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Case Control Study: Definition, Benefits & Examples

By Jim Frost 2 Comments

What is a Case Control Study?

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

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

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

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

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

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

Related posts : Observational Studies Explained and Control Groups in Experiments

Benefits of a Case Control Study

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

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

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

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

Limitations of a Case Control Study

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

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

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

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

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

Related post : Causation versus Correlation in Statistics

Use Matching to Control Confounders

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

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

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

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

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

Learn more about confounding variables .

Statistical Analysis of a Case Control Study

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

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

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

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

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

Ratios less than one might identify protective factors.

Learn more about Understanding Ratios .

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

Example Odds Ratio in a Case-Control Study

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

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

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

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

Learn more about Odds Ratios.

Epidemiology in Practice: Case-Control Studies (NIH)

Interpreting Results of Case-Control Studies (CDC)

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Reader Interactions

January 18, 2022 at 7:56 am

Great post, thanks for writing it!

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

January 18, 2022 at 7:41 pm

Hi Michael,

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

Comments and Questions Cancel reply

What Is A Case Control Study?

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

Explanation

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

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

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

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

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

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

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

Quick, inexpensive, and simple

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

Beneficial for studying rare diseases

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

Useful for preliminary research

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

Limitations

Subject to recall bias.

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

Difficulty finding a suitable control group

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

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

Do not demonstrate causation

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

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

Below are some examples of case-control studies:

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

Frequently asked questions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

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• 1 University of Nebraska Medical Center
• 2 Spectrum Health/Michigan State University College of Human Medicine
• PMID: 28846237
• Bookshelf ID: NBK448143

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

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

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

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

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

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

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

Disadvantages and Limitations

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

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

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

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

Copyright © 2024, StatPearls Publishing LLC.

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Conflict of interest statement

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

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

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

• Introduction
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• Clinical Significance
• Enhancing Healthcare Team Outcomes
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Study Design 101: Case Control Study

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

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

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

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

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

Disadvantages

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

Design pitfalls to look out for

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

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

Fictitious Example

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

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

Real-life Examples

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

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

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

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

Related Formulas

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

Related Terms

A patient with the disease or outcome of interest.

Confounding

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

A patient who does not have the disease or outcome.

Matched Design

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

Observed Assignment

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

Unmatched Design

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

Now test yourself!

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

a) True b) False

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

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

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What is a Case-Control Study?

Dupépé, Esther B MD, MSPH; Kicielinski, Kimberly P MD, MSPH; Gordon, Amber S MD; Walters, Beverly C MD, MSc, FRCSC

Department of Neurosurgery, University of Alabama at Birmingham

Department of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts

Department of Neurosurgery, Mobile Infirmary Medical Center, Mobile, Alabama

Correspondence: Beverly C. Walters, MD, MSc, FRCSC, Director of Clinical Research, Department of Neurosurgery, University of Alabama at Birmingham, FOT1008B, 1720 2nd Avenue South, Birmingham, AL 35294-3410. E-mail: [email protected]

Corresponding Article

Case-control (case-control, case-controlled) studies are beginning to appear more frequently in the neurosurgical literature. They can be more robust, if well designed, than the typical case series or even cohort study to determine or refine treatment algorithms. The purpose of this review is to define and explore the differences between case-control studies and other so-called nonexperimental, quasiexperimental, or observational studies in determining preferred treatments for neurosurgical patients.

Although randomized control trials (RCTs) represent the highest quality of a study design, 1-5 they are not always practical. Impediments to the successful execution of RCTs include cumbersome study protocols, high cost, lengthy duration, and difficulty enrolling an adequate number of patients. 3 , 5 , 6 They are additionally limited in surgical specialties (eg, neurosurgery) where so many variables may affect outcome and blinding is often impossible or impractical. 6 In addition, overall support for these expensive studies has decreased. Governmental support for research has fallen to less than half of that required for basic science studies. 7 With the decrease in NIH funding for clinical trials between 2006 and 2014, calculated at 24%, and an increase in industry funding of 43%, 8 clinical trials aimed at clinical questions outside of drugs and devices have dropped dramatically. These limitations necessitate the use of additional study designs including observational or so called quasiexperimental studies. Furthermore, the addition of nonrandomized studies enables researchers to fully utilize the complete gamut of clinical research tools. 9

There has been an increase in the reporting of observational studies including case-control trials in the medical literature, 10-12 including the neurosurgical literature. 12 , 13 Case-control studies can provide valuable information if they are well designed and implemented. 3 , 6 However, the quality of reporting has been questioned and case-control studies are found to be commonly mislabeled in the medical literature. 10 , 11 , 13-17 This inadequacy of quality reporting prompted the development of a standardized checklist, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). 16 , 17 Nesvick et al 13 reported that 48% of the studies described as case-control studies met their criteria, including adherence to the STROBE checklist, for the definition of a “true case-control study” within the JNS Publishing Group literature. In order to promote best practices in designing and reporting clinical research, study designs must be appropriately utilized and correctly labeled when reporting findings. Neurosurgical journals are actively taking steps to safeguard the future of quality in the medical literature. 18

It is our hope that a better understanding of the case-control study design and how it differs from other observational studies will promote improved quality in future reporting within the neurosurgical community. Additionally, a better understanding of the case-control study design will enable the clinician to be more critical when interpreting the significance of reported results.

DEFINITION OF CASE-CONTROL DESIGN

In a case-control study, an outcome of interest is first defined and then subjects are selected with (cases) and without (controls) the designated outcome. The investigator then looks

back in time to compare the two groups for a risk factor or other exposure or treatment of interest. Therefore, the case-control study design is inherently retrospective. 3 , 6 , 15 , 19-23 The other kind of comparative, predominantly retrospective, study with which the case-control is confused is the cohort study. This contrasts with the case-control study in that the groups treated in two different fashions are then compared for frequency of the outcome of interest. These 2 groups are often mislabeled as “cases” and “controls.” A key distinction of cohort studies from case-control studies is that the former may be conducted prospectively and can answer questions about disease incidence. 15 , 24 This difference underlies the different types of questions that can be answered using each design. case-control studies do not answer questions about prevalence or incidence, but they do enable investigators to test hypotheses regarding causation and therapeutic efficacy and are, therefore, analytic in nature. This contrasts with other observational study designs that are more descriptive in nature (eg, case series and cross-sectional studies). 24

While both case-control and cohort studies are longitudinal by design, cross-sectional studies, often mislabeled as case-control studies, reflect a single period in time (Figure 1 ). Cross sectional studies can provide information about prevalence and incidence (which case-control studies cannot) and can explore the relationships between variables. 15 A weak causal inference can be made by cross-sectional or other descriptive studies, but the questions about etiology and causation are better addressed by longitudinal studies, such as case-control or cohort studies. 15 To help readers of the literature (including reviewers of potential publications) determine which study design the authors are actually using, the Scottish Intercollegiate Guidelines Network (SIGN) 25 has adapted an algorithm from the National Institute for Health and Care Excellence (NICE) 26 that reduces the quandary to a simple format with easily understood application (Figure 2 ).

Case-control studies are also differentiated by how findings are reported and the statistics used to analyze data. These studies provide an estimate of the strength of association between a variable and an outcome of interest in the form of an odds ratio (OR). If the outcome of interest is rare and both the controls and the cases are at low risk for developing the outcome, the OR can approximate the relative risk (RR) 20 demonstrating a stronger association and inference of causality. This concept can be illustrated with a standard 2 × 2 table. In a cohort study, both incidence and RR (or risk ratio) can be calculated by a/(a + b) and [a/(a + b)]/[c/(c + d)], respectively. These calculations cannot be applied in case-control studies because the underlying concepts and derivation of the mathematical material are different. However, the RR can be approximated by the OR– the cross product of the 2 × 2 table, ac/cb. 20

Nested Case–Control Studies

Nested case-control studies refer to a case-control study that is carried out within a fully defined cohort. 19 For example, if our clinical question was focused upon whether we should be treating patients with odontoid fractures differently if they were older, our research question would be: if the patients who are treated with halo vest apparatuses and who fail to fuse are compared to the patients who do fuse within that treatment population, do those with nonunions tend to be older? In order to address this question using a cohort study design, a large sample size would be necessary, which is prohibitive when studying rare conditions such as this. This question could be answered (in fact, most certainly would have to be answered) within a nested case-control design. This was carried out by Lennarson et al 27 as illustrated in Figure  3 . The large cohort consists of those patients with type II odontoid fractures, but the study population is the subgroup treated with halo-vest apparatus. Within that group, patients are divided by the outcome of nonunion versus union and then looked at to see if age was a risk factor for the undesired outcome. The nested design was essential for answering the research question. There is doubt among some epidemiologists about whether this separation is even necessary; they claim that all case-control studies are ultimately “nested.” 28

ADVANTAGES OF A CASE-CONTROL DESIGN

While RCTs are considered the golden standard for prospectively studying the efficacy or effectiveness of an intervention on one or more outcomes, case-control studies address questions about a known outcome by retrospectively comparing exposure, treatment, or one or more risk factors in groups with or without an outcome of interest. Despite the essential difference in these types of scientific inquiry, similar clinical questions can be answered by a well-designed case-control study as illustrated above. This utility gives the case-control study design a unique advantage when studying clinical outcomes in populations affected by rare conditions and diseases.

Case-control studies are inexpensive, require a smaller number of subjects compared to cohort studies, including RCTs, can be conducted over a short period of time with minimal personnel, and are ideal for studying rare outcomes or outcomes that require a long latency period. 6 , 12 , 20 , 23 , 29 Because these studies are retrospective, it is possible to study multiple potential exposures or risk factors enabling an investigator to develop a hypothesis for further study. 20 The relatively low cost and short duration required to conduct these studies make them an appealing and efficient study design for a variety of clinical questions. Sample size calculations required for statistical significance typically yield far fewer subjects than necessary for enrollment in RCTs, 5 which is beneficial in studying uncommon conditions and outcomes. This is because in case-control studies, groups are divided by the outcome presence or absence looking backward for prevalence of exposure, treatment, or risk factors. In cohort studies, on the other hand, groups are divided by exposure, treatment or risk factors, looking forward to frequency of outcomes. Because frequency of outcomes is almost always smaller than prevalence of exposure, treatment, or risk factors, sample sizes in cohort studies are (usually) much larger than those of case-control studies.

Because most neurosurgical conditions are rare in the general population and may have long latency periods, this can often be an ideal study choice for neurosurgical clinical research. For many of these conditions, a cohort study (especially a randomized controlled trial) would not be feasible and thus would never be undertaken. 30 The most that could (or would most likely) be carried out prospectively is a case series from an institution that specializes in the area of interest, and only a small number of cases could be found. In a case-control study, on the other hand, the cases, those patients with the outcome of interest, are located and matched, sometimes quite easily, with similar patients, but without the outcome of interest. One also does not have to wait for the development of the outcome – it has already occurred, thus shortening the study process.

Cross-sectional studies are also inexpensive to conduct with minimal personnel requirements and can be carried out over a short period of time. This study design is primarily limited to reporting prevalence and caution must be used when attempting to deduce causal relationships. 24 Cross-sectional studies are also challenging in uncommon conditions unless the sample is chosen from the population of affected subjects. 24 Cohort studies allow for the calculation of incidence and provide a stronger inference of causation by determining the time course by measurement of risk factors prior to the occurrence of an outcome. However, prospective cohort studies are time consuming and expensive. The lengthy time required to conduct these studies makes them less efficient for studying rare outcomes. 24

DISADVANTAGES OF A CASE-CONTROL DESIGN

One disadvantage in case-control studies is their vulnerability to bias. 3 , 5 , 6 , 15 , 20-23 This includes both sampling and informational bias types. 6 , 20 The retrospective nature of these studies limits the investigator's control in selecting cases and can introduce sampling bias. 5 Random assignment of subjects to either the study or control group such as in RCTs, or including everyone who has developed the outcome of interest, may not be a feasible alternative to overcome this type of bias, although it would be effective. 20 Therefore, it is critical that the investigator recognizes that the sample of cases may not be entirely representative of the population being studied and takes steps to minimize this type of bias in their case selection. For example, cases and controls can be selected in a blinded fashion. 5 In cohort studies, this type of bias can be controlled by selection of the risk factor prior to the occurrence of the outcome. 24 An exception to this would be when an entire population is included, as in a nested case-control study, as discussed previously.

The retrospective aspect of case-control studies also introduces informational or differential measurement bias, specifically in the form of recall bias during data collection. 3 , 5 , 6 , 15 , 20-23 This type of bias does not occur in cohort studies where exposures and risk factors are determined prior to developing the outcome or disease of interest. 20 To reduce recall or informational bias, data should be used that was collected prior to the development of the outcome. Additionally, data collectors must use identical methods for extracting data on exposures in both control and case groups. 20

USES OF CASE-CONTROL STUDIES

Uses of case-control studies for inferring causation.

Case-control studies answer questions about etiology. 15 The original use of the case-control study was epidemiological for the identification of exposures (or risk factors) associated with an outcome or disease of interest. 20 They can be a cost-effective tool to evaluate potential cause and effect relationships. 12 , 23 Well-known examples of such studies reporting important results include maternal use of diethylstilbestrol and the development of vaginal cancer in their daughters and the prone sleeping position with sudden infant death syndrome. 20 , 31 , 32 Examples in the neurosurgical literature include the association between maternal periconceptional folic acid intake and the development of neural tube defects 33 , 34 and establishing cigarette smoking as a risk factor for subarachnoid hemorrhage. 35 , 36 Case-control studies may also provide evidence against a previously described association. An example was the case-control study of Marbacher et al, 37 which failed to demonstrate statins as a protective factor for aneurysm formation.

Several factors can strengthen the causal inference made by case-control study findings. Case-control studies are used to estimate the strength of an association between an exposure and an outcome in the form of an OR. If the risk of disease is approximately 10% or less in both exposed and unexposed subjects, then the OR can approximate the RR. 20 Strategies employed during study design can limit the effect of confounders. Excluding subjects with variables known to be associated with the outcome addresses the potential for those known variables to magnify the effect observed between the exposure and outcome (ie, effect modification), and thus, strengthening the findings of the current research question. Matching can eliminate the effect of known confounders (eg, age, gender) as well as potential confounders that are not easily measured, although such confounders are best addressed with an RCT design that in theory equalizes the presence of unidentified factors that may influence outcome. These potential confounders that are difficult to identify and measure can partly be addressed with a case-control study design through accounting for familial or genetic factors by matching twins and factors associated with the influence of institutional differences by matching for study center in a multicenter investigation. 38

Uses of Case–Control Studies for Therapeutic Effectiveness

The goal of studies on comparative treatments should always be in the realm of experimentation, ie, the randomized controlled trial. However, as mentioned, these are often unfeasible for various reasons – cost, rarity of the disease, the need for an expeditious assessment of outcome, etc. In addition, there are those who believe that case-control studies that are taken from the records of actual outcomes from treatments undertaken in the course of everyday medical care are more relevant and trustworthy. 39

If appropriately designed, the utility of case-control trials can extend beyond inferring causal relationships to yield information about the effect of a therapeutic treatment, including medical and surgical treatments. 5 , 6 A meta-analysis comparing results of RCTs with case-control and cohort studies reported in 5 major medical journals found that results in the treatment group were not overestimated in the latter study designs. 2 This application of case-control study design has 2 major potential uses in neurosurgical clinical research. The first is to test a preliminary hypothesis to determine if a lengthy and costly RCT is justified. The second is to compare treatments that are considered equivalent in practice. Additionally, this study design may be the only practical option for studying the treatment of a rare disease where enrollment would be insufficient for an RCT, or where randomization would be unethical.

Case-control studies can be designed to test preliminary hypotheses regarding therapeutic effectiveness. Common clinical treatments that are not uniformly used as standard of care can be studied. An example of such a strategy would be to define cases as those who develop an outcome and comparing them with controls without the outcome for the presence of a prior treatment or “exposure.”

For example, Walters et al 40 reported decreased infection rates in a case-control study of prophylactic antibiotic use in cerebrospinal fluid shunt surgery. In this study, cases were defined as patients with shunt infections and controls as patients who did not develop shunt infections. The 2 groups were then compared for the use of prophylactic antibiotics. Prior RCTs failed to demonstrate a decrease in shunt infection with the use of prophylactic antibiotics and the authors hypothesized that inadequate enrollment in the RCTs contributed to their failure to reject the null hypothesis and diluted these findings. 5 Utilizing a case-control analytic study design, fewer subjects were required to reach statistical significance and the authors were able to report solid evidence in support of using prophylactic antibiotics. 40

An additional use of case-control studies for therapeutic effectiveness is to investigate variables or exposures that can guide clinicians in choosing the preferred neurosurgical treatment when more than one therapeutic option exists. An example in the neurosurgical literature includes the study reported by Lennarson et al 27 described above utilizing a nested case-control study that evaluated age as a risk factor for nonfusion with halo-vest immobilization for treatment of type II odontoid fractures (Figure  3 ). The premise for this study was based on existing class III evidence showing no clear difference between the surgical interventions versus halo-vest immobilization for treatment of these fractures. The results of this study provided strong class II medical evidence that age is a significant risk factor for nonfusion with halo-vest immobilization. 27 Based on this finding, age can be used to define a treatment algorithm for choosing surgical intervention versus halo-vest immobilization. Similar case-control study designs could be used for other conditions with apparently equivocal treatment options and the findings could help guide the determination of appropriate neurosurgical treatment in subpopulations of patients.

LEVEL OF EVIDENCE PROVIDED BY CASE-CONTROL STUDIES

In the hierarchy of study designs used to produce evidence-based guidelines, the role of case-control studies has varied. The level of evidence designation for study designs is a hierarchical classification reflective of the inherent potential for bias that may skew the results that may be introduced by the study design. The more robust the study design, the higher the level of evidence. From the very beginning of the evidence-based policy development movement in the late 1970s, 41 through the 1980s, 42 and on into the 1990s, 43 case-control studies were listed among those categorized as level II studies, along with nonrandomized cohort studies, continuing into the 2000s. 2 As organized neurosurgery entered the guideline production business, we embraced this designation of case-control studies as “Class II,” along with nonrandomized controlled trials, and other cohort studies. 44 This paradigm was used routinely until a group utilized the North American Spine Society (NASS) approach, which, quite inexplicably, reduces case-control studies to Class III (out of V). 45 For more recent guidelines, however, a “modified NASS criteria” model has been employed that restores case-control studies to their previous Class II level of evidence, 46 as had been employed previously.

CRITERIA FOR DESIGNING A ROBUST CASE-CONTROL STUDY

• Eligibility, outcome, and exposure must be well defined.
• Eligibility criteria should account for variables with known associations to the outcome of interest. 38
• Eligibility should approximate the type of inclusion criteria that would be anticipated in a RCT (unless impractical or unethical). 5
• The definition of a case (ie, the outcome) must take into consideration all cases that might be missed in selection for inclusion in the study in order to minimize sampling bias. Attention should be given to potential cases that did not seek medical attention, those treated at other institutions, and those who were misdiagnosed. 24
• The definition of exposure should be defined prior to the start of the study with the goal of uniformity. 3 , 5 , 24
• The status of exposure should be determined for a time point that precedes development of the outcome. 15
• Control selection must be thought out in advance.
• The selection of controls should be from a population where they would be identified as a case if they developed the outcome of interest. 20
• The same population should be used for selection of cases and controls 19 , 23 and independent of the risk factor or exposure being studied. 23
• Controls must be defined by their outcome status (ie, those without the outcome of interest). 15
• Strategies to minimize selection bias in choosing controls should include selection from the same population used to select cases, the use of population-based controls if available (eg, if a national registry exists), using two or more control groups (different groups will inherently have different sampling biases and a stronger inference of causation is possible if results are similar between groups), and/or matching for factors that are known to be related to the outcome but not of interest to the current investigation. 20
• Matching controls can eliminate or reduce the effect of confounders thereby strengthening causal inference. 38
• Data collection must account for the potential of informational bias.
• If possible, data collected prior to developing the outcome should be used for analysis. 5 , 20
• Data collection should be blinded if possible and identical methods employed for data extraction in both cases and control groups. 5 , 20
• Statistical analysis should be appropriate.
• Findings should be reported as ORs and should be accompanied by confidence intervals. 3
• The use of matching and multivariate analysis to account for confounders should be employed to reduce any potential bias. 19 , 21 , 22
• If matching is used, matched analysis techniques must be used in the statistical analysis. 20
• The STROBE checklist should be consulted during study development and adherence should be verified prior to manuscript publication.
• Use of reporting guidelines strengthens the quality of reported results. 16-18
• The STROBE checklist should be utilized for case-control, cohort, and cross-sectional study designs. It is comprised of a 22 item checklist with 18 items generic to observational studies and 4 design specific items. 16 , 17

Observational study designs include case-control, cohort, and cross-sectional studies, and each study is distinct with a unique role in clinical research. Case-control studies can be a robust option in neurosurgical research compared to other observational study designs. A better understanding of the differences in design type will facilitate better study designs and further improve the quality of reporting. In our review, we explored those differences and how the case-control study design can contribute to the neurosurgical literature.

