case control study research in reverse

Case-control studies: research in reverse

Affiliation.

• 1 Family Health International, PO Box 13950, Research Triangle Park, NC 27709, USA. [email protected]
• PMID: 11844534
• DOI: 10.1016/S0140-6736(02)07605-5

Epidemiologists benefit greatly from having case-control study designs in their research armamentarium. Case-control studies can yield important scientific findings with relatively little time, money, and effort compared with other study designs. This seemingly quick road to research results entices many newly trained epidemiologists. Indeed, investigators implement case-control studies more frequently than any other analytical epidemiological study. Unfortunately, case-control designs also tend to be more susceptible to biases than other comparative studies. Although easier to do, they are also easier to do wrong. Five main notions guide investigators who do, or readers who assess, case-control studies. First, investigators must explicitly define the criteria for diagnosis of a case and any eligibility criteria used for selection. Second, controls should come from the same population as the cases, and their selection should be independent of the exposures of interest. Third, investigators should blind the data gatherers to the case or control status of participants or, if impossible, at least blind them to the main hypothesis of the study. Fourth, data gatherers need to be thoroughly trained to elicit exposure in a similar manner from cases and controls; they should use memory aids to facilitate and balance recall between cases and controls. Finally, investigators should address confounding in case-control studies, either in the design stage or with analytical techniques. Devotion of meticulous attention to these points enhances the validity of the results and bolsters the reader's confidence in the findings.

• Case-Control Studies*
• Reproducibility of Results
• Research Design
• Selection Bias*

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Lancet (London, England) , 01 Feb 2002 , 359(9304): 431-434 https://doi.org/10.1016/s0140-6736(02)07605-5   PMID: 11844534 

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• Published: 04 January 2019

Case-control studies: an efficient study design

• L. A. Harvey 1  

Spinal Cord volume  57 ,  pages 1–2 ( 2019 ) Cite this article

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Case-control studies provide estimates of how much more likely an outcome is amongst people who are subject to a particular exposure than amongst people who are not [ 1 , 2 , 3 , 4 ]. So they are helpful for answering questions about the aetiology of a disease or condition (i.e. an outcome).

Case-control studies are particularly useful for studying the cause of an outcome that is rare and for studying the effects of prolonged exposure. For example, a case-control study could be used to determine whether long-term use of indwelling catheters (the exposure) causes bladder cancer (the outcome) in people with spinal cord injury. (This is an example of a study of prolonged exposure on risk of a rare disease. Incidentally, the causal link between catheters and bladder cancer is contentious [ 5 , 6 , 7 ]).

In this example, the cases would be people with spinal cord injury, from the study base, who develop bladder cancer. It is important that all cases, or a random sample of all cases, from the study base are identified; they should not merely be a sample of convenience. (The study base might be, for example, all of the people with a spinal cord injury in a geographical area, or all of the people who, if they developed the disease of interest, would present at a particular hospital.) The controls should be sampled from the same study base of people with spinal cord injury. Controls must be sampled in a way that is not influenced by whether they are or are not exposed. So in our example, the controls would be a randomly selected group of people with spinal cord injury drawn from the same study base as the cases, irrespective of whether they do or do not have bladder cancer and irrespective of whether they have or have not been exposed to indwelling catheters. Theoretically, a person could be both a case and a control, although this is unlikely to happen because bladder cancer is rare. Data must be collected on exposures and outcomes of every participant. In the current example, data must be collected on the use of indwelling catheters and presence of bladder cancer. From these data, it is possible to construct an odds ratio that depicts how much more likely a person who has used indwelling catheters is to develop bladder cancer than a person who has not used indwelling catheters. Case-control studies are observational studies, so even if cases and controls are sampled without regard to exposure, it is still necessary to rigorously adjust for confounding.

Often, researchers conduct a different sort of study and erroneously call it a case-control study. In that design, researchers sample controls from a population that does not develop the disease of interest. For example, they sample people with spinal cord injury who do not develop bladder cancer. When controls are sampled in this way, the odds ratios may provide biased estimates of the causal effect, even if confounding is rigorously controlled.

