.

For example:

  • you need to pay careful attention to factors that may influence your results, confounding factors and biases – see explanation in ‘How to carry out a case-control study’ below
  • there may be challenges when accessing pre-existing data
  • you cannot draw definitive answers about the effects of your product as you haven’t randomly selected participants for your evaluation

How to carry out a case-control study

In a traditional case-control design, cases and controls are looked at retrospectively – that is, the health condition and the factor that might have caused it have already occurred when you start the study.

Sources of cases and controls typically include:

  • routinely collected data at medical facilities
  • disease registries
  • cross-sectional surveys

Some researchers use the term prospective case-control study when, for example, a prospective group exposed to an intervention is compared to a retrospective control.

Choosing your control

Selecting an appropriate control is an important part of a case-control study. The comparison group should be as similar as possible to the source population that produced the cases. This means the participants will be similar to each other in terms of factors that may influence the outcomes you’re looking at. Ideally, they will only differ in whether they received your digital product (cases) or not (controls).

There are 2 main types of case-control design: matched and unmatched.

Essentially, in an unmatched case-control design, a shared control group is selected for all cases at random given certain attributes. In a matched case-control design, controls are selected case-by-case based on specified characteristics. You should pick characteristics that have an effect on the usage of digital devices and services.

Commonly used matching factors include:

  • socio-economic status

However, think about other characteristics and attributes that might influence the use of your product, and the subsequent outcomes.

Confounding variables and biases

Confounding variables (variables other than the one you are interested in that may influence the results) and biases (errors that influence the sample selected and results observed) are important to consider when conducting any research. This is especially important in designs that are non-randomised.

  • selection bias can happen when participants are assigned without randomisation
  • attribution bias may occur when patients with unfavourable outcomes are less likely to attend follow-ups

Analysing your data

The analysis most commonly used in case-control studies is an odds ratio, which is the chance (odds) of the outcomes occurring in the case group versus the control group.

Example: Can telemedicine help with post-bariatric surgery care? A case-control design

In 2019, Wang and colleagues published a paper entitled Exploring the Effects of Telemedicine on Bariatric Surgery Follow-up: a Matched Case Control Study .

The study showed that people who go through bariatric surgery have better outcomes if they attend their follow-up appointments after surgery in comparison to those who do not. However, attending appointments can be challenging for people who live in remote areas. In Ontario, Canada, telemedicine suites were set up to enable healthcare provider-patient videoconferencing.

The researchers used a matched case-control study to investigate if telemedicine videoconferencing can support post-surgery appointment attendance rates in people who live further away from the hospital sites. They used the existing data from the bariatric surgery hospital programme to identify eligible patients.

All patients attending the bariatric surgery were offered telemedicine services. The cases were the participants who used telemedicine services; they were compared to those who did not (the controls).

Cases and controls were matched on various characteristics, specifically:

  • time since bariatric surgery
  • body mass index ( BMI )
  • travel distance from the hospital site

Researchers measured:

  • the percentage of appointments attended
  • rates of dropout
  • pre-and post-surgery weight and BMI
  • various physical and psychological outcomes

They also calculated rurality index to classify patients into urban, non-urban and rural areas. These variables were used to compare cases (those who used telemedicine) and controls (those who did not).

During the study period, they identified that 487 patients of 1,262 who received bariatric surgery used telemedicine services. Of those, 192 agreed to participate in the study.

They found that patients who used telemedicine did as well as patients who attended in person, both in terms of appointment attendance rates and in terms of physical and psychological outcomes.

Moreover, the researchers found that the cases (telemedicine users) came from more rural areas than the controls. The authors argued that this demonstrated that telemedicine can help overcome the known challenges for patients in more rural areas to attend appointments.

Randomising patients to telemedicine or withdrawing the telemedicine would be difficult, undesirable and possibly unethical. Case-control was a good alternative to assess the potential impact on patient outcomes in a service that is already up and running.

More information and resources

A 2003 study by Mann provides an accessible overview of observational research methods, including an explanation of biases and confounding variables.

On the website for Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ), there is a checklist of items that should be included in reports of case-control studies .

A 2016 study by Pearce offers considerations for the analysis of a matched case-control study.

Examples of case-control studies in digital health

In a 2020 study by Heuvel and others , researchers assessed a new digital health tool to monitor women at increased risk of preeclampsia at home. They investigated if the digital tool allows for fewer antenatal visits without compromising women’s safety, and whether it positively affects pregnancy outcomes. This study used a prospective case group compared to a retrospective control group.

In a 2019 study by Depp and others , the research team examined whether schizophrenia symptoms were associated with mobility (measured using GPS sensors). They compared participants with schizophrenia to healthy controls and they found that less mobility was associated with greater symptoms of schizophrenia.

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  • Open access
  • Published: 09 May 2024

Is the MIND diet useful for polycystic ovary syndrome? A case-control study

  • Mina Darand 1 ,
  • Narges Sadeghi 3 , 4 ,
  • Zahra Salimi 5 ,
  • Mahlagha Nikbaf-Shandiz 5 ,
  • Asieh Panjeshahin 2 , 7 ,
  • Hawal Lateef Fateh 6 &
  • Mahdieh Hosseinzadeh 2 , 7  

BMC Women's Health volume  24 , Article number:  282 ( 2024 ) Cite this article

Metrics details

Polycystic ovary syndrome (PCOS) is the most prevalent cause of ovulatory infertility and endocrine abnormalities in reproductive-age women. Although the MIND diet has been introduced to improve brain function, evidence shows that the MIND diet is rich in beneficial food groups that can have a preventive effect on other metabolic disorders. The present study was conducted to investigate the association between adherence to the MIND diet and PCOS.