The utility of the case-control studies includes answering epidemiological questions for risk factor identification as well as providing data on therapeutic effectiveness. The strength of case-control studies is dependent on well thought out clinical questions and quality study design. When used appropriately, findings can make important contributions to the literature that address causation or determine or refine treatment algorithms.

The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.

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The term “case control study” is often misused and inappropriately applied. The authors present a nice overview of what case control studies truly are, highlighting some of their advantages and their limitations. Although the use of true case-control studies in neurosurgery has, to date, been somewhat limited, the methodology holds substantial potential for answering important questions in our field.

Abhaya V. Kulkarni

Toronto, Canada

The authors have done a nice job of explaining the fundamental concepts of an underutilized but very useful retrospective study design, the case control study. As they point out, because many neurosurgical conditions are relatively uncommon, and because we are impatient to answer important clinical questions, the sample sizes and duration of prospective cohort studies and randomized trials may be impractical for many neurosurgical questions. The case control design offers potentially greater power with fewer patients, the ability to control for factors already known to affect the outcome of interest and, in situations where the risk of the outcome under question is low, the ability to estimate relative risk better than other retrospective designs.

To achieve these advantages, the design characteristics of the case control study must be rigorously applied. The authors outline those characteristics so that readers can avoid misinterpreting cross-sectional studies, case series, and less rigorous reports as case control studies. This is important, for the statistical analyses appropriate for supporting the conclusion from such studies, the amount of bias control possible, and the ability to estimates odds and relative risk are different depending on study design. The neurosurgeon hoping to appropriately incorporate the results of the studies she reads into practice needs to have at least a fundamental understanding of these issues related to study design to avoid placing unjustified confidence in the conclusions of a study done with insufficient rigor.

The case control study, while it has important strengths, is still a retrospective design with the problems that affect all retrospective studies. There may not be enough information available on enough subjects to analyze a particular factor of interest since the data collection was done after the fact. The potential for bias introduced by unmeasurable differences in patient selection, assessment, treatment, and follow-up is greater than in a rigorous prospective design. The immense power of prospective randomization to balance unknown and unmeasurable factors influencing outcome is not available to the retrospective researcher. Nonetheless, when a retrospective design is chosen for reasons of feasibility or practicality, it provides some of the strongest controls available under those constraints. It is a design that should be used more frequently in neurosurgical clinical research.

Stephen J. Haines

M inneapolis, Minnesota

Case–control; Observational study; Study design

What Isn’t a Case-Control Study?

Kicielinski, Kimberly P; Dupépé, Esther B; Gordon, Amber S; Mayo, Nancy E; Walters, Beverly C

Neurosurgery. 84(5):993-999, May 2019.

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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.

Case Control Study | Definition, Examples & Tips

Introduction

What is a case control study in research, when would you use a case control study, examples of case control studies, advantages of case control studies, disadvantages of case control studies.

A case control study is a type of observational research commonly used in the field of epidemiology. It is designed to help researchers identify factors that may contribute to a particular outcome, such as a disease or condition, by comparing subjects who have that outcome (cases) with those who do not (controls). The analysis approach is usually quantitative , but it's helpful to understand this research design , because this method is particularly useful for studying rare diseases or outcomes and can provide valuable insights into potential risk factors.

In this article, we will define what a case control study is, discuss when it is most appropriately used, and provide examples, along with the advantages and disadvantages of this research approach.

A case control study is a type of observational study commonly used to compare two groups of individuals who are largely similar except for the fact that one group has a specific condition or outcome while the second group of individuals, called the controls, do not have that condition or outcome. The primary goal of this study design is to compare factors between the two groups to identify what may be potentially contributing to the outcome or condition being studied.

Case control studies are usually retrospective, meaning they look backward and can use existing data to examine multiple risk factors that might explain why certain individuals developed the condition. In contrast, cohort studies are usually prospective, following individuals over a long period of time and analyzing an outcome, such as the development of a disease.

In a case control study, researchers first identify the cases, which are individuals who have the condition of interest. They then construct a second, very similar group of controls , who share many characteristics with the case group but do not have the condition. Researchers collect data on past exposures, behaviors, and other relevant variables from both the cases and the healthy controls.

By comparing the frequency and patterns of these exposures between an appropriate control group and a corresponding case group, researchers can identify any potentially relative risk factors associated with the condition. The quantitative measure commonly used to compare the strength of association between exposures and outcomes in case control studies is the odds ratio. Odds ratios are used for informing public health interventions and guiding future research.

This type of study is particularly valuable when studying rare diseases or conditions, as it allows researchers to gather data more quickly and efficiently than would be possible with a prospective cohort study. Additionally, case control studies are often less expensive and require fewer resources, making them a practical choice for many research questions .

However, it is important to note that case control studies can be prone to certain biases , such as recall bias and selection bias. Recall bias occurs when participants do not accurately remember past exposures, while selection bias can arise if cases and controls are not properly matched. Despite these limitations, case control studies remain a crucial method in health and epidemiological research, offering insights into the potential causes and risk factors of various health outcomes.

A case control study is particularly useful in several research scenarios, especially when the goal is to look at factors associated with rare diseases or conditions. This type of study is an efficient way to identify and evaluate risk factors associated with specific outcomes. Researchers often use case control studies when the condition under investigation has a low incidence rate, making it impractical to follow a large cohort over time to observe the development of the condition. By focusing on individuals who already have the condition and comparing them to those who do not, researchers can gain insights more quickly and with fewer resources.

This study design is also advantageous when time and funding are limited. Prospective studies can be time-consuming and costly, requiring long-term follow-up and extensive data collection. In contrast, case control studies are retrospective and can be conducted relatively quickly, as they rely on existing records and participant recall of past exposures. This makes them a cost-effective choice for preliminary investigations, allowing researchers to identify potential associations before committing to more extensive and expensive studies.

Case control studies are also appropriate when exploring multiple potential risk factors simultaneously. Since researchers collect detailed exposure information from both cases and controls, they can examine a wide range of variables and their potential associations with the condition. This flexibility is particularly useful in the early stages of research when the exact causes of a condition are not well understood.

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Case control studies have been instrumental in uncovering and evaluating factors associated with diseases and understanding potential underlying causes of various health conditions. These observational studies compare individuals with the outcome of interest to a comparison group of controls without the outcome, providing valuable insights into potential risk factors. Below are two examples that illustrate how case control studies can be used in different contexts.

Investigating lung cancer

One example of case control studies looks at historical factors of lung cancer such as smoking. Researchers select individuals diagnosed with lung cancer as the cases and a control group of individuals without lung cancer, matched by age, sex, and other relevant variables. Both groups are questioned about their smoking habits, including the duration and intensity of smoking.

The study can report a significantly higher prevalence of smoking among the cases compared to the controls, suggesting a strong association between smoking and lung cancer. Such findings can be crucial in establishing smoking as a major risk factor for lung cancer, leading to public health initiatives aimed at reducing smoking rates to improve health outcomes.

Exploring risk factors for myocardial infarction

Another important case control study might explore the risk factors for myocardial infarction (heart attack). Researchers select patients who had experienced a myocardial infarction as the cases and match them with a control group of individuals without a history of heart attacks but with similar health status and demographic characteristics. Data is collected on various exposures, such as diet, physical activity, family history of heart disease, and other historical factors to identify potential causes.

The analysis in this example reveals that factors like high cholesterol levels, hypertension, and lack of physical activity are more common among the cases than the controls. These findings can highlight the importance of managing cholesterol, blood pressure, and maintaining an active lifestyle to reduce the risk of myocardial infarction.

Case control studies offer several advantages that make them a valuable research method in epidemiology and public health. They are particularly useful when investigating rare diseases, working with limited resources, or exploring multiple risk factors. Below are three key advantages of case control studies.

Efficient for studying rare diseases

One of the primary advantages of case control studies is their efficiency in studying rare diseases. Since these studies start with individuals who already have the outcome of interest, researchers can gather sufficient data without needing to follow a large cohort over time. This is particularly beneficial when the condition is uncommon, as it allows researchers to focus their efforts on a smaller, more manageable sample size. By comparing these cases to a control group , researchers can quickly identify potential risk factors associated with the disease, accelerating the discovery of novel findings that might be difficult to obtain through other study designs like prospective cohort studies and retrospective cohort studies, which are designed around already established exposure or risk factors.

Cost-effective and time-efficient

Case control studies are generally more cost-effective and time-efficient compared to other epidemiological study designs, such as cohort studies. Because they are retrospective, case control studies utilize existing records and participant recall, reducing the need for long-term follow-up and extensive data collection. This makes them a practical choice for researchers with limited budgets and time constraints. The ability to conduct these studies relatively quickly allows for faster generation of insights and can inform the design of future, more comprehensive studies if necessary.

Ability to study multiple risk factors

Another significant advantage of case control studies is their ability to examine multiple risk factors simultaneously. When collecting data from both cases and controls, researchers can gather information on a wide range of exposures, behaviors, and other variables. This comprehensive data collection enables the analysis of various potential risk factors and their associations with the outcome of interest. This flexibility is particularly useful in the early stages of research when the exact causes of a condition are not well understood. By identifying several possible risk factors, case control studies can provide a broader understanding of the disease and guide further investigation.

While case control studies offer several advantages, they also come with notable disadvantages that researchers must consider. Below are two major disadvantages of case control studies.

Susceptibility to recall bias

One significant drawback of case control studies is their susceptibility to recall bias . Since these studies are retrospective, they rely on participants' memory and self-reported data regarding past exposures and behaviors. Cases and controls may recall information differently, especially if the condition being studied is severe or has a significant impact on the individual's life. Such recall bias may introduce effects from confounding variables and other factors to an analysis.

For example, individuals with a disease might be more likely to remember and report certain exposures they believe contributed to their condition, while controls may not recall these details as accurately. This discrepancy can lead to biased results, as the data collected may not accurately reflect actual past exposures. One way to minimize effects from recall bias is to collect data from multiple sources to triangulate findings.

Potential for selection bias

Another major disadvantage of case control studies is the potential for selection bias. Properly selecting and matching cases and controls is critical to ensure that the two groups are comparable in all relevant aspects except for the outcome of interest. If cases and controls are not appropriately matched, the contrasts observed between the groups may be due to systematic differences in who was selected rather than true associations between exposures and the outcome.

For instance, if the controls are not representative of the population that gave rise to the cases, the findings may not be generalizable. Additionally, the methods used to identify and recruit participants can also introduce bias, further complicating the interpretation of results. Selection bias can be mitigated by transparently describing the methods and assessing how representative the control group is of the population from which the cases emerged.

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

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

Case-control studies

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

Figure 1. Case-control study design.

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

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

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

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

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

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

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

Cohort studies

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

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

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

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

Figure 2. Cohort study design.

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

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

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

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

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

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

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

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

Saul Crandon

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

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

Very clear and informative! Thank you.

very informative article.

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

Very helpful information

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

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

Happy new year.

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

Saul you absolute melt! Really good work man

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

very helpful information provided here

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

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

Thank you this was so helpful amazing

Apreciated the information provided above.

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

Great to hear, thank you AJ!

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

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

Very much helpful

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An introduction to different types of study design

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

Quantitative study designs: Case Control

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial

Case Control

• Cross-Sectional Studies
• Study Designs Home

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

Stages of a Case-Control study

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

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

Which Clinical Questions does Case-Control best answer?

Case-Control studies are best used for Prognosis questions.

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

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

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

What does a strong Case-Control study look like?

A strong study will have:

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

What are the pitfalls to look for?

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

Critical appraisal tools 

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

CASP - Case Control Checklist

JBI – Critical appraisal checklist for case control studies

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

STROBE - Observational Studies checklists includes Case control

SIGN - Case-Control Studies Checklist

Real World Examples

Smoking and carcinoma of the lung; preliminary report

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

Anticholinergic drugs and risk of dementia: case-control study

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

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

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

References and Further Reading

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

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

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

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

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

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

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

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

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

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Case-Control Study: Definition, Real Life Examples

Design of Experiments > Case-Control Study

What is a Case-Control Study?

A case-control study is a retrospective study that looks back in time to find the relative risk between a specific exposure (e.g. second hand tobacco smoke) and an outcome (e.g. cancer). A control group of people who do not have the disease or who did not experience the event is used for comparison. The goal is figure out the relationship between risk factors and disease or outcome and estimate the odds of an individual getting a disease or experiencing an event.

Case-control studies have four main steps:

• The study begins by enrolling people who already have a certain disease or outcome.
• A second control group of similar size is sampled, preferably from a population identical in every way except that they don’t have the disease or condition being studied. They should not be selected because of an exposure status.
• People are asked about their exposure to risk factors.
• Finally, an odds ratio is calculated.
• Non-matched case-control study: this is the simplest form. Find a person with the disease and enroll them in the study. Then enroll a control and determine their exposure status.
• Matched case-control: Find a person with the disease and enroll them in the study. Match the person for some characteristic (e.g. sex, age, weight) with a control. This can eliminate or minimize confounding variables . However, it generally results in a longer study; the more characteristics being “matched”, the longer the study takes.

Advantages and Disadvantages

Advantages A case-control study is often the best choice for rare conditions or diseases . Let’s say 10 people in Duval county in Florida had a particularly rare disease. Random sampling for a cohort study would involve large numbers of people and may not pick up any of the diseased people at all. With a case-control study, all 10 people who have the disease can be identified (assuming they are in a medical database) and enrolled in the study. Random sampling could then be used on the non-diseased population to form the control group. Other Advantages :

• Short term study that doesn’t require waiting for events to happen, as they have already occurred.
• Inexpensive.
• Multiple risk factors can be studied at the same time.
• Quickly establishes associations between risk factors and disease. This can be especially useful with disease outbreaks, as causes can be identified with small sample sizes.
• Stronger than cross-sectional studies for establishing causation.

Disadvantages :

• Control groups can be difficult to find.
• Results can easily be tainted by recall bias , where people with the disease or condition are more likely to remember past details compared to people who don’t have the disease or condition.
• Is weaker than a cohort study for establishing causation.
• Usually not generalizable .

Examples from Real Life

• This study for non-Hodgkin lymphoma found a connection between the disease and inflammatory disorders like Sjögrens, Celiac and rheumatoid arthritis.
• This study investigated how increased consumption of fruits and vegetables protects against Cervical Intraepithelial Neoplasia.
• This INTERHEART study looked at second hand tobacco smoke and increased risk of myocardial infarction.

Microbe Notes

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

A case-control study (also known as a case-referent study) is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute.

• It is designed to help determine if an exposure is associated with an outcome (i.e., disease or condition of interest). In recent years, the case-control approach has emerged as a permanent method of epidemiological investigation.
• Case-control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/disease (the “cases”) with patients who do not have the condition/disease but are otherwise similar (the “controls”).
• In theory, the case-control study can be described simply. First, identify the cases (a group known to have the outcome) and the controls (a group known to be free of the outcome). Then, look back in time to learn which subjects in each group had the exposure(s), comparing the frequency of the exposure in the case group to the control group.
• This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is the odds ratio (OR).

Source: EBM Consult, LLC

Table of Contents

Interesting Science Videos

The Nature of Case-Control Studies

• By definition, a case-control study is always retrospective because it starts with an outcome then traces back to investigate exposures. When the subjects are enrolled in their respective groups, the outcome of each subject is already known by the investigator. This, and not the fact that the investigator usually makes use of previously collected data, is what makes case-control studies ‘retrospective’.
• The case-control study compares the prevalence of suspected causal factors between individuals with disease and controls who do not have the disease. If the prevalence of the factor is significantly different in cases than it is in controls, this factor may be associated with the disease.
• Although case-control studies can identify associations, they do not measure risk. An estimate of relative risk, however, can be derived by calculating the odds ratio.
• both exposure and outcome (disease) have occurred before the start of the study
• the study proceeds backward from effect to cause; and
• it uses a control or comparison group to support or refute an inference.

Steps Involved in Case-Control Studies

• By definition, a case-control study involves two populations – cases and controls.
• The focus is on a disease or some other health problem that has already developed.
• Case-control studies are basically comparison studies. Cases and controls must be comparable with respect to known “confounding factors” such as age, sex, occupation, social status, etc.
• The questions asked relate to personal characteristics and antecedent exposures which may be responsible for the condition studied.
• For example, one can use as “cases” the immunized children and use as “controls” un-immunized children and look for factors of interest in their past histories.

There are four basic steps in conducting a case-control study:

• Selection of cases and controls
• Measurement of exposure, and
• Analysis and interpretation

A. Selection of Cases and Controls

• The first is to identify a suitable group of cases and a group of controls.
• While the identification of cases is relatively easy, the selection of suitable controls may present difficulties.
• The definition of what constitutes a “case” is crucial to the case-control study.
• DIAGNOSTIC CRITERIA: The diagnostic criteria of the disease and the stage of the disease, if any (e.g., breast cancer Stage I) to be included in the study must be specified before the study is undertaken. Once the diagnostic criteria are established, they should not be altered or changed until the study is over.
• ELIGIBILITY CRITERIA: The second criterion is that of eligibility. A criterion customarily employed is the requirement that only newly diagnosed (incident) cases within a specified period of time are eligible than old cases or cases in advanced stages of the disease (prevalent cases).
• The cases may be drawn from hospitals or the general population.
• The cases should be fairly representative of all cases in the community.
• The controls must be free from the disease under study.
• They must be as similar to the cases as possible, except for the absence of the disease under study.
• As a rule, a comparison group is identified before a study is done, comprising of persons who have not been exposed to the disease or some other factor whose influence is being studied.
• Difficulties may arise in the selection of controls if the disease under investigation occurs in subclinical forms whose diagnosis is difficult.
• Selection of an appropriate control group is, therefore, an important prerequisite, for it is against this, we make comparisons, draw inferences and make judgments about the outcome of the investigation.
• The possible sources from which controls may be selected include hospitals, relatives, neighbors and the general population.
• If many cases are available, and a large study is contemplated, and if the cost to collect case and control is about equal, then one tends to use one control for each case. If the study group is small (say under 50) as many as 2,3, or even 4 controls can be selected for each study subject.
• To sum up, the selection of proper cases and controls is crucial to the interpretation of the results of case-control studies.

B. Matching

• The controls may differ from the cases in a number of factors such as age, sex, occupation, social status, etc.
• An important consideration is to ensure comparability between cases and controls. This involves what is known as “matching”.
• Matching is defined as the process by which we select controls in such a way that they are similar to cases with regard to certain pertinent selected variables (e.g., age) which are known to influence the outcome of the disease and which, if not adequately matched for comparability, could distort or confound the results.
• While matching it should be borne in mind that the suspected aetiological factor or the variable we wish to measure should not be matched, because, by matching, its aetiological role is eliminated in that study. The cases and controls will then become automatically alike with respect to that factor.
• There are several kinds of matching procedures such as group matching, pair matching, etc.

