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

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

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

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

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

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

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

Disadvantages and Limitations

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

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

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

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

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Volume 26, Number 11—November 2020

Case-Control Study of Use of Personal Protective Measures and Risk for SARS-CoV 2 Infection, Thailand

Cite This Article

We evaluated effectiveness of personal protective measures against severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Our case-control study included 211 cases of coronavirus disease (COVID-19) and 839 controls in Thailand. Cases were defined as asymptomatic contacts of COVID-19 patients who later tested positive for SARS-CoV-2; controls were asymptomatic contacts who never tested positive. Wearing masks all the time during contact was independently associated with lower risk for SARS-CoV-2 infection compared with not wearing masks; wearing a mask sometimes during contact did not lower infection risk. We found the type of mask worn was not independently associated with infection and that contacts who always wore masks were more likely to practice social distancing. Maintaining > 1 m distance from a person with COVID-19, having close contact for < 15 minutes, and frequent handwashing were independently associated with lower risk for infection. Our findings support consistent wearing of masks, handwashing, and social distancing to protect against COVID-19.

Evaluation of the effectiveness of mask-wearing to protect healthy persons in the general public from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease (COVID-19), is urgently needed ( 1 , 2 ). On February 27, 2020, during the early stages of the COVID-19 outbreak, the World Health Organization (WHO) announced that wearing a mask of any type was not recommended for asymptomatic persons ( 3 ). The rationale at that time was to avoid unnecessary cost, procurement burden, and a false sense of security ( 3 ). Several systematic reviews found no conclusive evidence to support widespread use of masks in public settings to protect against respiratory infectious diseases, such as influenza and severe acute respiratory syndrome (SARS) ( 4 – 6 ). However, China, South Korea, Japan, Thailand, and other countries in Asia have recommended the use of face masks among the general public since early in the COVID-19 pandemic ( 7 ). Evidence suggests that persons with COVID-19 can have a presymptomatic period, during which they can be contagious and transmit SARS-CoV-2 to others before symptoms develop ( 8 ). These findings led to a change in recommendations from the US Centers for Disease Control and Prevention on April 4, 2020, that advised all persons wear a cloth face covering when in public ( 9 ). On April 6 and June 5, 2020, WHO updated its advice on the use of masks for the general public and encouraged countries that issue the recommendations to conduct research on this topic ( 8 ).

Thailand has been implementing multiple measures against transmission of SARS-CoV-2 since the beginning of the outbreak ( 10 , 11 ). The country established thermal screening at airports on January 3, 2020, and detected an early case of COVID-19 outside China in a traveler from Wuhan, China, arriving at Bangkok Suvarnabhumi airport on January 8, 2020 ( 10 ). Thailand uses Surveillance and Rapid Response Teams (SRRTs), together with village health volunteers, to conduct contact tracing, educate the public about COVID-19, and monitor close contacts of persons with COVID-19 in quarantine ( 11 ). SRRTs are epidemiologic teams trained to conduct surveillance, investigations, and initial control of communicable diseases, such as SARS and influenza ( 12 , 13 ). More than 1,000 district-, provincial-, and regional-level SRRTs are working on COVID-19 contact tracing in Thailand.

By February 2020, public pressure to wear masks in Thailand was high. However, medical masks became difficult for the public to procure, and the government categorized medical masks as price-controlled goods. When the Ministry of Public Health (MoPH) designated COVID-19 a dangerous communicable disease, according to the Communicable Disease Act of 2015, government officials were empowered to quarantine case-contacts and close venues ( 14 , 15 ). On March 3, MoPH recommended public use of cloth face masks ( 16 ). On March 18, schools, universities, bars, nightclubs, and entertainment venues were closed ( 17 ). On March 26, when the country was reporting »100–150 new COVID-19 cases per day, the government declared a national state of emergency, prohibited public gatherings, and enforced wearing of face masks by all persons on public transport ( 18 ). On April 21, MoPH announced 19 new PCR-confirmed COVID-19 cases, bringing the total number of cases to 2,811 ( 18 ). Given the lack of evidence, we sought to evaluate the effectiveness of mask-wearing, handwashing, social distancing, and other personal protective measures against SARS-CoV-2 infection in public in Thailand.

Study Design

We conducted a retrospective case-control study by drawing persons with COVID-19 cases and noninfected controls from a cohort of contact tracing records of the central SRRT team at the Department of Disease Control (DDC), MoPH, Thailand. We included contact investigations of 3 large COVID-19 clusters in nightclubs, boxing stadiums, and a state enterprise office in Thailand.

Contacts were defined by DDC as persons who had activities with or were in the same location as a person with confirmed COVID-19 ( 19 , 20 ). The main aim of contact tracing was to identify and evaluate contacts, perform reverse transcription PCR (RT-PCR) diagnostic tests, and quarantine high-risk contacts, as defined by the MoPH ( Appendix ). RT-PCR was performed at laboratories certified for COVID-19 testing by the National Institute of Health of Thailand ( 19 , 20 ). Data on risk factors associated with SARS-CoV-2 infection, such as type of contact and use of mask, were recorded during contact investigations, but data sometimes were incomplete.

The central SRRT performed contact investigations for clusters of > 5 PCR-confirmed COVID-19 cases from the same location within a 1-week period ( 19 , 20 ). We used these data to identify contacts who were asymptomatic during March 1–31, 2020. We used all available contact tracing records of the central SRRT in the study.

We telephoned contacts during April 30–May 27, 2020, and asked details about their contact with a COVID-19 index patient, such as dates, locations, duration, and distance of contact. We asked whether contacts wore a mask during the contact with the index patient, the type of mask, and the frequency of wearing a mask, which we defined as compliance with mask-wearing. We asked whether and how frequently contacts washed their hands while with the index patient. We asked whether contacts performed social distancing and whether they had physical contact with the COVID-19 index patient. If they did not know, or could not remember, contact with the index patient, we asked whether they had contact with other persons at the location. We asked whether the contact shared a cup or a cigarette with other persons in the place they had contact or had highest risk for contact with the index patient and whether the COVID-19 index patient, if known to the respondent, had worn a mask ( Appendix , Additional Methods). We also asked, and verified by using DDC records, whether and when the contacts had symptoms and when COVID-19 was diagnosed.

For our study, we defined cases as asymptomatic contacts who later tested positive for SARS-CoV-2, on the basis of RT-PCR results available by April 21, 2020. We defined controls as asymptomatic contacts who did not have positive test results for SARS-CoV-2 by April 21, including those who tested negative and those who were not tested. We defined asymptomatic contacts as persons who had contact with or were in the same location as a symptomatic COVID-19 patient and had no symptoms of COVID-19 on the first day of contact. We defined index patients as persons identified from contact tracing data as the potential source of SARS-CoV-2 infection; cases (as defined above) also could be index patients. We defined primary index patients as persons whose probable sources of infection were before the study period, March 1–31; for whom we were not able to identify the source of infection; or whose probable sources of infection were outside the contact tracing data included in the study. We defined high-risk exposure as that which occurred when persons lived in the same household as a COVID-19 patient; had a direct physical contact with a COVID-19 patient; were < 1 m from a COVID-19 patient for >15 minutes; or were in the same closed environment, such as a room, nightclub, stadium, or vehicle, < 1 m from a COVID-19 patient for >15 minutes.

We used 21 days after March 31 as a cutoff date based on evidence that most persons with COVID-19 likely would develop symptoms within 14 days ( 21 ) and that it could take < 7 additional days for symptomatic persons under contact investigation to go to a healthcare facility and be tested for COVID-19. Our study follows the STROBE guidelines ( 22 ).

Statistical Analysis

To include only initially asymptomatic contacts in the study, we excluded persons who reported having symptoms of COVID-19 at the time of initial contact with an index patient. Because our study focused on the risk for infection in the community, we excluded contacts whose contact locations were healthcare facilities. We also excluded primary index patients if they were the first to have symptoms at the contact investigation location, had symptoms since the first day of visiting the location, or were the origin of infection based on the contact investigation.

We estimated secondary attack rates by using the percentage of new cases among asymptomatic contacts with high-risk exposure to enable comparison with other studies. We estimated odds ratios (ORs) and 95% CIs for associations between developing COVID-19 and factors evaluated. We used logistic regression with random effects for location and for index patients nested in the same location. If an asymptomatic contact had contact with > 1 symptomatic COVID-19 index patient, the interviewer identified the index patient as the symptomatic COVID-19 patient with the closest contact. The percentage of missing values for the variable indicating whether the index patients wore a mask was 27%; thus, we did not include this variable in our analyses. For other variables, we assumed that missing values were missing at random and used imputation by chained equations ( 23 , 24 ). We created 10 imputed datasets and the imputation model included the case-control indicator and variables used in the multivariable models, including sex, age group, contact place, shortest distance of contact, duration of contact at < 1 m, sharing dishes or cups, sharing cigarettes, handwashing, mask-wearing, and compliance with mask-wearing. We developed the final multilevel mixed-effect logistic regression models on the basis of previous knowledge and a purposeful selection method ( 25 ; Appendix , Additional Analyses). Because of collinearity between mask use and mask type, we conducted a separate analysis including mask type in the multilevel mixed-effects logistic regression model for SARS-CoV-2 infection. We also tested a predefined interaction between mask type and compliance with mask-wearing ( Appendix , Additional Analyses).

To clarify patterns of behavior and factors related to compliance with mask-wearing, we used multinomial logistic regression models and the imputed dataset to estimated OR and 95% CI for associations between 3 mask-wearing compliance categories, never, sometimes, or all the time; and for other practices, including handwashing and social distancing during the contact period. We used logistic regression to estimate p values for pairwise comparisons.

To estimate the proportional reduction in cases that would occur if exposure to risk factors was reduced, we estimated the population attributable fraction by using the imputed dataset and a direct method based on logistic regression, as described previously ( 26 , 27 ; Appendix , Additional Analyses). We performed all analyses by using Stata version 14.2 (StataCorp, https://www.stata.com ) and R version 4.0.0 (R Foundation for Statistical Computing, https://www.r-project.org ).

Characteristics of the Cohort Data

Flow diagram in case-control study of severe acute respiratory syndrome coronavirus 2 infections and contacts, Thailand, March–April 2020. COVID-19, coronavirus disease; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SRRT, Surveillance and Rapid Response Team of Ministry of Public Health.

Figure 1 . Flow diagram in case-control study of severe acute respiratory syndrome coronavirus 2 infections and contacts, Thailand, March–April 2020. COVID-19, coronavirus disease; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SRRT, Surveillance...

