case study control test

Study Design 101: Case Control Study

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

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

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

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

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

Disadvantages

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

Design pitfalls to look out for

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

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

Fictitious Example

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

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

Real-life Examples

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

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

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

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

Related Formulas

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

Related Terms

A patient with the disease or outcome of interest.

Confounding

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

A patient who does not have the disease or outcome.

Matched Design

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

Observed Assignment

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

Unmatched Design

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

Now test yourself!

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

a) True b) False

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

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

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

Advantages of case-control studies

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

Disadvantages of case-control studies

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

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

Sources in this article

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

George, T. (2023, June 22). What Is a Case-Control Study? | Definition & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/methodology/case-control-study/

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

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Case-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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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.

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

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

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

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

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

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

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

Disadvantages and Limitations

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

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

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

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

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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
• Issues of Concern
• Clinical Significance
• Enhancing Healthcare Team Outcomes
• Review Questions

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Analysis of matched case-control studies

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• Peer review
• Neil Pearce , professor 1 2
• 1 Department of Medical Statistics and Centre for Global NCDs, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
• 2 Centre for Public Health Research, Massey University, Wellington, New Zealand
• neil.pearce{at}lshtm.ac.uk
• Accepted 30 December 2015

There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even when it did not exist in the source population. Thus, a matched design may require controlling for the matching factors in the analysis. However, it is not the case that a matched design requires a matched analysis. Provided that there are no problems of sparse data, control for the matching factors can be obtained, with no loss of validity and a possible increase in precision, using a “standard” (unconditional) analysis, and a “matched” (conditional) analysis may not be required or appropriate.

Summary points

Matching in a case-control study does not control for confounding by the matching factors

A matched design may require controlling for the matching factors in the analysis

However, it is not the case that a matched design requires a matched analysis

A “standard” (unconditional) analysis may be most valid and appropriate, and a “matched” (conditional) analysis may not be required or appropriate

Matching on factors such as age and sex is commonly used in case-control studies. 1 This can be done for convenience (eg, choosing a control admitted to hospital on the same day as the case), to improve study efficiency by improving precision (under certain conditions) when controlling for the matching factors (eg, age, sex) in the analysis, or to enable control in the analysis of unquantifiable factors such as neighbourhood characteristics (eg, by choosing neighbours as controls and then controlling for neighbourhood in the analysis). The increase in efficiency occurs because it ensures similar numbers of cases and controls in confounder strata. For example, in a study of lung cancer, if controls are sampled at random from the source population, their age distribution will be much younger than that of the lung cancer cases. Thus, when age is controlled in the analysis, the young age stratum may contain mostly controls and few cases, whereas the old age stratum may contain mostly cases and fewer controls. Thus, statistical precision may be improved if controls are age matched to ensure roughly equal numbers of cases and controls in each age stratum.

There are two common misconceptions about case-control studies: that matching in itself eliminates confounding by the matching factors; and that if matching has been performed, then a “matched analysis” is required.

Matching in the design does not control for confounding by the matching factors. In fact, it can introduce confounding by the matching factors even when it did not exist in the source population. 1 The reasons for this are complex and will only be discussed briefly here. In essence, the matching process makes the controls more similar to the cases not only for the matching factor but also for the exposure itself. This introduces a bias that needs to be controlled in the analysis. For example, suppose we were conducting a case-control study of poverty and death (from any cause), and we chose siblings as controls (that is, for each person who died, we matched on family or residence by choosing a sibling who was still alive as a control). In this situation, since poverty runs in families we would tend to select a disadvantaged control for each disadvantaged person who had died and a wealthy control for each wealthy person who had died. We would find roughly equal percentages of disadvantaged people among the cases and controls, and we would find little association between poverty and mortality. The matching has introduced a bias, which fortunately (as we will illustrate) can be controlled by controlling for the matching factor in the analysis.

Thus, a matched design will (almost always) require controlling for the matching factors in the analysis. However, this does not necessarily mean that a matched analysis is required or appropriate, and it will often be sufficient to control for the matching factors using simpler methods. Although this is well recognised in both recent 2 3 and historical 4 5 texts, other texts 6 7 8 9 do not discuss this issue and present the matched analysis as the only option for analysing matched case-control studies. In fact, the more standard analysis may not only be valid but may be much easier in practice, and yield better statistical precision.

In this paper I explore and illustrate these problems using a hypothetical pair matched case-control study.

Options for analysing case-control studies

Unmatched case-control studies are typically analysed using the Mantel-Haenszel method 10 or unconditional logistic regression. 4 The former involves the familiar method of producing a 2×2 (exposure-disease) stratum for each level of the confounder (eg, if there are five age groups and two sex groups, then there will be 10 2×2 tables, each showing the association between exposure and disease within a particular stratum), and then producing a summary (average) effect across the strata. The Mantel-Haenszel estimates are robust and not affected by small numbers in specific strata (provided that the overall numbers of exposed or non-exposed cases or controls are adequate), although it can be difficult or impossible to control for factors other than the matching factors if some strata involve small numbers (eg, just one case and one control). Furthermore, the Mantel-Haenszel approach works well when there are only a few confounder strata, but will experience problems of small numbers (eg, strata with only cases and no controls) if there are too many confounders to adjust for. In this situation, logistic regression may be preferred, since this uses maximum likelihood methods, which enable the adjustment (given certain assumptions) of more confounders.

Suppose that for each case we have chosen a control who is in the same five year age group (eg, if the case is aged 47 years, then a control is chosen who is aged 45-49 years). We can then perform a standard analysis, which adjusts for the matching factor (age group) by grouping all cases and controls into five year age groups and using unconditional logistic regression 4 (or the Mantel-Haenszel method 10 ); if there are eight age groups then this analysis will just have eight strata (represented by seven age group dummy variables), each with multiple cases and controls. Alternatively we can perform a matched analysis (that is, retaining the pair matching of one control for each case) using conditional logistic regression (or the matched data methods, which are equivalent to the Mantel-Haenszel method); if there are 100 case-control pairs, this analysis will then have 100 strata.

The main reason for using conditional (rather than unconditional) logistic regression is that when the analysis strata are very small (eg, with just one case and one control for each stratum), problems of sparse data will occur with unconditional methods. 11 For example, if there are 100 strata, this requires 99 dummy variables to represent them, even though there are only 200 study participants. In this extreme situation, unconditional logistic regression is biased and produces an odds ratio estimate that is the square of the conditional (true) estimate of the odds ratio. 5 12

Example of age matching

Table 1 ⇓ gives an example of age matching in a population based case-control study, and shows the “true’ findings for the total population, the findings for the corresponding unmatched case-control study, and the findings for an age matched case-control study using the standard analysis. Table 2 ⇓ presents the findings for the same age matched case-control study using the matched analysis. All analyses were performed using the Mantel-Haenszel method, but this yields similar results to the corresponding (unconditional or conditional) logistic regression analyses.

Hypothetical study population and case-control study with unmatched and matched standard analyses

• View inline

Hypothetical matched case-control study with matched analysis

Table 1 ⇑ shows that the crude odds ratio in the total population is 0.86 (0.70 to 1.05), but this changes to 2.00 (1.59 to 2.51) when the analysis is adjusted for age (using the Mantel-Haenszel method). This occurs because there is strong confounding by age—the cases are mostly old, and old people have a lower exposure than young people. Overall, there are 390 cases, and when 390 controls are selected at random from the non-cases in the total population (which is half exposed and half not exposed), this yields the same crude (0.86) and adjusted (2.00) odds ratios, but with wider confidence intervals, reflecting the smaller numbers of non-cases (controls) in the case-control study.

Why matching factors need to be controlled in the analysis

Now suppose that we reconduct the case-control study, matching for age, using two very broad age groups: old and young (table 1 ⇑ ). The number of cases and controls in each age group are now equal. However, the crude odds ratio (1.68, 1.25 to 2.24) is different from both the crude (0.86) and the adjusted (2.00) odds ratios in the total population. In contrast, the adjusted odds ratio (2.00) is the same as that in the total population and in the unmatched case-control study (both of these adjusted odds ratios were estimated using the standard approach). Thus, matching has not removed age confounding and it is still necessary to control for age (this occurs because the matching process in a case-control study changes the association between the matching factor and the outcome and can create an association even if there were none before the matching was conducted). However, there is a small increase in precision in the matched case-control study compared with the unmatched case-control studies (95% confidence intervals of 1.42 to 2.81 compared with 1.38 to 2.89) because there are now equal numbers of cases and controls in each age group (table 1 ⇑ ).

A pair matched study does not necessarily require a pair matched analysis

However, control for simple matching factors such as age does not require a pair matched analysis. Table 2 ⇑ gives the findings that would have been obtained from a pair matched analysis (this is created by assuming that in each age group, and for each case, the control was selected at random from all non-cases in the same age group). The standard adjusted (Mantel-Haenszel) analysis (table 1 ⇑ ) yields an odds ratio of 2.00 (95% confidence interval 1.42 to 2.81); the matched analysis (table 2 ⇑ ) yields the same odds ratio (2.00) but with a slightly wider confidence interval (1.40 to 2.89).

Advantages of the standard analysis

So for many matched case-control studies, we have a choice of doing a standard analysis or a matched analysis. In this situation, there are several possible advantages of using the standard approach.

The standard analysis can actually yield slightly better statistical precision. 13 This may apply, for example, if two or more cases and their matched controls all have identical values for their matching factors; then combining them into a single stratum produces an estimator with lower variance and no less validity 14 (as indicated by the slightly narrower confidence interval for the standard adjusted analysis (table 1 ⇑ ) compared with the pair matched analysis (table 2 ⇑ ). This particularly occurs because combining strata with identical values for the matching factors (eg, if two case-control pairs all concern women aged 55-59 years) may mean that fewer data are discarded (that is, do not contribute to the analysis) because of strata where the case and control have the same exposure status. Further gains in precision may be obtained if combining strata means that cases with no corresponding control (or controls without a corresponding case) can be included in the analysis. When such strata are combined, a conditional analysis may still be required if the resulting strata are still “small,” 13 but an unconditional analysis will be valid and yield similar findings if the resulting strata are sufficiently large. This may often be the case when matching has only been performed on standard factors such as sex and age group.