C. Measurement of Exposure

• Definitions and criteria about exposure (or variables which may be of aetiological importance) are just as important as those used to define cases and controls.
• Information about exposure should be obtained in precisely the same manner both for cases and controls.
• This may be obtained by interviews, by questionnaires or by studying past records of cases such as hospital records, employment records, etc.
• It is important to recognize that when case-control studies are being used to test associations, the most important factor to be considered, even more, important than the P. values obtained, is the question of “bias” or systematic error which must be ruled out.

D. Analysis

The final step is analysis, to find out:

• Exposure rates among cases and controls to suspected factor.
• Estimation of disease risk associated with exposure (Odds ratio).

Advantages of Case-Control Studies

• Relatively easy to carry out.
• Rapid and inexpensive (compared with cohort studies).
• Require comparatively few subjects.
• Particularly suitable to investigate rare diseases or diseases about which little is known. But a disease which is rare in the general population (e.g., leukemia in adolescents) may not be rare in the special exposure group (e.g. prenatal X-rays).
• No risk to subjects.
• Allows the study of several different aetiological factors (e.g., smoking, physical activity and personality characteristics in myocardial infarction).
• Risk factors can be identified. Rational prevention and control programs can be established.
• No attrition problems, because case-control studies do not require follow-up of individuals into the future.
• Ethical problems are minimal.

Limitations of Case-Control Study

• Problems of bias relies on memory or past records, the accuracy of which may be uncertain; validation of information obtained is difficult or sometimes impossible.
• Selection of an appropriate control group may be difficult.
• We cannot measure incidence, and can only estimate the relative risk.
• Do not distinguish between causes and associated factors.
• Not suited to the evaluation of therapy or prophylaxis of disease.
• Another major concern is the representativeness of cases and controls.
• A hypothesis is necessary for case-control studies. Relationships will be observed only for those factors studied.
• Case-control studies are not useful for determining the spectrum of health outcomes resulting from specific exposures, because a definition of a case is required in order to do a case-control study.
• Gordis, L. (2014). Epidemiology (Fifth edition.). Philadelphia, PA: Elsevier Saunders.
• White, F., Stallones, L., & Last, J. M. (2013). Global public health: Ecological foundations. New York, NY: Oxford University Press.
• Park, K. (n.d.). Park’s textbook of preventive and social medicine.
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/
• https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/8-case-control-and-cross-sectional
• https://www.students4bestevidence.net/case-control-and-cohort-studies-overview/

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

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

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 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, other interesting articles.

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.

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

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

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

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.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

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

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

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

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

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

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

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

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.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Case Studies

• First Online: 08 May 2024

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• Claes Wohlin 7 ,
• Per Runeson 8 ,
• Martin Höst 9 ,
• Magnus C. Ohlsson 10 ,
• Björn Regnell 8 &
• Anders Wesslén 11  

This chapter presents case study research. It discusses definitions of case study research and why such research is vital in software engineering. A stepwise case study research process is presented. It includes essential aspects such as design, planning, data collection, data analysis, validity threats, and reporting. Three research methods regularly used in case study research are briefly presented as part of the process. The three research methods are interviews, observations, and archival analysis.

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Amschler Andrews, A., Pradhan, A.S.: Ethical issues in empirical software engineering: the limits of policy. Empirical Software Eng. 6 (2), 105–110 (2001). https://doi.org/10.1023/A:1011442319273

Article   Google Scholar  

Andersson, C., Runeson, P.: A spiral process model for case studies on software quality monitoring – method and metrics. Software Process Improv. Pract. 12 (2), 125–140 (2007). https://doi.org/10.1002/spip.311

Avison, D., Baskerville, R., Myers, M.: Controlling action research projects. Inf. Technol. People 14 (1), 28–45 (2001). https://doi.org/10.1108/09593840110384762

Ayala, C., Turhan, B., Franch, X., Juristo, N.: Use and misuse of the term ‘experiment’ in mining software repositories research. IEEE Trans. Software Eng. 48 (11), 4229–4248 (2022). https://doi.org/10.1109/TSE.2021.3113558

Basili, V.R., Weiss, D.M.: A methodology for collecting valid software engineering data. IEEE Trans. Software Eng. 10 (6), 728–737 (1984). https://doi.org/10.1109/TSE.1984.5010301

Baskerville, R.L., Wood-Harper, A.T.: A critical perspective on action research as a method for information systems research. Int. J. Inf. Technol. 11 (3), 235–246 (1996). https://doi.org/10.1080/026839696345289

Google Scholar  

Benbasat, I., Goldstein, D.K., Mead, M.: The case research strategy in studies of information systems. MIS Q. 11 (3), 369–386 (1987). https://doi.org/10.2307/248684

Brereton, P., Kitchenham, B., Budgen, D., Li, Z.: Using a protocol template for case study planning. In: Proceedings of the International Conference on Evaluation and Assessment in Software Engineering. University of Bari, Bari (2008). https://doi.org/10.14236/ewic/EASE2008.5

Book   Google Scholar  

Corbin, J., Strauss, A.: Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 3rd edn. SAGE, Newcastle upon Tyne (2008). https://doi.org/10.4135/9781452230153

Dalkey, N., Helmer, O.: An experimental application of the Delphi method to the use of experts. Manage. Sci. 9 (3), 458–467 (1963). https://doi.org/10.1287/mnsc.9.3.458

Dittrich, Y., Rönkkö, K., Eriksson, J., Hansson, C., Lindeberg, O.: Cooperative method development. Empirical Software Eng. 13 (3), 231–260 (2007). https://doi.org/10.1007/s10664-007-9057-1

Easterbrook, S., Singer, J., Storey, M.A., Damian, D.: Selecting empirical methods for software engineering research. In: Shull, F., Singer, J., Sjøberg, D.I.K. (eds.) Guide to Advanced Empirical Software Engineering. Springer, London (2008). https://doi.org/10.1007/978-1-84800-044-5_11

Eisenhardt, K.M.: Building theories from case study research. Acad. Manage. Rev. 14 (4), 532 (1989). https://doi.org/10.2307/258557

Flyvbjerg, B.: Five misunderstandings about case-study research. Qual. Inq. 12 (2), 219–245 (2006). https://doi.org/10.1177/1077800405284363

Gorschek, T., Garre, P., Larsson, S., Wohlin, C.: A model for technology transfer in practice. IEEE Software 23 (6), 88–95 (2006). https://doi.org/10.1109/MS.2006.147

Hannay, J.E., Sjøberg, D.I.K., Dybå, T.: A systematic review of theory use in software engineering experiments. IEEE Trans. Software Eng. 33 (2), 87–107 (2007). https://doi.org/10.1109/TSE.2007.12

Hassan, A.E.: The road ahead for mining software repositories. In: Proceedings of the Frontiers of Software Maintenance, pp. 48–57 (2008). https://doi.org/10.1109/fosm.2008.4659248

Hove, S.E., Anda, B.: Experiences from conducting semi-structured interviews in empirical software engineering research. In: Proceedings of the 11th IEEE International Software Metrics Symposium, pp. 1–10 (2005). https://doi.org/10.1109/METRICS.2005.24

Iversen, J.H., Mathiassen, L., Nielsen, P.A.: Managing risk in software process improvement: an action research approach. MIS Q. 28 (3), 395–433 (2004). https://doi.org/10.2307/25148645

Jedlitschka, A., Pfahl, D.: Reporting guidelines for controlled experiments in software engineering. In: Proceedings of the International Symposium on Empirical Software Engineering, pp. 95–104 (2005). https://doi.org/10.1109/ISESE.2005.1541818

Karahasanović, A., Anda, B., Arisholm, E., Hove, S.E., Jørgensen, M., Sjøberg, D.I.K., Welland, R.: Collecting feedback during software engineering experiments. Empirical Software Eng. 10 (2), 113–147 (2005). https://doi.org/10.1007/s10664-004-6189-4

Kitchenham, B., Pickard, L., Pfleeger, S.L.: Case studies for method and tool evaluation. IEEE Software, 52–62 (1995). https://doi.org/10.1109/52.391832

Kitchenham, B.A., Pfleeger, S.L., Pickard, L.M., Jones, P.W., Hoaglin, D., El Emam, K., Rosenberg, J.: Preliminary guidelines for empirical research in software engineering. IEEE Trans. Software Eng. 28 (8), 721–734 (2002). https://doi.org/10.1109/TSE.2002.1027796

Kitchenham, B.A., Al-Khilidar, H., Babar, M.A., Berry, M., Cox, K., Keung, J., Kurniawati, F., Staples, M., Zhang, H., Zhu, L.: Evaluating guidelines for reporting empirical software engineering studies. Empirical Software Eng. 13 (1), 97–121 (2007). https://doi.org/10.1007/s10664-007-9053-5

Kontio, J., Bragge, J., Lehtola, L.: The focus group method as an empirical tool in software engineering. In: Guide to Advanced Empirical Software Engineering, pp. 93–116. Springer, London (2008). https://doi.org/10.1007/978-1-84800-044-5_4

Lee, A.S.: A scientific methodology for MIS case studies. MIS Q. 13 (1), 33–50 (1989). https://doi.org/10.2307/248698

Lethbridge, T.C., Sim, S.E., Singer, J.: Studying software engineers: data collection techniques for software field studies. Empirical Software Eng. 10 , 311–341 (2005). https://doi.org/10.1007/s10664-005-1290-x

Manly, B.F.J.: Multivariate Statistical Methods – A Primer, 3rd edn. Chapman and Hall/CRC, Boca Raton (2004). https://doi.org/10.1201/b16974

McGregor, S.L.T.: Ethical considerations in research about organizations: compendium of strategies. Ethics Prog. 14 (2), 4–23 (2023). https://doi.org/10.14746/eip.2023.2.1

Méndez Fernández, D., Passoth, J.H.: Empirical software engineering: from discipline to interdiscipline. J. Syst. Software 148 , 170–179 (2019). https://doi.org/10.1016/j.jss.2018.11.019

Owen, S., Brereton, P., Budgen, D.: Protocol analysis: a neglected practice. Commun. ACM 49 (2), 117–122 (2006). https://doi.org/10.1145/1113034.1113039

Petersen, K., Wohlin, C.: The effect of moving from a plan-driven to an incremental and agile development approach: an industrial case study. J. Empirical Software Eng. 15 (6), 654–693 (2010). https://doi.org/10.1007/s10664-010-9136-6

Pfleeger, S.L.: Experimental design and analysis in software engineering. Ann. Software Eng. 1 , 219–253 (1995)

Ralph, P.: Empirical standards for software engineering research – case study and ethnography. https://acmsigsoft.github.io/EmpiricalStandards/docs/?standard=CaseStudy (2021). This is an online resource, edited by P. Ralph

Robinson, H., Segal, J., Sharp, H.: Ethnographically-informed empirical studies of software practice. Inf. Software Technol. 49 (6), 540–551 (2007). https://doi.org/10.1016/j.infsof.2007.02.007

Robson, C., McCartan, K.: Real World Research: A Resource for Users of Social Research Methods in Applied Settings, 4th edn. Wiley, Hoboken (2016)

Runeson, P., Skoglund, M.: Reference-based search strategies in systematic reviews. In: Proceedings of the International Conference on Empirical Assessment & Evaluation in Software Engineering (2009). https://doi.org/10.14236/ewic/EASE2009.4

Runeson, P., Höst, M., Rainer, A., Regnell, B.: Case Study Research in Software Engineering. Guidelines and Examples. John Wiley & Sons, Hoboken (2012)

Seaman, C.B.: Qualitative methods in empirical studies of software engineering. IEEE Trans. Software Eng. 25 (4), 557–572 (1999). https://doi.org/10.1109/32.799955

Sharp, H., Dittrich, Y., De Souza, C.R.B.: The role of ethnographic studies in empirical software engineering. IEEE Trans. Software Eng. 42 (8), 786–804 (2016). https://doi.org/10.1109/TSE.2016.2519887

Sjøberg, D.I.K., Bergersen, G.R.: Construct validity in software engineering. IEEE Trans. Software Eng. 49 (3), 1374–1396 (2023). https://doi.org/10.1109/tse.2022.3176725

Sjøberg, D.I.K., Hannay, J.E., Hansen, O., Kampenes, V.B., Karahasanovic, A., Liborg, N.K., Rekdal, A.C.: A survey of controlled experiments in software engineering. IEEE Trans. Software Eng. 31 (9), 733–753 (2005). https://doi.org/10.1109/TSE.2005.97

Stake, R.E.: The Art of Case Study Research. SAGE Publications, Thousand Oaks (1995)

Staron, M.: Action Research in Software Engineering. Springer International Publishing, Berlin (2020)

Strandberg, P.E.: Ethical interviews in software engineering. In: International Symposium on Empirical Software Engineering and Measurement, pp. 1–11 (2019). https://doi.org/10.1109/ESEM.2019.8870192

van Solingen, R., Berghout, E.: The Goal/Question/Metric Method: A Practical Guide for Quality Improvement and Software Development. McGraw-Hill International, New York (1999)

Verdecchia, R., Engström, E., Lago, P., Runeson, P., Song, Q.: Threats to validity in software engineering research: a critical reflection. Inf. Software Technol., 107329 (2023). https://doi.org/10.1016/j.infsof.2023.107329

Verner, J.M., Sampson, J., Tosic, V., Abu Bakar, N.A., Kitchenham, B.A.: Guidelines for industrially-based multiple case studies in software engineering. In: Proceedings of the International Conference on Research Challenges in Information Science, pp. 313–324 (2009). https://doi.org/10.1109/RCIS.2009.5089295

Vidoni, M.: A systematic process for mining software repositories: results from a systematic literature review. Inf. Software Technol. 144 , 106791 (2022). https://doi.org/10.1016/j.infsof.2021.106791

Wallace, C., Cook, C., Summet, J., Burnett, M.: Assertions in end-user software engineering: a think-aloud study. In: Proceedings of the Symposia on Human Centric Computing Languages and Environments, pp. 63–65 (2002). https://doi.org/10.1109/HCC.2002.1046348

Wohlin, C.: Case study research in software engineering—it is a case, and it is a study, but is it a case study? Inf. Software Technol. 133 , 106514 (2021). https://doi.org/10.1016/j.infsof.2021.106514

Wohlin, C., Rainer, A.: Is it a case study?–a critical analysis and guidance. J. Syst. Software 192 , 111395 (2022). https://doi.org/10.1016/j.jss.2022.111395

Wohlin, C., Runeson, P.: Guiding the selection of research methodology in industry–academia collaboration in software engineering. Inf. Software Technol. 140 , 106678 (2021). https://doi.org/10.1016/j.infsof.2021.106678

Wohlin, C., Šmite, D., Moe, N.B.: A general theory of software engineering: balancing human, social and organizational capitals. J. Syst. Software 109 , 229–242 (2015). https://doi.org/10.1016/j.jss.2015.08.009

Yin, R.K.: Case Study Research Design and Methods, 4th edn. Sage Publications, Beverly Hills (2009)

Zelkowitz, M.V., Wallace, D.R.: Experimental models for validating technology. IEEE Comput. 31 (5), 23–31 (1998). https://doi.org/10.1109/2.675630

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Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A. (2024). Case Studies. In: Experimentation in Software Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-69306-3_7

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Epidemiology in Practice: Case-Control Studies

Introduction.

A case-control study is designed to help determine if an exposure is associated with an outcome (i.e., disease or condition of interest). In theory, the case-control study can be described simply. First, identify the cases (a group known to have the outcome) and the controls (a group known to be free of the outcome). Then, look back in time to learn which subjects in each group had the exposure(s), comparing the frequency of the exposure in the case group to the control group.

By definition, a case-control study is always retrospective because it starts with an outcome then traces back to investigate exposures. When the subjects are enrolled in their respective groups, the outcome of each subject is already known by the investigator. This, and not the fact that the investigator usually makes use of previously collected data, is what makes case-control studies ‘retrospective’.

Advantages of Case-Control Studies

Case-control studies have specific advantages compared to other study designs. They are comparatively quick, inexpensive, and easy. They are particularly appropriate for (1) investigating outbreaks, and (2) studying rare diseases or outcomes. An example of (1) would be a study of endophthalmitis following ocular surgery. When an outbreak is in progress, answers must be obtained quickly. An example of (2) would be a study of risk factors for uveal melanoma, or corneal ulcers. Since case-control studies start with people known to have the outcome (rather than starting with a population free of disease and waiting to see who develops it) it is possible to enroll a sufficient number of patients with a rare disease. The practical value of producing rapid results or investigating rare outcomes may outweigh the limitations of case-control studies. Because of their efficiency, they may also be ideal for preliminary investigation of a suspected risk factor for a common condition; conclusions may be used to justify a more costly and time-consuming longitudinal study later.

Consider a situation in which a large number of cases of post-operative endophthalmitis have occurred in a few weeks. The case group would consist of all those patients at the hospital who developed post-operative endophthalmitis during a pre-defined period.

The definition of a case needs to be very specific:

• Within what period of time after operation will the development of endophthalmitis qualify as a case – one day, one week, or one month?
• Will endophthalmitis have to be proven microbiologically, or will a clinical diagnosis be acceptable?
• Clinical criteria must be identified in great detail. If microbiologic facilities are available, how will patients who have negative cultures be classified?
• How will sterile inflammation be differentiated from endophthalmitis?

There are not necessarily any ‘right’ answers to these questions but they must be answered before the study begins. At the end of the study, the conclusions will be valid only for patients who have the same sort of ‘endophthalmitis’ as in the case definition.

Controls should be chosen who are similar in many ways to the cases. The factors (e.g., age, sex, time of hospitalisation) chosen to define how controls are to be similar to the cases are the ‘matching criteria’. The selected control group must be at similar risk of developing the outcome; it would not be appropriate to compare a group of controls who had traumatic corneal lacerations with cases who underwent elective intraocular surgery. In our example, controls could be defined as patients who underwent elective intraocular surgery during the same period of time.

Matching Cases and Controls

Although controls must be like the cases in many ways, it is possible to over-match. Over-matching can make it difficult to find enough controls. Also, once a matching variable has been selected, it is not possible to analyse it as a risk factor. Matching for type of intraocular surgery (e.g., secondary IOL implantation) would mean including the same percentage of controls as cases who had surgery to implant a secondary IOL; if this were done, it would not be possible to analyse secondary IOL implantation as a potential risk factor for endophthalmitis.

An important technique for adding power to a study is to enroll more than one control for every case. For statistical reasons, however, there is little gained by including more than two controls per case.

Collecting Data

After clearly defining cases and controls, decide on data to be collected; the same data must be collected in the same way from both groups. Care must be taken to be objective in the search for past risk factors, especially since the outcome is already known, or the study may suffer from researcher bias. Although it may not always be possible, it is important to try to mask the outcome from the person who is collecting risk factor information or interviewing patients. Sometimes it will be necessary to interview patients about potential factors (such as history of smoking, diet, use of traditional eye medicines, etc.) in their past. It may be difficult for some people to recall all these details accurately. Furthermore, patients who have the outcome (cases) are likely to scrutinize the past, remembering details of negative exposures more clearly than controls. This is known as recall bias. Anything the researcher can do to minimize this type of bias will strengthen the study.

Analysis; Odds Ratios and Confidence Intervals

In the analysis stage, calculate the frequency of each of the measured variables in each of the two groups. As a measure of the strength of the association between an exposure and the outcome, case-control studies yield the odds ratio. An odds ratio is the ratio of the odds of an exposure in the case group to the odds of an exposure in the control group. It is important to calculate a confidence interval for each odds ratio. A confidence interval that includes 1.0 means that the association between the exposure and outcome could have been found by chance alone and that the association is not statistically significant. An odds ratio without a confidence interval is not very meaningful. These calculations are usually made with computer programmes (e.g., Epi-Info). Case-control studies cannot provide any information about the incidence or prevalence of a disease because no measurements are made in a population based sample.