This age and BMI frequency-matched case-control study was conducted on 216 women between January 2018 and March 2019 in Yazd, Iran. PCOS was diagnosed based on Rotterdam criteria. Participants were selected by convenience sampling method. The validated 178-item food frequency questionnaire was used to assess the usual dietary intake. Logistic regression was used to estimate the association between the MIND diet and PCOS.

The findings of the present study showed a significant inverse association between adherence to the MIND diet and PCOS in the crude model (OR for T3 vs. T1: 0.12 (95% CI: 0.05–0.25), P-value < 0.001) and multivariable-adjusted model including energy intake, age, BMI, waist circumference, marital status, pregnancy history, drug use history, education and physical activity (OR for T3 vs. T1 = 0.08 (95% CI: 0.03–0.19), P-value < 0.001). Adherence to the MIND diet had a protective effect of 92%.

Although the results of the present study showed that higher adherence to the MIND diet is associated with a lower risk of PCOS, more studies are needed to confirm these findings in the future.

Peer Review reports

One of the common endocrine metabolic disorders in women of reproductive age is polycystic ovary syndrome (PCOS), which is associated with reproductive, metabolic, and psychological features [ 1 ]. The prevalence of PCOS in women of reproductive age is 6 to 18% depending on the diagnostic criteria used and the population studied [ 2 , 3 , 4 , 5 ]. PCOS was diagnosed based on two of three following Rotterdam criteria: [ 1 ] anovulation and/or irregular menstruation (infrequent periods) [ 2 ], biochemical and/or clinical hyperandrogenism, and [ 3 ] polycystic ovaries (≥ 12 follicles measuring 2–9 mm in diameter and/or an ovarian volume > 10 mL in at least one ovary) [ 6 ]. PCOS is associated with intrinsic insulin resistance (IR), which in turn worsens the hormonal and clinical features of polycystic ovarian syndrome [ 1 , 7 , 8 ]. Rates of overweight, obesity and central obesity are higher in women with PCOS than those without PCOS which aggravates IR [ 9 , 10 , 11 ]. The first line of treatment considered for PCOS is lifestyle modification including adopting a healthy diet, regular exercise, and psychological support alongside drug therapy [ 12 ]. Previous reports suggest that, the diet of patients with PCOS is high in carbohydrates and fat, which increases the lipid inflammatory environment and IR of patients to some extent [ 13 ] and causes the progression of the disease. On the other hand, dietary modification and following the DASH, hypocaloric, Mediterranean, low-glycemic, and low-carbohydrate diets had improved BMI, insulin resistance, menstrual irregularity, and decreased testosterone levels in PCOS patients [ 14 ].

The MIND diet (a combination of the Mediterranean-DASH diet) has recently been recognized as a new dietary pattern, including 15 components, 10 of which are brain-healthy foods (green leafy vegetables, other vegetables, whole grains, beans, nuts, berries, olive oil, fish, chicken, and wine) and 5 of which are brain unhealthy foods (butter or margarine, cheese, red meat, fast foods or fried foods, and sweets or pastries) [ 15 ]. Although the association between the MIND diet and PCOS has not been investigated so far, few studies have examined the effect of this dietary pattern on chronic diseases. For instance, Mohammadpour et al. (2020) reported that adherence to the MIND diet significantly reduced general obesity but did not affect the likelihood of metabolic syndrome and abdominal obesity [ 16 ]. In contrast, Aminianfar et al. (2020) did not find a significant relationship between adherence to the MIND diet and the presence of central and general obesity in adult participants [ 17 ]. Although we have not yet reached definitive results about the effect of the MIND diet on PCOS, it seems that the components of this diet (especially one of its main components, olive oil) help to improve oxidative stress and, as a result, insulin resistance which is one of the major risk factors associated of PCOS [ 18 , 19 , 20 ].

Due to the continuous increase in the prevalence of PCOS and its related risk factors in recent decades and the increase in the rate of obesity, the prevalence of unhealthy dietary patterns and following them, we aimed to examine the association between adherence to the MIND diet and PCOS in the Iranian population.

Study design and participants

This frequency-matched case-control study was conducted on 216 women between January 2018 and March 2019. A sample of 108 PCOS newly-diagnosed patients (aged 18 to 45 years) was selected from women referred to Yazd Diabetes Clinic and Khatam Clinic in Yazd. Women with PCOS were diagnosed by an endocrinologist and based on Rotterdam criteria and the presence of at least two of the three following criteria: menstrual irregularities, clinical or biochemical signs of hyperandrogenism, and polycystic ovaries (≥ 12 follicles measuring 2–9 mm in diameter and/or an ovarian volume > 10 mL in at least one ovary) [ 21 – 23 ]. Women without a history of diseases such as hypothyroidism, hyperprolactinemia, congenital adrenal hyperplasia, Cushing syndrome or food allergies, and type 1 diabetes; without a history of using medications such as hormonal drugs, contraceptive pills, or other medicines that could change the androgens levels; women who did not drink alcohol and were not smokers; women who did not follow a specific diet in the last year and did not take nutritional supplements in the past three months and non-pregnant and non-lactating women were recruited. The control group included 108 women without PCOS (lacking Rotterdam diagnostic criteria) who had been referred to other departments of the same clinic such as orthopedics, dentistry, or optometry. Healthy controls were matched to PCOS women based on age and BMI. About 236 subjects were introduced by expert endocrinologist diagnosis to our study (117 subjects for the case group and 119 for the control). Finally, for the case group: 2 women were not willing to participate, and 7 had allergies to foods. for the control group: 5 women were not willing to participate, 4 cases were not newly diagnosed, and 2 had allergies to foods; as a result, 216 women including 108 cases (response rate (92%)) and 108 controls (response rate (90%)) completed study based on matching for age and BMI. Although the number of subjects in the control group was slightly higher than the case group, but after matching for age and BMI with the case group, 108 participants remained in each group. Other inclusion criteria were almost identical for the case and control groups. The participants’ recruitment procedures are represented in Fig.  1 .

figure 1

The participants’ recruitment procedures

Sample size calculation

Due to the limited number of similar articles, the appropriate reference for determining the sample size, considering alpha of 0.05 and a power of 90%, assuming that there is a 20% difference in adherence to dietary pattern in the two groups (P1 = 40%, P2 = 60%), and a 10% probability of sample loss, the minimum required sample size was calculated to be 108 women in each group.