The standard analysis may also enhance the clarity of the presentation, particularly when analysing subgroups of cases and controls selected for variables on which they were not matched, since it involves standard 2×2 tables for each subgroup. 15

A further advantage of the standard analysis is that it makes it easier to combine different datasets that have involved matching on different factors (eg, if some have matched for age, some for age and sex, and some for nothing, then all can be combined in an analysis adjusting for age, sex, and study centre). In contrast, one multicentre study 16 (of which I happened to be a coauthor) attempted to (unnecessarily) perform a matched analysis across centres. Because not all centres had used pair matching, this involved retrospective pair matching in those centres that had not matched as part of the study design. This resulted in the unnecessary discarding of the unmatched controls, thus resulting in a likely loss of precision.

Conclusions

If matching is carried out on a particular factor such as age in a case-control study, then controlling for it in the analysis must be considered. This control should involve just as much precision as was used in the original matching 14 (eg, if exact age in years was used in the matching, then exact age in years should be controlled for in the analysis), although in practice such rigorous precision may not always be required (eg, five year age groups may suffice to control confounding by age, even if age matching was done more precisely than this). In some circumstances, this control may make no difference to the main exposure effect estimate—eg, if the matching factor is unrelated to exposure. However, if there is an association between the matching factor and the exposure, then matching will introduce confounding that needs to be controlled for in the analysis.

So when is a pair matched analysis required? The answer is, when the matching was genuinely at (or close to) the individual level. For example, if siblings have been chosen as controls, then each stratum would have just one case and the sibling control; in this situation, an unconditional logistic regression analysis would suffer from problems of sparse data, and conditional logistic regression would be required. Similar situations might arise if controls were neighbours or from the same general practice (if each general practice only had one or a few cases), or if matching was performed on many factors simultaneously so that most strata (in the standard analysis) had just one case and one control.

Provided, however, that there are no problems of sparse data, such control for the matching factors can be obtained using an unconditional analysis, with no loss of validity and a possible increase in precision.

Thus, a matched design will (nearly always) require controlling for the matching factors in the analysis. It is not the case, however, that a matched design requires a matched analysis.

I thank Simon Cousens, Deborah Lawlor, Lorenzo Richiardi, and Jan Vandenbroucke for their comments on the draft manuscript. The Centre for Global NCDs is supported by the Wellcome Trust Institutional Strategic Support Fund, 097834/Z/11/B.

Competing interests: I have read and understood the BMJ policy on declaration of interests and declare the following: none.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/ .

• ↵ Rothman KJ, Greenland S, Lash TL, eds Design strategies to improve study accuracy. Modern epidemiology. 3rd ed . Lippincott Williams & Wilkins,  2008 .
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• ↵ Breslow NE, Day NE. Statistical methods in cancer research. Vol I: the analysis of case-control studies. IARC,  1980 .
• ↵ Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiologic research: principles and quantitative methods. Lifetime Learning Publications,  1982 .
• ↵ Dos Santos Silva I. Cancer epidemiology: principles and methods. IARC,  1999 .
• ↵ Keogh RH, Cox DR. Case-control studies. Cambridge University Press,  2014 doi:10.1017/CBO9781139094757 . .
• ↵ Lilienfeld DE, Stolley PD. Foundations of epidemiology. 3rd ed . Oxford University Press,  1994 .
• ↵ MacMahon B, Trichopolous D. Epidemiology: principles and methods. 2nd ed . Little Brown,  1996 .
• ↵ Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst  1959 ; 22 :719- 48 . 13655060 .
• ↵ Robins J, Greenland S, Breslow NE. A general estimator for the variance of the Mantel-Haenszel odds ratio. Am J Epidemiol  1986 ; 124 :719- 23 . 3766505 .
• ↵ Pike MC, Hill AP, Smith PG. Bias and efficiency in logistic analyses of stratified case-control studies. Int J Epidemiol  1980 ; 9 :89- 95 . doi:10.1093/ije/9.1.89 .  7419334 .
• ↵ Brookmeyer R, Liang KY, Linet M. Matched case-control designs and overmatched analyses. Am J Epidemiol  1986 ; 124 :693- 701 . 3752063 .
• ↵ Greenland S. Applications of stratified analysis methods. In: Rothman KJ, Greenland S, Lash TL, eds. Modern epidemiology. 3rd ed . Lippincott Williams & Wilkins,  2008 .
• ↵ Vandenbroucke JP, Koster T, Briët E, Reitsma PH, Bertina RM, Rosendaal FR. Increased risk of venous thrombosis in oral-contraceptive users who are carriers of factor V Leiden mutation. Lancet  1994 ; 344 :1453- 7 . doi:10.1016/S0140-6736(94)90286-0 .  7968118 .
• ↵ Cardis E, Richardson L, Deltour I, et al. The INTERPHONE study: design, epidemiological methods, and description of the study population. Eur J Epidemiol  2007 ; 22 :647- 64 . doi:10.1007/s10654-007-9152-z .  17636416 .
• Mansournia MA, Hernán MA, Greenland S. Matched designs and causal diagrams. Int J Epidemiol  2013 ; 42 :860- 9 . doi:10.1093/ije/dyt083 .  23918854 .
• Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology  2004 ; 15 :615- 25 . doi:10.1097/01.ede.0000135174.63482.43 .  15308962 .

EP717 Module 5 - Epidemiologic Study Designs – Part 2:

Case-control studies.

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Defining & Finding Cases and Controls

Case definitions, finding cases, selecting controls, test yourself, sources of controls, population controls, hospital/clinic controls, friend, neighbor, spouse, and relative controls, how many controls are needed.

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

For example, if one were to conduct a case-control study on the association between smoking and heart disease and simply defined the cases as someone who smokes and controls as someone who doesn't smoke raises a lot of questions. Does one or two cigarettes a year make one a smoker? Should someone who used to smoke regularly, but quit be classified as a smoker, a non-smoker, or neither?

 The CDC suggests the following definitions regarding classification of tobacco smoking:

• Current smoker: An adult who has smoked 100 cigarettes in his or her lifetime and who currently smokes cigarettes. Beginning in 1991 this group was divided into everyday smokers or somedays smokers.
• Every day smoker: An adult who has smoked at least 100 cigarettes in his or her lifetime, and who now smokes every day. Previously called a regular smoker
• Somedays smoker: An adult who has smoked at least 100 cigarettes in his or her lifetime, who smokes now, but does not smoke every day. Previously called an occasional smoker
• Former smoker: An adult who has smoked at least 100 cigarettes in his or her lifetime but who had quit smoking at the time of interview.
• Never smoker: An adult who has never smoked, or who has smoked less than 100 cigarettes in his or her lifetime

Another classic example of the importance of a clear case definition is a case-control study trying to determine whether use of a particular drug by pregnant women increases the risk of birth defects in their offspring. Should the investigators define a case as a child with any congenital defect large or small? Different drugs and other exposures have different effects and may influence one organ system but not others. Using an all-encompassing case definition like any congenital defect might lead to an underestimate of an important association or even a failure to recognize the association at all.

Typical sources for cases include:

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

Selection of control subjects hinges on how the cases are selected. The purpose of the controls is to estimate the exposure distribution in the source population, i.e., to estimate the odds of exposure in the overall source population from which the cases came . It is important to remember that these controls are not the unexposed controls in a laboratory experiment. Some of the controls in a case-control study will have the exposure of interest, and what they provide is an estimate of how prevalent the exposure is in the overall source population.

Selection of an appropriate control group is one of the most difficult aspects of conducting a case-control study. There are two key principles that should be followed in selecting controls:

• The comparison group ("controls") should be representative of the source population that produced the cases. The method of selecting and enrolling control subjects should meet the would criterion , i.e., if the controls had experienced the outcome, would they have been identified as cases in this study? If the answer is yes, then the controls are likely to be representative of the source population. If no, there is likely to be selection bias.
• The "controls" must be sampled in a way that is independent of the exposure , meaning that their selection should not be more (or less) likely if they have the exposure of interest.

If either of these principles are not adhered to, selection bias can result. Selection bias will be discussed in detail in the module on bias.

Consider the hypothetical example in the figure below, which summarizes the exposure distribution in diseased and non-diseased people in a sources population and compares it to the exposure distributions in the samples of cases and controls that were selected for a study.

Suppose the investigators were dealing with a rare disease that was present in only 24 people in a source population with 3.6 million non-diseased people. Suppose also that the true exposure distribution in the 24 cases was 17:8, or 2.1 to 1, and the exposure distribution in the non-diseased people in the source population was 421,101:3,178,899, or 0.13 to 1. If so, the true odds ratio in the population would be 2.1/0.13 = 16.15.

Suppose further that the investigators could only identify 12 cases who were willing to participate in the study, and they selected three times as many control subjects. Among the 12 sampled cases the exposure distribution was 8:4, or 2 to 1, and among the 36 sampled controls, the exposure distribution was 5:31, or 0.16. If so, the estimated odds ratio from the samples would be 2.0/0.16 = 12.5. Despite the fact that this was a very small sample, the sampling methodology provided exposure distributions that were similar to those in the entire source population, and these provided an estimated odds ratio that was reasonably close to the true value in this population.

"We identified patients entering Boston City Hospital from July 1976 until February 1978 with a spontaneous abortion at less than 20 weeks' gestation or premature delivery between 20 to 27 weeks' gestation (the case group). We used obstetric patients whose dates of delivery coincided with the cases' dates of spontaneous loss as a comparison group."

Do the controls in this study fulfill the would criterion ?