Risk Factors and Sampling

Another use for case-control studies is investigating risk factors for a rare disease, such as uveal melanoma. In this example, cases might be recruited by using hospital records. Patients who present to hospital, however, may not be representative of the population who get melanoma. If, for example, women present less commonly at hospital, bias might occur in the selection of cases.

The selection of a proper control group may pose problems. A frequent source of controls is patients from the same hospital who do not have the outcome. However, hospitalised patients often do not represent the general population; they are likely to suffer health problems and they have access to the health care system. An alternative may be to enroll community controls, people from the same neighborhoods as the cases. Care must be taken with sampling to ensure that the controls represent a ‘normal’ risk profile. Sometimes researchers enroll multiple control groups . These could include a set of community controls and a set of hospital controls.

Confounders

Matching controls to cases will mitigate the effects of confounders . A confounding variable is one which is associated with the exposure and is a cause of the outcome. If exposure to toxin ‘X’ is associated with melanoma, but exposure to toxin ‘X’ is also associated with exposure to sunlight (assuming that sunlight is a risk factor for melanoma), then sunlight is a potential confounder of the association between toxin ‘X’ and melanoma.

Case-control studies may prove an association but they do not demonstrate causation. Consider a case-control study intended to establish an association between the use of traditional eye medicines (TEM) and corneal ulcers. TEM might cause corneal ulcers but it is also possible that the presence of a corneal ulcer leads some people to use TEM. The temporal relationship between the supposed cause and effect cannot be determined by a case-control study.

Be aware that the term ‘case-control study’ is frequently misused. All studies which contain ‘cases’ and ‘controls’ are not case-control studies. One may start with a group of people with a known exposure and a comparison group (‘control group’) without the exposure and follow them through time to see what outcomes result, but this does not constitute a case-control study.

Case-control studies are sometimes less valued for being retrospective. However, they can be a very efficient way of identifying an association between an exposure and an outcome. Sometimes they are the only ethical way to investigate an association. If care is taken with definitions, selection of controls, and reducing the potential for bias, case-control studies can generate valuable information.

Case-Control Studies: Advantages and Disadvantages

Recommended Reading

• Open access
• Published: 07 September 2024

A case study of the informative value of risk of bias and reporting quality assessments for systematic reviews

• Cathalijn H. C. Leenaars   ORCID: orcid.org/0000-0002-8212-7632 1 ,
• Frans R. Stafleu 2 ,
• Christine Häger 1 &
• André Bleich 1  

Systematic Reviews volume  13 , Article number:  230 ( 2024 ) Cite this article

Metrics details

While undisputedly important, and part of any systematic review (SR) by definition, evaluation of the risk of bias within the included studies is one of the most time-consuming parts of performing an SR. In this paper, we describe a case study comprising an extensive analysis of risk of bias (RoB) and reporting quality (RQ) assessment from a previously published review (CRD42021236047). It included both animal and human studies, and the included studies compared baseline diseased subjects with controls, assessed the effects of investigational treatments, or both. We compared RoB and RQ between the different types of included primary studies. We also assessed the “informative value” of each of the separate elements for meta-researchers, based on the notion that variation in reporting may be more interesting for the meta-researcher than consistently high/low or reported/non-reported scores. In general, reporting of experimental details was low. This resulted in frequent unclear risk-of-bias scores. We observed this both for animal and for human studies and both for disease-control comparisons and investigations of experimental treatments. Plots and explorative chi-square tests showed that reporting was slightly better for human studies of investigational treatments than for the other study types. With the evidence reported as is, risk-of-bias assessments for systematic reviews have low informative value other than repeatedly showing that reporting of experimental details needs to improve in all kinds of in vivo research. Particularly for reviews that do not directly inform treatment decisions, it could be efficient to perform a thorough but partial assessment of the quality of the included studies, either of a random subset of the included publications or of a subset of relatively informative elements, comprising, e.g. ethics evaluation, conflicts of interest statements, study limitations, baseline characteristics, and the unit of analysis. This publication suggests several potential procedures.

Peer Review reports

Introduction

Researchers performing systematic reviews (SRs) face bias at two potential levels: first, at the level of the SR methods themselves, and second, at the level of the included primary studies [ 1 ]. To safeguard correct interpretation of the review’s results, transparency is required at both levels. For bias at the level of the SR methods, this is ensured by transparent reporting of the full SR methods, at least to the level of detail as required by the PRISMA statement [ 2 ]. For bias at the level of the included studies, study reporting quality (RQ) and/or risk of bias (RoB) are evaluated at the level of the individual included study. Specific tools are available to evaluate RoB in different study types [ 3 ]. Also, for reporting of primary studies, multiple guidelines and checklists are available to prevent missing important experimental details and more become available for different types of studies over time [ 4 , 5 ]. Journal endorsement of these types of guidelines has been shown to improve study reporting quality [ 6 ].

While undisputedly important, evaluation of the RoB and/or RQ of the included studies is one of the most time-consuming parts of an SR. Experienced reviewers need 10 min to an hour to complete an individual RoB assessment [ 7 ], and every included study needs to be evaluated by two reviewers. Besides spending substantial amounts of time on RoB or RQ assessments, reviewers tend to become frustrated because of the scores frequently being unclear or not reported (personal experience from the authors, colleagues and students). While automation of RoB seems to be possible without loss of accuracy [ 8 , 9 ], so far, this automation has not had significant impact on the speed; in a noninferiority randomised controlled trial of the effect of automation on person-time spent on RoB assessment, the confidence interval for the time saved ranged from − 5.20 to + 2.41 min [ 8 ].

In any scientific endeavour, there is a balance between reliability and speed; to guarantee reliability of a study, time investments are necessary. RoB or RQ assessment is generally considered to be an essential part of the systematic review process to warrant correct interpretation of the findings, but with so many studies scoring “unclear” or “not reported”, we wondered if all this time spent on RoB assessments is resulting in increased reliability of reviews.

Overall unclear risk of bias in the included primary studies is a conclusion of multiple reviews, and these assessments are useful in pinpointing problems in reporting, thereby potentially improving the quality of future publications of primary studies. However, the direct goal of most SRs is to answer a specific review question, and in that respect, unclear RoB/not reported RQ scores contribute little to the validity of the review’s results. If all included studies score “unclear” or “high” RoB on at least one of the analysed elements, the overall effect should be interpreted as inconclusive.

While it is challenging to properly evaluate the added validity value of a methodological step, we had data available allowing for an explorative case study to assess the informative value of various RoB and RQ elements in different types of studies. We previously performed an SR of the nasal potential difference (nPD) for cystic fibrosis (CF) in animals and humans, aiming to quantify the predictive value of animal models for people with CF [ 10 , 11 ]. That review comprised between-subject comparisons of both baseline versus disease-control and treatment versus treatment control. For that review, we performed full RoB and RQ analyses. This resulted in data allowing for comparisons of RoB and RQ between animal and human studies, but also between baseline and treatment studies, which are both presented in this manuscript. RoB evaluations were based on the Cochrane collaboration’s tool [ 12 ] for human studies and SYRCLE’s tool [ 13 ] for animal studies. RQ was tested based on the ARRIVE guidelines [ 14 ] for animal studies and the 2010 CONSORT guidelines [ 15 ] for human studies. Brief descriptions of these tools are provided in Table  1 .

All these tools are focussed on interventional studies. Lacking more specific tools for baseline disease-control comparisons, we applied them as far as relevant for the baseline comparisons. We performed additional analyses on our RQ and RoB assessments to assess the amount of distinctive information gained from them.

The analyses described in this manuscript are based on a case study SR of the nPD related to cystic fibrosis (CF). That review was preregistered on PROSPERO (CRD42021236047) on 5 March 2021 [ 16 ]. Part of the results were published previously [ 10 ]. The main review questions are answered in a manuscript that has more recently been published [ 11 ]. Both publications show a simple RoB plot corresponding to the publication-specific results.

For the ease of the reader, we provide a brief summary of the overall review methods. The full methods have been described in our posted protocol [ 16 ] and the earlier publications [ 10 , 11 ]. Comprehensive searches were performed in PubMed and Embase, unrestricted for publication date or language, on 23 March 2021. Title-abstract screening and full-text screening were performed by two independent reviewers blinded to the other’s decision (FS and CL) using Rayyan [ 17 ]. We included animal and/or human studies describing nPD in CF patients and/or CF animal models. We restricted to between-subject comparisons, either CF versus healthy controls or experimental CF treatments versus CF controls. Reference lists of relevant reviews and included studies were screened (single level) for snowballing. Discrepancies were all resolved by discussions between the reviewers.

Data were extracted by two independent reviewers per reference in several distinct phases. Relevant to this manuscript, FS and CL extracted RoB and RQ data in Covidence [ 18 ], in two separate projects using the same list of 48 questions for studies assessing treatment effects and studies assessing CF-control differences. The k  = 11 studies that were included in both parts of the overarching SR were included twice in the current data set, as RoB was separately scored for each comparison. Discrepancies were all resolved by discussions between the reviewers. In violation of the protocol, no third reviewer was involved.

RoB and SQ data extraction followed our review protocol, which states the following: “For human studies, risk of bias will be assessed with the Cochrane Collaboration’s tool for assessing risk of bias. For animal studies, risk of bias will be assessed with SYRCLE’s RoB tool. Besides, we will check compliance with the ARRIVE and CONSORT guidelines for reporting quality”. The four tools contain overlapping questions. To prevent unnecessary repetition of our own work, we created a single list of 48 items, which were ordered by topic for ease of extraction. For RoB, this list contains the same elements as the original tools, with the same response options (high/unclear/low RoB). For RQ, we created checklists with all elements as listed in the original tools, with the response options reported yes/no. For (RQ and RoB) elements specific to some of the included studies, the response option “irrelevant” was added. We combined these lists, only changing the order and merging duplicate elements. We do not intend this list to replace the individual tools; it was created for this specific study only.

In our list, each question was preceded by a short code indicating the tool it was derived from (A for ARRIVE, C for CONSORT, and S for SYRCLE’s) to aid later analyses. When setting up, we started with the animal-specific tools, with which the authors are more familiar. After preparing data extraction for those, we observed that all elements from the Cochrane tool had already been addressed. Therefore, this list was not explicit in our extractions. The extraction form always allowed free text to support the response. Our extraction list is provided with our supplementary data.

For RoB, the tools provide relatively clear suggestions for which level to score and when, with signalling questions and examples [ 12 , 13 ]. However, this still leaves some room for interpretation, and while the signalling questions are very educative, there are situations where the response would in our opinion not correspond to the actual bias. The RQ tools have been developed as guidelines on what to report when writing a manuscript, and not as a tool to assess RQ [ 14 , 15 ]. This means we had to operationalise upfront which level we would find sufficient to score “reported”. Our operationalisations and corrections of the tools are detailed in Table  2 .

Data were exported from Covidence into Microsoft’s Excel, where the two projects were merged and spelling and capitalisation were harmonised. Subsequent analyses were performed in R [ 21 ] version 4.3.1 (“Beagle Scouts”) via RStudio [ 22 ], using the following packages: readxl [ 23 ], dplyr [ 24 ], tidyr [ 25 ], ggplot2 [ 26 ], and crosstable [ 27 ].

Separate analyses were performed for RQ (with two levels per element) and RoB (with three levels per element). For both RoB and RQ, we first counted the numbers of irrelevant scores overall and per item. Next, irrelevant scores were deleted from further analyses. We then ranked the items by percentages for reported/not reported, or for high/unclear/low scores, and reported the top and bottom 3 (RoB) or 5 (RQ) elements.

While 100% reported is most informative to understand what actually happened in the included studies, if all authors continuously report a specific element, scoring of this element for an SR is not the most informative for meta-researchers. If an element is not reported at all, this is bad news for the overall level of confidence in an SR, but evaluating it per included study is also not too efficient except for highlighting problems in reporting, which may help to improve the quality of future (publications of) primary studies. For meta-researchers, elements with variation in reporting may be considered most interesting because these elements highlight differences between the included studies. Subgroup analyses based on specific RQ/RoB scores can help to estimate the effects of specific types of bias on the overall effect size observed in meta-analyses, as has been done for example randomisation and blinding [ 28 ]. However, these types of subgroup analyses are only possible if there is some variation in the reporting. Based on this idea, we defined a “distinctive informative value” (DIV) for RQ elements, based on the optimal variation being 50% reported and either 0% or 100% reporting being minimally informative. Thus, this “DIV” was calculated as follows:

Thus, the DIV could range from 0 (no informative value) to 50 (maximally informative), visualised in Fig.  1 .

Visual explanation of the DIV value

The DIV value was only used for ranking. The results were visualised in a heatmap, in which the intermediate shades correspond to high DIV values.

For RoB, no comparable measure was calculated. With only 10 elements but at 3 distinct levels, we thought a comparable measure would sooner hinder interpretation of informative value than help it. Instead, we show the results in an RoB plot split by population and study design type.

Because we are interested in quantifying the predictive value of animal models for human patients, we commonly perform SRs including both animal and human data (e.g. [ 29 , 30 ]). The dataset described in the current manuscript contained baseline and intervention studies in animals and humans. Because animal studies are often held responsible for the reproducibility crisis, but also to increase the external validity of this work, explorative chi-square tests (the standard statistical test for comparing percentages for binary variables) were performed to compare RQ and RoB between animal and human studies and between studies comparing baselines and treatment effects. They were performed with the base R “chisq.test” function. No power calculations were performed, as these analyses were not planned.

Literature sample

We extracted RoB and RQ data from 164 studies that were described in 151 manuscripts. These manuscripts were published from 1981 through 2020. Overall, 164 studies comprised 78 animal studies and 86 human studies, 130 comparisons of CF versus non-CF control, and 34 studies assessing experimental treatments. These numbers are detailed in a crosstable (Table  3 ).

The 48 elements in our template were completed for these 164 studies, which results in 7872 assessed elements. In total, 954 elements (12.1%) were irrelevant for various reasons (mainly for noninterventional studies and for human studies). The 7872 individual scores per study are available from the data file on OSF.

Of the 48 questions in our extraction template, 38 addressed RQ, and 10 addressed RoB.

Overall reporting quality

Of the 6232 elements related to RQ, 611 (9.8%) were deemed irrelevant. Of the remainder, 1493 (26.6% of 5621) were reported. The most reported elements were background of the research question (100% reported), objectives (98.8% reported), interpretation of the results (98.2% reported), generalisability (86.0% reported), and the experimental groups (83.5% reported). The least-reported elements were protocol violations, interim analyses + stopping rules and when the experiments were performed (all 0% reported), where the experiments were performed (0.6% reported), and all assessed outcome measures (1.2% reported).

The elements with most distinctive variation in reporting (highest DIV, refer to the “ Methods ” section for further information) were as follows: ethics evaluation (64.6% reported), conflicts of interest (34.8% reported), study limitations (29.3% reported), baseline characteristics (26.2% reported), and the unit of analysis (26.2% reported). RQ elements with DIV values over 10 are shown in Table  4 .

Overall risk of bias

Of the 1640 elements related to RoB, 343 (20.9%) were deemed irrelevant. Of the remainder, 219 (16.9%) scored high RoB, and 68 (5.2%) scored low RoB. The overall RoB scores were highest for selective outcome reporting (97.6% high), baseline group differences (19.5% high), and other biases (9.8% high); lowest for blinding of participants, caregivers, and investigators (13.4% low); blinding of outcome assessors (11.6% low) and baseline group differences (8.5% low); and most unclear for bias due to animal housing (100% unclear), detection bias due to the order of outcome measurements (99.4% unclear), and selection bias in sequence generation (97.1% unclear). The baseline group differences being both in the highest and the lowest RoB score are explained by the baseline values being reported better than the other measures, resulting in fewer unclear scores.

Variation in reporting is relatively high for most of the elements scoring high or low. Overall distinctive value of the RoB elements is low, with most scores being unclear (or, for selective outcome reporting, most scores being high).

Animal versus human studies

For RQ, the explorative chi-square tests indicated differences in reporting between animal and human studies for baseline values ( Χ 1  = 50.3, p  < 0.001), ethical review ( Χ 1  = 5.1, p  = 0.02), type of study ( Χ 1  = 11.2, p  < 0.001), experimental groups ( Χ 1  = 3.9, p  = 0.050), inclusion criteria ( Χ 1  = 24.6, p  < 0.001), the exact n value per group and in total ( Χ 1  = 26.0, p  < 0.001), (absence of) excluded datapoints ( Χ 1  = 4.5, p  = 0.03), adverse events ( Χ 1  = 5.5, p  = 0.02), and study limitations ( Χ 1  = 8.2, p  = 0.004). These explorative findings are visualised in a heatmap (Fig.  2 ).

Heatmap of reporting by type of study. Refer to Table  3 for absolute numbers of studies per category

For RoB, the explorative chi-square tests indicated differences in risk of bias between animal and human studies for baseline differences between the groups ( Χ 2  = 34.6, p  < 0.001) and incomplete outcome data ( Χ 2  = 7.6, p  = 0.02). These explorative findings are visualised in Fig.  3 .

Risk of bias by type of study. Refer to Table  3 for absolute numbers of studies per category. Note that the data shown in these plots overlap with those in the two preceding publications [ 10 , 11 ]

Studies assessing treatment effects versus studies assessing baseline differences

For RQ, the explorative chi-square tests indicated differences in reporting between comparisons of disease with control versus comparisons of treatment effects for the title listing the type of study ( X 1  = 5.0, p  = 0.03), the full paper explicitly mentioning the type of study ( X 1  = 14.0, p  < 0.001), explicit reporting of the primary outcome ( X 1  = 11.7, p  < 0.001), and reporting of adverse events X 1  = 25.4, p  < 0.001). These explorative findings are visualised in Fig.  2 .

For RoB, the explorative chi-square tests indicated differences in risk of bias between comparisons of disease with control versus comparisons of treatment effects for baseline differences between the groups ( Χ 2  = 11.4, p  = 0.003), blinding of investigators and caretakers ( Χ 2  = 29.1, p  < 0.001), blinding of outcome assessors ( Χ 2  = 6.2, p  = 0.046), and selective outcome reporting ( Χ 2  = 8.9, p  = 0.01). These explorative findings are visualised in Fig.  3 .

Overall, our results suggest lower RoB and higher RQ for human treatment studies compared to the other study types.

This literature study shows that reporting of experimental details is low, frequently resulting in unclear risk-of-bias assessments. We observed this both for animal and for human studies, with two main study designs: disease-control comparisons and, in a smaller sample, investigations of experimental treatments. Overall reporting is slightly better for elements that contribute to the “story” of a publication, such as the background of the research question, interpretation of the results and generalisability, and worst for experimental details that relate to differences between what was planned and what was actually done, such as protocol violations, interim analyses, and assessed outcome measures. The latter also results in overall high RoB scores for selective outcome reporting.

Of note, we scored this more stringently than SYRCLE’s RoB tool [ 13 ] suggests and always scored a high RoB if no protocol was posted, because only comparing the “Methods” and “Results” sections within a publication would, in our opinion, result in an overly optimistic view. Within this sample, only human treatment studies reported posting protocols upfront [ 31 , 32 ]. In contrast to selective outcome reporting, we would have scored selection, performance, and detection bias due to sequence generation more liberally for counterbalanced designs (Table  2 ), because randomisation is not the only appropriate method for preventing these types of bias. Particularly when blinding is not possible, counterbalancing [ 33 , 34 ] and Latin-square like designs [ 35 ] can decrease these biases, while randomisation would risk imbalance between groups due to “randomisation failure” [ 36 , 37 ]. We would have scored high risk of bias for blinding for these types of designs, because of increased sequence predictability. However, in practice, we did not include any studies reporting Latin-square-like or other counterbalancing designs.