P1 = the ratio of people who followed the dietary pattern among the women without.

PCOS P2 = the ratio of people who followed the dietary pattern among the women with PCOS.

Where \(P=\frac{{P}_{1}+{P}_{2}}{2}\) , \(Q=1-P\) , \({Q}_{1}=1-{P}_{1}\) , \({Q}_{2}=1-{P}_{2}\)

Anthropometric measurements

Body weight and height (in a fasting condition, with light clothes and no shoes) were measured using Omron digital scale to the nearest 0.1 kg and a nonstretched wall-mounted tape measure to the nearest 0.1 cm, respectively. BMI was computed as the ratio of measured weight in kilograms to height in meters squared and waist circumference (WC) was measured using a non-stretchable tape measure to the nearest 0.5 cm.

Physical activity assessment and other covariates

Physical activity level was evaluated by an International Physical Activity Questionnaire-Short Form (IPAQ-SH) and responses were presented to Metabolic Equivalent Task minutes per week (MET-min/week) [ 24 ]. Based on the level of physical activity, people were divided into 3 categories: inactive people, moderate people and active people. Inactive people: reported meth less than 600 MET-minute/ week Moderately active individuals: minimum reported MET greater than 600 MET-minute/ week People with intense meth activity more than 3000 MET-minute/ week [ 25 , 26 ]. Needed information including age, marital status, pregnancy history, Drug used history (anti-diabetic and anti-hypertensive drugs) and education was obtained using a validated self-administered questionnaire.

Dietary intake assessment

In a direct interview by a blinded nutritionist, participants’ typical dietary intake over the previous year was obtained using a 178-item semiquantitative food frequency questionnaire (FFQ) [ 27 ], which its validity has been approved in the previous studies [ 28 ]. The frequency intake of each food item was reported as daily, weekly, monthly, or yearly. Individuals’ food intake was converted to grams using the household scale guideline. Then, total energy and nutrient intake were calculated by transferring food intake (g/d) to Nutritionist IV.

MIND diet score

The MIND diet score includes 15 dietary components, 10 of which were recognized as brain-healthy food categories (green leafy vegetables, other vegetables, berries, nuts, beans, poultry, fish, whole grains, olive oil, and wine). The remaining items were known as brain-unhealthy food categories (red meats, butter and margarine, cheese, pastries and sweets, and fried/fast foods). In the present study, due to the lack of information, the score of wine consumption was not considered. As a result, 14 food categories were included in the MIND dietary pattern. The participants were categorized into tertile groups based on their intake of 14 components. Those in the lowest, middle, and highest tertiles of brain-healthy food intake were assigned scores of 0, 0.5, and 1, respectively. Conversely, individuals in the lowest tertile of brain-unhealthy food intake received a score of 1, while those in the middle and highest tertiles were assigned scores of 0.5 and 1, respectively. The total Mind score for each participant was calculated by adding up the scores for all dietary items. Finally, each participant was given a score ranging from 0 to 14 based on this calculation [ 29 ].

Statistical analysis

Kolmogorov-Smirnov test was used to examine the normal distribution of quantitative variables and then, categorical and quantitative variables were presented as frequency (percentage) and mean ± standard deviation (SD), respectively. Chi-squared and an independent t-test was performed for inter-group differences of quantitative and qualititative variables, respectively. Also, the comparison of dietary intakes between MIND tertiles was done by the one-way ANOVA test. Multivariate logistic regression was used in different models to investigate the association between MIND diet and PCOS. Model 1 was adjusted for energy intake. Further adjustment was for age, BMI, waist circumference, marital status, pregnancy history, drug use history and education. Physical activity was additionally adjusted in the model III. Data analysis was done using SPSS software version 24 (IBM, Armonk, NY, USA) and a P-value less than 0.05 was considered statistically significant.

Table  1 represents the general characteristics of participants in both groups (women with and without PCOS). The distribution of age, BMI, physical activity, marital status, pregnancy history, drug use history, and education was not different between the two groups (P˃0.05). The mean waist circumference was marginally significantly higher in women with PCOS than in healthy women. (P˃0.05).

Table  2 reports the characteristics of the study participants across tertiles of MIND diet scores. After MIND diet scores were categorized into three tertiles, no significant differences were observed in mean age, BMI, WC, marital status, pregnancy history, drug use for PCOS, education, and physical activity. Nevertheless, the prevalence of PCOS with increased adherence to the MIND diet significantly decreased ( P  < 0.001).

Dietary intakes of study participants based on tertiles of MIND diet scores are presented in Table  3 . Participants in the highest tertile of the MIND diet score had significantly greater intakes of carbohydrates, magnesium, folate, green leafy vegetables, other vegetables, whole grains, fish, and beans than those in the first tertile ( P  < 0.05). Conversely, their intake of fat, cholesterol, saturated fatty acids, monounsaturated fatty acids, butter, margarine, cheese, red meat and products, fast fried foods, and pastries and sweets were significantly lower ( P  < 0.05).

Crude and multivariable-adjusted odds ratios (95% confidence intervals) for PCOS across tertiles of MIND score are demonstrated in Table  4 . There was a significant inverse association between adherence to the MIND diet and the odds of PCOS in the crude model. Women in the highest tertile of the MIND diet score compared to those in the lowest tertile had 88% lower odds of PCOS (OR for T3 vs. T1: 0.12; 95% CI: 0.05–0.25, P-trend < 0.001). This association remained significant after adjustment for energy intake, age, BMI, waist circumference, marital status, pregnancy history, drug use history, education, and physical activity (OR for T3 vs. T1: 0.08; 95% CI: 0.03–0.20, P  < 0.001).