There are three main sources of control subjects:

• Friends, Neighbors, and Family Controls

A population-based case-control study is one in which the cases come from a precisely defined population, such as a fixed geographic area, and the controls are sampled directly from the same population. In this situation cases might be identified from a state cancer registry, for example, and the comparison group would logically be selected at random from the same source population.

Population controls can be identified from voter registration lists, tax rolls, drivers license lists, and telephone directories or by "random digit dialing" (which has the advantage that it includes unlisted numbers). High response rates are important regardless of the method of invitation to participate, because non-response bias can be introduced if response rates are low and non-responders differ from responders. For example, non-responders of lower socioeconomic status might not respond if they are forced to work multiple low-paying jobs.

If cases are obtained from a medical facility, the controls should be obtained from the same facility provided they meet two criteria:

• Control patients must have diseases that are unrelated to the exposure being studied. For example, for a study examining the association between smoking and lung cancer, it would not be appropriate to include patients with cardiovascular disease or emphysema as controls, since smoking is a risk factor for these conditions. Including patients who are more likely to have the exposures of interest than the source population will result in an underestimate of the true association.
• Control patients should have diseases with similar referral patterns as the cases, in order to minimize selection bias. For example, if the cases are women with cervical cancer who have been referred from all over the state, it would be inappropriate to use controls consisting of women with diabetes who had been referred primarily from local health centers in the immediate vicinity of the hospital. Similarly, it would be inappropriate to use patients from the emergency room, because the selection of a hospital for an emergency is different than for cancer, and this difference might be related to the exposure of interest.

The advantages of using controls who are patients from the same facility are:

• They are easier to identify
• They are more likely to participate than general population controls.
• They minimize selection bias because they generally come from the same source population (provided referral patterns are similar).
• Recall bias (remembering past exposures to a different degree than the cases) would be minimized, because they are sick, but with a different diagnosis.

Occasionally investigators will ask cases to nominate controls who are in one of these three categories because they have similar characteristics, such as genotype, socioeconomic status, or environment, i.e., factors that can cause confounding but are hard to measure and adjust for. By matching cases and controls on these factors, confounding by these factors will be controlled.

 Is this an appropriate control group?

For rare outcomes the number of cases that can be unrolled may be limited, making it difficult to achieve a precise estimate of the odds ratio. Statistical power can be increased somewhat by enrolling more controls than cases. Investigators will sometimes enroll 2, 3, or even 4 times as many controls as cases to increase statistical power, but there is very little advantage in exceeding a 4:1 ratio of controls to cases. Selecting more than four controls for each case usually means a lot more work to collect the additional data without any meaningful increase in statistical power.

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Case-control Study

Usmle guide: case-control study.

Introduction

Case-control studies are an essential research design used in medical and epidemiological research to investigate the relationship between an outcome (disease) and potential risk factors. This USMLE guide aims to provide a comprehensive overview of case-control studies, including their definition, design, advantages, disadvantages, and examples.

A case-control study is an observational study design that starts by identifying individuals with a particular disease or outcome of interest (cases) and compares them to individuals without the disease or outcome (controls). The study then investigates the exposure or risk factors that might be associated with the disease.

Study Design

Selection of Cases : Cases are individuals who have the outcome or disease under investigation. They can be identified from hospitals, medical records, disease registries, or other sources.

Selection of Controls : Controls are individuals without the outcome or disease and should be representative of the population from which the cases arise. Controls can be selected from the general population, hospitals, or through random digit dialing.

Matching : In some case-control studies, matching is used to ensure cases and controls are similar in certain characteristics (e.g., age, sex, socioeconomic status) to minimize confounding variables.

Data Collection : Information on exposure or risk factors is collected retrospectively from cases and controls using interviews, questionnaires, or medical records. It is crucial to ensure both groups are asked the same questions and using the same methods.

Data Analysis : Statistical analysis is performed to determine the association between the exposure/risk factor and the disease/outcome. Commonly used statistical measures include odds ratios (OR) and confidence intervals (CI).

Advantages of Case-Control Studies

Efficient : Case-control studies are useful when studying rare diseases or outcomes, as it allows for a more efficient use of resources compared to cohort studies.

Cost-effective : Case-control studies are generally less expensive and time-consuming than other study designs, such as cohort studies.

Suitable for studying rare exposures : Case-control studies are ideal for investigating potential risk factors that are rare in the general population.

Retroactive data collection : Since cases have already developed the disease, data collection can be done retrospectively, reducing the potential for bias related to follow-up.

Disadvantages of Case-Control Studies

Selection bias : There is a potential for selection bias if cases and controls are not representative of the population from which they arise.

Recall bias : Since data collection is retrospective, participants may have difficulty recalling past exposures accurately, leading to recall bias.

Limited causal inference : While case-control studies can establish an association between an exposure and an outcome, they cannot determine causality.

Research Question : Is exposure to secondhand smoke associated with the development of lung cancer?

Selection of Cases : Identify individuals diagnosed with lung cancer from hospital records.

Selection of Controls : Select individuals without lung cancer from the general population, matched for age and gender.

Data Collection : Interview cases and controls about their exposure to secondhand smoke during different periods of their lives.

Data Analysis : Calculate the odds ratio (OR) to determine the association between exposure to secondhand smoke and the development of lung cancer.

Case-control studies are a valuable research design for investigating the association between exposures and outcomes. Understanding their design, advantages, and limitations is crucial for medical professionals and researchers to critically evaluate and conduct epidemiological studies.

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A Case-Control Study to Identify Associations Between Modifiable Preconception Care Indicators and Pregnancy Outcomes

Original research, ashwini kamath mulki, md, mbbs, mph; melanie b. johnson, mpa; nicole m. burgess, bs; kyle shaak, mph; katie nisbett, pharmd; katarzyna jabbour, pharmd, bcps; roya hamadani, mph; beth careyva, md.

Corresponding Author:  Ashwini Kamath Mulki, MD, MBBS, MPH, VHP; Family Health Center Email:  [email protected] DOI:  10.3122/jabfm.2024.240133R1 Keywords:  Case-Control Studies, Counseling, Pennsylvania, Preconception Care, Pregnancy, Pregnancy Outcome, Preterm Birth, Primary Health Care, Retrospective Studies Dates:  Submitted: 03-26-2024; Revised: 08-09-2024; Accepted: 08-19-2024 Status:  In production for ahead of print. 

PURPOSE : This study explored gaps and opportunities in preconception care with a focus on determining whether modifiable preconception care indicators are associated with preterm births.

METHODS : This retrospective case-control study explored pre-pregnancy data of patients ≥18 years old who delivered preterm (cases) versus full term (controls) between June 1, 2018, and May 31, 2019, at a health care network in Pennsylvania. Cases were matched 1:2 with controls based on age, parity, and history of preterm delivery. A literature review yielded 11 key indicators of quality preconception care. Documentation of counseling on these indicators were extracted from patient charts from their most recent primary care visit prior to pregnancy (preconception care) and their pregnancy intake visit (prenatal care). Bivariate analyses were used to assess whether any of the 11 preconception indicators were associated with preterm birth. All analyses were conducted utilizing SPSS statistical software.

RESULTS : Our sample included 663 patient charts: 221 preterm births and 442 term births. Elevated blood pressure (>120/80) in the preconception period (Odds Ratio [OR] = 1.84) and at the prenatal intake visit (OR = 1.68) was significantly associated with preterm birth. In addition, patients with BMI ≤18 or ≥30 at their prenatal visit were nearly twice as likely (OR = 1.85) to have pregnancies resulting in preterm birth.

CONCLUSIONS : Our study highlights BMI and BP as key focus points for preconception counseling. Additional studies are needed to determine whether pregnancy outcomes other than preterm birth may be influenced by these and other preconception care indicators. 

ABSTRACTS IN PRESS

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• v.16(4); Oct-Dec 2013

Design and data analysis case-controlled study in clinical research

Sanjeev v. thomas.

Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India

Karthik Suresh

1 Department of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Louiseville, USA

Geetha Suresh

2 Department of Justice Administration, University of Louisville, Louiseville, USA

Clinicians during their training period and practice are often called upon to conduct studies to explore the association between certain exposures and disease states or interventions and outcomes. More often they need to interpret the results of research data published in the medical literature. Case-control studies are one of the most frequently used study designs for these purposes. This paper explains basic features of case control studies, rationality behind applying case control design with appropriate examples and limitations of this design. Analysis of sensitivity and specificity along with template to calculate various ratios are explained with user friendly tables and calculations in this article. The interpretation of some of the laboratory results requires sound knowledge of the various risk ratios and positive or negative predictive values for correct identification for unbiased analysis. A major advantage of case-control study is that they are small and retrospective and so they are economical than cohort studies and randomized controlled trials.

Introduction

Clinicians think of case-control study when they want to ascertain association between one clinical condition and an exposure or when a researcher wants to compare patients with disease exposed to the risk factors to non-exposed control group. In other words, case-control study compares subjects who have disease or outcome (cases) with subjects who do not have the disease or outcome (controls). Historically, case control studies came into fashion in the early 20 th century, when great interest arose in the role of environmental factors (such as pipe smoke) in the pathogenesis of disease. In the 1950s, case control studies were used to link cigarette smoke and lung cancer. Case-control studies look back in time to compare “what happened” in each group to determine the relationship between the risk factor and disease. The case-control study has important advantages, including cost and ease of deployment. However, it is important to note that a positive relationship between exposure and disease does not imply causality.

At the center of the case-control study is a collection of cases. [ Figure 1 ] This explains why this type of study is often used to study rare diseases, where the prevalence of the disease may not be high enough to permit for a cohort study. A cohort study identifies patients with and without an exposure and then “looks forward” to see whether or not greater numbers of patients with an exposure develop disease.

Comparison of cohort and case control studies

For instance, Yang et al . studied antiepileptic drug (AED) associated rashes in Asians in a case-control study.[ 1 ] They collected cases of confirmed anti-epileptic induced severe cutaneous reactions (such as Stevens Johnson syndrome) and then, using appropriate controls, analyzed various exposures (including type of [AED] used) to look for risk factors to developing AED induced skin disease.