The contact tracing records of the central SRRT included 1,716 persons who had contact with or were in the same location as a person with diagnosed COVID-19 in an investigation of 3 large clusters ( Figure 1 ). Overall, 18 primary index patients were identified: 11 from the nightclub cluster, 5 from the boxing stadiums cluster, and 2 from the state enterprise office cluster. Timelines of primary index patients from the 3 clusters varied ( Appendix Figures 1–3); we excluded the 18 primary index patients from our analyses.

Characteristics of Cases and Controls

After interviewing each contact by phone and applying the exclusion criteria ( Figure 1 ), our analysis included 1,050 asymptomatic persons who had contact with or were in the same location as a symptomatic COVID-19 index patient during March 1–31, 2020. The median age of persons included was 38 years (IQR 28–51 years); 55% were male, and 45% were female ( Table 1 ). Most (61%; n = 645) asymptomatic contacts included in the study were associated with the boxing stadiums cluster, 36% (n = 374) were related to the nightclub cluster, and 3% (n = 31) were related to the state enterprise office cluster. Overall, 890 (84.8%) contacts were considered to have high-risk exposure.

Among 1,050 asymptomatic contacts included in our analysis, 211 (20.1%) tested positive for SARS-CoV-2 by April 21, 2020, and were classified as cases; 839 (79.9%) never tested positive and were controls. Of the 211 cases, 195 (92.4%) had high-risk exposures and 150 (71.1%) had symptoms before COVID-19 diagnosis by RT-PCR ( Appendix ). The last date that a COVID-19 case was detected was April 9, 2020. Among the 839 controls, 695 (82.8%) had high-risk exposures and 719 (85.7%) were tested by PCR at least once.

Development and transmission of severe acute respiratory syndrome coronavirus 2 among asymptomatic contacts, Thailand, March–April 2020. Clusters indicate coronavirus disease (COVID-19) contacts from nightclubs (A); boxing stadiums (B), and the state enterprise office (C). Black nodes represent primary index patients, red dots cases (contacts of primary index patients who had COVID-19), green dots noninfected controls, and orange dots patients with confirmed COVID-19 who could not be contacted by the study team. Black lines represent household contacts, purple lines contacts at workplaces, and gray lines contacts at other locations.

Figure 2 . Development and transmission of severe acute respiratory syndrome coronavirus 2 among asymptomatic contacts, Thailand, March–April 2020. Clusters indicate coronavirus disease (COVID-19) contacts from nightclubs (A); boxing stadiums (B), and the...

Among asymptomatic contacts included in the study, 228 had contact with a COVID-19 index patient at nightclubs, 144 at boxing stadiums, and 20 at the state enterprise office ( Figure 2 ). The others had contacts with a COVID-19 index patient at workplaces (n = 277), households (n = 230), and other places (n = 151). Among 890 asymptomatic contacts with high-risk exposures included in the study, the secondary attack rate from boxing stadiums was 86% (111/129), the secondary attack rate for nightclubs was 18.2% (34/187), the household secondary attack rate was 16.5% (38/230), the workplace secondary attack rate was 4.9% (10/205), and the secondary attack rate at other places was 1.4% (2/139).

Bivariate Analyses

Our analysis showed risk for SARS-CoV-2 infection was negatively associated with personal protective measures ( Table 1 ). Crude odds ratio (OR) for infection was 0.08 (95% CI 0.02–0.31) for those maintaining a distance of > 1 m from a COVID-19 patient; 0.13 (95% CI 0.04–0.46) for those whose duration of contact was ≤15 minutes; 0.41 (95% CI 0.18–0.91) for those who performed handwashing sometimes; 0.19 (95% CI 0.08–0.46) for those who washed hands often; and 0.16 (95% CI 0.07–0.36) for those wearing a mask all the time during contact with a COVID-19 patient. We noted a higher risk for SARS-CoV-2 infection among persons sharing dishes or cups (OR 2.71; 95% CI 1.48–4.94) and sharing cigarettes (OR 6.12; 95% CI 2.12–17.72) with other persons in general, not necessarily including a COVID-19 patient. In the bivariate model, type of mask was associated with risk for infection (p = 0.003).

Multivariable Analyses

We found a negative association between risk for SARS-CoV-2 infection and maintaining a distance of > 1 m from a COVID-19 patient (adjusted odds ratio [aOR] 0.15; 95% CI 0.04–0.63); duration of contact < 15 minutes (aOR 0.24; 95% CI 0.07–0.90); handwashing often (aOR 0.33; 95% CI 0.13–0.87); and wearing a mask all the time during contact with a COVID-19 patient (aOR 0.23; 95% CI 0.09–0.60) ( Table 1 ). Wearing masks sometimes during contact with a COVID-19 patient was not statistically significantly associated with lower risk for infection (aOR 0.87; 95% CI 0.41–1.84). Sharing cigarettes with other persons was associated with higher risk for infection (aOR 3.47; 95% CI 1.09–11.02).

Compliance with mask-wearing during contact with a COVID-19 patient was strongly associated with lower risk for infection in the multivariable model. Because of collinearity with mask-wearing compliance, we did not include mask type in the final model. We included mask type in a separate multivariable model and found type of mask was not independently associated with infection (p = 0.54) ( Appendix Table 1 ). We found no evidence of effect modification between mask type and mask-wearing compliance.

Association Between Mask-Wearing Compliance and Other Social Distancing Practices

Because mask-wearing throughout the contact period was negatively associated with COVID-19, we further explored characteristics of participants to ascertain whether wearing a mask produced a potential false sense of security. We found that during the contact period, 25% of persons who wore masks all the time reported maintaining > 1 m distance from contacts compared with 18% of persons who did not wear a mask (pairwise p = 0.03). In addition, persons who wore masks all the time were more likely to report duration of contact < 15 minutes (26% vs. 13% for those who did not wear a mask; pairwise p<0.001) and washing hands often (79% vs. 26% for those who did not wear a mask; pairwise p<0.001) ( Table 2 ). We found that 43% of persons who wore masks sometimes were likely to wash their hands often compared with those who did not wear masks (26%; pairwise p<0.001), but they were more likely to have physical contact (50% vs. 42%; pairwise p = 0.03) and report duration of contact >60 minutes (75% vs. 67%; pairwise p = 0.04) compared with those who did not wear masks.

Our findings provide evidence that mask-wearing, handwashing, and social distancing are independently associated with lower risk for SARS-CoV-2 infection in the general public in community settings in Thailand. We observed that wearing masks throughout the period of exposure to someone infected with SARS-CoV-2 was associated with lower risk for infection, but wearing masks only sometimes during the period was not. This evidence supports recommendations to wear masks consistently and correctly at all times in public ( 2 , 7 – 9 ).

The effectiveness of mask-wearing we observed is consistent with previous studies, including a randomized-controlled trial showing that consistent face mask use reduced risk for influenza-like illness ( 28 ), 2 case-control studies that found that mask-wearing was associated with lower risk for SARS infection ( 29 , 30 ), and a retrospective cohort study that found that mask-wearing by index patients or family members at home was associated with lower risk for SARS-CoV-2 infection ( 31 ). Previous studies found use of surgical masks or similar 12–16-layer cotton reusable masks demonstrated protection against coronavirus infection in the community ( 32 ), but we did not observe a difference between wearing nonmedical and medical masks in the general population. Our results suggest that wearing nonmedical masks in public can potentially reduce transmission of SARS-CoV-2. Another study found perception of risk of developing COVID-19 can increase a person’s likelihood of wearing a medical mask in nonmedical settings (T.D. Huynh, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.26.20044388v1 ). However, given supply shortages, medical masks should be reserved for use by healthcare workers.

We found a negative association between risk for SARS-CoV-2 infection and social distancing, consistent with previous studies that found that > 1 m physical distancing was associated with a large protective effect and distances of >2 m could be more effective ( 32 ). Our findings on effectiveness of hand hygiene also were consistent with reports in previous studies ( 33 ).

In this study, secondary attack rates at different venues varied widely. The household secondary attack rate in our study (16.5%) is comparable with ranges reported previously (11%–23%) ( 34 , 35 ), and relatively high compared with workplaces (4.9%) and other settings (1.4%). Although quarantine measures can be challenging and sometimes impractical, household members should immediately separate a person who develops symptoms of COVID-19; the sick person should stay in a specific room; use a separate bathroom, if possible; and not share dishes, cups, and other utensils ( 36 ). All household members should wear masks, frequently wash their hands, and perform social distancing to the extent possible ( 37 ).

The high number of COVID-19 cases associated with nightclub exposures in Bangkok is comparable to a COVID-19 outbreak associated with the Itaewon nightclub cluster in Seoul, South Korea, during May 2020 ( 38 ), in which persons visited several nightclubs in the same area during a short period of time. The secondary attack rate in boxing stadiums was high (86%) but similar to a cluster of COVID-19 cases associated with a football match in Italy during February 2020 ( 39 ). The secondary attack rate of COVID-19 at a choir practice in the United States was reported to be 53.3%–86.7% ( 40 ). Secondary attack rates in public gathering places with high densities of persons shouting and cheering, such as football and boxing stadiums, are still uncertain but appear to be high.

Clear and consistent public messaging from policy makers likely can prevent a false sense of security and promote compliance with social distancing in Thailand. We found that those who wore masks throughout the time they were exposed to a COVID-19 patient also were more likely to wash their hands and perform social distancing. Traditional and social media outlets can support public health responses by working with governments to provide consistent, simple, and clear messages ( 41 ). In Thailand, daily briefings from the Centre for COVID-19 Situation Administration provided clear, consistent messages on social distancing, how to put on a mask, and how to wash hands, which might have improved public confidence with the recommendations. Consistent public messages on how to wear masks correctly also are needed, particularly for those who wear masks sometimes or incorrectly, such as not covering both nose and mouth. We found that persons who only intermittently wore masks during exposure also did not practice social distancing adequately.

Our study has several limitations. First, because our findings were based on contacts related to 3 major COVID-19 clusters in Thailand during March 2020, they might not be generalizable to all settings. Second, estimated ORs were conditioned on reported contact with index patients; our study did not evaluate or consider the probability of having contact with other infected persons in the community, which could have occurred. Third, because only 89% of controls were tested, those not tested could have been infected; therefore, cases might have been missed in persons with mild or no symptoms or who did not report symptoms or seek care or testing. Nonetheless, we believe that misclassification likely was minimal because those who were not tested with RT-PCR were low-risk contacts; the small number likely would not change our main findings and recommendations on personal protective measures. Fourth, identifying every potential contact can be nearly impossible because some persons might have had contact with >1 COVID-19 patient. Hence, our estimated secondary attack rates among contacts with high-risk exposure could be overestimated or underestimated. Fifth, population attributable fraction is based on several assumptions, including causality, and should be interpreted with caution ( 42 , 43 ). Finally, findings were subject to common biases of retrospective case-control studies, including memory bias, observer bias, and information bias ( 44 ). To reduce potential biases, we used structured interviews in which each participant was asked the same set of defined questions.