To the best of our knowledge, this is the first case-control study that investigated the association between adherence to the MIND diet and odds of PCOS. A significant inverse association between adherence to the MIND diet and PCOS was found, in such a way that whatever the score adherence to the MIND diet was higher, the occurrence of PCOS was lower.

Although no study examines the association between the MIND diet and PCOS, some studies investigated the relationships between the MIND diet and other metabolic disorders relevant to PCOS, such as obesity, metabolic syndrome, and CVD [ 30 , 31 , 32 ]. According to the Mohammadpour et al. study, higher adherence to the MIND diet was associated with an increased risk of general obesity but a reduced risk of low-HDL-C levels. However, adherence to the MIND diet did not affect the risk of metabolic syndrome or abdominal obesity [ 32 ]. Aminianfar et al. reported that MIND diet adherence was not significantly correlated with both central and general obesity among adults [ 31 ].

Although the causes of PCOS are not yet fully understood, insulin resistance has been implicated as a significant factor [ 33 ]. Being overweight and obese can worsen insulin resistance and features of metabolic syndrome, which is also a common finding in PCOS [ 34 , 35 ]. The MIND dietary pattern is a combination of the Mediterranean (MD) and Dietary Approaches to Stop Hypertension (DASH) dietary patterns. It is believed to have some advantages over these two eating patterns. The advantage of the MIND dietary pattern is assigning separate groups for green leafy vegetables and berries, as well as cakes and pastries [ 34 ]. The beneficial effects of the MIND diet may have been relevant to the use of olive oil as a major source of dietary fats and phenolic compounds [ 36 ]. Some trial studies reported that oleic acid had anti-inflammatory properties and improved insulin resistance [ 37 ]. As it is clear from the results of our study, with the increased adherence to the MIND dietary pattern from tertile 1 to 3, the mean magnesium intake has increased significantly. Components of the MIND diet are rich sources of magnesium, which has a crucial role in regulating several biological processes in the human body [ 38 ]. Specifically, magnesium regulates the insulin receptors and improves insulin receptor sensitivity by increasing tyrosine kinase activity [ 39 ]. Magnesium also down-regulates the inflammatory response by inhibiting NF-κB, as well as magnesium reduces insulin resistance by restoring antioxidant enzyme activity and scavenging oxygen radicals [ 40 , 41 , 42 , 43 ].

Previous studies showed a strong association between insulin resistance and elevated serum homocysteine levels in PCOS patients and obese women [ 44 , 45 ]. On the other hand, there is an association between inadequate folate intake and higher circulating homocysteine concentrations [ 46 ]. The MIND diet, as a plant-based diet, is a rich source of folic acid due to its recommendation of whole grains, beans, fruits, and vegetables, especially green leafy vegetables, which appear useful for PCOS patients [ 47 ].

Here are some strengths attributed to the current study. This is the first case-control study that investigated the association between adherence to the MIND diet and odds of PCOS. Moreover, enrollment of newly diagnosed subjects declined the recall bias. However, it should be noted that this study had some limitations due to the case-control design, and no causal associations can be identified. Due to the retrospective nature of the FFQ, the probability of recall bias should be addressed. The residual confounders cannot be removed while we have controlled for possible confounding. This study was conducted on a small population of women, so it cannot be generalized to all PCOS patients, and more studies on a larger scale should be done.

The results of the current study showed a significant inverse association between PCOS and adherence to the MIND diet. Given the limitations we had in the study, further mechanism-based investigations on a larger scale are needed to confirm the results.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

  • Polycystic ovary syndrome

European Society for Human Reproduction and Embryology/American Society for Reproductive Medicine

Mediterranean-DASH Intervention for Neurodegenerative Delay

International Physical Activity Questionnaire-Short

Metabolic Equivalent Task

Food Frequency Questionnaire

Tehran Lipid and Glucose Study

Body Mass Index

Physical Activity

Waist Circumference

Hip Circumference

Monounsaturated Fatty Acid

Polyunsaturated Fatty Acid

Saturated Fatty Acid

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Acknowledgements

Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors did not receive support from any organization for the submitted work.

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Department of Nutrition, School of Allied Medical Science, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

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Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

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All authors contributed to the study conception and design. M.H. supervision, study concept and design analysis of data, review, and editing. M.D. drafting and analysis of data, statistical analysis. N.S., Z.S., and M.N. writing the manuscript. A.P. and H.L. revising and final approval of the version to be submitted. All authors reviewed the manuscript and approved the final manuscript submitted for publication.

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Correspondence to Mahdieh Hosseinzadeh .

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Darand, M., Sadeghi, N., Salimi, Z. et al. Is the MIND diet useful for polycystic ovary syndrome? A case-control study. BMC Women's Health 24 , 282 (2024). https://doi.org/10.1186/s12905-024-03090-3

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DOI : https://doi.org/10.1186/s12905-024-03090-3

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  • Polycystic ovarian syndrome

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ISSN: 1472-6874

case control study groups

A case-control study of the clinical and economic impact of infections caused by Carbapenemase-producing Enterobacterales (CPE)

Affiliations.

  • 1 Infectious Disease Service, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim de La Barceloneta, 25-29, 08003, Barcelona, Spain.
  • 2 Universitat Pompeu Fabra (UPF), Barcelona, Spain.
  • 3 CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III (CIBERINFEC ISCIII), Madrid, Spain.
  • 4 Microbiology Service, Laboratori de Referència de Catalunya, El Prat de Llobregat (Barcelona), Spain.
  • 5 Methodology and Biostatistics Support Unit, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain.
  • 6 Infectious Disease Service, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim de La Barceloneta, 25-29, 08003, Barcelona, Spain. [email protected].
  • 7 Universitat Pompeu Fabra (UPF), Barcelona, Spain. [email protected].
  • 8 CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III (CIBERINFEC ISCIII), Madrid, Spain. [email protected].
  • PMID: 38700659
  • DOI: 10.1007/s15010-024-02268-z

Purpose: The aim was to analyse the clinical and economic impact of carbapenemase-producing Enterobacterales (CPE) infections.