One of the “non-story” elements that is reported relatively well, particularly for human treatment studies, is the blinding of participants, investigators, and caretakers. This might relate to scientists being more aware of potential bias of participants; they may consider themselves to be more objective than the general population, while the risk of influencing patients could be considered more relevant.

The main strength of this work is that it is a full formal analysis of RoB and RQ in different study types: animal and human, baseline comparisons, and treatment studies. The main limitation is that it is a single case study from a specific topic: the nPD test in CF. The results shown in this paper are not necessarily valid for other fields, particularly as we hypothesise that differences in scientific practice between medical fields relate to differences in translational success [ 38 ]. Thus, it is worth to investigate field-specific informative values before selecting which elements to score and analyse in detail.

Our comparisons of different study and population types show lower RoB and higher RQ for human treatment studies compared to the other study types for certain elements. Concerning RQ, the effects were most pronounced for the type of experimental design being explicitly mentioned and the reporting of adverse events. Concerning RoB, the effects were most pronounced for baseline differences between the groups, blinding of investigators and caretakers, and selective outcome reporting. Note, however, that the number of included treatment studies is a lot lower than the number of included baseline studies, and that the comparisons were based on only k  = 12 human treatment studies. Refer to Table  3 for absolute numbers of studies per category. Besides, our comparisons may be confounded to some extent by the publication date. The nPD was originally developed for human diagnostics [ 39 , 40 ], and animal studies only started to be reported at a later date [ 41 ]. Also, the use of the nPD as an outcome in (pre)clinical trials of investigational treatments originated at a later date [ 42 , 43 ].

Because we did not collect our data to assess time effects, we did not formally analyse them. However, we had an informal look at the publication dates by RoB score for blinding of the investigators and caretakers, and by RQ score for ethics evaluation (in box plots with dot overlay), showing more reported and fewer unclear scores in the more recent publications (data not shown). While we thus cannot rule out confounding of our results by publication date, the results are suggestive of mildly improved reporting of experimental details over time.

This study is a formal comparison of RoB and RQ scoring for two main study types (baseline comparisons and investigational treatment studies), for both animals and humans. Performing these comparisons within the context of a single SR [ 16 ] resulted in a small, but relatively homogeneous sample of primary studies about the nPD in relation to CF. On conferences and from colleagues in the animal SR field, we heard that reporting would be worse for animal than for human studies. Our comparisons allowed us to show that particularly for baseline comparisons of the nPD in CF versus control, this is not the case.

The analysed tools [ 12 , 13 , 15 ] were developed for experimental interventional studies. While some of the elements are less appropriate for other types of studies, such as animal model comparisons, our results show that many of the elements can be used and could still be useful, particularly if the reporting quality of the included studies would be better.

Implications

To correctly interpret the findings of a meta-analysis, awareness of the RoB in the included studies is more relevant than the RQ on its own. However, it is impossible to evaluate the RoB if the experimental details have not been reported, resulting in many unclear scores. With at least one unclear or high RoB score per included study, the overall conclusions of the review become inconclusive. For SRs of overall treatment effects that are performed to inform evidence-based treatment guidelines, RoB analyses remain crucial, even though the scores will often be unclear. Ideally, especially for SRs that will be used to plan future experiments/develop treatment guidelines, analyses should only include those studies consistently showing low risk of bias (i.e. low risk on all elements). However, in practice, consistently low RoB studies in our included literature samples (> 20 SRs to date) are too scarce for meaningful analyses. For other types of reviews, we think it is time to consider if complete RoB assessment is the most efficient use of limited resources. While these assessments regularly show problems in reporting, which may help to improve the quality of future primary studies, the unclear scores do not contribute much to understanding the effects observed in meta-analyses.

With PubMed already indexing nearly 300,000 mentioning the term “systematic review” in the title, abstract, or keywords, we can assume that many scientists are spending substantial amounts of time and resources on RoB and RQ assessments. Particularly for larger reviews, it could be worthwhile to restrict RoB assessment to either a random subset of the included publications or a subset of relatively informative elements. Even a combination of these two strategies may be sufficiently informative if the results of the review are not directly used to guide treatment decisions. The subset could give a reasonable indication of the overall level of evidence of the SR while saving resources. Different suggested procedures are provided in Table  5 . The authors of this work would probably have changed to such a strategy during their early data extraction phase, if the funder would not have stipulated full RoB assessment in their funding conditions.

We previously created a brief and simple taxonomy of systematised review types [ 44 ], in which we advocate RoB assessments to be a mandatory part of any SR. We would still urge anyone calling their review “systematic” to stick to this definition and perform some kind of RoB and/or RQ assessment, but two independent scientists following a lengthy and complex tool for all included publications, resulting in 74.6% of the assessed elements not being reported, or 77.9% unclear RoB, can, in our opinion, in most cases be considered inefficient and unnecessary.

Our results show that there is plenty of room for improvement in the reporting of experimental details in medical scientific literature, both for animal and for human studies. With the current status of the primary literature as it is, full RoB assessment may not be the most efficient use of limited resources, particularly for SRs that are not directly used as the basis for treatment guidelines or future experiments.

Availability of data and materials

The data described in this study are available from the Open Science Platform ( https://osf.io/fmhcq/ ) in the form of a spreadsheet file. In the data file, the first tab shows the list of questions that were used for data extraction with their respective short codes. The second tab shows the full individual study-level scores, with lines per study and columns per short code.

Abbreviations

• Cystic fibrosis

High risk of bias

Low risk of bias

No, not reported

• Nasal potential difference
• Risk of bias
• Reporting quality

Systematic review

Unclear risk of bias

Yes, reported

Drucker AM, Fleming P, Chan AW. Research techniques made simple: assessing risk of bias in systematic reviews. J Invest Dermatol. 2016;136(11):e109–14.

Article   PubMed   Google Scholar  

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Article   PubMed   PubMed Central   Google Scholar  

Page MJ, McKenzie JE, Higgins JPT. Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review. BMJ Open. 2018;8(3):e019703.

Wang X, Chen Y, Yang N, Deng W, Wang Q, Li N, et al. Methodology and reporting quality of reporting guidelines: systematic review. BMC Med Res Methodol. 2015;15:74.

Zeng X, Zhang Y, Kwong JS, Zhang C, Li S, Sun F, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med. 2015;8(1):2–10.

Turner L, Shamseer L, Altman DG, Schulz KF, Moher D. Does use of the CONSORT statement impact the completeness of reporting of randomised controlled trials published in medical journals? A Cochrane review Syst Rev. 2012;1:60.

Savovic J, Weeks L, Sterne JA, Turner L, Altman DG, Moher D, et al. Evaluation of the Cochrane collaboration’s tool for assessing the risk of bias in randomized trials: focus groups, online survey, proposed recommendations and their implementation. Syst Rev. 2014;3:37.

Arno A, Thomas J, Wallace B, Marshall IJ, McKenzie JE, Elliott JH. Accuracy and efficiency of machine learning-assisted risk-of-bias assessments in “real-world” systematic reviews : a noninferiority randomized controlled trial. Ann Intern Med. 2022;175(7):1001–9.

Jardim PSJ, Rose CJ, Ames HM, Echavez JFM, Van de Velde S, Muller AE. Automating risk of bias assessment in systematic reviews: a real-time mixed methods comparison of human researchers to a machine learning system. BMC Med Res Methodol. 2022;22(1):167.

Leenaars C, Hager C, Stafleu F, Nieraad H, Bleich A. A systematic review of the effect of cystic fibrosis treatments on the nasal potential difference test in animals and humans. Diagnostics (Basel). 2023;13(19):3098.

Leenaars CHC, Stafleu FR, Hager C, Nieraad H, Bleich A. A systematic review of animal and human data comparing the nasal potential difference test between cystic fibrosis and control. Sci Rep. 2024;14(1):9664.

Higgins JPT, Savović J, Page MJ, Elbers RG, Sterne JAC. Chapter 8: Assessing risk of bias in a randomized trial. Cochrane Handbook for Systematic Reviews of Interventions. 2022.

Google Scholar  

Hooijmans CR, Rovers MM, de Vries RB, Leenaars M, Ritskes-Hoitinga M, Langendam MW. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol. 2014;14:43.

Kilkenny C, Browne WJ, Cuthill IC, Emerson M, Altman DG. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 2010;8(6):e1000412.

Begg C, Cho M, Eastwood S, Horton R, Moher D, Olkin I, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement JAMA. 1996;276(8):637–9.

Leenaars C, Stafleu F, Bleich A. The nasal potential difference test for diagnosing cystic fibrosis and assessing disease severity: a systematic review. 2021.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.

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

Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 20: updated guidelines for reporting animal research. J Cereb Blood Flow Metab. 2020;40(9):1769–77.

Knowles MR, Gatzy JT, Boucher RC. Aldosterone metabolism and transepithelial potential difference in normal and cystic fibrosis subjects. Pediatr Res. 1985;19(7):676–9.

Team RC. a language and environment for statistical computing. R Foundation for Statistical Computing. 2021.

RStudio_Team. RStudio: integrated development for R. Boston, MA.: RStudio, Inc.; 2019. Available from: http://www.rstudio.com/ .

Wickham H, Bryan J. readxl: read Excel files. R package version 1.3.1. 2019.

Wickham H, François R, Henry L, Müller K. dplyr: a grammar of data manipulation. R package version 1.0.3. 2021.

Wickham H, Girlich M. tidyr: tidy messy data. R package version 1.2.0. 2022.

Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.

Book   Google Scholar  

Chaltiel D. Crosstable: crosstables for descriptive analyses. R package version 0.5.0. 2022.

Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U, Donnan GA. Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke. 2008;39(10):2824–9.

Leenaars C, Stafleu F, de Jong D, van Berlo M, Geurts T, Coenen-de Roo T, et al. A systematic review comparing experimental design of animal and human methotrexate efficacy studies for rheumatoid arthritis: lessons for the translational value of animal studies. Animals (Basel). 2020;10(6):1047.

Leenaars CHC, Kouwenaar C, Stafleu FR, Bleich A, Ritskes-Hoitinga M, De Vries RBM, et al. Animal to human translation: a systematic scoping review of reported concordance rates. J Transl Med. 2019;17(1):223.

Kerem E, Konstan MW, De Boeck K, Accurso FJ, Sermet-Gaudelus I, Wilschanski M, et al. Ataluren for the treatment of nonsense-mutation cystic fibrosis: a randomised, double-blind, placebo-controlled phase 3 trial. Lancet Respir Med. 2014;2(7):539–47.

Rowe SM, Liu B, Hill A, Hathorne H, Cohen M, Beamer JR, et al. Optimizing nasal potential difference analysis for CFTR modulator development: assessment of ivacaftor in CF subjects with the G551D-CFTR mutation. PLoS ONE. 2013;8(7): e66955.

Reese HW. Counterbalancing and other uses of repeated-measures Latin-square designs: analyses and interpretations. J Exp Child Psychol. 1997;64(1):137–58.

Article   Google Scholar  

Zeelenberg R, Pecher D. A method for simultaneously counterbalancing condition order and assignment of stimulus materials to conditions. Behav Res Methods. 2015;47(1):127–33.

Richardson JTE. The use of Latin-square designs in educational and psychological research. Educ Res Rev. 2018;24:84–97.

King G, Nielsen R, Coberley C, Pope JE, Wells A. Avoiding randomization failure in program evaluation, with application to the Medicare Health Support program. Popul Health Manag. 2011;14(Suppl 1):S11-22.

Meier B, Nietlispach F. Fallacies of evidence-based medicine in cardiovascular medicine. Am J Cardiol. 2019;123(4):690–4.

Van de Wall G, Van Hattem A, Timmermans J, Ritskes-Hoitinga M, Bleich A, Leenaars C. Comparing translational success rates across medical research fields - a combined analysis of literature and clinical trial data. Altex. 2023;40(4):584–94.

PubMed   Google Scholar  

Knowles MR, Gatzy JT, Boucher RC. Increased bioelectric potential differences across respiratory epithelia in cystic fibrosis. N Engl Med. 1981;305:1489–95.

Unal-Maelger OH, Urbanek R. Status of determining the transepithelial potential difference (PD) of the respiratory epithelium in the diagnosis of mucoviscidosis. Monatsschr Kinderheilkd. 1988;136(2):76–80.

Dorin JR, Dickinson P, Alton EW, Smith SN, Geddes DM, Stevenson BJ, et al. Cystic fibrosis in the mouse by targeted insertional mutagenesis. Nature. 1992;359(6392):211–5.

Alton EW, Middleton PG, Caplen NJ, Smith SN, Steel DM, Munkonge FM, et al. Non-invasive liposome-mediated gene delivery can correct the ion transport defect in cystic fibrosis mutant mice. Nat Genet. 1993;5(2):135–42.

Caplen NJ, Alton EW, Middleton PG, Dorin JR, Stevenson BJ, Gao X, et al. Liposome-mediated CFTR gene transfer to the nasal epithelium of patients with cystic fibrosis. Nat Med. 1995;1(1):39–46.

Leenaars C, Tsaioun K, Stafleu F, Rooney K, Meijboom F, Ritskes-Hoitinga M, et al. Reviewing the animal literature: how to describe and choose between different types of literature reviews. Lab Anim. 2021;55(2):129–41.

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The authors kindly acknowledge Dr. Hendrik Nieraad for his help in study classification.

Open Access funding enabled and organized by Projekt DEAL. This research was funded by the BMBF, grant number 01KC1904. During grant review, the BMBF asked for changes in the review design which we incorporated. Publication of the review results was a condition of the call. Otherwise, the BMBF had no role in the collection, analysis and interpretation of data, or in writing the manuscript.

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Leenaars, C.H.C., Stafleu, F.R., Häger, C. et al. A case study of the informative value of risk of bias and reporting quality assessments for systematic reviews. Syst Rev 13 , 230 (2024). https://doi.org/10.1186/s13643-024-02650-w

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Clinical profile, microbiology and outcomes in infective endocarditis treated with aortic valve replacement: a multicenter case-control study

• Håvard Dingen 1 , 2 ,
• Stina Jordal 2 ,
• Sorosh Bratt 3 , 4 ,
• Pål Aukrust 5 , 6 ,
• Rolf Busund 7 ,
• Øyvind Jakobsen 7 ,
• Magnus Dalén 3 , 4 ,
• Thor Ueland 5 , 6 , 8 ,
• Peter Svenarud 3 , 4 ,
• Rune Haaverstad 9 , 10 ,
• Sahrai Saeed 9   na1 &
• Ivar Risnes 9 , 11   na1  

BMC Infectious Diseases volume  24 , Article number:  913 ( 2024 ) Cite this article

Metrics details

Aortic valve infective endocarditis (IE) is associated with significant morbidity and mortality. We aimed to describe the clinical profile, risk factors and predictors of short- and long-term mortality in patients with aortic valve IE treated with aortic valve replacement (AVR) compared with a control group undergoing AVR for non-infectious valvular heart disease.

Between January 2008 and December 2013, a total of 170 cases with IE treated with AVR (exposed cohort) and 677 randomly selected non-infectious AVR-treated patients with degenerative aortic valve disease (controls) were recruited from three tertiary hospitals with cardiothoracic facilities across Scandinavia. Crude and adjusted hazard ratios (HR) were estimated using Cox regression models.

The mean age of the IE cohort was 58.5 ± 15.1 years (80.0% men). During a mean follow-up of 7.8 years (IQR 5.1-10.8 years), 373 (44.0%) deaths occurred: 81 (47.6%) in the IE group and 292 (43.1%) among controls. Independent risk factors associated with IE were male gender, previous heart surgery, underweight, positive hepatitis C serology, renal failure, previous wound infection and dental treatment (all p  < 0.05). IE was associated with an increased risk of both short-term (≤ 30 days) (HR 2.86, [1.36–5.98], p  = 0.005) and long-term mortality (HR 2.03, [1.43–2.88], p  < 0.001). In patients with IE, chronic obstructive pulmonary disease (HR 2.13), underweight (HR 4.47), renal failure (HR 2.05), concomitant mitral valve involvement (HR 2.37) and mediastinitis (HR 3.98) were independent predictors of long-term mortality. Staphylococcus aureus was the most prevalent microbe (21.8%) and associated with a 5.2-fold increased risk of early mortality, while enterococci were associated with the risk of long-term mortality (HR 1.78).

Conclusions

In this multicenter case-control study, IE was associated with an increased risk of both short- and long-term mortality compared to controls. Efforts should be made to identify, and timely treat modifiable risk factors associated with contracting IE, and mitigate the predictors of poor survival in IE.

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Introduction

Infective endocarditis (IE) is a potentially severe infection that most commonly affects heart valves. The disease is associated with a poor prognosis despite optimal medical and surgical treatment, with mortality around 20–30% at one year [ 1 ]. The epidemiology of IE has changed towards elderly patients and with Staphylococcus aureus (S. aureus) as the predominant causative organism in high-income countries [ 2 ]. Complications of IE include septic embolism, heart failure and uncontrolled infection, which all are indications for cardiac surgery [ 3 ]. Surgery is potentially lifesaving and is indicated in 50–70% of cases of left-sided IE [ 3 , 4 , 5 , 6 ]. Although international guidelines provide recommendations for cardiac surgery in patients with IE, the clinical decision should also consider the age of the patient, comorbidities, and the availability of appropriate surgical expertise [ 3 , 7 ].

Overall, the rates of IE are increasing with annual incidence rates varying from 7 to 14 per 100,000 person-years in recent studies [ 4 , 8 ]. This may be explained by an elderly population, advances in medical care including an increasing number of patients receiving prosthetic heart valves, surgery in patients with congenital heart disease and the increasing use of cardiac implantable electronic devices. Few studies, however, have described the incidence or epidemiology of IE in Scandinavia, and we still need data on variables that predict short- and long-term prognosis of IE. The present study is a large multicenter collaboration between tertiary hospitals with cardiothoracic facilities in Scandinavia. The objectives were to: (1) describe incidence, clinical profile and epidemiology of patients with aortic site IE treated with aortic valve (AV)-surgery compared with a control group undergoing AVR due to non-infectious valvular heart disease; and (2) assess predictors of short- and long-term mortality in patients with aortic valve IE treated with AVR.

Materials and methods

Source population and research design.

The study was a collaborative project between Karolinska University Hospital, Stockholm, Sweden; University Hospital of North Norway, Tromsø, Norway and Haukeland University Hospital, Bergen, Norway. The study was designed in accordance with the Declaration of Helsinki and conducted in compliance with Norwegian and Swedish legislation with approval obtained by the Regional Committees for Medical and Health Research Ethics in Norway (South-Eastern Norway - REK Helse Sør-Øst C, 2017/768) and Sweden (Swedish Ethical Review Authority 2017/2113-31/2). The need for informed consent was waived for Swedish patients by the Swedish Ethical Review Authority, while in Norway, patients still alive at the time of registry building provided informed consent.

Between January 2008 and December 2013, a source population of 10,347 patients ≥ 18 years undergoing primary aortic valve surgery in all participant centers was considered for the study. A total of 170 underwent aortic valve surgery due to IE (exposed cohort) and remaining 10.177 underwent aortic valve surgery due to non-infectious aortic valve disease. Among the latter group, a total of 677 patients were randomly selected as controls (non-exposed cohort) in a ratio of 1:4. A flow chart of the study design (dual design, a case-control and a retrospective cohort study) is presented in Fig.  1 . The diagnosis of IE was based upon the modified Duke criteria for IE [ 9 – 10 ]. Cases were identified from departmental databases and categorized according to the modified Duke criteria and the International Classification of Diseases (ICD) version 10 (I33.0, I38, I33.9). Microbial isolates were cultured from blood and from excised valves.

Study flow chart

Most of the included patients were treated with AVR (98.8%), while the remainder were treated with aortic valve repair. Coronary artery bypass grafting (CABG) was concurrently performed in 14.5% of included patients, while none were operated with aortic homograft.