Matching may improve the efficiency of case-control designs. However, a common misunderstanding is that matched case-control studies need only involve collecting data on a convenient sample of cases and a convenient sample of people who are matched to the cases on a few variables [ 8 ]. This is not correct. As far back as 1986 Rothman said that:

''..because [case control studies] need not be expensive nor time-consuming to conduct….many studies have been conducted by would-be investigators who lack even a rudimentary appreciation for epidemiologic principles. Occasionally such haphazard research can produce fruitful or even extremely important results, but often the results are wrong because basic research principles have been violated ” (cited p. 431 [ 9 ]).

Importantly, a matched case-control design still requires that cases be people from the study base with the condition of interest and controls still need to be sampled from the same study base without regard to exposure. In a matched design, there is the additional complexity that cases and controls are matched on variables that are likely to confound estimates (e.g. time since injury or age). A well-conducted matched case-control design may be more efficient and therefore requires a smaller sample size than an unmatched study. However, matching on a variable that is not actually a confounder may reduce efficiency. Moreover, matching on a variable that is affected by the exposure (a mediator) or is affected by both the exposure and the outcome (a collider) may introduce bias. For example, while it might be tempting to match for smoking status because those who smoke are more likely to develop bladder cancer [ 5 , 6 ], this would only be necessary if smoking status influences the likelihood of using indwelling catheters (which would seem unlikely). It is therefore important to consider whether a variable is a true confounder before matching on it [ 10 ].

Spinal Cord values carefully designed case-control studies because they provide a very efficient way of estimating the causal effect of an exposure on the risk of developing a rare condition. However, they need to be grounded in key epidemiological principles to ensure that the results are trustworthy.

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Harvey, L.A. Case-control studies: an efficient study design. Spinal Cord 57 , 1–2 (2019). https://doi.org/10.1038/s41393-018-0234-4

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Case-Control Studies: Research in Reverse