Methods: Case-control study. Adult patients with CPE infections were considered cases, while those with non-CPE infections were controls. Matching criteria were age (± 5 years), sex, source of infection and microorganism (ratio 1:2). Primary outcome was 30-day mortality. Secondary outcomes were 90-day mortality, clinical failure, hospitalisation costs and resource consumption.

Results: 246 patients (82 cases and 164 controls) were included. Klebsiella pneumoniae OXA-48 was the most common microorganism causing CPE infections. CPE cases had more prior comorbidities (p = 0.007), septic shock (p = 0.003), and were more likely to receive inappropriate empirical and definitive antibiotic treatment (both p < 0.001). Multivariate analysis identified septic shock and inappropriate empirical treatment as independent predictors for 7-day and end-of-treatment clinical failure, whereas Charlson Index and septic shock were associated with 30- and 90-day mortality. CPE infection was independently associated with early clinical failure (OR 2.18, 95% CI, 1.03-4.59), but not with end-of-treatment clinical failure or 30- or 90-day mortality. In terms of resource consumption, hospitalisation costs for CPE were double those of the non-CPE group. CPE cases had longer hospital stay (p < 0.001), required more long-term care facilities (p < 0.001) and outpatient parenteral antibiotic therapy (p = 0.007).

Conclusions: The CPE group was associated with worse clinical outcomes, but this was mainly due to a higher comorbidity burden, more severe illness, and more frequent inappropriate antibiotic treatment rather than resistance patterns as such. However, the CPE group consumed more healthcare resources and incurred higher costs.

Keywords: Carbapenem-Resistant Enterobacterales; Carbapenemase; Hospital costs; Mortality; Multiple drug resistance.

© 2024. The Author(s).

Association between BMI and oncologic outcomes in epithelial ovarian cancer: a predictors-matched case-control study

  • Gynecologic Oncology
  • Open access
  • Published: 07 May 2024

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case control study groups

  • Gabriel Levin   ORCID: orcid.org/0000-0003-1282-5379 1 ,
  • Yoav Brezinov 2 ,
  • Yossi Tzur 2 ,
  • Tomer Bar-Noy 2 ,
  • Melica Nourmoussavi Brodeur 1 ,
  • Shannon Salvador 1 ,
  • Susie Lau 1 &
  • Walter Gotlieb 1  

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We aimed to study the association between obesity and survival in ovarian cancer (OC) patients, accounting for confounders as disease stage, histology, and comorbidities.

Retrospective matched case-control study of consecutive patients, with epithelial OC. Obese (body mass index [BMI] ≥ 35 kg m −2 ) patients were matched in a 1:4 ratio with patients having lower BMIs (BMI < 35 kg m −2 ) based on disease stage, cytoreduction state, tumor histology and ASA score. We compared the 3-year and total recurrence-free survival and overall survival through Kaplan–Meier survival curves and Cox proportional hazards.

Overall, 153 consecutive patients were included, of whom 32 (20.9%) had a BMI ≥ 35. and 121 a BMI < 35. The median follow-up time was 39 months (interquartile range 18–67). Both study groups were similar in multiple prognostic factors, including American Society of Anesthesiologists physical status, completion of cytoreduction, histology and stage of disease ( p  = 0.981, p  = 0.992, p  = 0.740 and p  = 0.984, respectively). Ninety-five (62.1%) patients underwent robotic surgery and conversion rate from robotic to laparotomy was similar in both groups 2 (6.3%) in obese group vs. 6 (5.0%) in lower BMI patients, p  = 0.673. During the follow-up time, the rate of recurrence was similar in both groups; 21 (65.6%) in obese group vs. 68 (57.1%), p  = 0.387 and the rate of death events was similar; 16 (50.0%) in obese group vs. 49 (40.5%), p  = 0.333). The 3-year OS was higher in the obese group (log rank p  = 0.042) but the 3-year RFS was similar in both groups (log rank p  = 0.556). Median total OS was similar in both groups 62 months (95% confidence interval 25–98 months) in obese vs. 67 months (95% confidence interval 15–118) in the lower BMI group, log rank p  = 0.822. Median RFS was similar in both groups; 61 months (95% confidence interval 47–74) in obese, vs. 54 (95% confidence interval 43–64), log rank p  = 0.842. In Cox regression analysis for OS, including obesity, age, laparotomy and neoadjuvant treatment – only neoadjuvant treatment was independently associated with longer OS: odds ratio 1.82 (95% confidence interval 1.09–3.05) and longer RFS: odds ratio 2.16 (95% confidence interval 1.37–3.41).

Conclusions

In the present study on consecutive cases of ovarian cancer, obesity did not seem to be associated with outcome, except for an apparent improved 3-year survival that faded away thereafter.

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case control study groups

Impact of body mass index on ovarian cancer survival varies by stage

case control study groups

Obesity and survival among women with ovarian cancer: results from the Ovarian Cancer Association Consortium

Severe obesity impacts recurrence-free survival of women with high-risk endometrial cancer: results of a french multicenter study.

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Introduction

Ovarian cancer (OC) is the deadliest of gynecologic malignancies [ 1 , 2 ], with an average lifetime risk of approximately 1.5%, [ 1 , 3 ]. It is mostly diagnosed at a late stage and the stage at diagnosis is the most important predictor of overall survival [ 3 ]. Other main prognostic factors, among others, are age, tumor grade, and the amount of residual disease after cytoreduction [ 4 ].