As many countries begin to relax social distancing measures, our findings provide evidence supporting consistent mask-wearing, handwashing, and adhering to social distancing recommendations to reduce SARS-CoV-2 transmission in public gatherings. Wearing nonmedical masks in public could help slow the spread of COVID-19. Complying with all measures could be highly effective; however, in places with a high population density, additional measures might be required.

Clear and consistent public messaging on personal protective recommendations is essential, particularly for targeting those who wear masks intermittently or incorrectly. Our data showed that no single protective measure was associated with complete protection from COVID-19. All measures, including mask-wearing, handwashing, and social distancing, can increase protection against COVID-19 in public settings.

Dr. Pawinee Doung-ngern is the head of Communicable Disease Unit, Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand. Her primary research interests include the public health and epidemiology of communicable diseases.

Acknowledgment

This article was preprinted at https://www.medrxiv.org/content/10.1101/2020.06.11.20128900 .

DOI: 10.3201/eid2611.203003

Original Publication Date: September 14, 2020

Table of Contents – Volume 26, Number 11—November 2020

Please use the form below to submit correspondence to the authors or contact them at the following address:

Direk Limmathurotsakul, 420/6 Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Rajvithi Road, Bangkok 10400, Thailand

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What is a case-control study in medical research?

case control study example article

A case-control study is a type of medical research investigation often used to help determine the cause of a disease, particularly when investigating a disease outbreak or rare condition.

If public health scientists want a quick and easy way to highlight clues about the cause of a new disease outbreak, they can compare two groups of people: Cases, the term for people who already have the disease, and controls, or people not affected by the disease.

Other terms used to describe case-control studies include epidemiological, retrospective, and observational.

What is a case-control study?

Case control study on questionnaire

A case-control study is a way of carrying out a medical investigation to confirm or indicate what is likely to have caused a condition.

They are usually retrospective, meaning that the researchers look at past data to test whether a particular outcome can be linked back to a suspected risk factor and prevent further outbreaks.

Prospective case-control studies are less common. These involve enrolling a specific selection of people and following that group while monitoring their health. Cases emerge as people who develop the disease or condition under investigation as the study progresses. Those unaffected by the disease form the control group.

To test for specific causes, the scientists need to create a hypothesis about possible causes of the outbreak or disease. These are known as risk factors.

They compare how often the people in the group of cases had been exposed to the suspected cause against how often members of the control group had been exposed. If more participants in the case group experience the risk factor, this suggests that it is a likely cause of the disease.

Researchers might also uncover likely risk factors not mentioned in their hypothesis by studying the medical and personal histories of the people in each group. A pattern may emerge that links the condition to certain factors.

If a specific risk factor has already been identified for a disease or condition, such as age, sex, smoking, or eating red meat, the researchers can use statistical methods to adjust the study to account for that risk factor, helping them to identify other possible risk factors more easily.

Case-control research is a vital tool used by epidemiologists, or researchers who look into the factors affecting health and illness of populations.

Just one risk factor could be investigated for a particular outcome. A good example of this is to compare the number people with lung cancer who have a history of smoking with the number who do not. This will indicate the link between lung cancer and smoking.

Why is it useful?

There are multiple reasons for the use of case-control studies.

Relatively quick and easy

Case-control studies are usually based on past data, so all of the necessary information is readily available, making them quick to carry out. Scientists can analyze existing data to look at health events that have already happened and risk factors that have already been observed.

A retrospective case-control study does not require scientists to wait and see what happens in a trial over a period of days, weeks, or years.

The fact that the data is already available for collation and analysis means that a case-control study is useful when quick results are desired, perhaps when clues are sought for what is causing a sudden disease outbreak.

A prospective case-control study may also be helpful in this scenario as researchers can collect data on suspected risk factors while they monitor for new cases.

The time-saving advantage offered by case-control studies also means they are more practical than other scientific trial designs if the exposure to a suspected cause occurs a long time before the outcome of a disease.

For example, if you wanted to test the hypothesis that a disease seen in adulthood is linked to factors occurring in young children, a prospective study would take decades to carry out. A case-control study is a far more feasible option.

Does not need large numbers of people

Numerous risk factors can be evaluated in case-control studies since they do not require large numbers of participants to be statistically meaningful. More resources can be dedicated to the analysis of fewer people.

Overcomes ethical challenges

As case-control studies are observational and usually about people who have already experienced a condition, they do not pose the ethical problems seen with some interventional studies.

For example, it would be unethical to deprive a group of children of a potentially lifesaving vaccine to see who developed the associated disease. However, analyzing a group of children with limited access to that vaccine can help determine who is at most risk of developing the disease, as well as helping to guide future vaccination efforts.

Limitations

While a case-control study can help to test a hypothesis about the link between a risk factor and an outcome, it is not as powerful as other types of study in confirming a causal relationship.

Case-control studies are often used to provide early clues and inform further research using more rigorous scientific methods.

The main problem with case-control studies is that they are not as reliable as planned studies that record data in real time, because they look into data from the past.

The main limitations of case-control studies are:

‘Recall bias’

When people answer questions about their previous exposure to certain risk factors their ability to recall may be unreliable. Compared to people not affected by a condition, individuals with a certain disease outcome may be more likely to recall a certain risk factor, even if it did not exist, because of a temptation to make their own subjective links to explain their condition.

This bias may be reduced if data about the risk factors – exposure to certain drugs, for example – had been entered into reliable records at the time. But this may not be possible for lifestyle factors, for example, because they are usually investigated by questionnaire.

An example of recall bias is the difference between asking study participants to recall the weather at the time of the onset of a certain symptom, versus an analysis of scientifically measured weather patterns around the time of a formal diagnosis.

Finding a measurement of exposure to a risk factor in the body is another way of making case-control studies more reliable and less subjective. These are known as biomarkers. For example, researchers may look at results of blood or urine tests for evidence of a specific drug, rather than asking a participant about drug use.

Cause and effect

An association found between a disease and a possible risk does not necessarily mean one factor directly caused the other.

In fact, a retrospective study can never definitively prove that a link represents a definite cause, as it is not an experiment. There are, though, questions that can be used to test the likelihood of a causal relationship, such as the extent of the association or whether there is a ‘dose response’ to increasing exposure to the risk factor.

One way of illustrating the limitations of cause-and-effect is to look at associations found between a cultural factor and a particular health effect. The cultural factor itself, such as a certain type of exercise, may not be causing the outcome if the same cultural group of cases shares another plausible common factor, such as a certain food preference.

Some risk factors are linked to others. Researchers have to take into account overlaps between risk factors, such as leading a sedentary lifestyle, being depressed, and living in poverty.

If researchers conducting a retrospective case-control study find an association between depression and weight gain over time, for example, they cannot say with any certainty that depression is a risk factor for weight gain without bringing in a control group containing people who follow a sedentary lifestyle.

‘Sampling bias’

The cases and controls selected for study may not truly represent the disease under investigation.

An example of this occurs when cases are seen in a teaching hospital, a highly specialized setting compared with most settings in which the disease may occur. The controls, too, may not be typical of the population. People volunteering their data for the study may have a particularly high level of health motivation.

Other limitations

There are other limitations to case-control studies. While they are good for studying rare conditions, as they do not require large groups of participants, they are less useful for examining rare risk factors, which are more clearly indicated by cohort studies.

Finally, case-control studies cannot confirm different levels or types of the disease being investigated. They can look at only one outcome because a case is defined by whether they did or did not have the condition.

Last medically reviewed on May 16, 2018

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Research Article

A Case-Control Study on the Risk Factors for Meningococcal Disease among Children in Greece

* E-mail: [email protected]

Affiliation Department of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, Larissa, Greece

Affiliation Aghia Sophia Children’s Hospital, National and Kapodistrian University of Athens School of Medicine, Athens, Greece

Affiliation National Reference Centre for Meningitis, National School of Public Health, Athens, Greece

The aim of this study was to identify environmental or genetic risk factors that are associated with invasive meningococcal disease (IMD) in children in Greece.

A case-control study was performed in 133 children (44 cases and 89 controls) aged between 0–14 years, who were hospitalized in a children's hospital in Athens. Demographics and possible risk factors were collected by the use of a structured questionnaire. To investigate the association of mannose binding lectin (MBL) with IMD, a frequency analysis of the haplotypes of the MBL2 gene and quantitative measurement of MBL serum protein levels were performed using Nanogen NanoChipR 400 technology and immuno-enzyme techniques, respectively.

The multivariate analysis revealed that changes in a child's life setting (relocation or vacation, OR = 7.16), paternal smoking (OR = 4.51), upper respiratory tract infection within the previous month (OR = 3.04) and the density of people in the house/100m 2 (OR = 3.16), were independent risk factors associated with IMD. Overall 18.8% of patients had a MBL2 genotype with low functionality compared to 10.1% of healthy controls, but this was not statistically significant (p = 0.189).

Prevention strategies aimed at reducing parental smoking and other risk factors identified in this study could decrease the risk of IMD among children in Greece.

Citation: Hadjichristodoulou C, Mpalaouras G, Vasilopoulou V, Katsioulis A, Rachiotis G, Theodoridou K, et al. (2016) A Case-Control Study on the Risk Factors for Meningococcal Disease among Children in Greece. PLoS ONE 11(6): e0158524. https://doi.org/10.1371/journal.pone.0158524

Editor: Daniela Flavia Hozbor, Universidad Nacional de la Plata, ARGENTINA

Received: January 26, 2016; Accepted: June 16, 2016; Published: June 28, 2016

Copyright: © 2016 Hadjichristodoulou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The authors received no specific funding for this work.

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

Introduction

Invasive meningococcal disease (IMD) is a contagious bacterial disease caused by a meningococcus ( Neisseria meningitidis ), a Gram-negative bacterium that is classified into 13 capsular groups according to its capsular polysaccharides. Six of these (A, B, C, Y, X and W), are of clinical significance as they cause invasive infections. In Europe, groups B and C are mainly responsible for IMD [ 1 ]. In the USA, groups B, C and Y cause a high proportion of IMD [ 2 ], while in Africa group A is predominant and groups W, X and C are also endemic [ 3 ]. Meningitis and septicemia are the two main clinical forms of IMD, and sometimes both clinical forms are found in the same patient. Meningococcal meningitis is a serious infection of the meninges that can cause severe brain damage and other sequelae. In meningococcal septicaemia, the onset of the symptoms is sudden and death can follow within hours. IMD has a high fatality rate and many survivors develop permanent sequelae [ 4 – 5 ].