Chapter Contents Basic Case-Control Study Design 52 Advantages and Disadvantages 53 Selection of Case and Control Groups 54 Case Group 54 Control Group 54 Measurement of Exposure Information 56 Control for Confounding 56 Conclusion 57 Epidemiologists benefit greatly from having case-control study designs in their research armamentarium. Case-control studies can yield important scientific findings with relatively little time, money, and effort compared with other study designs. This seemingly quick road to research results entices many newly trained epidemiologists. Indeed, investigators implement case-control studies more frequently than any other analytical epidemiological study. Unfortunately, case-control designs also tend to be more susceptible to biases than other comparative studies. Although easier to do, they are also easier to do wrong. Five main notions guide investigators who do, or readers who assess, case-control studies. First, investigators must explicitly define the criteria for diagnosis of a case and any eligibility criteria used for selection. Second, controls should come from the same population as the cases, and their selection should be independent of the exposures of interest. Third, investigators should blind the data gatherers to the case or control status of participants or, if impossible, at least blind them to the main hypothesis of the study. Fourth, data gatherers need to be thoroughly trained to elicit exposure in a similar manner from cases and controls; they should use memory aids to facilitate and balance recall between cases and controls. Finally, investigators should address confounding in case-control studies, either in the design stage or with analytical techniques. Devotion of meticulous attention to these points enhances the validity of the results and bolsters the reader’s confidence in the findings. Case-control studies contribute greatly to the research toolbox of an epidemiologist. They embody the strengths and weaknesses of observational epidemiology. Moreover, epidemiologists use them to study a huge variety of associations. To show this variety, we searched PubMed for topics investigated with case-control studies ( Panel 5.1 ). We identified diverse diseases and exposures, with outcomes ranging from hip fracture to premature ejaculation, and exposures ranging from hair dyes to vitamin D. Panel 5.1 Examples of Topics in the Literature Investigated With Case-Control Studies Exposure Outcome Uterine fibromas Postpartum haemorrhage Breastfeeding Pertussis Shiftwork Violence against nurses Periodontitis Breast cancer History of migraine Concussion Hypothyroidism Unruptured cerebral aneurysms Statins Polyneuropathy Vitamin D Early childhood fracture HPV Invasive cervical cancer Vitamin B 12 Premature ejaculation Human Papillomavirus Colorectal cancer Untreated psoriasis Male fertility Atopy Melanoma Body mass index, hormone therapy Cutaneous melanoma Micronutrients Hip fracture Antidepressants Colorectal cancer Agricultural occupation Testicular cancer Childhood obesity Hypertriglyceridaemia Hair dyes Connective tissue disorders Digital rectal examination Metastatic prostate cancer Statins Dementia Paracetamol use Ovarian cancer Physical activity Breast cancer Influenza vaccination Recurrent myocardial infarction   The strength of case-control studies can be appreciated in early research done by investigators hoping to understand the cause of AIDS. Case-control studies identified risk groups (e.g., homosexual men, intravenous drug users, and blood-transfusion recipients) and risk factors (e.g., multiple sex partners, receptive anal intercourse in homosexual men, and not using condoms) for AIDS. Based on such studies, blood banks restricted high-risk individuals from donating blood, and educational programmes began to promote safer behaviours. As a result of these precautions, the speed of transmission of HIV-1 was greatly reduced, even before the virus had been identified. By comparison with other study types, case-control studies can yield important findings in a relatively short time, and with relatively little money and effort. This apparently quick road to research results entices many newly trained epidemiologists. However, case-control studies tend to be more susceptible to biases than other analytical, epidemiological designs. A notable friend of ours (the late David L. Sackett, personal communication, 2001) told us that he would trust only six people in the world to do a proper case-control study. And Ken Rothman comments in his book that ‘because it need not be extremely expensive nor time-consuming to conduct a case-control study, many studies have been conducted by would-be investigators who lack even a rudimentary appreciation for epidemiological principles. Occasionally such haphazard research can produce fruitful or even extremely important results, but often the results are wrong because basic research principles have been violated’. Basic Case-Control Study Design Case-control designs might seem easy to understand, but many clinicians stumble over them. Because this type of study runs backwards by comparison with most other studies, it often confuses researchers and readers alike. Indeed, it so confuses researchers that they frequently do not know what type of study they have done (and readers do not know the difference). For example, in a review of 124 published articles in four US obstetrics and gynaecology journals labelled as ‘case-control’ studies, clearly 30% were not case-control studies. Most of the mislabelled case-control studies were actually retrospective cohort studies. This mislabelling of studies as ‘case-contol’ extends to other specialties as well. In a review of studies in diabetes labelled as ‘case-control’, 43.8% were mislabelled and thereby misleading. Certainly, researchers, reviewers, editors, and readers need better training in methods and terminology. In cohort studies, study groups are defined by exposure. In case-control studies, however, study groups are defined by outcome ( Fig. 5.1 ). To study the association between smoking and lung cancer, therefore, people with lung cancer are enrolled to form the case group, and people without lung cancer are identified as controls. Researchers then look back in time to ascertain each person’s exposure status (smoking history), hence the retrospective nature of this study design. Investigators compare the frequency of smoking exposure in the case group with that in the control group, and calculate a measure of association. Fig. 5.1 Schematic diagram of a case-control study design. Unlike cohort studies, case-control studies cannot yield incidence rates. Instead, they provide an odds ratio, derived from the proportion of individuals exposed in each of the case and control groups. When the cumulative incidence rate of an outcome in the population of interest is low (usually under 5% suffices in both the exposed and unexposed), the odds ratio from a case-control study is a good estimate of relative risk. Epidemiologists refer to this condition as the rare disease assumption, which pertains to a type of case-control study that ascertains cases after the end of the risk period of interest, with controls being selected from among those who did not become cases. This represents the type of case-control study that we address in this chapter, usually labelled a cumulative case-control study. Of note, although beyond the scope of this chapter, this rare disease assumption is not needed for other case-control study designs in which researchers estimate the incidence density ratio. Advantages and Disadvantages Researchers often tout case-control studies as the most efficient epidemiological study design. Indeed, they tend to take less time, less money, and less effort. That makes sense when the incidence rate of an outcome is low, because in a cohort design the researchers would have to follow up many individuals to identify one with the outcome. Case-control studies are also efficient in the investigation of diseases that have a long latency period (e.g., cancer), in which instance a cohort study would involve many years of follow-up before the outcome became evident. However, cohort studies can be more efficient than case-control studies. If the frequency of exposure is low, for example, case-control studies quickly become inefficient. Researchers would have to examine many cases and controls to find one who had been exposed. For instance, a case-control study of oral contraceptive use and transmission of HIV-1 would be impractical in parts of Africa because of the rarity of use of oral contraceptives. As a rule of thumb, cohort designs are more efficient in settings in which the incidence of outcome is higher than the prevalence of exposure. Finally, many methodological issues affect the validity of the results of case-control studies, and two factors (i.e., choosing a control group and obtaining exposure history) can greatly affect a study’s vulnerability to bias.