Study endpoints and follow-up

The primary outcome was all-cause mortality at short-term (within 30 days after AVR) and long-term follow-up. Follow-up was complete in all patients and calculated from the date of operation to the date of death or censoring, with February 1st, 2024, as closing date. The predictors of long-term mortality were calculated at 6-year follow-up at which the difference in survival estimates between IE and controls was greatest.

Cardiovascular risk factors and comorbidities

Information regarding smoking history, body mass index (BMI) and other relevant risk factors and comorbidities was obtained from the electronic patient record. Obesity was defined as BMI > 30 kg/m 2 and underweight as BMI < 18.5 kg/m 2 . Left ventricular ejection fraction (LVEF) was measured by Simpson biplane method. Coronary artery disease (CAD) was defined as previous myocardial infarction, CABG or significant coronary obstructions on coronary angiography prior to valve surgery. Cardiovascular disease (CVD) was defined as a composite of CAD as defined above, stroke and/or peripheral artery disease. Renal failure was defined as estimated glomerular filtration rate < 60 mL/min at baseline.

Statistical methods

SPSS version 29.0 (IBM Corporation, Armonk, NY) was used for data management and statistical analyses. Baseline characteristics were compared using chi-square test for categorical variables and t-test for numerical variables. For the case-control study, after univariate analysis, multivariate logistic regression was used to pinpoint independent risk factors of IE by a backward elimination procedure. Association between risk factors and IE were estimated by the odds ratio (OR) and 95% confidence interval (CI). The effects of IE on short- and long-term survival were visualized by survival curves using the Kaplan-Meier method. Univariate predictors of short- and long-term survival were assessed by cox-regression analyses and presented as hazard ratio (HR) and 95% CI. A multivariate cox regression analysis was used to pinpoint independent risk factors of survival by a backward elimination procedure. All significant predictors of survival in the univariate analyses, or those clinically relevant, were entered into the multivariate models. All statistical tests were performed at the 2-sided, α = 0.05 significance level.

Baseline characteristics of IE cases versus controls

The main demographic and clinical characteristics of IE cases versus controls are presented in Table  1 . Patients with IE were predominantly men (80.0%) and younger than the controls (59 ± 15 versus 69 ± 12 years, p  < 0.001). The IE group included 124 patients (72.9%) with native valve endocarditis (NVE) and 46 patients (27.1%) with prosthetic valve endocarditis (PVE). Patients with PVE were older (mean 62.4 versus 57.1 years), had more severe symptoms (NYHA ≥ 3 74.4% versus 57.8%), and were more likely to have hypercholesterolemia (37.0 vs. 20.2%), atrial fibrillation (AF) at baseline (52.2% versus 34.7%) and AV block (28.3 versus 11.3%) compared to NVE ( p  < 0.05 for all).

Recent dental procedures (31.2% versus 3.6%), previous wound infections (7.1% versus 0.1%), intravenous drug use (IVDU) (17.0% versus 0.0%), positive serology for viral hepatitis (hepatitis B and C) and renal failure (36.5% versus 17.8%) were all more prevalent in the IE group ( p  < 0.001 for all; Table  1 ). A higher prevalence of chronic obstructive pulmonary disease (COPD) (16.7% versus 9.8%, p  = 0.013) and smoking history (65.9% versus 53.8%, p  = 0.005) was also observed in the IE-cases. AF at baseline was more prevalent in the IE group (39.4% versus 16.7%, p  < 0.001), while the prevalence of combined pre- and postoperative AF was comparable (60.7% vs. 57.8%, p  = 0.500).

Patients with IE had significantly longer aortic cross clamp time, cardiopulmonary bypass time, postoperative mechanical ventilation duration, as well as greater postoperative drainage volume and need for blood transfusion ( p  < 0.05 for all).

Microbiology and infection data in the IE group

Lack of infection control was evident in 49.4% and septic emboli in 34.1% of patients with IE preoperatively. Valve vegetations on echocardiography were identified in 141 (82.9%) patients with IE. The prevalence of concomitant infective mitral valve disease (MVD) was 18.2% ( n  = 31). Isolated microorganisms are presented in Table  2 . The most frequently isolated microorganisms were S. aureus (21.8%) (equally represented both in NVE and PVE), streptococci of the viridans group (21.2%) and enterococci (19.4%). Non-viridans streptococci (33.1%) were the most prevalent pathogenic microbes among patients with NVE, while S. aureus (21.7%) was most prevalent in patients with PVE. The proportions of septic embolism to different organs in patients with IE are presented in Fig.  2 , with the brain being the target organ for septic emboli in 20% of cases and multi-organs in 6.5% of cases.

Bar chart showing the proportions of septic embolism to different organs in patients with infective endocarditis

Predictors of infective endocarditis

Covariates of IE are presented in Table  3 . In a multivariable-adjusted model, younger age (OR 1.03 per year), male gender (OR 2.30), previous heart surgery (OR 6.75), the presence of AF at baseline (OR 2.76), positive hepatitis C serology (OR 20.83), previous wound infection (OR 22.97), previous dental treatment (OR 17.13), underweight (OR 9.91) and renal failure (OR 3.52) were associated with a higher risk of having IE. The presence of hypertension (OR 0.44) and hypercholesterolemia (OR 0.37), which were more prevalent in the elderly control group, were associated with lower odds of IE in the entire study population (all p  < 0.01). COPD was associated with a higher likelihood of IE in univariate analysis (OR 1.82, p  = 0.014), but did not remain a significant predictor in the multivariable-adjusted model (OR 1.88, p  = 0.071). Removing hypertension and hypercholesterolemia from the same primary model did not change our results (data not shown).

Survival data

Entire study population: ie is an independent predictor of total mortality.

During a mean follow-up of 7.8 years (median 7.6 years, IQR 5.1–10.8 years), a total of 373 (44.0%) deaths occurred: 81 (47.6%) in the IE group and 292 (43.1%) in the control group. The early mortality rate (≤ 30 days) was 7.1% ( n  = 12) among patients with IE and 2.5% ( n  = 17) in the control group ( p  = 0.004). The difference in mortality rates was greatest at 6-year follow-up: 40.0% (68/170 patients) in the IE group and 26.1% (177/677 patients) in the control group. Figure  3 (Kaplan-Meier curve) shows survival probability rates in patients with IE and controls separately for short-term mortality (≤ 30 days) (panel A) and long-term mortality at closing date (panel B). Importantly, this was confirmed by a multivariate Cox regression model, in which IE was a strong and independent predictor of long-term mortality (HR 2.03, [1.43–2.86], p  < 0.001), also after adjusting for other variables that predicted total mortality (Table  4 ). When long-term mortality was assessed at total follow-up of maximum 16 years in the same multivariate Cox regression model, the prognostic impact of IE somehow attenuated, but remained highly significant (HR 1.73, [1.29–2.32], p  < 0.001). However, Kaplan-Meier curves showed a cross-over at 12-years with a higher likelihood of death in controls, potentially reflecting that other factors than IE may influence the total mortality in the entire cohort after such a long time.

Kaplan-Meier curves demonstrating short-term mortality ( 3A ) and long-term mortality ( 3B ) in patients with infective endocarditis (IE) versus controls

Short-term mortality in patients with IE

In univariate Cox regression analysis, there was a significant increased risk of short-term (≤ 30 days) all-cause mortality in the IE group compared to controls (HR 2.86, [1.36–5.98], p  = 0.005; Table  5 ). Concomitant infective MVD (HR: 4.85, [1.56–15.03], p  = 0.006) and septic embolism (HR 4.01, [1.21–13.31], p  = 0.023) were predictors of mortality within 30 days in the IE cohort. There was no significant difference in early mortality between NVE and PVE patients ( p  = 0.171). In a univariate Cox-regression analysis, IE caused by S. aureus was associated with an increased risk of early mortality (HR 5.24, [1.66–16.50], p  = 0.005) compared with IE caused by other microbes (Table  2 ). Short-term mortality rates in patients with S. aureus were 18.9% compared to 3.8% in IE group caused by other microbes ( p  = 0.001).

Long-term mortality in patients with IE

Total mortality after 6 years showed the largest differences between IE and controls (Fig.  2 B) and this time point was therefore chosen to analyze predictors of long-term mortality in the IE group (Table  6 ). In univariate Cox regression models, age ≥ 60 years (HR: 1.80, [1.10–2.95], p  = 0.020), diabetes mellitus (DM) (HR: 1.98, [1.13–3.47], p  = 0.017), COPD (HR: 2.48, [1.46–4.23], p  = 0.001), previous wound infections (HR 2.34, [1.20–4.90], p  = 0.024), preoperative renal failure (HR: 2.06; [1.28–3.32], p  = 0.003), concomitant infective MVD (HR: 1.94, [1.12–3.37], p  = 0.018), multi-organ failure (HR: 3.95, [2.24–6.96], p  < 0.001) and mediastinitis (HR: 3.65, [1.14–11.72], p  = 0.030), were all associated with increased long-term mortality. No difference was found in long-term mortality between NVE and PVE patients ( p  = 0.526). No association was found between gender and long-term mortality ( p  = 0.830). Similarly, LVEF < 50% and NYHA functional class ≥ 3 were not associated with increased risk of mortality. IE caused by enterococci had a higher long-term mortality rate than IE caused by other microorganisms (HR 1.78, [1.05–3.03], p  = 0.033) (Table  2 ). In a multivariate Cox regression model, underweight (HR 4.47), mediastinitis (HR 3.98), concomitant infective MVD (HR 2.37), COPD (HR 2.13) and preoperative renal failure (HR 2.05) were identified as independent predictors of all-cause mortality in patients with IE (all p  < 0.05). Age, baseline AF and DM were not associated with the risk of all-cause mortality in the same multivariate model ( p  > 0.1). When IE caused by enterococci was introduced in the same multivariate Cox regression model, it did not retain a significant association with all-cause mortality (HR 1.39, [0.78–2.47], p  = 0.263) (data not shown).

The key findings of our study are: (1) Patients who underwent AVR due to IE were mostly men, younger and had an almost three-fold increased risk of early mortality and nearly two-fold increased risk of long-term mortality after 6 years compared to patients who underwent AVR due to degenerative valvular heart disease (non-infectious group) in adjusted analyses. However, at maximum follow-up of 16 years, Kaplan-Meier curves showed a cross-over at approximately 12 years, showing a trend towards increasing risk of death in controls in comparison to the younger IE group who were successfully treated; (2) COPD, underweight, renal failure, concomitant infective MVD and mediastinitis were independent risk factors of all-cause long-term mortality in patients with IE; (3) IE caused by S. aureus was associated with an increased risk of early mortality and IE caused by enterococci had a higher long-term mortality rate than IE caused by other microorganisms.

Antibiotic prophylaxis before dental or surgical procedures for prevention of IE has been debated for decades due to limited evidence. However, recent studies showed that antibiotic prophylaxis in high-risk individuals led to a significant reduction of IE after invasive dental procedures ( 11 – 12 ). Consequently, the 2023 ESC guidelines strengthened their recommendation of antibiotic prophylaxis, while updating their risk categories for IE [ 3 ]. Our study showed that recent dental treatment was associated with a high risk of IE, supporting the need for preventive action. Conversely, previous studies from our group indicated no association between origin of the IE-causing bacteria and findings during oral infection screening but suggested an association between marginal bone loss – as a marker for reduced oral and/or general health – and mortality [ 13 ].

While male gender was identified as a risk factor of acquiring IE, gender per se had no impact on short- or long-term mortality in patients with IE, consistent with previous results [ 14 ]. Our finding of 7.1% short-term (≤ 30 days) mortality rates for IE was less than those reported by previous studies (25–40%), with in-hospital mortality of 20% [ 14 , 15 , 16 ]. Although surgical treatment of IE is associated with an overall better prognosis, this is influenced by the fact that AVR is performed in a selected group of patients, excluding patients with excessive comorbidities, higher surgical risk or poor rehabilitation potentials following surgery. However, the improved short-term prognosis in our study may be explained by the timely and optimal medical treatment before surgery, and the practice of relatively low threshold for surgery in some of our institutions. In general, European guidelines have been followed and differences in practice between institutions have not been investigated in this study.

Long-term mortality was 48% among patients with IE in our study, which is in line with other studies [ 14 , 17 ]. Notably, the rate of mortality progressively increased and peaked at 6-year follow-up (40%) in patients with IE compared with controls (26%). However, when long-term mortality was assessed at total follow-up of maximum 16 years, Kaplan-Meier curves showed a cross-over at approximately 12 years, showing a trend towards increasing risk of death in controls in comparison to the younger IE group, who remained relatively stable. This may be explained by the fact that the controls undergoing cardiac surgery were older and had a higher burden of cardiovascular risk factors (hypertension, hypercholesterolemia, and obesity) at baseline. Furthermore, they may have developed other age-related comorbid conditions during follow-up, suggesting that other factors than IE were the main risk factors for long-term mortality in the control cohort at this time point.

The ‘obesity paradox’ describes the association between obesity and lower risk of mortality after cardiac surgery compared to patients with normal weight and underweight [ 18 ]. However, obesity was not associated with a lower risk of long-term mortality neither in the entire study population nor control group, in whom the prevalence of traditional cardiovascular risk factors was higher. Our study identified underweight, and not obesity, as an independent risk factor of increased long-term mortality in patients with IE. This may suggest a possible interaction between nutritional status, antimicrobial host immune function, bacterial proliferation, and persistent inflammation in patients with IE. Furthermore, underweight may also indicate the severity, duration, and an advanced stage of the disease in patients with IE. Hence, diagnosing IE in an early stage, and appropriate management of IE is essential in terms of better postoperative outcomes. In addition to its correlation with systemic inflammation and metabolic disruption, underweight can also reflect cachexia secondary to malignancies or other advanced systemic diseases, as competing risks.

We demonstrated that most patients with IE had a smoking history (65.9%) and a relatively high prevalence of COPD (16.7%). Although previous studies have not shown COPD to be a risk factor of extrapulmonary infections [ 19 ], our study identified COPD as a risk factor of IE. Furthermore, survival data with multivariate Cox regression identified COPD as an independent predictor of long-term all-cause mortality in the entire study population, but this association was specifically stronger within patients with IE, hence being an effect modifier in IE. Interestingly, smoking history, but not COPD was a predictor of long-term mortality in patients operated due to valvular heart disease. With COPD both being a risk factor of IE and an independent predictor of long-term mortality, it is of great importance that clinicians address smoking history and assist their patients in smoking cessation.

In patients with aortic valve IE, the prevalence of concomitant infective MVD was 18.2% and a predictor of both early and long-term all-cause mortality. The mitral valve might be affected in different ways in aortic valve IE, i.e., either due to a kissing vegetation/lesion or local extension to the mitral valve through the aortic root. Furthermore, aortic valve IE can lead to left ventricular remodeling/dilatation, resulting in mitral regurgitation. In primary aortic valve endocarditis, secondary involvement of the mitral valve is well documented and timely surgery may preserve the mitral valve apparatus, favorably affecting long-term prognosis [ 20 ]. Moreover, case reports showed that even severe mitral regurgitation resolved rapidly after AVR [ 21 ]. Multivalvular IE has a poor prognosis and has been reported in about 15% of patients with IE, which corresponds well to our findings [ 22 ]. Early diagnosis and treatment of IE is important and may prevent secondary valve infections and associated complications.

Patients with IE had significantly longer aortic cross-clamp time, cardiopulmonary bypass time, mechanical ventilation time, as well as more mediastinal drainage and increased need for blood transfusion. Furthermore, we found that postoperative complications such as stroke, renal failure and multi-organ failure were more prevalent in the IE group. Interestingly, postoperative pneumonia was more prevalent in the control group, even though patients with IE had a significantly higher prevalence of COPD and smoking history. This may be explained by the aggressive antibiotic treatment for IE preventing pulmonary infection, in addition to the fact that controls were older and had a higher burden of traditional cardiovascular diseases, including obesity which may be a risk factor for postoperative pneumonia [ 23 ].

Mediastinitis is a rare, but feared complication after sternotomy, and appears to increase the risk of sudden cardiac death [ 24 ]. Our group recently demonstrated that mediastinitis after CABG-surgery was associated with a poor long-term prognosis and a nearly two-fold increased risk of all-cause mortality ( 24 – 25 ). In our current study, mediastinitis was identified as a strong and independent predictor of long-term mortality in the multivariable-adjusted Cox models, both in IE cohort and controls, having an almost four-fold increased risk of all-cause long-term mortality.

DM is a well-established risk factor for infections and CVD [ 26 ]. Moreover, previous studies have shown that patients with DM and IE are older and show a higher prevalence of cardiovascular risk factors and an impaired prognosis [ 27 , 28 , 29 ]. In the present study, however, DM was not associated with a higher prevalence of IE, and although it was associated with an overall worse prognosis in patients with IE, this association was not significant in the multivariable-adjusted model. However, in the entire study population, DM was a significant predictor of all-cause mortality independent of IE. Similarly, previous heart surgery was associated with a higher risk of IE, but was somewhat surprisingly not a predictor of short- or long-term mortality in patients with IE.

S. aureus is the most common microbe in IE and has been shown to be overtaking streptococci as the most frequent causative microorganism [ 30 ]. Furthermore, staphylococcal IE occurs more often with healthcare-associated IE. Patients with S. aureus as the pathogenic microbe had a higher risk of early short-term mortality as compared to other microbiology. IE caused by S. aureus is associated with aggressive disease with an increased risk of complications and in-hospital mortality [ 3 , 28 ]. Interestingly, we found no association between S. aureus and an increased risk of long-term mortality at 6 years. On the contrary, IE caused by enterococci was associated with an increased risk of long-term mortality. International guidelines recommend a combination of aminoglycosides and β-lactam antibiotics in the treatment of enterococcal IE [ 9 ]. Combination-therapy with dual ß-lactams (ampicillin in combination with ceftriaxone), a combination with collateral anti-bacterial synergistic effects [ 31 ], is considered equally efficient and recommended in case of kidney failure or high-level aminoglycoside resistance (HLAR) [ 3 ]. However, the need for long-term dual antimicrobial therapy, rising HLAR and enterococcal IE being more prevalent in elderly patients may partly explain the increased long-term mortality rates compared to IE caused by other microbes [ 32 ].

Strength and limitations

The present study benefits from a large sample size from multiple tertiary hospitals with cardiothoracic facilities across Scandinavia, maintaining the same diagnostic criteria and treatment with active and prospective epidemiologic surveillance of IE after aortic valve surgery. A dual case-control study design enabled us to compare the association between risk factors and clinical outcomes between the exposed (IE) and control groups. The limitations are the retrospective nature of the study, the fact that the control group was not matched for age and sex and underwent AVR for valvular heart disease. Information regarding reconstruction of the left ventricular outflow tract or aortic root were not obtained. Furthermore, data concerning different types of MV-lesions or MV-procedures were not collected. We presented information regarding local abscesses and septic emboli, but did not gather information regarding other local complications, such as pseudoaneurysms and fistulas. This is important because these conditions contribute to the complexity of surgery and increase associated surgical risk.

Aortic valve IE is associated with both increased short- and long-term mortality compared to controls undergoing AVR. Male gender, previous heart surgery, baseline atrial fibrillation, underweight, positive hepatitis C serology, previous wound infection and dental procedures, and renal failure were all associated with IE. Staphylococcus aureus was the most prevalent microbe and equally represented both in native valve and prosthetic valve endocarditis. Renal failure, concomitant infective MVD and the presence of Staphylococcus aureus were risk factors of early mortality. COPD, underweight, renal failure, concomitant infective MVD and mediastinitis were independent risk factors of long-term all-cause mortality in patients with IE. Risk reduction through preventive measures is better than cure. Therefore, all efforts should be made to identify and timely treat the modifiable risk factors associated with IE.

Data availability

Data are available by the corresponding author upon reasonable request.