Choosing controls is very important aspect of case-control study design. The investigator must weigh the need for the controls to be relevant against the tendency to over match controls such that potential differences may become muted. In general, one may consider three populations: Cases, the relevant control population and the population at large. For the study above, the cases include patients with AED skin disease. In this case, the relevant control population is a group of Asian patients without skin disease. It is important for controls to be relevant: In the anti-epileptic study, it would not be appropriate to choose a population across ethnicities since one of the premises of the paper revolves around particularly susceptibility to AED drug rashes in Asian populations.

One popular method of choosing controls is to choose patients from a geographic population at large. In studying the relationship between non-steroidal anti-inflammatory drugs and Parkinson's disease (PD), Wahner et al . chose a control population from several rural California counties.[ 2 ] There are other methods of choosing controls (using patients without disease admitted to the hospital during the time of study, neighbors of disease positive cases, using mail routes to identify disease negative cases). However, one must be careful not to introduce bias into control selection. For instance, a study that enrolls cases from a clinic population should not use a hospital population as control. Studies looking at geography specific population (e.g., Neurocysticercosis in India) cannot use controls from large studies done in other populations (registries of patients from countries where disease prevalence may be drastically different than in India). In general, geographic clustering is probably the easiest way to choose controls for case-control studies.

Two popular ways of choosing controls include hospitalized patients and patients from the general population. Choosing hospitalized, disease negative patients offers several advantages, including good rates of response (patients admitted to the hospital are generally already being examined and evaluated and often tend to be available to further questioning for a study, compared with the general population, where rates of response may be much lower) and possibly less amnestic bias (patients who are already in the hospital are, by default, being asked to remember details of their presenting illnesses and as such, may more reliably remember details of exposures). However, using hospitalized patients has one large disadvantage; these patients have higher severity of disease since they required hospitalization in the first place. In addition, patients may be hospitalized for disease processes that may share features with diseases under study, thus confounding results.

Using a general population offers the advantage of being a true control group, random in its choosing and without any common features that may confound associations. However, disadvantages include poor response rates and biasing based on geography. Administering long histories and questions regarding exposures are often hard to accomplish in the general population due to the number of people willing (or rather, not willing) to undergo testing. In addition, choosing cases from the general population from particular geographic areas may bias the population toward certain characteristics (such as a socio-economic status) of that geographic population. Consider a study that uses cases from a referral clinic population that draws patients from across socio-economic strata. Using a control group selected from a population from a very affluent or very impoverished area may be problematic unless the socio-economic status is included in the final analysis.

In case-controls studies, cases are usually available before controls. When studying specific diseases, cases are often collected from specialty clinics that see large numbers of patients with a specific disease. Consider for example, the study by Garwood et al .[ 3 ] which looked at patients with established PD and looked for associations between prior amphetamine use and subsequent development various neurologic disorders. Patients in this study were chosen from specialty clinics that see large numbers of patients with certain neurologic disorders. Case definitions are very important when planning to choose cases. For instance, in a hypothetical study aiming to study cases of peripheral neuropathy, will all patients who carry a diagnosis of peripheral neuropathy be included? Or, will only patients with definite electromyography evidence of neuropathy be included? If a disease process with known histopathology is being studied, will tissue diagnosis be required for all cases? More stringent case definitions that require multiple pieces of data to be present may limit the number of cases that can be used in the study. Less stringent criteria (for instance, counting all patients with the diagnosis of “peripheral neuropathy” listed in the chart) may inadvertently choose a group of cases that are too heterogeneous.

The disease history status of the chosen cases must also be decided. Will the cases being chosen have newly diagnosed disease, or will cases of ongoing/longstanding disease also be included? Will decedent cases be included? This is important when looking at exposures in the following fashion: Consider exposure X that is associated with disease Y. Suppose that exposure X negatively affects disease Y such that patients that are X + have more severe disease. Now, a case-control study that used only patients with long-standing or ongoing disease might miss a potential association between X and Y because X + patients, due to their more aggressive course of disease, are no longer alive and therefore were not included in the analysis. If this particular confounding effect is of concern, it can be circumvented by using incident cases only.

Selection bias occurs when the exposure of interest results in more careful screening of a population, thus mimicking an association. The classic example of this phenomenon was noted in the 70s, when certain studies noted a relationship between estrogen use and endometrial cancer. However, on close analysis, it was noted that patients who used estrogen were more likely to experience vaginal bleeding, which in turn is often a cause for close examination by physicians to rule out endometrial cancer. This is often seen with certain drug exposures as well. A drug may produce various symptoms, which lead to closer physician evaluation, thus leading to more disease positive cases. Thus, when analyzed in a retrospective fashion, more of the cases may have a particular exposure only insofar as that particular exposure led to evaluations that resulted in a diagnosis, but without any direct association or causality between the exposure and disease.

One advantage of case-control studies is the ability to study multiple exposures and other risk factors within one study. In addition, the “exposure” being studied can be biochemical in nature. Consider the study, which looked at a genetic variant of a kinase enzyme as a risk factor for development of Alzheimer's disease.[ 4 ] Compare this with the study mentioned earlier by Garwood et al .,[ 3 ] where exposure data was collected by surveys and questionnaires. In this study, the authors drew blood work on cases and controls in order to assess their polymorphism status. Indeed, more than one exposure can be assessed in the same study and with planning, a researcher may look at several variables, including biochemical ones, in single case-control study.

Matching is one of three ways (along with exclusion and statistical adjustment) to adjust for differences. Matching attempts to make sure that the control group is sufficiently similar to the cases group, with respects to variables such as age, sex, etc., Cases and controls should not be matched on variables that will be analyzed for possible associations to disease. Not only should exposure variables not be included, but neither should variables that are closely related to these variables. Lastly, overmatching should be avoided. If the control group is too similar to the cases group, the study may fail to detect the difference even if one exists. In addition, adding matching categories increases expense of the study.

One measure of association derived from case control studies are sensitivity and specificity ratios. These measures are important to a researcher, to understand the correct classification. A good understanding of sensitivity and specificity is essential to understand receiver operating characteristic curve and in distinguishing correct classification of positive exposure and disease with negative exposure and no disease. Table 1 explains a hypothetical example and method of calculation of specificity and sensitivity analysis.

Hypothetical example of sensitivity, specificity and predictive values

Interpretation of sensitivity, specificity and predictive values

Sensitivity and specificity are statistical measures of the performance of a two by two classification of cases and controls (sick or healthy) against positives and negatives (exposed or non-exposed).[ 5 ] Sensitivity measures or identifies the proportion of actual positives identified as the percentage of sick people who are correctly identified as sick. Specificity measures or identifies the proportion of negatives identified as the percentage of healthy people who are correctly identified as healthy. Theoretically, optimum prediction aims at 100% sensitivity and specificity with a minimum of margin of error. Table 1 also shows false positive rate, which is referred to as Type I error commonly stated as α “Alpha” is calculated using the following formula: 100 − specificity, which is equal to 100 − 90.80 = 9.20% for Table 1 example. Type 1 error is also known as false positive error is referred to as a false alarm, indicates that a condition is present when it is actually not present. In the above mentioned example, a false positive error indicates the percent falsely identified healthy as sick. The reason why we want Type 1 error to be as minimum as possible is because healthy should not get treatment.

The false negative rate, which is referred to as Type II error commonly stated as β “Beta” is calculated using the following formula: 100 − sensitivity which is equal to 100 − 73.30 = 26.70% for Table 1 example. Type II error is also known as false negative error indicates that a condition is not present when it should have been present. In the above mentioned example, a false negative error indicates percent falsely identified sick as healthy. A Type 1 error unnecessarily treats a healthy, which in turn increases the budget and Type II error would risk the sick, which would act against study objectives. A researcher wants to minimize both errors, which not a simple issue because an effort to decrease one type of error increases the other type of error. The only way to minimize both type of error statistically is by increasing sample size, which may be difficult sometimes not feasible or expensive. If the sample size is too low it lacks precision and it is too large, time and resources will be wasted. Hence, the question is what should be the sample size so that the study has the power to generalize the result obtained from the study. The researcher has to decide whether, the study has enough power to make a judgment of the population from their sample. The researcher has to decide this issue in the process of designing an experiment, how large a sample is needed to enable reliable judgment.

Statistical power is same as sensitivity (73.30%). In this example, large number of false positives and few false negatives indicate the test conducted alone is not the best test to confirm the disease. Higher statistical power increase statistical significance by reducing Type 1 error which increases confidence interval. In other words, larger the power more accurately the study can mirror the behavior of the study population.

The positive predictive values (PPV) or the precision rate is referred to as the proportion of positive test results, which means correct diagnoses. If the test correctly identifies all positive conditions then the PPV would be 100% and negative predictive value (NPV) would be 0. The calculative PPV in Table 1 is 11.8%, which is not large enough to predict cases with test conducted alone. However, the NPV 99.9% indicates the test correctly identifies negative conditions.

Clinical interpretation of a test

In a sample, there are two groups those who have the disease and those who do not have the disease. A test designed to detect that disease can have two results a positive result that states that the disease is present and a negative result that states that the disease is absent. In an ideal situation, we would want the test to be positive for all persons who have the disease and test to be negative for all persons who do not have the disease. Unfortunately, reality is often far from ideal. The clinician who had ordered the test has the result as positive or negative. What conclusion can he or she make about the disease status for his patient? The first step would be to examine the reliability of the test in statistical terms. (1) What is the sensitivity of the test? (2) What is the specificity of the test? The second step is to examine it applicability to his patient. (3) What is the PPV of the test? (4) What is the NPV of the test?