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What Is A Case Control Study?

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

Explanation

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

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

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

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

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

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

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

Quick, inexpensive, and simple

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

Beneficial for studying rare diseases

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

Useful for preliminary research

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

Limitations

Subject to recall bias.

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

Difficulty finding a suitable control group

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

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

Do not demonstrate causation

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

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

Below are some examples of case-control studies:

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

Frequently asked questions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Case-control study

Tarani Chandola, CCSR.

A case-control study is a type of observational study design that is often used in epidemiology. Two groups of people are compared; one with the condition/disease (‘cases’) and a similar group of people who do not have the condition or disease (‘controls’).

The proportion of each group having a history of a particular exposure or characteristic of interest is then compared. If there is a greater proportion of cases who are exposed to a particular factor compared to controls, there is an association between the exposure and the disease.

Case control studies are usually cheaper and easier to do than longitudinal and experimental study designs but they suffer from a number of biases including recall bias in a person’s recollection of their history of exposure to the factor of interest.

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

A nested case control study utilises data from a longitudinal cohort study to select a subset of matched controls to compare with the incident cases. In a case-cohort study, all incident cases in the cohort are compared to a random subset of participants who do not develop the disease of interest. In contrast, in a nested-case-control study, some number of controls are selected for each case from that case's matched risk set.

By matching on factors such as age and selecting controls from relevant risk sets, the nested case control model is generally more efficient than a case-cohort design with the same number of selected controls. This is similar to propensity score matching techniques.

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Key references

• Doll R, Hill AB (1952) A study of the aetiology of carcinoma of the lung. British Medical Journal 2:1271–1286
• Rothman K (2002) Epidemiology. An Introduction. Oxford University Press, Oxford, England
• Schulz KF, Grimes DA (2002) Case-control studies: research in reverse;. Lancet: 359: 431–34

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Methodology Series Module 2: Case-control Studies

Maninder singh setia.

Epidemiologist, MGM Institute of Health Sciences, Navi Mumbai, Maharashtra, India

Case-Control study design is a type of observational study. In this design, participants are selected for the study based on their outcome status. Thus, some participants have the outcome of interest (referred to as cases), whereas others do not have the outcome of interest (referred to as controls). The investigator then assesses the exposure in both these groups. The investigator should define the cases as specifically as possible. Sometimes, definition of a disease may be based on multiple criteria; thus, all these points should be explicitly stated in case definition. An important aspect of selecting a control is that they should be from the same ‘study base’ as that of the cases. We can select controls from a variety of groups. Some of them are: General population; relatives or friends; and hospital patients. Matching is often used in case-control control studies to ensure that the cases and controls are similar in certain characteristics, and it is a useful technique to increase the efficiency of the study. Case-Control studies can usually be conducted relatively faster and are inexpensive – particularly when compared with cohort studies (prospective). It is useful to study rare outcomes and outcomes with long latent periods. This design is not very useful to study rare exposures. Furthermore, they may also be prone to certain biases – selection bias and recall bias.

Introduction

Case-Control study design is a type of observational study design. In an observational study, the investigator does not alter the exposure status. The investigator measures the exposure and outcome in study participants, and studies their association.

In a case-control study, participants are selected for the study based on their outcome status. Thus, some participants have the outcome of interest (referred to as cases), whereas others do not have the outcome of interest (referred to as controls). The investigator then assesses the exposure in both these groups. Thus, by design, in a case-control study the outcome has to occur in some of the participants that have been included in the study.