Meningococcal infections are transmitted between people through respiratory droplets or secretions. N . meningitidis inhabits the mucosal membrane of the nose and throat, where it usually causes no harm [ 6 – 7 ]. There is substantial evidence that approximately 10% of the general population are asymptomatic carriers, although this rate varies with age, and is associated with a peak in early adulthood [ 8 – 10 ]. Several polysaccharide and conjugate vaccines are available for the protection of humans from the most common capsular groups of IMD. Polysaccharide vaccines are available in bivalent (A, C), trivalent (A, C, W), and quadrivalent (A, C, W, Y) forms. Conjugate vaccines, which are more immunogenic and can provide herd immunity, are available in monovalent (A or C), quadrivalent (A, C, W, Y), or combinatorial (group C and Haemophilus influenzae type b) forms [ 10 ]. Recently, a new vaccine against group B has been developed based on reverse vaccinology [ 11 ]. In Greece, the vaccine against group C has been included in the National Vaccination Programme (NVP) since 2006; recently, a vaccine against group B was made available in Greece but is not yet included in the NVP.

The risk of meningococcal infection in an individual is dependent on the balance of the virulence of the strain and the host’s immune response. Moreover, several environmental risk factors have been associated with the disease in several countries [ 12 – 13 ]. Both active and passive smoking in particular have been found to increase the risk of IMD in pediatric populations [ 14 – 16 ]. Other risk factors include crowded living conditions, close contact with an infected person, a history of recent upper respiratory tract infections and low socio-economic status [ 17 – 20 ]. Finally, individual risk factors such as an underlying disease (e.g., malignancies) or asplenia are also associated with a higher risk of developing IMD [ 21 – 22 ].

Genetic mutations of MBL(an acute protein phase that contributes to the elimination of bacteria by activating the complement system) have been identified as possible risk factors associated with IMD in several studies [ 23 – 25 ]. As contradictory results exist regarding the role of MBL in IMD, and there is no available evidence from Greece, we decided to investigate the role of MBL further as a predisposing factor for IMD. The aim of the current study was therefore to identify possible environmental or genetic factors that increase the predisposition of children in Greece to developing IMD, including an analysis of MBL serum protein levels and haplotype analysis of MBL2 gene.

Materials and Methods

Ethics statement.

Approval of the study protocol was received by the Ethics Committee of Aghia Sofia Children’s Hospital, which waived the need for written consent. Parents or guardians were informed about the aim of the study, and they provided written consent for their child’s participation in the study.

Study design

A case-control study was performed using 133 children (44 cases and 89 controls). All participants were children aged between 0–14 years, who had been hospitalized in two children's hospitals (Aghia Sophia and P & A Kyriakou) in Athens, Greece, within a 2-year period from January 2011 to December 2012.

Cases were had been hospitalized with a diagnosis of IMD (meningococcal meningitis and/or sepsis). In all cases, N . meningitidis was identified in samples of biological material (blood or CSF) in the laboratory and isolated using bacterial cultures or molecular techniques, such as polymerase chain reaction (PCR). In addition, to increase the reliability and the power of our genetic research, the frequency analysis of the gene polymorphisms of MBL2 , included 45 extra blood samples from the National Reference Centre for Meningitis (NRCM). These samples were collected from cases of IMD in children aged 0–14 years, who had become ill during the same period (2011–2012) and had been admitted to various hospitals across Greece. Our study was designed to encompass two spring seasons in order to capture any respective seasonal increase in IMD.

Controls were children hospitalized in the surgical wards of the two hospitals with a diagnosis that was unrelated to IMD or other infections. All controls were matched to cases using the sex and age (year of birth) of each child, and the week of admission. When it was possible, at least two controls were randomly selected for every case.

Data collection

In both cases and controls, a whole blood sample (3–4 mL) was collected on the day the child was admitted to the hospital, in order to study the MBL2 haplotypes and the serum levels of MBL protein. For each blood sample, 0.5 mL was stored immediately at -80°C, in order to be used in the frequency analysis of MBL2 polymorphisms. After centrifugation of the remaining blood sample, plasma was collected and kept readily cryopreserved at -80°C for the quantitative measurement of MBL.

Questionnaire

A questionnaire was distributed to the parents of both cases and controls, in order to obtain information on the following: their child’s demographic details (sex, age, race, height, weight, mother’s/father’s educational status, social security status, use of paediatrician or general practitioner services, family income, specific population group); family history (number of family members, number of children, birth order, family medical history, family medical history of meningitis); housing environment (size of the house in square meters, number of household members, heating system, type of house [single family house or block of flats], exposure to passive smoking at home). Moreover, the questionnaire included questions regarding perinatal history and breastfeeding; history of hospitalization; vaccination history; medications/ social history during the last month: attendance at nursery school, elementary/high school/college attendance; participation in sporting events; attendance at parties and playgrounds; church attendance; visits to restaurants or coffee bars; kissing other people; use of public transport; and change of life setting (relocation or vacation). If the answer to the question of change in life setting was positive the parents were asked to specify. Parents were also asked to report if their children had signs and symptoms compatible with upper respiratory infection (fever and/or cough and/or sore throat and/or rhinitis) during the last month. It should be noted that some questions in the questionnaire (e.g., kissing other people and coffee bars attendance) were relevant to older children (≥12 years old), while other questions (e.g., nursery and play areas attendance) were relevant to children aged ≤10 years.

Genetic analysis of MBL

MBL2 includes three polymorphic sites in exon 1 (cd52, cd54, cd57) forming the haplotypes AO, OO, and AA, three polymorphisms in the area of the promoter (-550, -221, +4) forming the haplotypes HY, LY and LX and four polymorphic sites located in exon 4 (carbohydrate recognition domain-CRD). The frequency analysis of MBL2 polymorphisms was performed by the Choremeio Research Laboratory of Medical Genetics at the University of Athens. Of the 44 cases of IMD, only 36 agreed to participate in the genetic analysis while 69 out of the 89 of the controls agreed to participate (response rates of 81.8% and 77.5%, respectively). To increase the power of the study, 45 cases of IMD from NRCM were included in the genetic analysis to achieve a total number of 81 cases of IMD analyzed.

Genomic DNA was isolated from 350 μl of peripheral blood, using the BioRobotR M48 System (Qiagen, Hilden, Germany) and the MagAttractR DNA Blood Midi M48 Kit (Qiagen, Hilden, Germany). For the characterization of the six SNPs in MBL2 , we developed an advanced high throughout methodology using the Nanogen NanoChipR 400 system (NC400, Nanogen Inc). Three separate regions of the MBL2 gene containing SNPs were designed for PCR amplification using the PrimerQuestSM tool provided by IDTR (Integrated DNA Technologies). The set-up of all PCR reactions was performed automatically using the Biorobot 3000 platform (Qiagen, Hilden, Germany) and carried out in a Techne TC-412 thermal cycler using the amplicon-down format of the Nanogen protocol. Results were estimated by the instrument's software.

The quantitative measurement of the MBL levels of serum was performed in 43 of 44 (97.7%) patients, and in 73 of 89 (82.0%) controls at the Department of Hygiene and Epidemiology at the University of Thessaly, using the MBL Oligomer ELISA Kit (BioPorto Diagnostics Co.).

Statistical analysis

All data collected from the study participants (questionnaire, clinical and laboratory results) were entered into an electronic database using Epi-info software (version 3.5.3, CDC, Atlanta). Statistical analysis was performed using IBM SPSS Statistics software (v.22.0. Armonk, NY: IBM Corp.).

Quantitative variables were presented either as mean values with standard deviation or as a median value with the interquartile range (IQR). Qualitative variables were presented as absolute and relative frequencies with the corresponding 95% confidence intervals (95% CI). The receiver operating characteristic (ROC) curve analysis was conducted to determine the optimal cutoff values of the quantitative variable density (number of persons per 100 m 2 ), which was used to distinguish between IMD cases and controls.

In the univariate analysis, the Chi-square test or the Fisher's exact test was used to investigate the associations between the qualitative variables. The Chi-square test for trend was used to assess any dose response relationship between the ordinal factors (e.g., number of cigarettes per day) and IMD. The Mann-Whitney test was used to explore differences between IMD cases and controls with regards to quantitative non-normally distributed variables.

In the multivariate analysis, multiple logistic regression analysis was performed, using the backward stepwise conditional method with a removal criterion of p-value equal to or greater than 0.10, inorder to identify the independent risk factors for the onset of IMD by calculating the odds ratios (ORs) and the corresponding 95% CI. The dependent variable was IMD and as independent variables were used all the statistically significant risk factors found in the univariate analysis together with age and gender were used as independent variables. For the genetic analysis of MBL, the necessary sample size was calculated to be 200 children (100 per group) assuming a study power of 0.80, alpha 0.05, and considering 10.0% of low MBL in controls to identify a significant OR 3.00 (cases vs. controls). A result with a p-value <0.05 was considered to be statistically significant.

Participant characteristics

During the initial study period, 50 cases of IMD were hospitalized in both hospitals. In two cases, the diagnosis was not confirmed and the guardians of four patients (all females) refused to participate in the study. Overall, 44 cases of IMD participated in the study (response rate: 91.1%), of whom 27 (61.3%) were males and 17 (38.7%) females. The median age was 3 years (IQR: 1–4 years). Regarding race, 93.2% of them were white, 2.3% black, and 4.5% were from an other racial background. Also, 18 IMD cases (~41%) were second-generation immigrants, of whom 13 (~72%) were born in Greece while their parents were from Albania. Seven patients presented with septicemia, nine had meningitis and 28 patients had both. Sixteen patients were diagnosed using PCR and 28 through culture tests. The majority of cases were caused by group B meningococcus (36/44), one patient had group A and one had group W, while meningococcus was nongroupable in six patients. Furthermore, 23 out of 40 (57.5%) cases of IMD had been vaccinated with meningococcal meningitis C vaccine.

Regarding the controls, 99 patients were initially recorded but only 89 controls participated in the study (response rate: 89.9%). Out of these 89 controls, 65 (73%) were males and 24 (27%) were females, while 98.8%were white, 0% were black and 1.2% had another racial background. The median age of controls was 3 years (IQR: 1–5 years). Approximately 24% of the controls were second-generation immigrants.