Suppose the test result had come as positive. In this example the test has a sensitivity of 73.3% and specificity of 90.8%. This test is capable of detecting the disease status in 73% of cases only. It has a false positivity of 9.2%. The PPV of the test is 11.8%. In other words, there is a good possibility that the test result is false positive and the person does not have the disease. We need to look at other test results and the clinical situation. Suppose the PPV of this test was close to 80 or 90%, one could conclude that most likely the person has the disease state if the test result is positive.

Suppose the test result had come as negative. The NPV of this test is 99.9%, which means this test gave a negative result in a patient with the disease only very rarely. Hence, there is only 0.1% possibility that the person who tested negative has in fact the disease. Probably no further tests are required unless the clinical suspicion is very high.

It is very important how the clinician interprets the result of a test. The usefulness of a positive result or negative result depends upon the PPV or NPV of the test respectively. A screening test should have high sensitivity and high PPV. A confirmatory test should have high specificity and high NPV.

Case control method is most efficient, for the study of rare diseases and most common diseases. Other measures of association from case control studies are calculation of odds ratio (OR) and risk ratio which is presented in Table 2 .

Different ratio calculation templates with sample calculation

Absolute risk means the probability of an event occurring and are not compared with any other type of risk. Absolute risk is expressed as a ratio or percent. In the example, absolute risk reduction indicates 27.37% decline in risk. Relative risk (RR) on the other hand compares the risk among exposed and non-exposed. In the example provided in Table 2 , the non-exposed control group is 69.93% less likely compared to exposed cases. Reader should keep in mind that RR does not mean increase in risk. This means that while a 100% likely risk among those exposed cases, unexposed control is less likely by 69.93%. RR does not explain actual risk but is expressed as relative increase or decrease in risk of exposed compared to non-exposed.

OR help the researcher to conclude whether the odds of a certain event or outcome are same for two groups. It calculates the odds of a health outcome when exposed compared to non-exposed. In our example an OR of. 207 can be interpreted as the non-exposed group is less likely to experience the event compared to the exposed group. If the OR is greater than 1 (example 1.11) means that the exposed are 1.11 times more likely to be riskier than the non-exposed.

Event rate for cases (E) and controls (C) in biostatistics explains how event ratio is a measure of how often a particular statistical exposure results in occurrence of disease within the experimental group (cases) of an experiment. This value in our example is 11.76%. This value or percent explains the extent of risk to patients exposed, compared with the non-exposed.

The statistical tests that can be used for ascertain an association depends upon the variable characteristics also. If the researcher wants to find the association between two categorical variables (e.g., a positive versus negative test result and disease state expressed as present or absent), Cochran-Armitage test, which is same as Pearson Chi-squared test can be used. When the objective is to find the association between two interval or ratio level (continuous) variables, correlation and regression analysis can be performed. In order to evaluate statistical significant difference between the means of cases and control, a test of group difference can be performed. If the researcher wants to find statically significant difference among means of more than two groups, analysis of variance can be performed. A detailed explanation and how to calculate various statistical tests will be published in later issues. The success of the research directly and indirectly depends on how the following biases or systematic errors, are controlled.

When selecting cases and controls, based on exposed or not-exposed factors, the ability of subjects to recall information on exposure is collected retrospectively and often forms the basis for recall bias. Recall bias is a methodological issue. Problems of recall method are: Limitations in human ability to recall and cases may remember their exposure with more accuracy than the controls. Other possible bias is the selection bias. In case-control studies, the cases and controls are selected from the same inherited characteristics. For instance, cases collected from referral clinics often exposed to selection bias cases. If selection bias is not controlled, the findings of association, most likely may be due to of chance resulting from the study design. Another possible bias is information bias, which arises because of misclassification of the level of exposure or misclassification of disease or other symptoms of outcome itself.

Case control studies are good for studying rare diseases, but they are not generally used to study rare exposures. As Kaelin and Bayona explains[ 6 ] if a researcher want to study the risk of asthma from working in a nuclear submarine shipyard, a case control study may not be a best option because a very small proportion of people with asthma might be exposed. Similarly, case-control studies cannot be the best option to study multiple diseases or conditions because the selection of the control group may not be comparable for multiple disease or conditions selected. The major advantage of case-control study is that they are small and retrospective and so they are economical than cohort studies and randomized controlled trials.

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Home / Resources / ISACA Journal / Issues / 2021 / Volume 5 / A Technology Control Testing Automation Case Study

A technology control testing automation case study.

Many organizations, particularly those highly regulated by government entities, such as banking and insurance, employ a Three Lines of Defense model for risk management. In this model, originally conceived by The Institute of Internal Auditors (The IIA) for the financial services industry to better manage risk, the first line is operational management, the second line is risk monitoring and oversight, and the third line is audit. 1 Although different organizations may delineate the roles for each line differently, the essential construct is widely applied through financial institutions and technology domains such as cybersecurity. Typically, the model is embedded within the organization’s documented enterprise risk management framework, which governs the risk management approach. The framework itself is required by regulators, such as the US Office of the Comptroller of the Currency (OCC) for financial institutions within the United States, and recommended by international standards, such as International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 27001. 2 In the case of information security within US financial institutions, the applicable standard is 12 CFR Appendix B to Part 30 Interagency Guidelines of Establishing Information Security Standards, which states that financial institutions must have a comprehensive written security program to ensure security and confidentiality of customer information. 3 More explicitly, each institution must have procedures to manage and control risk. Correspondingly, the Sarbanes-Oxley Act of 2002 (SOX) Section 404 requires organizations to assess internal controls related to their financial systems. 4

To achieve these and other regulatory requirements, organizations have introduced risk assessment and control testing processes to ensure that adequate controls exist in line with their established risk management framework. Most have adopted industry-standard control frameworks, such as the US National Institute of Standards and Technology (NIST) Risk Management Framework (RMF), 5 the Risk Management Association’s Enterprise Risk Management Framework, 6 COBIT ® 2019 7 or ISO 27001. 8

A typical risk and control process (e.g., NIST RMF) provides guidance for several steps to categorize, identify, implement, assess and monitor controls. To accomplish the “assess” portion of the process, first line of defense elements within an organization evaluate or test the control to see if it is achieving the intended objective. The assess function is arguably the most critical element of the process. If the system is tested properly, it will be fundamentally perly, it will be fundamentally secure. 9

Control Testing Overview

Technology control testing involves four basic steps: gather evidence, analyze the evidence, assess effectiveness and substantiate the results. The first three steps can range in time and complexity; well-designed and automated controls can quickly be tested, while those more complex and manual in nature may require more time and resources to thoroughly evaluate. The risk framework also dictates when controls will be tested—usually annually, but sometimes quarterly. Typically, organizations use a binary effective or ineffective rating in their assessment, though some may have a three or five scale option.

Control testing has three fundamental challenges:

• Time and investment cost —Meeting the equired standards for control assurance requires people and time, which are cost factors to any organization. In addition, thelarger the control portfolio, the more people required to conduct testing.
• Continuous monitoring —Control testing is done on a set frequency (e.g., annually or quarterly), which can be inefficient. Alternatively, a continuous monitoring capability can provide cost reductions through improved efficiency and effectiveness, reduce risk velocity, reduce remediation cost, and reduce impact on second- and third-line validation activities. 10
• Scope coverage —Technology controls are typically implemented over a large scope of systems and processes; control testing frameworks dictate sampling methods to extrapolate overall effectiveness. However, sampling has limitations, including the potential for flawed population selection, nadequate coverage of the true population and inability to attain assurance about risk reduction holistically throughout the technology environment.

The Benefits of Automation

The automation of controls and the testing process can substantially reduce the impact of control testing challenges. Specifically, automation can reduce the manual overhead associated with evidence gathering and sample analysis, provide continuous monitoring capabilities to enable remediation prior to formal testing, and increase scope coverage to 100 percent of systems, eliminating sampling. The degree to which automation provides the maximum benefit depends on:

• Whether the control itself is automated or manual (i.e., control owners manually generate reports on a periodic basis for control testing evidence)
• The degree to which the control life cycle process is manual (i.e., the organization uses a technology system or application to manage the testing life cycle))

The greatest benefit is achieved when controls are automated and the associated systems, including the risk management/control system of record, are in place.

THE AUTOMATION OF CONTROLS AND THE TESTING PROCESS CAN SUBSTANTIALLY REDUCE THE IMPACT OF CONTROL TESTING CHALLENGES.

Case study: technology change management.

A US Midwest regional banking institution has a technology footprint of approximately 300 systems and 250 active technology controls. The organization has a mature risk management framework with an established system of record for managing the risk and control portfolio. In addition, the first line of defense technology risk the organization uses is the ServiceNow governance, risk and compliance (GRC) module to feed RSA Archer. ServiceNow is an international vendor product that provides digital workflows to increase productivity, including technology, customer and employee workflows. 11 RSA Archer is an international vendor product that provides integrated risk management capabilities. 12 The GRC module permits technology-specific control workflow management and other technology specific capabilities (i.e., support for continuous monitoring). The key aspect of this infrastructure is that ServiceNow GRC is also used by the organization for technology service management such as incident management, change management, access provisioning and configuration management. This integration point is significant because ServiceNow GRC has capabilities internal to the integration that support automation without requiring external techniques such as remote process automation (RPA), a generic capability that automates manually intensive processes such as keystroke date entry.

The first line of defense risk team (the risk team aligned with the operational technology teams responsible for the controls) evaluated the COBIT ® - based control inventory to determine which existing controls offered the maximum automation benefit (e.g., reduction in testing life cycle time, maximum coverage and continuous monitoring potential) with minimal cost (e.g., effort to redesign controls, development time and impact to the ecosystem). The team identified the change management controls that optimally met this objective. The change management control domain is referenced in COBIT 2019’s Build, Acquire, and Implement (BAI) processes of BAI105 Manage organizational change enablement, BAI106 Manage changes and BAI107 Manage change acceptance and transitioning. 13 The fundamental objectives of change management controls include ensuring that a defined change process exists for technology systems, ensuring that the changes are appropriately managed and approved to avoid incidents and outages, and ensuring that appropriate governance is in place to effectively manage system changes.