Abbreviations

Atrial fibrillation

Atrioventricular

• Aortic valve replacement

Body mass index

Coronary artery bypass grafting

Chronic obstructive pulmonary disease

Cardiovascular disease

Diabetes mellitus

Estimated glomerular filtration rate

• Infective endocarditis

Left ventricular ejection fraction

Left ventricular end-diastolic dimension

Mitral valve disease

New York Heart Association

Murdoch DR, Corey GR, Hoen B, Miró JM, Fowler VG Jr, Bayer AS, et al. International collaboration on endocarditis-prospective Cohort Study (ICE-PCS) investigators. Clinical presentation, etiology, and outcome of infective endocarditis in the 21st century: the international collaboration on endocarditis-prospective cohort study. Arch Intern Med. 2009;169(5):463–73.

Article   PubMed   PubMed Central   Google Scholar  

Cahill TJ, Baddour LM, Habib G, Hoen B, Salaun E, Pettersson GB, et al. Challenges in Infective Endocarditis. J Am Coll Cardiol. 2017;69(3):325–44.

Article   PubMed   Google Scholar  

Delgado V, Ajmone Marsan N, de Waha S, Bonaros N, Brida M, Burri H, ESC Scientific Document Group, et al. 2023 ESC guidelines for the management of endocarditis. Eur Heart J. 2023;44(39):3948–4042.

Chu VH, Park LP, Athan E, Delahaye F, Freiberger T, Lamas C, et al. International collaboration on Endocarditis (ICE) Investigators*. Association between surgical indications, operative risk, and clinical outcome in infective endocarditis: a prospective study from the International collaboration on Endocarditis. Circulation. 2015;131(2):131–40.

Iung B, Doco-Lecompte T, Chocron S, Strady C, Delahaye F, Le Moing V, et al. AEPEI Study Group. Cardiac surgery during the acute phase of infective endocarditis: discrepancies between European Society of Cardiology guidelines and practices. Eur Heart J. 2016;37(10):840–8.

Habib G, Erba PA, Iung B, Donal E, Cosyns B, Laroche C, et al. EURO-ENDO investigators. Clinical presentation, aetiology and outcome of infective endocarditis. Results of the ESC-EORP EURO-ENDO (European infective endocarditis) registry: a prospective cohort study. Eur Heart J. 2019;40(39):3222–32.

Otto CM, Nishimura RA, Bonow RO, Carabello BA, Erwin JP 3rd, Gentile F, et al. 2020 ACC/AHA Guideline for the management of patients with Valvular Heart Disease: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice guidelines. Circulation. 2021;143(5):e35–71.

PubMed   Google Scholar  

Baddour LM, Wilson WR, Bayer AS, Fowler VG Jr, Tleyjeh IM, Rybak MJ, et al. American Heart Association Committee on Rheumatic Fever, Endocarditis, and Kawasaki Disease of the Council on Cardiovascular Disease in the Young, Council on Clinical Cardiology, Council on Cardiovascular surgery and anesthesia, and Stroke Council. Infective endocarditis in adults: diagnosis, antimicrobial therapy, and management of complications: A Scientific Statement for Healthcare professionals from the American Heart Association. Circulation. 2015;132(15):1435–86.

Article   PubMed   CAS   Google Scholar  

Durack DT, Lukes AS, Bright DK. New criteria for diagnosis of infective endocarditis: utilization of specific echocardiographic findings. Duke Endocarditis Service. Am J Med. 1994;96(3):200–9.

Habib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Del Zotti F, ESC Scientific Document Group. 2015 ESC Guidelines for the management of infective endocarditis: The Task Force for the Management of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by: European Association for Cardio-Thoracic Surgery (EACTS), the European Association of Nuclear Medicine (EANM). Eur Heart J. 2015;36(44):3075–3128.

Thornhill MH, Crum A, Campbell R, Stone T, Lee EC, Bradburn M, et al. Temporal association between invasive procedures and infective endocarditis. Heart. 2023;109(3):223–31.

Thornhill MH, Gibson TB, Yoon F, Dayer MJ, Prendergast BD, Lockhart PB, et al. Antibiotic Prophylaxis Against Infective endocarditis before Invasive Dental procedures. J Am Coll Cardiol. 2022;80(11):1029–41.

Thoresen T, Jordal S, Lie SA, Wünsche F, Jacobsen MR, Lund B. Infective endocarditis: association between origin of causing bacteria and findings during oral infection screening. BMC Oral Health. 2022;22(1):491.

Abegaz TM, Bhagavathula AS, Gebreyohannes EA, Mekonnen AB, Abebe TB. Short- and long-term outcomes in infective endocarditis patients: a systematic review and meta-analysis. BMC Cardiovasc Disord. 2017;17(1):291.

Park LP, Chu VH, Peterson G, Skoutelis A, Lejko-Zupa T, Bouza E, et al. International collaboration on Endocarditis (ICE) investigators. Validated risk score for Predicting 6-Month Mortality in Infective Endocarditis. J Am Heart Assoc. 2016;5(4):e003016.

Erdem H, Puca E, Ruch Y, Santos L, Ghanem-Zoubi N, Argemi X, et al. Portraying infective endocarditis: results of multinational ID-IRI study. Eur J Clin Microbiol Infect Dis. 2019;38(9):1753–63.

Tahon J, Geselle PJ, Vandenberk B, Hill EE, Peetermans WE, Herijgers P, et al. Long-term follow-up of patients with infective endocarditis in a tertiary referral center. Int J Cardiol. 2021;331:176–82.

Mariscalco G, Wozniak MJ, Dawson AG, Serraino GF, Porter R, Nath M, et al. Body Mass Index and Mortality among adults undergoing cardiac surgery: a Nationwide Study with a systematic review and Meta-analysis. Circulation. 2017;135(9):850–63.

Lange P. Chronic obstructive pulmonary disease and risk of infection. Pneumonol Alergol Pol. 2009;77(3):284–8.

Piper C, Hetzer R, Körfer R, Bergemann R, Horstkotte D. The importance of secondary mitral valve involvement in primary aortic valve endocarditis; the mitral kissing vegetation. Eur Heart J. 2002;23(1):79–86.

Friess JO, Bruelisauer T, Hurni S, Pasic M, Erdoes G, Eberle B. Resolution of severe secondary mitral valve regurgitation following aortic valve replacement in infective endocarditis. SAGE Open Med Case Rep. 2021;9:2050313X211034377.

PubMed   PubMed Central   Google Scholar  

Álvarez-Zaballos S, González-Ramallo V, Quintana E, Muñoz P, de la Villa-Martínez S, Fariñas MC, et al. On Behalf of games. Multivalvular endocarditis: a Rare Condition with Poor Prognosis. J Clin Med. 2022;11(16):4736.

Covarrubias J, Grigorian A, Schubl S, Gambhir S, Dolich M, Lekawa M, et al. Obesity associated with increased postoperative pulmonary complications and mortality after trauma laparotomy. Eur J Trauma Emerg Surg. 2021;47(5):1561–8.

Risnes I, Aukrust P, Lundblad R, Ueland T, Rynning SE, Solheim E, et al. Troponin T and N-Terminal Pro-brain Natriuretic peptide are Associated with Long-Term all-cause mortality in patients with Post-sternotomy Mediastinitis following coronary artery bypass grafting: a 15-Year Follow-Up study. Cardiology. 2023;148(6):599–603.

Risnes I, Aukrust P, Lundblad R, Rødevand O, Ueland T, Rynning SE, et al. Increased levels of NT-proBNP and troponin T 2 years after coronary artery bypass grafting complicated by mediastinitis. Front Cardiovasc Med. 2023;10:1008825.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Martín-Timón I, Sevillano-Collantes C, Segura-Galindo A, Del Cañizo-Gómez FJ. Type 2 diabetes and cardiovascular disease: have all risk factors the same strength? World J Diabetes. 2014;5(4):444–70.

Chirillo F, Bacchion F, Pedrocco A, Scotton P, De Leo A, Rocco F, et al. Infective endocarditis in patients with diabetes mellitus. J Heart Valve Dis. 2010;19(3):312–20.

Kourany WM, Miro JM, Moreno A, Corey GR, Pappas PA, Abrutyn E, et al. Influence of diabetes mellitus on the clinical manifestations and prognosis of infective endocarditis: a report from the International collaboration on endocarditis-merged database. Scand J Infect Dis. 2006;38(8):613–9.

Chu VH, Cabell CH, Benjamin DK Jr, Kuniholm EF, Fowler VG Jr, Engemann J, Sexton DJ, Corey GR, Wang A. Early predictors of in-hospital death in infective endocarditis. Circulation. 2004;109(14):1745–9.

Selton-Suty C, Célard M, Le Moing V, Doco-Lecompte T, Chirouze C, Iung B, et al. AEPEI Study Group. Preeminence of Staphylococcus aureus in infective endocarditis: a 1-year population-based survey. Clin Infect Dis. 2012;54(9):1230–9.

Marino A, Munafò A, Zagami A, Ceccarelli M, Di Mauro R, Cantarella G, et al. Ampicillin Plus Ceftriaxone Regimen against Enterococcus faecalis Endocarditis: A literature review. J Clin Med. 2021;10(19):4594.

Beganovic M, Luther MK, Rice LB, Arias CA, Rybak MJ, LaPlante KL. A review of Combination Antimicrobial Therapy for Enterococcus faecalis Bloodstream infections and Infective endocarditis. Clin Infect Dis. 2018;67(2):303–9.

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This study had no financial support.

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Sahrai Saeed and Ivar Risnes contributed equally to this work.

Authors and Affiliations

Department of Internal Medicine, Stord Hospital, Stord, Norway

Håvard Dingen

Department of Medicine, Section of Infectious diseases, Haukeland University Hospital, Bergen, Norway

Håvard Dingen & Stina Jordal

Department of Cardiothoracic Surgery, Karolinska University Hospital, Stockholm, Sweden

Sorosh Bratt, Magnus Dalén & Peter Svenarud

Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

Research Institute of Internal Medicine & Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway

Pål Aukrust & Thor Ueland

Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

Department of Cardiothoracic and Vascular Surgery, University Hospital North Norway, Tromsø, Norway

Rolf Busund & Øyvind Jakobsen

Thrombosis Research Center (TREC), Division of internal medicine, University Hospital of North Norway, Tromsø, Norway

Thor Ueland

Department of Cardiology, Haukeland University Hospital, Bergen, Norway

Rune Haaverstad, Sahrai Saeed & Ivar Risnes

Institute of Clinical Science, Medical Faculty, University of Bergen, Bergen, Norway

Rune Haaverstad

Department of Thoracic and Cardiovascular Surgery, Oslo University Hospital Ullevål, Oslo, Norway

Ivar Risnes

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IR and PA contributed to the study design, data collection, quality checking and editing the manuscript. SB, RB, ØJ, MD, TU and PS have contributed to data collection and editing the manuscript. RH has contributed to review and editing the manuscript. HD, SJ and SS contributed substantially to analysis, interpretation, literature search, and writing of the manuscript. SS (guarantor) takes the responsibility for the content of the manuscript, including the data and analysis.

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Correspondence to Sahrai Saeed .

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Dingen, H., Jordal, S., Bratt, S. et al. Clinical profile, microbiology and outcomes in infective endocarditis treated with aortic valve replacement: a multicenter case-control study. BMC Infect Dis 24 , 913 (2024). https://doi.org/10.1186/s12879-024-09782-3

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Exploring the effects of short-course antibiotics on children’s gut microbiota by using 16S rRNA gene sequencing: a case-control study

• Yuhan Zhou 1 ,
• Xianglian Chen 1 ,
• Tongtong Wang 1 &
• Riyan Huang 1  

BMC Pediatrics volume  24 , Article number:  562 ( 2024 ) Cite this article

Metrics details

With the widespread use of antibiotics, more attention has been paid to their side effects. We paid extra attention to the impact of antibiotics on children’s bodies. Therefore, we analyzed the characteristic changes in the gut microbiota of children after antibiotic treatment to explore the pathogenesis of antibiotic-associated diseases in more depth and to provide a basis for diagnosis and treatment.

We recruited 28 children with bronchopneumonia in the western district of Zhuhai, China, and divided them into three treatment groups based on antibiotic type. We took stool samples from children before and 3–5 days after antibiotic treatment. 16S rRNA gene sequencing was used to analyze the effects of antibiotic therapy on the gut microbiota of children. Continuous nonparametric data are represented as median values and analyzed using the Wilcoxon rank-sum test.

While alpha diversity analysis found no significant changes in the mean abundance of the gut microbiota of children after a short course of antibiotic treatment, beta diversity analysis demonstrated significant changes in the composition and diversity of the gut microbiota of children even after a short course of antibiotic therapy. We also found that meloxicillin sulbactam can inhibit the growth of Proteobacteria, Bacteroidetes, and Verrucomicrobia, ceftriaxone inhibits Verrucomicrobia and Bacteroides, and azithromycin inhibits Fusobacteria, Actinobacteria, Proteobacteria, and Verrucomicrobia. We further performed a comparative analysis at the genus level and found significantly different clusters in each group. Finally, we found that azithromycin had the greatest effect on the metabolic function of intestinal microbiota, followed by ceftriaxone, and no significant change in the metabolic process of intestinal microbiota after meloxicillin sulbactam treatment.

Conclusions

Antibiotic treatment significantly affects the diversity of intestinal microbiota in children, even after a short course of antibiotic treatment. Different classes of antibiotics affect diverse microbiota primarily, leading to varying alterations in metabolic function. Meanwhile, we identified a series of intestinal microbiota that differed significantly after antibiotic treatment. These groups of microbiota could be used as biomarkers to provide an additional basis for diagnosing and treating antibiotic-associated diseases.

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Introduction

Pathogenic bacteria are a significant cause of infectious diseases in children, such as sepsis, bacterial meningitis, and infectious diarrhea. If not treated properly and timely, it can cause serious consequences [ 1 ]. Antibiotics play a vital role in treating bacterial infectious diseases in children. It has also contributed significantly to the reduction of complications and mortality. However, with the wide application of antibiotics, people have found that antibiotics can also cause various harmful effects on the human body, such as antibiotic-associated diarrhea (AAD), an allergic rash, fungal infection, multi-drug-resistant bacteria, and so on [ 2 , 3 , 4 ]. Researchers also found that antibiotic exposure increases the risk of numerous diseases, such as obesity, diabetes, allergies, asthma, and inflammatory bowel diseases [ 5 ].

In addition to killing disease-causing bacteria, antibiotics can affect bacteria that colonize the gut. It will break the original microbial balance of the intestine and cause enteric dysbacteriosis, which is more evident in children [ 6 ]. Previous studies have shown that colonization of the gut microbiota begins during the fetal stage and plays a crucial role in the development and maturation of the fetal gut. Similarly, gut microbiota’s role in children’s growth and development is not limited. Gut microbiota can participate in or affect the body’s metabolic and immune processes by maintaining a dynamic balance and producing chemicals [ 7 ]. Various studies have recently examined the relationship between disease and gut microbiota [ 8 ]. Researchers want to seek different approaches to diagnosis and treatment by uncovering the role of gut microbiota in physiological processes and disease progression.

It is known that antibiotics can cause dysbiosis of the microbiota, inhibiting beneficial bacteria and causing an overgrowth of opportunistic pathogens, resulting in a wide range of clinical manifestations [ 9 ]. However, the mechanism has yet to be particularly well known. Most of the gut microbiota in previous studies was cultured by bacteria. Still, most of the culture conditions were only suitable for the growth of some bacteria, so the results were limited. The development of research techniques, particularly at the molecular level and biological information, has provided us with a different perspective on understanding gut microbiota. In addition, previous studies have shown that probiotics are an effective treatment for enteric dysbacteriosis and AAD [ 10 , 11 ]. However, the mechanism of action needs to be better understood, and it is also critical to note that inappropriate use of probiotics may lead to drug-induced intestinal microbiota disorders.

For these reasons, we sequenced the 16S rRNA V3/ V4 region of stool samples from children treated with different antibiotics. We want to know the changes in the gut microbiome after antibiotic therapy to provide additional evidence to study the mechanism and treatment of intestinal microbiota disorders.

Human subjects

For the study, we collected 56 stool samples from 28 children, 17 boys, and 11 girls, aged between 5 months and 13 years, in Zhuhai, China. According to different antibiotics, we divided all samples into three research groups, namely research group 1 (RG1, n  = 13), research group 2 (RG2, n  = 8), and research group 3 (RG3 n  = 7). The medicine used in RG1 was meloxicillin sulbactam(Suzhou Erye Pharmaceutical Co. LTD). The medicine used in RG2 was ceftriaxone(Shenzhen Lijian Pharmaceutical Co. LTD), and the RG3 was medicine(Hainan Puli Pharmaceutical Co. LTD). We collected specimens from the three research groups before drug treatment (RGA) and after 3–5 days of treatment (RGB). Key laboratory test data of each research group have been collated in the additional table, including WBC, CRP, PCT, and pathogens (Additional table). All children in our study were treated according to antibiotic use criteria. In our study, we relied on the criteria for antibiotic use: Community-acquired pneumonia diagnosis and treatment standard for children (2019 edition), published by the National Health Commission of the People’s Republic of China and the State Administration of Traditional Chinese Medicine.

Inclusion criteria:

(a) Inclusion age range: children aged 1 month to 14 years; (b) Children with bronchopneumonia; (c) All participants voluntarily joined the study and informed consent from their legal guardians.

Exclusion criteria:

(a) Participants had a history of antibiotic use within four weeks before the study; (b) Participants had a history of gastrointestinal diseases within four weeks before the study, such as abdominal pain, vomiting, diarrhea, constipation, etc.; (c) Participants with a history of probiotics, prebiotics, or any other medications three months before the study that could affect their gut microbiota.

Sample collection and genomic DNA extraction

This study was approved by the Ethics Committee of the Fifth Affiliated Hospital of Zunyi Medical University (Zhuhai). All participants and legal guardians volunteered to participate in this study, and all legal guardians signed informed consent forms. We obtained 56 stool samples from 28 children in three groups. Stool samples were collected and frozen at -80℃ within 15 min. After all the samples are collected, they are transported to the laboratory of the research institution. The microbial community DNA was extracted using MagPure Stool DNA KF kit B (Magen, China) following the manufacturer’s instructions. DNA was quantified with a Qubit Fluorometer using a Qubit dsDNA BR Assay kit (Invitrogen, USA), and the quality was checked by running an aliquot on 1% agarose gel.

Library construction

Variable regions V4 of bacterial 16S rRNA gene was amplified with degenerate PCR primers, 515F (5’-GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’- GGACTACHVGGGTWTCTAAT-3’). Both forward and reverse primers are labeled with Illumina adapter, pad, and linker sequences. PCR enrichment was performed in a 50 µL reaction containing a 30ng template, fusion PCR primer, and PCR master mix. PCR cycling conditions were as follows: 95 °C for 3 min, 30 cycles of 95 °C for 45 s, 56 °C for 45 s, 72 °C for 45 s, and a final extension for 10 min at 72 °C for 10 min. The PCR product was purified using Agencourt AMPure XP beads and eluted in an elution buffer. The Agilent Technologies 2100 Bioanalyzer qualifies the library. The validated libraries were used for sequencing on DNB MGISeq 2000 platform (BGI, Shenzhen, China) following the standard pipelines of DNB and generating 2 × 300 bp paired-end reads.