Univariate analysis

All statistically significant risk factors, according to the univariate analysis, are presented in Table 1 . None of the other parameters included in the questionnaire, including gender, age, race, pets, normal birth, breastfeeding insurance status, annual family income, parental education level, maternal smoking, maternal cigarettes consumption per day, fever, cough, headache, vaccination status for N . meningitidis type C, or parental occupation, were found to have any statistically significant association with the occurrence of IMD. Moreover, factors such as the use of antibiotics, participation in sports activities, participation in parties, or kissing other people during the previous month were not statistically significant. Finally, no statistically significant difference was found in the serum protein levels of MBL between cases and controls.

https://doi.org/10.1371/journal.pone.0158524.t001

As shown in Table 1 , a change in life setting in the previous month (vacation or relocation) was identified as a risk factor (p = 0.010). The most frequent answer within the positive responses in the relocation/vacation question among cases, was vacation to another town within Greece (60%) or abroad (20%) for ≥5days. Moreover, 20% reported a relocation without specifying whether it was in the same town or in another town. Finally, it should be noted that most of the vacations were to celebrate Easter, or to visit relatives and friends in the migrants’ country of origin (mainly Albania).

Parental smoking (p = 0.030, OR = 2.48) and paternal smoking (p = 0.004, OR = 3.19) were identified to be associated with IMD, although the difference for maternal smoking was not statistically significant (p = 0.150, OR = 1.74). Moreover, for fathers a dose-response was revealed between number of cigarettes per day and therisk of IDM. The OR for smoking ≥20 cigarettes per day was 3.32 (95% C.I.: 1.46–7.58), compared to 2.56 (95% C.I.: 0.62–10.53) for smoking 1–19 cigarettes per day. The mean number of cigarettes per day smoked by the fathers was double of that of the mothers (20 vs. 10).

Multivariate analysis

As shown in Table 2 , the multivariate analysis revealed the following independent risk factors: relocation or vacation during the last month (OR = 7.16; 95% CI: 1.80–28.50), paternal smoking (OR = 4.51; 95% CI: 1.60–12.67), recent history (past 30 days) of viral respiratory infection (OR = 3.04; 95% CI: 1.17–7.91) and crowd density at home (OR = 3.16; 95% CI: 1.16–8.60).

https://doi.org/10.1371/journal.pone.0158524.t002

MBL2 haplotype analysis

According to the analysis of the MBL polymorphisms in exon 1 and in the promoter region, the patients were classified into three groups of high (HYA / HYA, HYA / LYA, LYA / LYA, and LYA / LXA), moderate (LXA / LXA, HYA / O and LYA / O) and low (LXA / O and O / O) functionality of the MBL2 gene, except for one sample. The low functionality of MBL2 , was observed in 18.8% of the patients and in 10.1% of the control group, but this difference was not statistically significant (p = 0.189) ( Table 3 ).

https://doi.org/10.1371/journal.pone.0158524.t003

This study revealed that a recent history of relocation or holiday, paternal smoking, a recent history of viral upper respiratory tract infection and crowded home conditions were independent risk factors for IMD. One interesting and possibly important novel finding of our study was the association between a change in the life setting either by relocation or vacation in the previous month with IMD (OR = 7.16). During these trips, extensive social activities are expected together with changes in pharyngeal flora as indicated by previous studies [ 26 ]. The precise mechanism by which travelling for a vacation could be a risk factor is unknown, but a recent case-crossover study reported that travelling abroad was independently associated with meningococcal carriage [ 27 ]. Relocation/vacation activities could be linked with changes in the flora of the nasopharynx as a result of environmental changes, including colonization by strains of N . meningitidis . It is known that the risk of invasive IMD is higher in newly colonized people [ 28 ]. This finding could have implications related to the systematic vaccination of unvaccinated young people who will be travelling or relocating. This prevention activity has a number of practical limitations related to the cost of the vaccine, the need for booster doses and the time needed to achieve protective immunity. Moreover, as reported by Tzanakaki et al., the suggested coverage of the 4CmenB vaccine would be 89% in Greece [ 29 ].

Our findings are in line with well-known risk factors for IMD, such as a history of viral respiratory tract infections [ 30 ], and parental passive smoking. A stronger association between paternal smoking together with a dose–response effect has been identified previously, while in a previous study maternal smoking was identified as more important risk factor [ 31 ]. The most plausible explanation for our findings was the fact that the fathers in our study smoked a higher average number (20 per day) of cigarettes compared to mothers (10 per day), while both parents contribute almost equally to child care. A meta-analysis found a significant association, between passive smoking and IMD in children [ 32 ]. In addition, a positive, statistically significant association between passive smoking and the carriage of N . meningitidis has also been documented in previous studies. It has been suggested that this increased risk may be attributed to the increased ability of the bacteria to adhere to mucosa in the presence of smoke [ 33 ].

Finally, multiple logistic regression analysis has revealed that the crowd density in the house (number of persons per 100 square meters of the house) was an independent risk factor for IMD. It should be noted that a nationwide population-based case-control study among preschool children reported that the risk of IMD increased with an increasing household density [ 34 ]. A number of studies have demonstrated a positive correlation between low socio-economic status and the risk of invasive meningococcal infections [ 17 – 20 ]. For this purpose, increased population density in the household has been considered as an indirect indicator of low socio-economic status.

The present study also attempted to explore the correlation between a particular genotype of the MBL2 gene and its predisposition to IMD. The patients were classified into three groups of high, moderate and low functionality of the MBL2 gene, based on the combination of polymorphisms in exon 1 and in the region of the promoter. Patients mostly had the low functionality genotype, compared to healthy controls ( Table 3 , 18.8% vs. 10.1%, p = 0.189). The difference was not statistically significant, probably because of the relatively small number of cases and controls, which resulted in an underpowered study. Several studies have reported different results regarding the association between the MBL protein and IMD. Summerfield et al. [ 34 ] were the first to report the possible link between polymorphisms of the MBL2 gene and the appearance of unusual and severe infections in adults. Moreover, the correlation between IMD and MBL2 polymorphisms was found in a case-control study by Hibberd et al. [ 23 ]. This study revealed a significantly higher frequency of homozygosity and heterozygosity in patients with IMD compared to healthy controls, which was associated with an increased risk for IMD [ 23 ]. The authors estimated that approximately 32% of IMD cases might be attributed to MBL2 polymorphisms. Other studies supported these findings [ 24 – 25 ]. However, another case-control study including 5500 Europeans (296 case and 5196 controls) questioned the above findings and the authors concluded that there was no correlation between MBL2 polymorphisms and IMD [ 35 ].

Our results are subject to several limitations, with the most significant being the limited number of participants. The study power for the effect of MBL was estimated at 0.31,which was low. Thus, the fact that we did not identify a statistically significant difference does not mean that one does not exist in reality. Another limitation was related to the case-control design. Given the absence of the criterion for a temporal association in the case-control studies, we cannot claim that the associations observed in the present study between several risk factors and the outcome are causative. Moreover, our case-control study design is prone to information bias. In particular, the parents of the cases may report (recall) several exposures more readily than controls. However, on the other hand, it is not likely that parents of cases over-reported their smoking habits, and consequently, the exposure of their children to passive smoking. On the contrary, it would be expected that parents would under-report their smoking activities, which could lead to an underestimation of the impact of parental smoking on the risk associated with IMD. A further limitation of our study was that symptoms that were suggestive of an infection of the upper respiratory tract were based on self-reports, without the implementation of serological tests for the detection of viral antigens. Finally, we matched cases to controls by age and thus we lost the opportunity to verify the age as a risk factor. However, as age is a well-known strong confounder, we preferred to control for age to be able to study the other possible risk factors.

In conclusion, our case-control study indicated that paternal smoking, a recent history of upper respiratory tract viral infection, crowded households and recent relocation/vacation activities were independent risk factors for IMD. Additional studies are needed to explore in detail the role of relocation or holidays as risk factors for IMD and to assess the actual risk posed. Preventive activities aimed at reducing parental smoking and other risk factors could decrease the risk of IMD among children in Greece.

Author Contributions

Conceived and designed the experiments: CH VV MT. Performed the experiments: VV KT. Analyzed the data: CH AK. Contributed reagents/materials/analysis tools: GT VS. Wrote the paper: CH GM GR. Provided expertise and editing: GR. All the authors provided constructive comments and approved the final version of the manuscript.

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 outcome of interest. The researcher then looks at historical factors to identify if some exposure(s) is/are found more commonly in the cases than the controls. If the exposure is found more commonly in the cases than in the controls, the researcher can hypothesize that the exposure may be linked to the outcome of interest.

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

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

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

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

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

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

Disadvantages and Limitations

The most commonly cited disadvantage in case-control studies is the potential for recall bias. [2] Recall bias in a case-control study is the increased likelihood that those with the outcome will recall and report exposures compared to those without the outcome. In other words, even if both groups had exactly the same exposures, the participants in the cases group may report the exposure more often than the controls do. Recall bias may lead to concluding that there are associations between exposure and disease that do not, in fact, exist. It is due to subjects' imperfect memories of past exposures. If people with Kaposi's sarcoma are asked about exposure and history (e.g., HIV, asbestos, smoking, lead, sunburn, aniline dye, alcohol, herpes, human papillomavirus), the individuals with the disease are more likely to think harder about these exposures and recall having some of the exposures that the healthy controls.

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

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

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

The major method for analyzing results in case-control studies is the odds ratio (OR). The odds ratio is the odds of having a disease (or outcome) with the exposure versus the odds of having the disease without the exposure. The most straightforward way to calculate the odds ratio is with a 2 by 2 table divided by exposure and disease status (see below). Mathematically we can write the odds ratio as follows.

Odds ratio = [(Number exposed with disease)/(Number exposed without disease) ]/[(Number not exposed to disease)/(Number not exposed without disease) ]

This can be rewritten as:

Odds ratio = [ (Number exposed with disease) x (Number not exposed without disease) ] / [ (Number exposed without disease ) x (Number not exposed with disease) ]

The odds ratio tells us how strongly the exposure is related to the disease state. An odds ratio of greater than one implies the disease is more likely with exposure. An odds ratio of less than one implies the disease is less likely with exposure and thus the exposure may be protective. For example, a patient with a prior heart attack taking a daily aspirin has a decreased odds of having another heart attack (odds ratio less than one). An odds ratio of one implies there is no relation between the exposure and the disease process.

Odds ratios are often confused with Relative Risk (RR), which is a measure of the probability of the disease or outcome in the exposed vs unexposed groups. For very rare conditions, the OR and RR may be very similar, but they are measuring different aspects of the association between outcome and exposure. The OR is used in case-control studies because RR cannot be estimated; whereas in randomized clinical trials, a direct measurement of the development of events in the exposed and unexposed groups can be seen. RR is also used to compare risk in other prospective study designs.