In general, obesity is associated with greater mortality in patients with cancer [ 5 ]. However, in some specific cancers such as lung cancer, renal cell carcinoma, and melanoma obese patients have longer survival than their slimmer counterparts [ 5 ]. Several studies, including meta-analyses, have examined the association between obesity and OC survival, yielding conflicting results [ 6 ].

Considering the conflicting evidence, this study addresses the association of obesity with survival in OC patients taking into account confounders such as disease stage, histology, and comorbidities.

Materials and methods

Study population.

The study population was composed of all consecutive patients who were diagnosed with and treated for International Federation of Gynecology and Obstetrics (FIGO) stages I–IV epithelial OC, including serous, endometrioid, clear cell and mucinous types, between 2006 and 2022. We categorized patient by body mass index (BMI) into two groups: (1) obese (BMI ≥ 35 kg m −2 ) and (2) lower BMI (BMI < 35 kg m −2 ).

Study design

We matched cases (obese) to controls (lower BMI) in a 1:4 ration, matched by disease stage, cytoreduction status at end of surgery, American Society of Anesthesiologists (ASA) score and tumor histology. No tolerance for matching was allowed. Cases with no matching controls—were excluded ( n  = 8). Data for the study were collected retrospectively from a prospectively maintained computerized database. We extracted information for each patient, including age, BMI, histologic type, grade, International Federation of Gynecology and Obstetrics (FIGO) stage, extent of cytoreduction at surgery and residual disease, administration of neoadjuvant chemotherapy (NACT), and type of surgery (robotic vs. laparotomy vs. conversion from robotics to laparotomy). The extent of cytoreduction was defined as R0 if no macroscopic disease was observed at the end of the operation; R1 cytoreduction was defined as total disease measuring less than 1 cm, and R2 was defined as total disease measuring more than 1 cm.

Overall survival (OS) was defined as the time from diagnosis to either last follow-up or death. In patients who had recurrence-progression-free survival (PFS) was defined as the time from diagnosis to recurrence. Recurrences were diagnosed clinically by patient symptoms or radiologically when triggered by abnormal CA-125 levels.

Statistical analysis

Statistical analysis was performed using SPSS 29 (IBM, Chicago, IL). Matching of cases to controls was performed by SPSS. Statistical significance was calculated using the chi-square test and Fischer’s exact test for differences in categorical variables, and the Mann–Whitney U test for continuous variables. Kaplan–Meier survival curves were used to calculate survival estimates (PFS and OS), and the log-rank test was used to quantify survival differences according to different variables. Cox regression analysis was performed to determine independent factors associated with OS and PFS.

Ethics approval

An institutional review board approval (protocol #15–070) was granted for this study, with yearly reviews.

Overall, 153 patients were included in this study during 2006–2022, 32 (20.9%) obese patients (BMI ≥ 35.0) were matched with 121 patients with lower BMI. The median follow-up time was 39 months [interquartile range 18–67] and was similar in both groups ( p  = 0.572) (Table  1 ). The median BMI of the study groups were as follows: Obese 38.0 [IQR 36.0–41.9], lower BMI 24.6 [IQR 21.2–27.8]. Both study groups had similar ASA, residual disease after cytoreduction, histology and stage of disease ( p  = 0.981, p  = 0.992, p  = 0.740 and p  = 0.984, respectively). Ninety-five (62.1%) patients underwent robotic surgery and conversion rate from robotic to laparotomy was similar in both groups 2 (6.3%) in obese group vs. 6 (5.0%) in lower BMI patients, p  = 0.673. During the follow-up time, the rate of recurrence was similar in both groups; 65.6% (21 patients) in the obese group vs. 57.1% (68 patients), p  = 0.387 and the rate of death events was similar; 50.0% (16 patients) in the obese group vs. 40.5% (49 patients), p  = 0.333. Figure  1 presents 3-year OS in both study groups. The 3-year OS was higher in the obese group (log rank p  = 0.042). Figure  2 present 3-year RFS, which was similar in both group (log rank p  = 0.556). Survival curves for total OS and RFS are presented in Figs.  3 and 4 . Median total OS was similar in both groups 62 months (95% confidence interval 25–98 months) in obese vs. 67 months (95% confidence interval 15–118) in lower BMI group, log rank p  = 0.822. Median RFS was similar in both groups; 61 months (95% confidence interval 47–74) in obese vs. 54, 95% (confidence interval 43–64), log rank p  = 0.842. Table 2 presents Cox regression for total OS, including obesity, age, laparotomy, and neoadjuvant treatment. Neoadjuvant treatment was the only independent factor associated with longer OS: odds ratio 1.82 (95% confidence interval 1.09–3.05). Table 3 presents Cox regression for RFS. Once again, neoadjuvant treatment was the only independent factor associated with longer RFS: odds ratio 2.16 (95% confidence interval 1.37–3.41).

figure 1

3-year overall survival of obese vs. lower BMI OC patients

figure 2

3-year recurrence-free survival of obese vs. lower BMI OC patients

figure 3

Total overall survival of obese vs. lower BMI OC patients

figure 4

Total recurrence-free survival of obese vs. lower BMI OC patients

The major finding from this study is that obese women (BMI ≥ 35 kg m −2 ) have similar OS to their slimmer counterpart. There is an advantage in 3-year OS for obese patients, probably driven by lower recurrence rates during this period, and OS curves meet back at 5-years of follow-up. The only independent factor associated with OS and RFS is treatment with neoadjuvant chemotherapy.