The control inventory analysis revealed the organization’s 15 change management controls (a mix of manual and automated evidence, but primarily system reports) could be redesigned into two controls with four attributes. The 15 controls relied on sampling 25 of the approximately 300 applications, so the existing construct was limited in coverage. No continuous monitoring existed. All controls were tested on a semi-annual basis and typically took approximately 810 hours (.38 full-time employee [FTE]; this is found by dividing the hours savings by 1750, the annual equivalent hours of an FTE).

Working with control designers, risk officers, control owners and ServiceNow GRC internal developers, the team streamlined (redesigned) the existing 15 manual controls into one control with four attributes (approval types required), developing more than 2,000 indicators (ServiceNow automated trigger queries) to automate the control testing life cycle, increase coverage to 100 percent of nearly 300 applications and establish continuous monitoring to ensure full effectiveness for the forthcoming test cycle.

THE CONTINUOUS MONITORING ASPECT ALLOWED THE CHANGE MANAGEMENT TEAM TO CORRECT POTENTIAL TESTING EXCEPTIONS PRIOR TO THE ACTUAL TEST, THUS ENSURING 100 PERCENT EFFECTIVENESS WHEN THE TEST EVENT DOES OCCUR.

The team first redesigned the 15 manual controls into a single control with four attributes (all types of change management approvals). This effort streamlined the disparate controls into a manageable, cohesive element against which logical software queries could be most effectively applied. The 2,000 indicators were required to run against the larger population of 270 applications and the four designed attributes, thus allowing full coverage of the target population rather than using a sampling of 25 applications in the prior state. The corresponding time savings in labor resulted from the automation itself coupled with the need to only evaluate exceptions versus individual change records in the sampled population. The effort achieved continuous monitoring by establishing a system-generated advisory to the change management team when change records were out of compliance. The continuous monitoring aspect allowed the change management team to correct potential testing exceptions prior to the actual test, thus ensuring 100 percent effectiveness when the test event does occur. A summary of the effort is outlined in figure 1.

The net investment of the effort included approximately 80 hours of resource time (control design and development/user acceptance/implementation) with a duration of approximately 45 days (executed in parallel with other duties). The team expects to further reduce the .11 FTE current state requirement once SOX requirements are further analyzed, refined and accepted to comply with the higher rigor SOX necessitates.

In this case study, the organization realized an optimal solution using automation for change management, recognizing significant resource time savings, attaining 100 percent scope coverage, and implementing on-demand and weekly continuous monitoring. The continuous monitoring process enables the control owner to monitor and remediate exceptions prior to testing.

The control analysis inventory review identified a possible 60 percent of the remaining portfolio could similarly be automated, particularly those domains using ServiceNow (most notably access management and incident management). Although the investment level of effort remains a constant, the potential value depends heavily on the control area—the team does not expect to realize a 70 percent reduction in control testing times across that 60 percent population. For the 40 percent remaining population, the team will identify longerterm automation opportunities and explore external techniques such as RPA.

THE VALUE PROPOSITION OF AUTOMATION CAN BE REALIZED BY ANY ORGANIZATION WITH THE APPROPRIATE RISK MANAGEMENT FRAMEWORK AND ASSOCIATED SYSTEMS.

Automation will permit the first-line team to expand control testing capacity without adding significant resources, expand the coverage for system controls to reduce the risk profile and improve control effectiveness through continuous monitoring. The second and third lines of defense will realize a corresponding benefit in their oversight responsibilities. The value proposition of automation can be realized by any organization with the appropriate risk management framework and associated systems.

Author’s Note

The author wishes to acknowledge the contribution of Keith Mangine, Matt Foos, Ralph Baisden, Anusha Akarapu and Aaron Kramer in the execution of this use case and their ongoing partnership.

1 Mehta, ; “Three Lines of Defense for Cyber Security Professionals,” Governance,Risk, and Compliance, 3 October 2019, https://grcmusings.com/3-lines-of-defense-for-cyber-security-professionals/ 2 International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC), ISO/IEC 27001 Information Security Management , Switzerland, https://www.iso.org/isoiec-27001-information-security.html 3 Office of the Comptroller of the Currency, United States Code of Federal Regulations, 12 CFR Appendix B to Part 30 Interagency Guidelines of Establishing Information Security Standards, USA, 1995, https://www.ecfr.gov/current/title-12/chapter-I/part-30 4 US Congress, H.R.3763 107th Congress Sarbanes-Oxley Act of 2002, 30 July 2002, https://www.congress.gov/bill/107th-congress/house-bill/3763/text 5 National Institute of Standards and Technology (NIST), “Risk Management Framework (RMF) Overview,” FISMA Implementation Project: CSRC, 30 November 2016, https://csrc.nist.gov/projects/risk-management/about-rmf 6 The Risk Management Association (RMA), Enterprise Risk Management Framework, https://www.rmahq.org/erm-framework 7 ISACA ® , COBIT ® 2019, USA, 2018, https://www.isaca.org/resources/cobit 8 Op cit International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 9 Dubsky, L.; “Assessing Security Controls:Keystone of the Risk Management Framework,” ISACA ® Journal , vol. 6, 2016, https://www.isaca.org/archives 10 Vohradsky, D.; “A Practical Approach to Continuous Control Monitoring,” ISACA Journal , 2, 2015, https://www.isaca.org/archives 11 ServiceNow, “About ServiceNow,” https://www.servicenow.com/company.html 12 Archer Integrated Risk Management, https://www.archerirm.com/ 13 Op cit ISACA

Michael Powers, Ph.D., CRISC

Is an IT risk director at a US Midwest regional banking institution and adjunct professor in quantitative statistics, project management and cybersecurity for three universities. He can be reached at [email protected] .

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Control and Coordination Case Study Based Questions Class 10

Students who are studying in CBSE class 10 board, need to get the knowledge about the Control and Coordination Case Study Based Questions. Case based questions are generally based on the seen passages from the chapter Control and Coordination. Through solving the case based questions, students can understand each and every concept. 

 With the help of Control and Coordination Case Study Based Questions, students don’t need to memorise each answer. As answers for these case studies are already available in the given passage. Questions are asked through MCQs so student’s won’t take time to mark the answers. These multiple choice questions can help students to score the weightage of Control and Coordination. 

Control and Coordination Case Study Based Questions with Solutions 

Selfstudys provides case studies for the Class 10 Science chapter Control and Coordination with solutions. The Solutions can be helpful for students to refer to if there is a doubt in any of the case studies problems. The solutions from the Selfstudys website are easily accessible and free of cost to download. This accessibility can help students to download case studies from anywhere with the help of the Internet. 

Control and Coordination Case Study Based Questions with solutions are in the form of PDF. Portable Document Format (PDF) can be downloaded through any of the devices: smart phone, laptop. Through this accessibility, students don't need to carry those case based questions everywhere. 

Features of Control and Coordination Case Study Based Questions

Before solving questions, students should understand the basic details of Control and Coordination. Here are the features of case based questions on Control and Coordination are:

• These case based questions start with short or long passages. In these passages some concepts included in the chapter can be explained.
• After reading the passage, students need to answer the given questions. These questions are asked in the Multiple Choice Questions (MCQ). 
• These case based questions are a type of open book test. These case based questions can help students to score well in the particular subject. 
•  These Control and Coordination Case Study Based Questions can also be asked in the form of CBSE Assertion and Reason .

Benefits of Solving Control and Coordination Case Study Based Questions

According to the CBSE board, some part of the questions are asked in the board exam question papers according to the case studies. As some benefits of solving Control and Coordination Case Study Based Questions can be obtained by the students. Those benefits are: 

• Through solving case studies students will be able to understand every concept included in the chapter Control and Coordination
• Passages included in the case study are seen passages, so students don’t need to struggle for getting answers. As these questions and answers can be discussed by their concerned teacher. 
• Through these students can develop their observation skills. This skill can help students to study further concepts clearly. 
• Case studies covers all the concepts which are included in the Control and Coordination

How to Download Control and Coordination Case Based Questions? 

Students studying in CBSE class 10 board, need to solve questions based on case study. It is necessary for students to know the basic idea of Control and Coordination Case Study Based Questions. Students can obtain the basic idea of case based questions through Selfstudys website. Easy steps to download it are:

• Open Selfstudys website. 

• Bring the arrow towards CBSE which is visible in the navigation bar. 

• A pop-up menu will appear, Select case study from the list. 

• New page will appear, select 10 from the list of classes. 

• Select Science from the subject list. 

• And in the new page, you can access the Control and Coordination Case Study Based Questions. 

Tips to solve Control and Coordination Case Study Questions-

Students should follow some basic tips to solve Control and Coordination Case Study Based Questions. These tips can help students to score good marks in CBSE Class 10 Science. 

• Generally, the case based questions are in the form of Multiple Choice Questions (MCQs). 
• Students should start solving the case based questions through reading the given passage. 
• Identify the questions and give the answers according to the case given. 
• Read the passage again, so that you can easily answer the complex questions. 
• Answer according to the options given below the questions provided in the Control and Coordination Case Study Based Questions. 

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Contextual hospital conditions and the risk of nosocomial sars-cov-2 infection: a matched case-control study with density sampling in a large portuguese hospital.

1. Introduction

2. materials and methods.