Issues of Concern

The main issues of concern with a case-control study are recall bias, its retrospective nature, the need for a careful collection of measured variables, and the selection of an appropriate control group. [3] These are discussed above in the disadvantages section.

Clinical Significance

A case-control study is a good tool for exploring risk factors for rare diseases or when other study types are not feasible. Many times an investigator will hypothesize a list of possible risk factors for a disease process and will then use a case-control study to see if there are any possible associations between the risk factors and the disease process. The investigator can then use the data from the case-control study to focus on a few of the most likely causative factors and develop additional hypotheses or questions. Then through further exploration, often using other study types (such as cohort studies or randomized clinical studies) the researcher may be able to develop further support for the evidence of the possible association between the exposure and the outcome.

Enhancing Healthcare Team Outcomes

Case-control studies are prevalent in all fields of medicine from nursing and pharmacy to use in public health and surgical patients. Case-control studies are important for each member of the health care team to not only understand their common occurrence in research but because each part of the health care team has parts to contribute to such studies. One of the most important things each party provides is helping identify correct controls for the cases. Matching the controls across a spectrum of factors outside of the elements of interest take input from nurses, pharmacists, social workers, physicians, demographers, and more. Failure for adequate selection of controls can lead to invalid study conclusions and invalidate the entire study.

2x2 table with calculations for the odds ratio and 95% confidence interval for the odds ratio

Article Details

Article Editor:

Methodology Series Module 2: Case-control Studies., Setia MS,, Indian journal of dermatology, 2016 Mar-Apr [PubMed PMID: 27057012]

Bias in observational study designs: case-control studies., Sedgwick P,, BMJ (Clinical research ed.), 2015 Jan 30 [PubMed PMID: 25636996]

Efficient sampling in unmatched case-control studies when the total number of cases and controls is fixed., Groenwold RHH,van Smeden M,, Epidemiology (Cambridge, Mass.), 2017 Jul 4 [PubMed PMID: 28682849]

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Evidence-Based Practice

Case-Control Studies

To the Editor The JAMA Guide to Statistics and Methods article on case-control studies 1 contains several misconceptions.

First, Dr Irony stated that “the RR [relative risk] cannot be determined from a case-control study. A case-control study can only estimate the OR [odds ratio], which is the ratio of odds and not the ratio of probabilities.” This is true for studies using epidemic sampling (also known as cumulative incidence sampling), in which controls are selected from those who did not develop the outcome by the end of the risk period. However, the vast majority of case-control studies sample controls from the source population over the entire risk period under study (density sampling). With such sampling, the OR exactly estimates the rate ratio from the corresponding cohort study. 2 - 4

Blakely T, Pearce N, Lynch J. Case-Control Studies. JAMA. 2019;321(8):806–807. doi:10.1001/jama.2018.20253

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Case Control Study: Definition, Benefits & Examples

Research Assistant at Princeton University

Undergraduate at Princeton University

Julia Simkus is a Psychology student at Princeton University. She will graduate in May of 2023 and go on to pursue her doctorate in Clinical Psychology.

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

What is a Case-Control study?

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 are used to identify any associations between an exposure and an outcome and to help researchers form hypotheses about a particular population.

Researchers will first identify the two groups, and then they will 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.

Below are some examples of case-control studies:

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.

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?

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

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. https://doi.org/10.1111/head.13423

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Case-Control Study: Understanding the Basics and Importance

In this article, you will learn about the benefits and limitations of a case-control study and its importance in health research.

Case-control studies play a very important role in medical research and have contributed significantly to our understanding of the etiology of numerous illnesses and ailments. These studies are especially beneficial for researching uncommon or complicated diseases, or for looking into potential risk factors for diseases when a randomized controlled trial would be impracticable or unethical.

This article is intended to provide you with useful information on the case-control study. It covers the basics, such as the distinction between a cohort and a case-control study, before delving into more complex themes to help you obtain a deeper grasp of the subject.

What is a case-control study?

A case-control study is a sort of observational study that is frequently used in medical research to determine the causes of a particular disease or condition. A case-control study compares a group of people with the disease or condition of interest, called as cases, to a control group of people who do not have the ailment, the controls. The study’s purpose is to detect variations in exposures, risk factors, or other features between the two groups that may be related to illness development.

They are frequently employed as a first phase in the research process to detect potential illness risk factors before doing broader, more extensive research.

The effectiveness of a case-control study is one of its key advantages. Researchers are not required to monitor a huge number of people over a lengthy period of time to identify who will acquire the disease because the study is undertaken after the condition has already happened. They can instead recruit a smaller sample of patients and controls and compare their features.

When is a case-control study used?

In medical research, a case-control study is typically employed in these situations:

Cohort vs. case-control study

Cohort studies and case-control studies are two methods of observational studies that are commonly utilized in medical research. While they have certain commonalities, they also have some significant differences.

A cohort study is a method of observational research that follows a group of people over time to see if a certain exposure or intervention is connected to a specific result. Individuals in a cohort study are defined based on their exposure statuses, such as a risk factor or a specific treatment, and are monitored over time to see if they develop the particular disease of interest.

A case-control study, on the other hand, is a method of observational research that compares a group of people, cases, with an illness or condition to a group of people, control, who do not have the ailment. A case-control study aims to discover differences in exposures, risk factors, or other features between the two groups that may be connected to illness development.

Key differences between cohort and case-control study

case control study example article

Benefits and limitations of case-control studies

Limitations

Examples of case-control studies

The connection between smoking and cancer: A study examined the smoking behaviors of people with lung cancer to those who did not have lung cancer and discovered a substantial connection between smoking and the development of lung cancer.

Pesticide exposure and Parkinson’s disease : A study compared pesticide exposure in people with Parkinson’s disease to people without the condition and discovered a substantial connection between pesticide exposure and Parkinson’s disease development.

Diet and breast cancer: A study examined the diets of women with breast cancer to those of women who did not have the disease and discovered a connection between high-fat diets and an elevated risk of breast cancer.

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Case-Control Study

A nested case control study is a case control study within an existing cohort (cohort study)

From: The Practical Guide to Clinical Research and Publication , 2021

Related terms:

Pharmacovigilance in Pregnancy

Gweneth Levy MD , in Pharmacovigilance: A Practical Approach , 2019

Case-Control Studies

Case-control studies declare a specific outcome of interest (i.e., specific birth defect) and determine whether cases with the event of interest and controls had similar or different rates of exposure to a particular drug or class of drugs ( Box 8.14 ). Case-control studies are performed retrospectively because the pregnancy outcomes are already known. Data are collected through interviews, questionnaires, or medical chart review. Often they are conducted as part of current ongoing case-control surveillance studies, such as the US National Birth Defects Prevention Study 32 or the Slone Epidemiology Center Birth Defects Study. 33 An example of two large case-control studies that supported product labeling showed that maternal use of paroxetine during the first trimester of pregnancy was associated with a two- to threefold increased risk of right ventricular outflow tract obstructions. 34,35

Biostatistics Used for Clinical Investigation of Coronary Artery Disease

Chul Ahn , in Translational Research in Coronary Artery Disease , 2016

Case–Control Study

A case–control study is always retrospective because it starts with an outcome such as disease and then looks backward in time for exposures that might have caused the outcome. Here, a case means a subject with a disease or outcome of interest where a control means a subject without a disease or outcome of interest. A case–control study aims to retrospectively determine the exposure to the risk factor of interest from cases and controls. The investigators ascertain the prevalence of exposure to a risk factor in both groups of cases and controls through chart reviews or other means. If cases have significantly higher prevalence rate of the exposure than controls, then the exposure is significantly associated with an increased risk of the outcome.

Case–control studies are especially useful for outcomes that are rare or that take a long time to develop, such as cardiovascular disease and cancer. These studies often require less time, effort, and money than cohort studies. Therefore, a case–control study may be the only feasible method for very rare disorders or those with long lag between exposure and outcome. A case–control study can examine many risk factors at once. Disadvantage of a case–control study includes reliance on recall or records to determine exposure status, difficulty in establishing cause and effect due to temporal backwards relationship, and potential recall and selection bias. Incidence-prevalence bias (also called Neyman bias) also occurs in a case–control study. Suppose that cases are interviewed 1 month after the coronary attack in a study that investigates association between tobacco smoking and acute myocardial infarction (AMI). If death occurs more frequently in smokers with AMI, the remaining cases will show lower frequency of smoking than the dead AMI patients, which will decrease the association between smoking and AMI. This bias occurs only if the risk factor influences mortality from the disease being studied [5] .

Matched case–control study designs are commonly implemented to eliminate confounding in clinical studies. The main potential benefit of matching in case–control studies is a gain in efficiency [6] .

Example: Pierre-Louis et al. [7] used a case–control design to investigate the severity of coronary artery disease by coronary angiography in age-matched and sex-matched patients with diabetes mellitus with atrial fibrillation versus sinus rhythm.

Understanding, Controlling, and Preventing Infectious Diseases

Benjamin Schwartz , Stacey W. Martin , in Principles and Practice of Pediatric Infectious Diseases (Fourth Edition) , 2012

In a case-control study , the investigator identifies a group of people with a disease or outcome of interest (cases) and compares their exposures with those in a selected group of people who do not have disease (controls). Differences between the groups are expressed by an odds ratio, which compares the odds of an exposure in case and control groups (see Table 1-1 ). Case-control studies are retrospective in that disease status is known and serves as the basis for selecting the two comparison groups; exposures are then determined by reviewing available records or by interview.

A major advantage of case-control studies is their efficiency in studying uncommon diseases or those with a long latency. Case-control studies also can evaluate multiple exposures that may contribute to a single outcome. They tend to be less costly and more time-efficient than cohort studies because study subjects can be identified from existing sources (such as hospital or laboratory records, disease registries, or surveillance reports) and, after identification of suitable control subjects, data on prior exposures can be collected rapidly. Case-control studies also have several drawbacks: bias can be introduced during selection of cases and controls and in retrospectively determining exposures, and inferring causation from statistically significant associations can be complicated by difficulty in determining the temporal sequence of exposure and disease in a retrospective study.

Ashley Conley , in Disaster Epidemiology , 2018

Case-control studies are commonly used when the route of exposure is unknown and there is not a clearly defined group of people that can be identified as exposed and unexposed. In these studies, cases are individuals that have the disease and controls are those that do not have the disease. The measure of association for case-control studies is the odds ratio. The data to calculate an odds ratio can be put in a two-by-two table, as shown in Table 12.3 . The calculation for the odds ratio is shown below ( Eq. 12.2 ) ( USDHHS, 2012 ).