Obesity has emerged as a significant factor influencing survival outcomes in many malignancies [ 7 ]. As it is theoretically modifiable, it is drawing increasing attention from the medical community. While the prevalence of obesity continues to rise, understanding and addressing the impact of obesity on OC survival is becoming increasingly crucial, with potential implications for clinical management and public health strategies. In OC, research has inconsistently shown different results. Some studies support that obese individuals with OC face a higher risk of both diagnosis at advanced stages and poorer OS [ 8 , 9 , 10 , 11 ]. These studies raise several mechanisms that possibly underlie this association, including chronic inflammation, hormonal imbalances, and insulin resistance, which can facilitate the growth and spread of OC. Furthermore, obesity can complicate surgical and chemotherapy interventions, potentially leading to suboptimal treatment outcomes. However, other studies found contrasting results [ 12 , 13 , 14 , 15 ], suggesting the ‘obesity paradox’ [ 16 ] . Furthermore, many studies have found no association of BMI with OC survival [ 17 , 18 , 19 ].

Importantly, many of these studies did not account for prognostic factors other than BMI, which may explain these conflicting findings. It is therefore, that our study, which matched patients for comorbidities (e.g., ASA), disease stage, level of cytoreduction and the type of OC — sheds some additional light on the apparent controversy.

In our study, the 3-year survival rate was superior in obese patients. The recurrence-free survival in the first 3-years did not reach statistical significance, however the curves do separate. It is suggested that the superior 3-year OS may be driven by lower recurrence among obese patients in this period. One explanation may be that chemotherapy doses are calculated by weight formulas, and obese patients receive higher doses if not reduced as per patient’s tolerance [ 20 ]. However, it is possible that this gap may be explained by the obesity paradox.

The concept of the “obesity paradox” in cancer highlightens the unexpected phenomenon where obese individuals exhibit better outcomes in certain cancer types. While obesity is a well-established risk factor for various malignancies, recent studies have suggested that obese patients with cancer, particularly those with advanced stages or undergoing aggressive treatments, might experience improved survival rates. This intriguing observation has prompted extensive research into potential mechanisms underlying this paradox, such as enhanced energy reserves, metabolic adaptations, and differences in tumor biology [ 21 , 22 , 23 ].

Our study results indicating that obesity has no independent association with overall survival is in line with some previous literature [ 17 , 18 ], however others have showed adverse outcomes [ 24 ], although none of these studies accounted for confounders as in our study.

Interestingly, the use of neoadjuvant chemotherapy was the only independent factor associated with improved survival in our study. While there are many confounders, this goes in line with a large study which underlined that higher provision of neoadjuvant chemotherapy is associated with improvements in median overall survival and larger decrease in the short-term mortality [ 25 ].

Our study has some limitations. First, this is a retrospective study, carrying inherent biases such as selection and information bias. Although in our study, all data were complete for each patient. Furthermore, as this is a retrospective study, it is impossible to account for many confounders, as provider bias, different treatment protocols as OC patients may undergo multiple lines of chemotherapy and dose interruptions. Furthermore, we must acknowledge the sample size of our study, which might be too small to identify statistical significance and to generalize. Further studies with larger sample size are needed to deepen the understanding of our findings. Nevertheless, the strength of our study is the matching in a 1:4 ratio for the most significant factors of disease outcome as disease stage and degree ofcytoreduction, as well as histology and patient’s comorbidities.

In this study, we found a temporary association, with obese patients having a limited overall survival advantage for the first 3 years, that then fades away with no further association with oncological outcome.

Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

Abbreviations

  • Ovarian cancer

Overall survival,

Recurrence-free survival

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Open access funding provided by Hebrew University of Jerusalem. This study was supported by grants from the Israel Cancer Research Fund, the Gloria’s Girls Fund, and the Susan and Jonathan Wener Fund.

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Gabriel Levin, Melica Nourmoussavi Brodeur, Shannon Salvador, Susie Lau & Walter Gotlieb

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GL, and WG: Conceptualization, formal analysis, investigation and methodology, writing—— original draft, and writing—— review and editing. YB, YT, TB, MB, SS, SL, and SS data acquisition, investigation, drafting and revising.

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Levin, G., Brezinov, Y., Tzur, Y. et al. Association between BMI and oncologic outcomes in epithelial ovarian cancer: a predictors-matched case-control study. Arch Gynecol Obstet (2024). https://doi.org/10.1007/s00404-024-07537-8

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DOI : https://doi.org/10.1007/s00404-024-07537-8

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case control study groups

COMMENTS

  1. What Is a Case-Control Study?

    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.".

  2. Case-control study

    A case-control study (also known as case-referent study) is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case-control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have the condition with patients who do not have ...

  3. Case Control Studies

    A case-control study is a type of observational study commonly used to look at factors associated with diseases or outcomes.[1] The case-control study starts with a group of cases, which are the individuals who have the outcome of interest. The researcher then tries to construct a second group of individuals called the controls, who are similar to the case individuals but do not have the ...

  4. Case Control Study: Definition & Examples

    Examples. A case-control study is an observational study where researchers analyzed two groups of people (cases and controls) to look at factors associated with particular diseases or outcomes. Below are some examples of case-control studies: Investigating the impact of exposure to daylight on the health of office workers (Boubekri et al., 2014).

  5. Case Control Study: Definition, Benefits & Examples

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

  6. Epidemiology in Practice: Case-Control Studies

    A case-control study is designed to help determine if an exposure is associated with an outcome (i.e., disease or condition of interest). In theory, the case-control study can be described simply. First, identify the cases (a group known to have the outcome) and the controls (a group known to be free of the outcome).

  7. Case Control Studies

    A case-control study is a type of observational study commonly used to look at factors associated with diseases or outcomes. The case-control study starts with a group of cases, which are the individuals who have the outcome of interest. The researcher then tries to construct a second group of individuals called the controls, who are similar to ...

  8. Methodology Series Module 2: Case-control Studies

    The investigator then assesses the exposure in both these groups. Case-control studies are less expensive and quicker to conduct (compared with prospective cohort studies at least). The measure of association in this type of study is an odds ratio. This type of design is useful for rare outcomes and those with long latent periods.