Sequencing and bioinformatics analysis

Raw reads were filtered to remove adaptors and low-quality and ambiguous bases, and then paired-end reads were added to tags by the Fast Length Adjustment of Short reads program (FLASH, v1.2.11) to get the tags [ 12 ]. The tags were clustered into Operational Taxonomic Units (OUTs) with a cutoff value of 97% using UPARSE software (v7 0.0.1090) [ 13 ] and chimera sequences were compared with the Gold database using UCHIME (v4.2.40) [ 14 ] to detect. Then, OTU representative sequences were taxonomically classified using Ribosomal Database Project (RDP) Classifier v.2.2 with a minimum confidence threshold of 0.6 and trained on the Greengenes database v201305 by QIIME v1.8.0 [ 15 ]. The USEARCH global was used to compare all Tags back to OTU to get the OTU abundance statistics table of each sample [ 16 ]. The OTU Rank curve was plotted using the R package version 3.1.1. Alpha and beta diversity were estimated by MOTHUR (v1.31.2) [ 17 ] and QIIME (v1.8.0) [ 15 ] at the OTU level, respectively. The sample cluster was conducted by QIIME (v1.8.0) [ 15 ] based on UPGMA. MetaCyc functions were predicted using the PICRUSt software [ 18 ]. Principal Coordinate Analysis (PCoA) was performed by QIIME (v1.8.0) [ 15 ]. Barplot of different classification levels was plotted with R package v3.4.1 and R package “gplots”, respectively. LEfSe cluster or LDA analysis was conducted by LEfSe. Significant Species or functions were determined by R (v3.4.1) based on Wilcox-test or Kruskal-Test.

Statistical analysis

We use IBM SPSS Statistics 27.0 software for data documentation and statistical analysis. Parametric data of age are expressed as the mean and standard deviation. Continuous nonparametric data are represented as median values and analyzed using the Wilcoxon rank-sum test. P  < 0.05 is considered statistically significant.

Study participants feature

We recruited 28 children with bronchopneumonia (male: female, 17: 11; average age 3.93 ± 3.06 years). According to different antibiotics, they were divided into three research groups. RG1 was treated with meloxicillin sulbactam (male: female, 6: 7; average age 3.00 ± 1.78 years), study group 2 was treated with ceftriaxone (male: female, 7: 1; average age 2.75 ± 2.25 years), and study group 3 was treated with azithromycin (male: female, 4: 3; average age 7.00 ± 3.92 years) (Table  1 ).

Species sequencing coverage

The rarefaction curves (Fig.  1 a) reflect the depth and coverage of sequencing. In this study, the ends of the most rarefaction curves tend to be flat, demonstrating that the current amount of data can reflect the vast majority of the species information in the sample and that the sequencing depth and representation are acceptable. More data will yield only a few new OTUs. The OTU Rank curve (Fig.  1 b) had a wide abscissa but a steep slope, indicating that the species richness in the samples was high, but the species composition was not uniform.

Rarefaction curve and OTU Rank curve ( a ) The rarefaction curves of sample species. The abscissa is the amount of sample sequencing data, and the ordinate is the actual number of OTUs measured. Blue is for the pre-antibiotic treatment group, and orange is for the post-antibiotic group. ( b ) OTU Rank curves. The abscissa is ordered according to the number of OTUs, with the ordinate being the relative abundance of OTUs. The different color curves represent different samples, with M for the meloxicillin sulbactam-treated group, X for the ceftriaxone-treated group, and Z for the azithromycin-treated group

Analysis of gut microbiota diversity

We performed diversity analysis separately for each of the three research groups. In alpha diversity, chao1 algorithm results represent species richness within each group and are shown as boxplots (Fig.  2 a, b, c). There were no significant differences in mean species richness among the three study groups before and after antibiotic treatment ( P  = 0.05, P  = 0.33, P  = 0.80), which might be related to the shorter duration of antibiotic treatment. We would obtain a different result if the course of antibiotic therapy were longer. The beta diversity was analyzed by the unweighted-unifrac algorithm and shown by box plots (Fig.  2 d, e, f). There were significant differences in microbiota composition before and after antibiotic treatment in the three study groups ( P <​ 0.01, P  < 0.01, P  = 0.04). The diversity of gut microbiota increased significantly after meloxicillin sulbactam and ceftriaxone treatment. But result decreased substantially after treatment with azithromycin.

Alpha and beta diversity. ( a , b , c ) Alpha diversity box plot. The five lines from bottom to top are minimum, first quartile, median, third quartile, and maximum. The abscissa denotes the group, and the ordinate is the Chao index. ( d , e , f ) beta diversity box plot. The five lines from bottom to top are minimum, first quartile, median, third quartile, and maximum. The abscissa denotes the group; the ordinate is the Unweighted Unifrac index. Different colors indicate different study groups. RG1A is the pre-treatment group of meloxicillin sulbactam, and RG1B is the post-treatment group of meloxicillin sulbactam. RG2A is the pre-treatment group of ceftriaxone, and RG2B is the post-treatment group of ceftriaxone. RG3A is the pre-treatment group of azithromycin, and RG3B is the post-treatment group of azithromycin

​Changes in gut microbiota after antibiotic therapy

The stacked bar chart of species composition shows that at the phylum level, Actinobacteria, Firmicutes, Proteobacteria, Fusobacteria, Verrucomicrobia, and Bacteroidetes were the main compositions of gut microbiota in children (Fig.  3 a, b, c). The composition of the gut microbiota differed in the three study groups after antibiotic treatment (Table  2 ). The relative abundance of Fusobacteria (0.95%, 3.19%) and Actinobacteria (2.01%, 11.71%) increased significantly, while the relative abundance of Proteobacteria (13.61%, 9.24%), Bacteroidetes (44.12%, 37.71%) and Verrucomicrobia (4.33%, 1.79%) decreased significantly after treatment with meloxicillin sulbactam. There was no significant difference in Firmicutes (34.96%, 35.62%). After ceftriaxone treatment, the relative abundance of Proteobacteria (9.43%, 16.67%), Actinobacteria (6.20%, 12.39%), Firmicutes (28.73%, 38.51%), and cyanobacteria (<​ 0.01%, 2.21%) increased significantly. The relative abundance of Verrucomicrobia (8.72%, 2.17%) and Bacteroidetes (45.77%, 26.37%) significantly decreased, and there was no significant difference in Fusobacteria (1.00%, 0.94%). After azithromycin treatment, only the relative abundance of Bacteroidetes (40.19%, 58.67%) increased significantly, and the relative abundance of Fusobacteria (1.28%, 0.17%), Actinobacteria (4.45%, 2.99%), Proteobacteria (15.49%, 2.23%), and Verrucomicrobia (0.55%, 0.28%) decreased significantly. There was no significant difference in Firmicutes (37.92%, 35.54%).

Bar graph of species composition The abscissa represents the groups, RG1A is the pre-treatment group of meloxicillin sulbactam, and RG1B is the post-treatment group of meloxicillin sulbactam. RG2A is the pre-treatment group of ceftriaxone, and RG2B is the post-treatment group of ceftriaxone. RG3A is the pre-treatment group of azithromycin, and RG3B is the post-treatment group of azithromycin. The ordinate is the proportion of species composition (phylum level). Different colors correspond to different species. Species with abundances less than 0.5% of the sample not annotated at this taxonomic level were combined into Others

No data indicates

Linear discriminant analysis Effect Size (LEfSe) is used to identify species with significant differences in abundance between different groups. The microbiota abundance of LDA Score > 2 in each group was considered significantly higher than that in the other group, and the larger the score, the more pronounced the difference ( P  < 0.05). We use the evolutionary clade diagram (Fig.  4 a, c, e) and the histogram of the distribution of LDA values (Fig.  4 b, d, f) to demonstrate. In this study, 27 microbiota relative abundance increased significantly after meloxicillin sulbactam treatment. The most obvious one is Lactococcus (LDA value 4.17, P  < 0.01), 13 microbiota relative abundance decreased significantly, and the most obvious one is Prevotellaceae (LDA value 4.83, P  < 0.05) (Fig.  4 a, b). After ceftriaxone treatment, the relative abundance of 11 bacterial groups increased significantly, the most obvious one is Actinomycetales (LDA value 4.69, P  < 0.05), and 13 bacterial groups decreased significantly, the most obvious one is Bacteroidaceae (LDA value 5.08, P  < 0.05) (Fig.  4 c, d). After azithromycin treatment, the relative abundance of 6 bacteria groups increased significantly, the most obvious is Bacteroidia (LDA value 4.69, P  < 0.05). 17 bacteria groups decreased significantly, and the most obvious is Proteobacteria (LDA value 4.83, P  < 0.05) (Fig.  4 e, f). These groups of microbiota can be used as biomarkers. They could combine with the microbiota’s biological function to further investigate the mechanisms of antibiotic effects on the children, thus providing additional methods and evidence for diagnosis and treatment.

Cluster diagram of LEfSe and LDA diagram ( a , c , e ). LEfSe cluster graph. The nodes with different colors represent microbial communities that play an essential role in the groups. A colored circle represents a biomarker, and the legend in the upper right corner is the name of the biomarker. The diameter of the circle is proportional to the relative abundance. From the inside out, the circles are the species at the level of phylum, class, order, family, and genus. ( b , d , f ) LDA diagram. It is the distribution map of LDA values of different species, the color represents the corresponding groups, and the length of the bar chart represents the contribution of different species (LDA Score). The figure shows species with significant differences in abundance between different groups under the condition that the LDA Score is greater than the set value (default setting is 2)

Functional difference analysis of metabolic levels

Previous studies have found that gut microbiota participates in the body’s life activities and metabolic processes by producing chemicals. This study confirmed that antibiotics affect the composition ratio and abundance of gut microbiota. After antibiotic treatment, we also analyzed differences in gut microbiota function at the metabolic level to explore how the shift in microbiota affected the body’s metabolic process. As shown in the figure, in the RG1 group, glycan degradation decreased after meloxicillin sulbactam treatment, but the difference was insignificant (Fig.  5 a, P  = 0.07). In the RG2 group, antibiotic resistance increased significantly after ceftriaxone treatment (Fig.  5 b, P  = 0.01), while the polymeric compound degradation decreased significantly (Fig.  5 b, P  < 0.05). In group RG3, nucleoside and nucleotide biosynthesis function, glycolysis function, and secondary metabolite biosynthesis were increased significantly (Fig.  5 c, P  < 0.01, P  = 0.01, P  < 0.05), aldehyde and polymeric compound degradation, aldehyde degradation, alcohol degradation, and aromatic compound degradation were decreased significantly (Fig.  5 c, P  < 0.01, P  < 0.05, P  < 0.05).

Analysis of the functional differences. Path difference of the Wilcox test results. Shown on the left is a bar plot showing the relative abundance of the channels for each group. In the middle is the log 2 value of the mean close abundance ratio for the same path in both groups and the right panel shows the p-values and FDR values obtained from the Wilcox test. If the p-value is less than 0.05, the pathway is significantly different between the two groups

The side effects of antibiotic treatment on the human body should not be ignored, especially in children. Early exposure to antibiotics can significantly increase the risk of certain diseases, which may be related to a shift in the colonizing microbiota of the child’s gut [ 19 ]. Even a short course of antibiotics can take a long time to restore balance among the microbiota and may have long-term effects on colonizing the gut microbiota. This study further seeks to understand antibiotics’ impact on children from a gut microbiota perspective.

From the alpha diversity results, we can see that short-course antibiotic therapy may not significantly affect the mean abundance of gut microbiota, which is the same conclusion reached in other similar studies [ 20 ]. It may be related to the course of antibiotics, and the outcome may be different if treatment is prolonged. However, beta diversity analysis showed that antibiotic therapy, even short approaches, significantly affected the composition and homogeneity of gut microbiota. The study showed that gut microbiota diversity increased dramatically after meloxicillin sulbactam and ceftriaxone. However, it decreased significantly after treatment with azithromycin.

From the sample analysis, at the phylum level, the gut microbiota of the children consisted mainly of Actinobacteria, Firmicutes, Proteobacteria, Fusobacteria, Verrucomicrobia, and Bacteroides, in agreement with other studies [ 21 ]. At the same time, we found that different antibiotics had different effects on different groups of bacteria. Meloxicillin sulbactam inhibited the growth of Proteobacteria, Bacteroidetes, and Verrucomicrobia, while Fusobacteria and Actinobacteria showed significant increases but had limited effect on Firmicutes. Ceftriaxone had an inhibitory effect on the Verrucomicrobia and Bacteroides, and a substantial increase in Proteobacteria, Actinobacteria, Firmicutes, and Cyanobacteria, with little impact on Fusobacteria. Azithromycin treatment inhibited Fusobacteria, Actinobacteria, Proteobacteria, and Verrucomicrobia, with a significant increase in Bacteroides, but had little effect on Firmicutes. By killing or inhibiting the growth of some bacteria, antibiotics cause a considerable increase in the abundance of others, thus disrupting the original homeostasis of the gut, which may include harmful bacteria or opportunistic pathogens. Cyanobacteria, for example, showed a significant increase in abundance after treatment with ceftriaxone, which may have been acquired by eating seafood. Treatment with ceftriaxone creates an imbalance in the gut microbiota, which may be responsible for the apparent increase in the abundance of Cyanobacteria and other microbiota. Studies have found that Cyanobacteria can produce neurotoxins that may cause neurodegeneration in humans [ 22 ].

LEfSe was used to analyze further and compare the microbiota at the genus level of each study group. The microbiota affected by the treatment with meloxicillin sulbactam had the most significant number of species, with 40 groups of microbiota found to be significantly different. Ceftriaxone was followed by 26 species of microbiota that differed significantly. The effects of azithromycin were relatively minor, with 23 groups of microbiota found to be quite different. In terms of bacterial metabolic function, we discovered that azithromycin has the most significant effect on bacterial microbiota’s metabolic process and considerably inhibits the degradation function of amines, aldehydes, aromatic compounds, and other chemical substances. Ceftriaxone promotes antibiotic resistance in the body, while meloxicillin sulbactam has little effect on metabolism. Whether these bacterial imbalances and changes in metabolic function cause clinical symptoms or even have a more significant impact on the body could be a direction for future research.

According to the findings, while most children did not develop significant symptoms after a short course of antibiotics, they significantly altered the composition and function of the microbiota. Clinically, a precise diagnosis is lacking, even when symptoms are present. In our study, the groups of gut microbiota that changed substantially could be used as biomarkers to provide additional evidence for diagnosing antibiotic-associated diseases. In terms of treatment, we can supplement with different probiotics based on changes in specific microbial communities, which play a very significant role in restoring and establishing colonizing bacteria in the child’s gut.

There are still some limitations to our study. The time range of the study was narrow, and only the short-term effects of antibiotic treatment on the gut microbiota of children were analyzed, while the long-term effects were lacking. In addition, the sample size of this study is small, and more samples are needed to prove our findings further. In the future, we may also find specific changes in the microbiota after each antibiotic treatment through more different types of antibiotic studies, thus providing an additional basis for diagnosis and treatment.

Antibiotic treatment significantly affects the diversity of gut microbiota in children, even with short courses of antibiotics. This study confirmed that different classes of antibiotics mainly affected diverse microbiota, resulting in various metabolic function changes. We identified a range of gut microbiota that significantly differed after antibiotic treatment, and they could be used as biomarkers to diagnose and treat antibiotic-associated disease.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA010070) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa [ 23 , 24 ].

Abbreviations

Antibiotic-Associated Diarrhea

Operational Taxonomic Units

Linear Discriminant Analysis Effect Size

Collaborators GBDLRI. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the global burden of Disease Study 2016. Lancet Infect Dis. 2018;18(11):1191–210.

Article   Google Scholar  

Capurso L, Koch M. [Side effects of antibiotic: diarrhea and Clostridium difficile infection]. Recenti Prog Med. 2021;112(1):4–26.

PubMed   Google Scholar  

Hum SW, Shaikh KJ, Musa SS, Shaikh N. Adverse events of Antibiotics used to treat Acute Otitis Media in children: a systematic Meta-analysis. J Pediatr. 2019;215:139–e143137.

Article   CAS   PubMed   Google Scholar  

Haddad SF, Allaw F, Kanj SS. Duration of antibiotic therapy in Gram-negative infections with a particular focus on multidrug-resistant pathogens. Curr Opin Infect Dis. 2022;35(6):614–20.

Blaser MJ. Antibiotic use and its consequences for the normal microbiome. Science. 2016;352(6285):544–5.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Silverman MA, Konnikova L, Gerber JS. Impact of antibiotics on necrotizing enterocolitis and Antibiotic-Associated Diarrhea. Gastroenterol Clin North Am. 2017;46(1):61–76.

Article   PubMed   PubMed Central   Google Scholar  

Deering KE, Devine A, O’Sullivan TA, Lo J, Boyce MC, Christophersen CT. Characterizing the composition of the Pediatric gut microbiome: a systematic review. Nutrients 2019, 12(1).

Ren X, Gamallat Y, Liu D, Zhu Y, Meyiah A, Yan C, Shang D, Xin Y. The distribution characteristics of intestinal microbiota in children with community-acquired pneumonia under five years of age. Microb Pathog. 2020;142:104062.

Bhattacharyya M, Debnath AK, Todi SK. Clostridium difficile and antibiotic-associated Diarrhea. Indian J Crit Care Med. 2020;24(Suppl 4):S162–7.

Wan CM, Yu H, Liu G, Xu HM, Mao ZQ, Xu Y, Jin Y, Luo RP, Wang WJ, Fang F. [A multicenter randomized controlled study of Saccharomyces boulardii in the prevention of antibiotic-associated diarrhea in infants and young children]. Zhonghua Er Ke Za Zhi. 2017;55(5):349–54.

CAS   PubMed   Google Scholar  

Investigating Group for Prevention of AADiCwPbCB. [Multicenter, randomized, controlled clinical trial on preventing antibiotic-associated diarrhea in children with pneumonia using the live Clostridium butyricum and Bifidobacterium combined Powder]. Zhonghua Er Ke Za Zhi. 2012;50(10):732–6.

Google Scholar  

Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957–63.

Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8.

Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194–200.

Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.

Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1.

Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75(23):7537–41.

Wilkinson TJ, Huws SA, Edwards JE, Kingston-Smith AH, Siu-Ting K, Hughes M, Rubino F, Friedersdorff M, Creevey CJ. CowPI: a Rumen Microbiome Focussed Version of the PICRUSt Functional Inference Software. Front Microbiol. 2018;9:1095.

Ahmadizar F, Vijverberg SJH, Arets HGM, de Boer A, Lang JE, Garssen J, Kraneveld A, Maitland-van der Zee AH. Early-life antibiotic exposure increases the risk of developing allergic symptoms later in life: a meta-analysis. Allergy. 2018;73(5):971–86.

Han S, Zhang Q, Ding Y, Chu P, Zhang J, Shi J, Jia S, Yang C, Lu J, Wang Y. One-Week effects of Antibiotic Treatment on Gut Microbiota of late neonates with pneumonia or meningitis. Front Pediatr. 2021;9:723617.

Chen X, Zhang D, Sun H, Jiang F, Shen Y, Wei P, Shen X. Characterization of the gut microbiota in Chinese children with overweight and obesity using 16S rRNA gene sequencing. PeerJ. 2021;9:e11439.

Sini P, Dang TBC, Fais M, Galioto M, Padedda BM, Luglie A, Iaccarino C, Crosio C. Cyanobacteria, Cyanotoxins, and neurodegenerative diseases: dangerous liaisons. Int J Mol Sci 2021, 22(16).

Chen T, Chen X, Zhang S, Zhu J, Tang B, Wang A, Dong L, Zhang Z, Yu C, Sun Y, et al. The genome sequence Archive Family: toward Explosive Data Growth and Diverse Data types. Genomics Proteom Bioinf. 2021;19(4):578–83.

Members C-N, Partners. Database resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res. 2022;50(D1):D27–38.

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Acknowledgements

The authors would like to thank all the participants for their participation in this clinical trial.

This study was supported by the Science and Technology Foundation of Guizhou Provincial Health Commission (gzwkj2022-137).

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YHZ conceived the study and designed the experiments. TTW and RYH recruited subjects and collected specimens. XLC performed experiments and analyzed the data. TTW wrote the manuscript. YHZ revised the manuscript. All authors read and approved the final manuscript.

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Zhou, Y., Chen, X., Wang, T. et al. Exploring the effects of short-course antibiotics on children’s gut microbiota by using 16S rRNA gene sequencing: a case-control study. BMC Pediatr 24 , 562 (2024). https://doi.org/10.1186/s12887-024-05042-0

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DOI : https://doi.org/10.1186/s12887-024-05042-0

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