• Case-control eligibility criteria
• Cases, controls, and matching
• Risk factors
• Statistical analysis

4. Discussion

Supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Almeida, F.; Correia, S.; Leal, C.; Guedes, M.; Duro, R.; Andrade, P.; Pedrosa, A.; Rocha-Pereira, N.; Lima-Alves, C.; Azevedo, A. Contextual Hospital Conditions and the Risk of Nosocomial SARS-CoV-2 Infection: A Matched Case-Control Study with Density Sampling in a Large Portuguese Hospital. J. Clin. Med. 2024 , 13 , 5251. https://doi.org/10.3390/jcm13175251

Almeida F, Correia S, Leal C, Guedes M, Duro R, Andrade P, Pedrosa A, Rocha-Pereira N, Lima-Alves C, Azevedo A. Contextual Hospital Conditions and the Risk of Nosocomial SARS-CoV-2 Infection: A Matched Case-Control Study with Density Sampling in a Large Portuguese Hospital. Journal of Clinical Medicine . 2024; 13(17):5251. https://doi.org/10.3390/jcm13175251

Almeida, Francisco, Sofia Correia, Cátia Leal, Mariana Guedes, Raquel Duro, Paulo Andrade, Afonso Pedrosa, Nuno Rocha-Pereira, Carlos Lima-Alves, and Ana Azevedo. 2024. "Contextual Hospital Conditions and the Risk of Nosocomial SARS-CoV-2 Infection: A Matched Case-Control Study with Density Sampling in a Large Portuguese Hospital" Journal of Clinical Medicine 13, no. 17: 5251. https://doi.org/10.3390/jcm13175251

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CASE-CONTROL STUDY OF INTESTINAL PARASITES IN PATIENTS WITH INTESTINAL CANCER IN SOHAG, EGYPT

• Nagwa Ibrahim Seleem , Nada Abd EL-FATTAH EL-NADI , +1 author M. R. GabAllah
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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.

Peer Review reports

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

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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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Test Cases vs Test Scenarios: A Complete Guide

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Modified thoracoabdominal nerve block via perichondral approach: an alternative for perioperative pain management in laparoscopic cholecystectomy in a middle-income country

• Luisa Fernanda Castillo-Dávila 1 ,
• Carlos Jesús Torres-Anaya 1 ,
• Raquel Vazquez-Apodaca 1 ,
• Hector Borboa-Olivares 2 ,
• Salvador Espino-y-Sosa 3 &
• Johnatan Torres-Torres 3  

BMC Anesthesiology volume  24 , Article number:  304 ( 2024 ) Cite this article

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Laparoscopic cholecystectomy is known for its minimally invasive nature, but postoperative pain management remains challenging. Despite the enhanced recovery after surgery (ERAS) protocol, regional analgesic techniques like modified perichondral approach to thoracoabdominal nerve block (M-TAPA) show promise. Our retrospective study evaluates M-TAPA’s efficacy in postoperative pain control for laparoscopic cholecystectomy in a middle-income country.

This was a retrospective case-control study of laparoscopic cholecystectomy patients at Hospital General de Mexico in which patients were allocated to the M-TAPA or control group. The data included demographic information, intraoperative variables, and postoperative pain scores. M-TAPA blocks were administered presurgery. Outcomes: opioid consumption, pain intensity, adverse effects, and time to rescue analgesia. Analysis of variance (ANOVA) compared total opioid consumption between groups, while Student’s t test compared pain intensity and time until the first request for rescue analgesia.

Among the 56 patients, those in the M-TAPA group had longer surgical and anesthetic times ( p  < 0.001), higher ASA 3 scores (25% vs. 3.12%, p  = 0.010), and reduced opioid consumption ( p  < 0.001). The M-TAPA group exhibited lower postoperative pain scores ( p  < 0.001), a lower need for rescue analgesia ( p  = 0.010), and a lower incidence of nausea/vomiting ( p  = 0.010).

Bilateral M-TAPA offers effective postoperative pain control after laparoscopic cholecystectomy, especially in middle-income countries, by reducing opioid use and enhancing recovery.

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Introduction

Laparoscopic cholecystectomy is a cornerstone procedure in modern surgery and is renowned for its minimally invasive approach and swift recovery compared to open surgery [ 1 ]. However, managing postoperative pain, which can range from moderate to severe within the first 24 h, remains a clinical challenge [ 2 ]. This pain, driven primarily by somatic components followed by neuropathic and shoulder-referred pain, exhibits considerable variability among patients [ 3 ].

To address this challenge, the enhanced recovery after surgery (ERAS) protocol advocates for a multimodal analgesic strategy [ 4 ]. However, the efficacy of nonopioid analgesics and adjuvant anesthetics has limitations, spurring exploration of regional analgesic techniques such as ultrasound-guided nerve blocks or local infiltration [ 5 , 6 ].

By targeting the anterolateral abdominal wall innervated by the thoracoabdominal nerves, the modified perichondral approach to thoracoabdominal nerve block (M-TAPA) has emerged as a promising analgesic modality [ 7 , 8 , 9 ]. M-TAPA, introduced by Tulgar et al. in 2019, offers broader coverage of both anterior and lateral cutaneous branches of the thoracoabdominal nerves, surpassing the limitations of conventional techniques such as the transversus abdominis plane block [ 10 ]. While initial studies have demonstrated its efficacy in abdominal surgeries, including laparoscopic cholecystectomy, further research is needed to establish its clinical utility and compare it with existing regional analgesic methods [ 11 , 12 ].

Healthcare in middle-income countries faces considerable challenges due to resource limitations and underdeveloped healthcare infrastructure. These nations often lack access to advanced treatments and pain management techniques, underscoring the importance of developing effective and economically viable approaches to address perioperative pain. In this context, our study aimed to assess the efficacy of M-TAPA for postoperative pain management compared to that of conventional analgesia in laparoscopic cholecystectomy patients in a middle-income country.

Study design and participants

This retrospective case-control study involved adult patients who underwent laparoscopic cholecystectomy at the Hospital General de Mexico “Dr. Eduardo Liceaga” in Mexico City from January 2023 to July 2023. Patients were retrospectively categorized into two groups based on the analgesic technique they received during surgery: those who received M-TAPA were assigned to the M-TAPA group, while those who received local infiltration were allocated to the control group. The study was conducted with the approval of the institutional research and bioethics boards at the Hospital General de Mexico “Dr. Eduardo Liceaga” (Approval number: 1384 − 295/23). Exclusion criteria included allergies to local anesthetics or contraindications to nerve block procedures.

Data collection

The data, including demographic information; intraoperative variables such as surgical and anesthetic duration; intraoperative opioid consumption; and postoperative pain scores assessed using the visual analog scale (VAS) [ 13 , 14 ], which ranges from 0 to 10, at awake, 30, and 120 min postoperatively, were extracted from the patients’ medical records. Each of these variables of interest was then transferred to an electronic database for analysis. Adverse effects related to analgesia, such as nausea and vomiting, were also recorded. Furthermore, our analysis incorporated variables such as time to mobilization and time to complete oral intake to assess postoperative recovery. Additionally, the time until the first request for rescue analgesia was documented as a measure of postoperative pain management efficacy.

Anesthesia and perioperative management protocol

No preoperative premedication was administered to the patients. The anesthesia technique was standardized for all patients in the operating room, involving electrocardiography, non-invasive blood pressure monitoring, capnography, peripheral oxygen saturation, neuromonitoring, a multi-gas analyzer, and temperature monitoring, alongside the initiation of a 0.9% NaCl infusion at a rate of 4 ml/kg.

Anesthesia induction comprised propofol (1.2 mg/kg), rocuronium (0.6 mg/kg), and fentanyl (4 mcg/kg) based on ideal body weight. Following endotracheal intubation, maintenance anesthesia involved a blend of 2% sevoflurane and 50% air in 50% O2 (3.5 L/min). Mechanical ventilation was executed in volume-controlled mode, with tidal volume set at 6–8 ml/kg according to ideal body weight to maintain end-tidal carbon dioxide at 30 to 35 mm Hg.

During surgery, anesthesia depth was regulated using end-tidal sevoflurane, maintaining sevoflurane concentration at 0.8-1 MAC.

In the M-TAPA group, the regional blockade was bilaterally administered by a single anesthesiologist post-general anesthesia induction and pre-surgical procedure. Sevoflurane maintained anesthesia and in most cases as heart rate and blood pressure values remained within a 20% variation, there was no need for additional boluses of fentanyl (1 mcg/kg).

In the control group, intraoperative management involved fentanyl infusion titration, supplemented in some cases with dexmedetomidine or lidocaine infusion, while ensuring non-invasive blood pressure and heart rate remained within 20% variation.

Patients were transferred to the post-anesthesia care unit upon completion of surgery, once neuromuscular functions were fully restored, and adequate tidal volume was observed.

Standard analgesia comprised intravenous administration of paracetamol 1 g every 8 h, metamizole 1 g every 8 h, or ketorolac 60 mg every 8 h. The initial doses of paracetamol and metamizol or ketorolac were administered in the last 30 min of surgery. Additionally, 100 mg of tramadol was intravenously administered to all patients in the no-block group before surgery termination. Pain intensity was assessed using a 0–10 numeric rating scale (NRS) at rest and during movement. Tramadol 50–100 mg was administered intravenously as a rescue analgesic to patients with a resting NRS > 4.

Description of the M-TAPA block technique

After administering anesthesia and before surgery began, an experienced anesthesiologist performed the M-TAPA block while the patient laid on their back. Using a high-frequency linear transducer (7–14 MHz, SonoSite M-Turbo), the anesthesiologist located the external, internal, and transverse abdominal muscles near the tenth rib cartilage, aiming to clearly visualize the underside of the cartilage.

Once the injection site was prepped, a 22G x 100 mm peripheral nerve block needle (Stimuplex ® Ultra 360 ® , B-Braun, USA) was carefully guided toward the underside of the tenth rib cartilage between the internal oblique and transverse abdominal muscles using real-time ultrasound imaging. After confirming the correct placement of the saline solution and ensuring that there was no blood aspiration, 20 ml of 0.375% ropivacaine was injected, ensuring effective anesthetic spread between the muscle layers. The same process was performed bilaterally. All blocks were conducted by an experienced anesthesiologist [ 12 , 15 ].

The primary outcome measure was total opioid consumption during the first postoperative hour. The secondary outcomes included postoperative pain intensity, incidence of adverse effects related to analgesia, and time to the first request for rescue analgesia.