  9. Case-control study: Design, measures, classic example

    A case-control study investigates two groups of patients: a study ("case") group who have known medical condition(s), disease state, and a control group who are a similar group of patients without the medical condition(s), disease state, to be investigated in the study group. Case-control studies have a control group whereas case series do ...

  10. A Practical Overview of Case-Control Studies in Clinical Practice

    In a case-control study the researcher identifies a case group and a control group, with and without the outcome of interest. Such a study design is called observational because the researcher does not control the assignment of a subject to one of the groups, unlike in a planned experimental study. In a.

  11. Case-control and Cohort studies: A brief overview

    Case-control studies. Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups.

  12. A Practical Overview of Case-Control Studies in Clinical Practice

    Case-control studies are one of the major observational study designs for performing clinical research. The advantages of these study designs over other study designs are that they are relatively quick to perform, economical, and easy to design and implement. Case-control studies are particularly appropriate for studying disease outbreaks, rare diseases, or outcomes of interest. This article ...

  13. Research Guides: Study Design 101: Case Control Study

    A case-control study was conducted to investigate if exposure to zinc oxide is a more effective skin cancer prevention measure. The study involved comparing a group of former lifeguards that had developed cancer on their cheeks and noses (cases) to a group of lifeguards without this type of cancer (controls) and assess their prior exposure to ...

  14. LibGuides: Quantitative study designs: Case Control

    Case Control. In a Case-Control study there are two groups of people: one has a health issue (Case group), and this group is "matched" to a Control group without the health issue based on characteristics like age, gender, occupation. In this study type, we can look back in the patient's histories to look for exposure to risk factors that ...

  15. Case-control study in medical research: Uses and limitations

    A case-control study can help provide extra insight on data that has already been collected. A case-control study is a way of carrying out a medical investigation to confirm or indicate what is ...

  16. Overview of Case-Control Design

    In a case-control study the same cases are identified and classified as to whether they belong to the exposed or unexposed cohort. Instead of obtaining the denominators for the rates or risks, however, a control group is sampled from the entire source population that gives rise to the cases. Individuals in the control group are then classified ...

  17. Selecting & Defining Cases and Controls

    Selection of the Controls. As noted above, it is always useful to think of a case-control study as being nested within some sort of a cohort, i.e., a source population that produced the cases that were identified and enrolled. In view of this there are two key principles that should be followed in selecting controls:

  18. Case-control study: comparative studies

    A case-control study is a type of observational study. It looks at 2 sets of participants. One group has the condition you are interested in (the cases) and one group does not have it (the ...

  19. Observational Studies: Cohort and Case-Control Studies

    Cohort studies and case-control studies are two primary types of observational studies that aid in evaluating associations between diseases and exposures. In this review article, we describe these study designs, methodological issues, and provide examples from the plastic surgery literature. Keywords: observational studies, case-control study ...

  20. Case-Control Study- Definition, Steps, Advantages, Limitations

    A case-control study (also known as a case-referent study) is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. It is designed to help determine if an exposure is associated with an outcome (i.e., disease or condition of interest).

  21. Is the MIND diet useful for polycystic ovary syndrome? A case-control study

    About 236 subjects were introduced by expert endocrinologist diagnosis to our study (117 subjects for the case group and 119 for the control). Finally, for the case group: 2 women were not willing to participate, and 7 had allergies to foods. for the control group: 5 women were not willing to participate, 4 cases were not newly diagnosed, and 2 ...

  22. A case-control study of the clinical and economic impact of ...

    The CPE group was associated with worse clinical outcomes, but this was mainly due to a higher comorbidity burden, more severe illness, and more frequent inappropriate antibiotic treatment rather than resistance patterns as such. ... Methods: Case-control study. Adult patients with CPE infections were considered cases, while those with non-CPE ...

  23. Nutrients

    Results are based on secondary data analyses of a retrospective case-control study of 100 preterm and 200 term pregnancies, where case and control groups were analyzed together. Data collection was based on a self-administered questionnaire, health documentation, and maternal serum VD laboratory tests.

  24. Grab Control! Choosing the Right Comparison Group in an Observational Study

    Control Groups for Case-Control Studies. Remember that case-control studies have groups formed based on the outcome. Those with the outcome of interest are referred to as "cases," while those without the outcome of interest are the "controls". Selection of controls is often the most difficult aspect of conducting a case-control study.

  25. Chapter 10 EPI Flashcards

    In a study begun in 1965, a group of 3,000 adults in Baltimore were asked about alcohol con- sumption. The occurrence of cases of cancer between 1981 and 1995 was studied in this group. This is an example of: a. A cross-sectional study b. A prospective cohort study c. A retrospective cohort study d. A clinical trial e. A case-control study

  26. Case Control

    A case-control study was conducted to investigate if exposure to zinc oxide is a more effective skin cancer prevention measure. The study involved comparing a group of former lifeguards that had developed cancer on their cheeks and noses (cases) to a group of lifeguards without this type of cancer (controls) and assess their prior exposure to ...

  27. Association between BMI and oncologic outcomes in epithelial ...

    Objective We aimed to study the association between obesity and survival in ovarian cancer (OC) patients, accounting for confounders as disease stage, histology, and comorbidities. Methods Retrospective matched case-control study of consecutive patients, with epithelial OC. Obese (body mass index [BMI] ≥ 35 kg m−2) patients were matched in a 1:4 ratio with patients having lower BMIs (BMI ...

  28. Partner Case Study Series

    The Navnit Group is a Mumbai, India-based network of businesses spanning the automotive, infrastructure, marine, adventure sports, aviation, and financial services sectors. As part of its consulting services, Cloud 9 Infosystems offers a two-week engagement to integrate Office 365, Microsoft Entra ID, Azure Rights Management, and Azure Monitor ...