Table 12.3 . Example Two-by-Two Table

In a case-control study conducted by Ward, Spokes, and McAnulty (2011) , the authors looked at risk factors for hospitalization in Sydney, Australia, due to the 2009 H1N1 influenza A pandemic strain in individuals >16 years old ( Ward et al., 2011 ). The pandemic strain of H1N1 emerged in 2009 and spread quickly across the globe, disproportionately affecting children and young adults. During the 2009–10 influenza season, there was an increase in pediatric deaths and hospitalizations for children and young adults not traditionally seen in prior influenza seasons ( World Health Organization Collaborating Center for Surveillance, Epidemiology, and Control of Influenza, 2010 ). The case-control study analyzed data from 302 case-patients and 603 controls and identified pregnancy, immune suppression, preexisting lung disease, asthma, heart disease, diabetes, and smoking as risk factors for hospitalization from July 1, 2009–August 31, 2009 ( Ward, Spokes, & McAnulty, 2011 ).

Uveal malignant melanoma: epidemiologic aspects

Arun D. Singh , ... Stefan Seregard , in Clinical Ophthalmic Oncology , 2007

Although several case–control studies have evaluated occupation as a risk factor for uveal melanoma ( Table 35.5 ), there is no consistent evidence indicating occupational exposure to UV light or other agents as a risk factor. 13, 58 In an exploratory study, agriculture and farming work was associated with uveal melanoma, but specific exposure to a group of chemicals could not be clearly identified. 63 Studies from England and Canada have shown a significant excess of uveal melanoma cases in electrical workers, managers, technical workers, and other indoor workers such as scientists, judges, and teachers. 58

The association of occupational exposure to artificial UV light remains questionable. In a French case–control study involving only 50 patients, an increased risk of uveal melanoma was reported in welders, 64 but in a larger study of 412 patients, also from France, no statistically significant association with any occupation could be identified. 65 Other case–control studies have also yielded conflicting results for occupational UV light exposure and the risk of uveal melanoma. 61, 63 With regard to recreational UV exposure, some authors have suggested that the use of a sunlamp may be a significant risk determinant for uveal melanoma, but more studies are needed. 60, 66

Polyphenols in the Prevention and Treatment of Vascular and Cardiac Disease, and Cancer

Taisha Doo , Gertraud Maskarinec , in Polyphenols in Human Health and Disease , 2014

3.7 Flavones

In addition to three case-control studies, 41,45,46 two cohorts 42,43 estimated breast cancer risk in relation to this flavonoid with low intake levels. All three case-control studies observed significant inverse associations with a 13–27% lower risk for women in the highest intake categories. There was also a trend in the Iowa Women’s Study (HR=0.94; 95% CI: 0.87–1.02; p trend= 0.09) for flavones as a group 42 but not in the Women’s Health Health Study, which reported a combined estimate for flavonols and flavones. 43

Ahmed Awaisu , ... Mohamed Izham Mohamed Ibrahim , in Encyclopedia of Pharmacy Practice and Clinical Pharmacy , 2019

Case–Control Studies

Case–control study design is used to determine association between risk factors or exposures and outcomes. It is a useful design to study exposures in rare diseases or diseases that take long time to develop ( Newman et al., 2013 ). It investigates exposures in individuals with and those without the outcome of interest. Nevertheless, case–control studies can help to identify harmful or beneficial exposures. Furthermore, the outcome of interest can be undesirable (e.g., mortality) or desirable (e.g., microbiological cure). As the name suggests, in a case–control study design, there are two groups of subjects: (1) cases (individuals with the outcome of interest) and (2) controls (individuals without the outcome of interest) ( Newman et al., 2013 ). Cases are randomly selected based on prespecified eligibility criteria from a population of interest. Appropriate representative controls for the cases selected are then identified. The researchers then retrospectively investigate possible exposures to the risk factor. Fig. 1 represents a schematic diagram of a case–control study.

case control study example article

Figure 1 . Case–control study design.

Case–control studies are relatively inexpensive, less time-consuming to conduct, allow investigation of several possible exposures or associations, and are suitable for rare diseases. Selection of the control group is a critical component of case–control studies. Case–control studies have several drawbacks: confounding must be controlled, subject to recall, observation, and selection biases.

OR is the measure of association used for the analysis of case–control studies. This is defined as the odds of exposure to a factor in those with a condition or disease compared with those who do not have the condition or disease.

Understanding Research Designs

Vera Ehrenstein , Timothy L. Lash , in Women and Health (Second Edition) , 2013

The Nested Case-Control Design

Nested case-control studies are conducted – ‘nested’ – in fully enumerated populations, such as cohorts recruited for other studies. Cases are outcomes of interest occurring among the cohort members over the observation period. Each time a case is identified, controls are sampled from the case’s risk set , defined as the cohort members who have not developed the disease of interest as of the index date. Persons in the risk set are at risk of developing the outcome of interest on the index date. The sampling strategy is therefore referred to as risk-set sampling , and implies matching of controls to cases on calendar time (the index date). A person selected as a control for a given case who develops the outcome of interest later during the study period herself becomes ‘a case’ and should be included in the case series, with her own index date and risk set for sampling controls ( Figure 7.3 ). More generally, one samples the control experience from the eligible person-time (as in density sampling ). 8 The odds ratio in a nested case-control study with risk-set or density sampling can be interpreted as an estimate of the underlying incidence rate ratio.

Meat Consumption and Cancer

Amanda J. Cross , Rashmi Sinha , in International Encyclopedia of Public Health (Second Edition) , 2017

Salted Foods

Case-control studies have found a positive association between stomach cancer risk with consumption of salted meat and fish ( Boeing et al., 1991 ; Ward and Lopez-Carrillo, 1999 ; Buiatti et al., 1989 ; Haenszel et al., 1972 ; Kono et al., 1988 ; Lee et al., 1995 ; Palli et al., 1992 ; Ramon et al., 1993 ); in addition, a cohort study found a two-fold increased risk of stomach cancer with salted fish consumption ( Kneller et al., 1991 ). Salted meat and fish has also been associated with a 2.6-fold increased risk of colorectal cancer ( Knekt et al., 1999 ). Some foods, such as salted fish, are preserved using nitrite salts and are thus a source of both salt and exogenous N -nitroso compounds (NOC) from the reaction between the nitrite and the secondary amines present in the fish. Chinese salted fish, for example, contains high levels of NOCs.

Foundations of evidence-based gerontological occupational therapy practice

Mary Krieger RN, MLIS , ... Charlotte B. Royeen PhD, OTR/L, FAOTA , in Occupational Therapy with Aging Adults , 2016

Case-control study

Case-control studies start with the outcome of interest, for example, a disease, and then look backward in time to detect possible causes or risk factors for that disease. 21 A case-control study answers the question of “what happened” and is retrospective. The study group is comprised of individuals who have the disease, and the control group includes individuals who have not developed the disease. Ideally, there should not be any differences in other characteristics between the individuals in the study group (case) and the control group other than those with or without the disease. A case-control study is useful for the identification of the causes of disease and side effects of treatment. It can be conducted in a shorter time period than a cohort study and is relatively less expensive to undertake. 32

In a case-control study, selection of appropriate control individuals and the possibility of recall bias (a patient interprets the possible causes of his or her disease by recalling events or experiences) are two major sources of bias. A checklist of appraisal questions for a case-control study is as follows: 73 , 91

Were the cases clearly defined?

Were the cases representative of a defined population?

How were the controls selected, and were they drawn from the same population as the cases?

Did the authors identify confounding factors, such as genetic, environmental, and socioeconomic factors?

Were study measures identical for cases and controls?

Were study measures objective or subjective, and is recall bias likely if they were subjective?

A good example of a case-control study is the following resource:

Stott, D. J., Buttery, A. K., Bowman, A., Agnew, R., Burrow, K., Mitchell, S. L., et al. (2006). Comprehensive geriatric assessment and home-based rehabilitation for elderly people with a history of recurrent nonelective hospital admissions. Age and Aging , 35 (5), 487-491. (PubMed ID: 16772361)

Quantitative Study Designs: Case Control

Quantitative study designs.

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 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 example article

(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:

What are the pitfalls to look for?

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

Anticholinergic drugs and risk of dementia: case-control study

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

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.datasciencecentral.com/case-control-study/

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case control study example article

A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19

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Interpretation

Evidence before this study

Added value of this study, implications of all the available evidence, 1. introduction.

Intensive Care National Audit & Research Centre Reports. COVID-19 Report 2020-07-31. https://www.icnarc.org/Our-Audit/Audits/Cmp/Reports (6 August 2020).

Office for National Statistics. Coronavirus (COVID-19) related deaths by ethnic group, England and Wales: 2 March 2020 to 10 April 2020. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/coronavirusrelateddeathsbyethnicgroupenglandandwales/2march2020to10april2020 (6 August 2020).

Public Health England Public Health Profiles. https://fingertips.phe.org.uk/search/ethnicity#page/0/gid/1/pat/6/par/E12000007/ati/102/are/E09000002/cid/4/tbm/1/page-options/ovw-do-0 (4 Sept 2020)

Office for National Statistics. Deaths involving COVID-19 by local area and socioeconomic deprivation: deaths occurring between 1 March and 30 June 2020. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19bylocalareasanddeprivation/deathsoccurringbetween1marchand30june2020 (6 August 2020).

2.1 Study design and participants

Latest research from Lambeth DataNet, Lambeth Together, 2019. https://lambethtogether.net/latest-research-from-lambeth-datanet/ (6 August 2020).

2.2 Data sources and processing

2.3 Exposures

Office for National Statistics. Ethnic group, national identity and religion. https://www.ons.gov.uk/methodology/classificationsandstandards/measuringequality/ethnicgroupnationalidentityandreligion (6 August 2020).

BMI: preventing ill health and premature death in black, Asian and other minority ethnic groups. https://www.nice.org.uk/guidance/ph46/chapter/1-Recommendations (16 June 2020).

Ministry of housing, communities and local government. The English indices of deprivation 2019 (IoD2019). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/835115/IoD2019_Statistical_Release.pdf (6 August 2020)

2.4 Outcomes

2.5 statistical analysis.

2.6 Sensitivity analyses

2.7 Ethics

2.8 data availability, 2.9 role of funding, 3.1 relationship between covid-19 admission and sociodemographic and comorbidity profiles.

3.2 Clinical presentation of COVID-19 in patients requiring hospital admission

3.3 Risk of in-hospital mortality with COVID-19

3.4 Sensitivity analyses

Harrison E.M., Docherty A.B., Barr B., et al. ISARIC4C investigators. Ethnicity and outcomes from COVID-19: the ISARIC CCP-UK prospective observational cohort study of hospitalised patients. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3618215 (4 Sept 2020).