Statistical analysis

Continuous variables are expressed as medians and interquartile ranges (IQRs), and inferential testing was performed using the Mann‒Whitney U test; categorical data are expressed as numbers and percentages and were analyzed using the chi‒square test or Fisher’s exact test. Analysis of variance (ANOVA) was used to compare total opioid consumption between groups, and Student’s t test was used to compare pain intensity and time until the first request for rescue analgesia. A p -value of less than 0.05 was considered to indicate statistical significance. (StataCorp. 2020, Stata Statistical Software: Release 17. College Station, TX: StataCorp LLC).

Characteristics of the study population

A total of 56 patients were analyzed in this study, with a predominance of 69.6% females and 30.4% males. The mean age of the patients was 38.18 ± 9.57 years. Clinical characteristics such as age, sex, and body mass index were significantly similar between the study groups ( Table  1 ) . However, compared with those in the control group, a significantly greater proportion of patients in the M-TAPA group underwent ASA 3 assessment (25% vs. 3.12%, p  = 0.010).

Characteristics of the surgical event

Surgical and anesthetic times were significantly longer in the M-TAPA group than in the control group (119.75 versus 81.25, p  = 0.0004 and 153.25 versus 107.06, p  = 0.0001, respectively). Additionally, there was a greater use of anesthetic adjuvants in the control group (8.33% versus 78.12%, p  = 0.0001), as did increased opioid consumption (341.58 versus 577.53, p  = 0.0001) ( Tables  1 and Fig.  1 ) .

Opioid consumption between groups

Postoperative evaluation between groups

There were no significant differences in the VAS scores at awakening or 30 min after the operation between the study groups ( Table  2 ) . However, upon recovery, the M-TAPA group exhibited significantly lower pain scores than did the control group (1.25 versus 3.88, p  = 0.0001) ( Fig.  2 ) . In Fig.  3 , the Kaplan-Meier survival curve compares the probability of patients in the control group and the M-TAPA group requiring tramadol rescue analgesia over time post-surgery. The log-rank test indicated a statistically significant difference between the survival curves (χ² =21.30, df = 1, p  < 0.0001), demonstrating a longer duration until rescue analgesia in the M-TAPA group compared to the control group. This finding underscores the efficacy of M-TAPA in providing prolonged pain relief following laparoscopic cholecystectomy. Additionally, the hazard ratio (HR) calculated using the Mantel-Haenszel method was 8.333 (95%CI: 3.973 to 17.48), indicating a significantly lower risk of requiring rescue analgesia in the M-TAPA group compared to the control group. Furthermore, consistent with these survival analysis results, there was a reduced need for rescue analgesia with tramadol at 60 min post-surgery in the M-TAPA group compared to the control group (16.67% versus 50%, p  = 0.010). A greater incidence of nausea and/or vomiting was recorded in the control group than in the M-TAPA group (15.62% versus 0, p  = 0.010) ( Table  2 ) . Furthermore, analysis revealed that the M-TAPA group had significantly shorter times for early mobilization (8.2 h versus 10.6 h, p  = 0.010), and quicker resumption of oral intake (17.3 h versus 24.7 h, p  = 0.001) compared to the control group.

Postoperative pain assessment in the recovery area

Depicts a Kaplan-Meier survival curve illustrating the time to rescue analgesia following laparoscopic cholecystectomy, with a focus on the need for tramadol rescue analgesia at 60 min post-surgery

The findings of our study underscore the efficacy of the M-TAPA block in alleviating postoperative pain following laparoscopic cholecystectomy. While the analgesic effectiveness of this technique is independent of the socioeconomic context, conducting the study in a middle-income country enhances the universal applicability and external validity of the M-TAPA block. It demonstrates that this approach can be particularly beneficial in settings with limited resources, where cost-effective and easily implementable pain management strategies are crucial. In our study, the administration of M-TAPA post anesthesia led to a significant reduction in postoperative pain levels, decreased opioid consumption, and a lower requirement for rescue analgesia during the recovery period. These outcomes not only highlight the effectiveness of M-TAPA in improving patient comfort but also emphasize its potential as a valuable tool in resource-limited environments.

This study contributes to the growing body of evidence supporting the use of M-TAPA in abdominal surgeries. In line with the findings of Bilge et al. and Güngör et al. [ 11 , 12 ]. , we found that M-TAPA provides effective analgesia, evidenced by a significant reduction in the need for rescue analgesia and lower postoperative pain scores compared to control groups receiving alternative analgesic approaches. Additionally, we observed a higher proportion of ASA 3 patients in the M-TAPA group, suggesting that this technique may be particularly beneficial for patients with more complex physical statuses.

Our results also align with Tulgar et al.‘s research [ 15 ], which underscores M-TAPA’s efficacy in abdominal surgeries, supporting our own observation of decreased opioid consumption and improved patient satisfaction. Furthermore, our study aligns with Chen et al.‘s findings, emphasizing the versatility of M-TAPA, as we illustrate its potential efficacy, even in obese patients [ 16 ].

The cadaveric studies by Ciftci et al. and the clinical cases presented by Aikawa et al. provide a robust anatomical and clinical basis for M-TAPA efficacy [ 17 , 18 ]. Our findings support this understanding by demonstrating a reduced need for rescue analgesia, lower opioid consumption, and better postoperative recovery in the M-TAPA group compared to the control group. Our study did not directly assess the impact on recovery using instruments like the QoR-40 questionnaire employed in Bilge et al.‘s study or the QoR-15 questionnaire used in Suzuka et al.‘s research [ 12 , 19 ]. Nonetheless, we have emphasized the potential for improved recovery based on objective metrics, such as notably shorter durations for early mobilization and faster resumption of oral intake observed in the M-TAPA group compared to the control group. These specific findings regarding functional aspects of recovery, namely mobilization and oral intake, serve as objective indicators suggesting that the M-TAPA technique may contribute to a swifter and smoother recovery process following laparoscopic cholecystectomy.

The consistent findings across several studies, including our own, underscore the reliability and practicality of this technique for postoperative pain management in abdominal surgeries.

Strengths and limitations

Our study utilized objective and quantitative measures, such as intraoperative opioid consumption and postoperative pain intensity assessed using the visual analog scale, thereby enhancing the reliability and validity of our findings. The inclusion of demographic and clinical variables also allowed for a more comprehensive analysis and appropriate comparison between study groups.

However, several limitations must be acknowledged. The sample size and retrospective design of the present study may introduce selection and confounding biases, potentially compromising its internal validity. Uncontrolled factors such as variations in surgical technique, team experience, and anesthesiologist expertise could influence outcomes. Furthermore, long-term follow-up data for assessing surgical complications and late-stage recovery were lacking. The single-center design limits generalizability, and the absence of definitive recommendations on the optimal LA concentration underscores the need for prospective studies. Despite these limitations, our findings strongly support the efficacy of M-TAPA blockade for perioperative pain management, warranting further multicenter investigations.

Clinical implications

Our study findings suggest that integrating M-TAPA into standard anesthesia protocols for laparoscopic cholecystectomy, particularly in a middle-income country setting, can notably enhance pain management outcomes. By decreasing intraoperative opioid usage and postoperative pain intensity, M-TAPA represents a promising strategy for improving patient comfort, safety, and overall surgical experience. This approach not only helps mitigate opioid-related side effects but also holds potential for expediting recovery, reducing hospital stays, and optimizing resource utilization in healthcare settings. These results highlight the importance of developing accessible and effective pain management strategies in resource-constrained environments, underscoring the need for greater consideration of middle-income countries in research and clinical practice related to perioperative pain management to improve outcomes and enhance the quality of care in these challenging settings.

Our study highlights the effectiveness of M-TAPA for managing postoperative pain after laparoscopic cholecystectomy, especially in middle-income countries. By reducing opioid use and providing prolonged analgesia, MWA offers a safer alternative to traditional methods, potentially leading to the transformation of perioperative care. However, further research is needed to validate its broad applicability in diverse surgical settings.

Data availability

The datasets used and/or analyzed during the current study are available upon reasonable request. Access to the data can be requested via the following Google Drive link: https://drive.google.com/file/d/1uQWopPm2vmltwrh52_TKzjEvL1Z_Cqu-/view? usp=drivesdk. Please contact [email protected] or [email protected] to request access.

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Acknowledgements

We would like to extend our sincere gratitude to the Instituto Nacional de Perinatología “Isidro Espinosa de los Reyes” for their financial support, which covered the publication charges for this study. Their generous contribution has been invaluable in facilitating the dissemination of our research findings.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Luisa Fernanda Castillo-Dávila, Carlos Jesús Torres-Anaya & Raquel Vazquez-Apodaca

Community Interventions Research Branch, Instituto Nacional de Perinatología “Isidro Espinosa de los Reyes”, Mexico City, Mexico

Hector Borboa-Olivares

Department of Reproductive and Perinatal Health Research, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico

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LFCD: Conceptualization, Writing – original draft and editing. LFCD and CJTA: Methodology and Investigation. RVA: Writing – the original draft. HBO and SEyS: Methodology and editing. JTT: Conceptualization, Methodology, Formal analysis, Writing – original draft and editing.

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The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Hospital General de Mexico “Dr. Eduardo Liceaga” (protocol code 1384 − 295/23). Informed consent was obtained from all subjects involved in the study.

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Castillo-Dávila, L.F., Torres-Anaya, C.J., Vazquez-Apodaca, R. et al. Modified thoracoabdominal nerve block via perichondral approach: an alternative for perioperative pain management in laparoscopic cholecystectomy in a middle-income country. BMC Anesthesiol 24 , 304 (2024). https://doi.org/10.1186/s12871-024-02690-8

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Published : 31 August 2024

DOI : https://doi.org/10.1186/s12871-024-02690-8

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• Laparoscopic cholecystectomy
• Modified thoracoabdominal nerve block (M-TAPA)
• Perioperative pain management
• Opioid consumption
• Postoperative analgesia

BMC Anesthesiology

ISSN: 1471-2253

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