Griffith G.J., Morris T.T., Tudball M., et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. medRxiv preprint doi: https://doi.org/10.1101/2020.05.04.20090506 (6 August 2020)

Contributors

Declaration of competing interest, acknowledgments, appendix. supplementary materials.

Availability of data and materials The authors declare that all data supporting the findings of this study are available within the article (and its supplementary information files). Individual participant data will not be made available.

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DOI: https://doi.org/10.1016/j.eclinm.2020.100574

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Study Design 101

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."

Disadvantages

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

Related Terms

A patient with the disease or outcome of interest.

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

A patient who does not have the disease or outcome.

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

Observed Assignment

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

Unmatched Design

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

Now test yourself!

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

a) True b) False

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

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

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National Center for Biotechnology Information - Case Control Studies

case-control study , in epidemiology , observational (nonexperimental) study design used to ascertain information on differences in suspected exposures and outcomes between individuals with a disease of interest (cases) and comparable individuals who do not have the disease (controls). Analysis yields an odds ratio (OR) that reflects the relative probabilities of exposure in the two populations. Case-control studies can be classified as retrospective (dealing with a past exposure) or prospective (dealing with an anticipated exposure), depending on when cases are identified in relation to the measurement of exposures. The case-control study was first used in its modern form in 1926. It grew in popularity in the 1950s following the publication of several seminal case-control studies that established a link between smoking and lung cancer .

Case-control studies are advantageous because they require smaller sample sizes and thus fewer resources and less time than other observational studies. The case-control design also is the most practical option for studying exposure related to rare diseases. That is in part because known cases can be compared with selected controls (as opposed to waiting for cases to emerge, which is required by other observational study designs) and in part because of the rare disease assumption, in which OR mathematically becomes an increasingly better approximation of relative risk as disease incidence declines. Case-control studies also are used for diseases that have long latent periods (long durations between exposure and disease manifestation) and are ideal when multiple potential risk factors are at play.

The primary challenge in designing a case-control study is the appropriate selection of cases and controls. Poor selection can result in confounding, in which correlations that are unrelated to the exposure exist between case and control subjects. Confounding in turn affects estimates of the association between disease and exposure, causing selection bias, which distorts OR figures. To overcome selection bias, controls typically are selected from the same source population as that used for the selection of cases. In addition, cases and controls may be matched by relevant characteristics. During the analysis of study data, multivariate analysis (usually logistic regression) can be used to adjust for the effect of measured confounders.

Bias in a case-control study might also result if exposures cannot be measured or recalled equally in both cases and controls. Healthy controls, for example, may not have been seen by a physician for a particular illness or may not remember the details of their illness. Choosing from a population with a disease different from the one of interest but of similar impact or incidence may minimize recall and measurement bias, since affected individuals may be more likely to recall exposures or to have had their information recorded to a level comparable to cases.

What Is a Case Study?

An in-depth study of one person, group, or event

Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.

case control study example article

Cara Lustik is a fact-checker and copywriter.

case control study example article

Verywell / Colleen Tighe

Benefits and Limitations

Types of case studies, how to write a case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in various fields, including psychology, medicine, education, anthropology, political science, and social work.

The purpose of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, it is important to follow the rules of APA format .

A case study can have both strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult to impossible to replicate in a lab. Some other benefits of a case study:

On the negative side, a case study:

Researchers may choose to perform a case study if they are interested in exploring a unique or recently discovered phenomenon. The insights gained from such research can help the researchers develop additional ideas and study questions that might be explored in future studies.

However, it is important to remember that the insights gained from case studies cannot be used to determine cause and effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of Freud's work and theories were developed through the use of individual case studies. Some great examples of case studies in psychology include:

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse had denied her the opportunity to learn language at critical points in her development.

This is clearly not something that researchers could ethically replicate, but conducting a case study on Genie allowed researchers the chance to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might utilize:

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers utilize depends on the unique characteristics of the situation as well as the case itself.

There are also different methods that can be used to conduct a case study, including prospective and retrospective case study methods.

Prospective case study methods are those in which an individual or group of people is observed in order to determine outcomes. For example, a group of individuals might be watched over an extended period of time to observe the progression of a particular disease.

Retrospective case study methods involve looking at historical information. For example, researchers might start with an outcome, such as a disease, and then work their way backward to look at information about the individual's life to determine risk factors that may have contributed to the onset of the illness.

Where to Find Data

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research.

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Here are a few additional pointers to keep in mind when formatting your case study:

A Word From Verywell

Case studies can be a useful research tool, but they need to be used wisely. In many cases, they are best utilized in situations where conducting an experiment would be difficult or impossible. They are helpful for looking at unique situations and allow researchers to gather a great deal of information about a specific individual or group of people.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines that you are required to follow. If you are writing your case study for professional publication, be sure to check with the publisher for their specific guidelines for submitting a case study.

Simply Psychology. Case Study Method .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal. The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.

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What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on January 30, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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

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

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

Prevent plagiarism. Run a free check.

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

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

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

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

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

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

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

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

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IMAGES

Diagram. Case-control study.
Case-Control Studies: Research in Reverse
Study case control design
Overview of case, control and matching criteria for the study sample
Unmatched Case-Control
Case Control Study Example

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Approaching Case Studies
Case Study Writing
Case Control Study Part 1
Case-control study design
Case Study Presentation
Chapter 5 Part 1 Case control study

COMMENTS

A Practical Overview of Case-Control Studies in Clinical Practice
For example, if we want to evaluate the role of a specific risk factor for two different diseases, under the nested case-control study we need two control groups, whereas for a case-cohort design, we need only one subcohort which can be used to assess the effect of the risk factor on both diseases.
Case Control Studies
In the example above about Kaposi's sarcoma, the researcher could ask both the cases and controls about exposures to HIV, asbestos, smoking, lead, sunburns, aniline dye, alcohol, herpes, human papillomavirus, or any number of possible exposures to identify those most likely associated with Kaposi's sarcoma.
Case-Control Study of Human Papillomavirus and Oropharyngeal Cancer
We performed a hospital-based, case-control study of 100 patients with newly diagnosed oropharyngeal cancer and 200 control patients without cancer to evaluate associations between HPV...
Case-Control Study of Use of Personal Protective Measures and Risk for
Cite This Article Abstract We evaluated effectiveness of personal protective measures against severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Our case-control study included 211 cases of coronavirus disease (COVID-19) and 839 controls in Thailand.
Case-control study in medical research: Uses and limitations
Case-control research is a vital tool used by epidemiologists, or researchers who look into the factors affecting health and illness of populations. Just one risk factor could be investigated...
Case Control Study: Definition, Benefits & Examples
For example, medical researchers study disease X and use a case-control study design to identify risk factors. They create two groups using available medical records from hospitals. Individuals with disease X are in the case group, while those without it are in the control group.
A Case-Control Study on the Risk Factors for Meningococcal ...
A case-control study was performed using 133 children (44 cases and 89 controls). All participants were children aged between 0-14 years, who had been hospitalized in two children's hospitals (Aghia Sophia and P & A Kyriakou) in Athens, Greece, within a 2-year period from January 2011 to December 2012.
(PDF) Case-control studies
Case-control studies have been used to estimate the odds ratios (ORs) for involvement in road traffic accidents; the 'cases' were drivers injured or killed in road traffic accidents, and...
Case Control Studies Article
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 ...
LibGuides: Evidence-Based Practice: Case Control Studies
Case Control study design simply compares a sample population with certain conditions or characteristics and compares them with a sample population without the certain conditions or characteristics. This design is descriptive and historical/retrospective in nature. Koffel, 2011.
Case-Control Studies
This is true for studies using epidemic sampling (also known as cumulative incidence sampling), in which controls are selected from those who did not develop the outcome by the end of the risk period. However, the vast majority of case-control studies sample controls from the source population over the entire risk period under study (density ...
Case-Control Study
Case-control studies may prove an association between exposures and outcomes, but they can not demonstrate causation. Examples Investigating the impact of exposure to daylight on the health of office workers (Boubekri et al., 2014).
Case-Control Study: Understanding the Basics and Importance
A case-control study is a sort of observational study that is frequently used in medical research to determine the causes of a particular disease or condition. A case-control study compares a group of people with the disease or condition of interest, called as cases, to a control group of people who do not have the ailment, the controls.
Case-Control Study
A good example of a case-control study is the following resource: Stott, D. J., Buttery, A. K., Bowman, A., Agnew, R., Burrow, K., Mitchell, S. L., et al. (2006). Comprehensive geriatric assessment and home-based rehabilitation for elderly people with a history of recurrent nonelective hospital admissions. Age and Aging, 35 (5), 487-491.
Research Design: Case-Control Studies
As an actual example of a case-control study, children with autism spectrum disorder (ASD) may be compared with normally developing children to determine whether a history of maternal antidepressant use during pregnancy is more frequent among cases than among controls; if it is, and if the association remains statistically significant after adjusting for confounding variables, one may ...
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 ...
A case-control and cohort study to determine the relationship between
This case-control study assessed the association between ethnicity and risk of severe COVID-19 in an ethnically diverse inner city location, taking into account the local contextual population demography and individual-level comorbidity burden and socioeconomic deprivation. ... For example, a recent very large cohort study not specifically ...
Case-control studies: basic concepts
For example, suppose that one wants to investigate in a case-control study whether two different types of oral contraceptives give a different risk of venous thrombosis: 'third-generation oral contraceptives' vs 'second-generation oral contraceptives' (this was once a real and hotly debated question 26 ).
Case Control
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
Case-control study
case-control study, in epidemiology, observational (nonexperimental) study design used to ascertain information on differences in suspected exposures and outcomes between individuals with a disease of interest (cases) and comparable individuals who do not have the disease (controls).
What Is an Observational Study?
Case-control studies bring together two groups, a case study group and a control group. The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group. For example, if you compared smokers (the case ...
Case Study: Definition, Examples, Types, and How to Write
A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in various fields, including psychology, medicine, education, anthropology, political science, and social work.
What Is a Case Study?
Step 1: Select a case Step 2: Build a theoretical framework Step 3: Collect your data Step 4: Describe and analyze the case When to do a case study A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject.
250 C++ Program Examples & Solutions
List of Switch case C++ Programming Examples. Write C++ program to print number of days in a month using switch case. Write C++ program to print day of week name using switch case. Write C++ program to create calculator using switch Statement. Write C++ program to check even or odd number using switch case.