As seen in Figure 1 , at the time of entry into the study (sampling of participants), some of the study participants have the outcome (cases) and others do not have the outcome (controls). During the study procedures, we will examine the exposure of interest in cases as well as controls. We will then study the association between the exposure and outcome in these study participants.

Example of a case-control study

Examples of Case-Control Studies

Smoking and lung cancer study.

In their landmark study, Doll and Hill (1950) evaluated the association between smoking and lung cancer. They included 709 patients of lung carcinoma (defined as cases). They also included 709 controls from general medical and surgical patients. The selected controls were similar to the cases with respect to age and sex. Thus, they included 649 males and 60 females in cases as well as controls.

They found that only 0.3% of males were non-smokers among cases. However, the proportion of non-smokers among controls was 4.2%; the different was statistically significant ( P = 0.00000064). Similarly they found that about 31.7% of the female were non-smokers in cases compared with 53.3% in controls; this difference was also statistically significant (0.01< p <0.02).

Melanoma and tanning (Lazovic et al ., 2010)

The authors conducted a case-control study to study the association between melanoma and tanning. The 1167 cases - individuals with invasive cutaneous melanoma – were selected from Minnesota Cancer Surveillance System. The 1101 controls were selected randomly from Minnesota State Driver's License list; they were matched for age (+/- 5 years) and sex.

The data were collected by self administered questionnaires and telephone interviews. The investigators assessed the use of tanning devices (using photographs), number of years, and frequency of use of these devices. They also collected information on other variables (such as sun exposure; presence of freckles and moles; and colour of skin, hair, among other exposures.

They found that melanoma was higher in individuals who used UVB enhances and primarily UVA-emitting devices. The risk of melanoma also increased with increase in years of use, hours of use, and sessions.

Risk factors for erysipelas (Pitché et al, 2015)

Pitché et al (2015) conducted a case-control study to assess the factors associated with leg erysipelas in sub-Saharan Africa. This was a multi-centre study; the cases and controls were recruited from eight countries in sub-Saharan Africa.

They recruited cases of acute leg cellulitis in these eight countries. They recruited two controls for each case; these were matched for age (+/- 5 years) and sex. Thus, the final study has 364 cases and 728 controls. They found that leg erysipelas was associated with obesity, lympoedema, neglected traumatic wound, toe-web intertrigo, and voluntary cosmetic depigmentation.

We have provided details of all the three studies in the bibliography. We strongly encourage the readers to read the papers to understand some practical aspects of case-control studies.

Selection of Cases and Controls

Selection of cases and controls is an important part of this design. Wacholder and colleagues (1992 a, b, and c) have published wonderful manuscripts on design and conduct of case-control of studies in the American Journal of Epidemiology. The discussion in the next few sections is based on these manuscripts.

Selection of case

The investigator should define the cases as specifically as possible. Sometimes, definition of a disease may be based on multiple criteria; thus, all these points should be explicitly stated in case definition.

For example, in the above mentioned Melanoma and Tanning study, the researchers defined their population as any histologic variety of invasive cutaneous melanoma. However, they added another important criterion – these individuals should have a driver's license or State identity card. This probably is not directly related to the clinic condition, so why did they add this criterion? We will discuss this in detail in the next few paragraphs.

Selection of a control

The next important point in designing a case-control study is the selection of control patients.

In fact, Wacholder and colleagues have extensively discussed aspects of design of case control studies and selection of controls in their article.

According to them, an important aspect of selecting a control is that they should be from the same ‘study base’ as that of the cases. Thus, the pool of population from which the cases and controls will be enrolled should be same. For instance, in the Tanning and Melanoma study, the researchers recruited cases from Minnesota Cancer Surveillance System; however, it was also required that these cases should either have a State identity card or Driver's license. This was important since controls were randomly selected from Minnesota State Driver's license list (this also included the list of individuals who have the State identity card).

Another important aspect of a case-control study is that we should measure the exposure similarly in cases and controls. For instance, if we design a research protocol to study the association between metabolic syndrome (exposure) and psoriasis (outcome), we should ensure that we use the same criteria (clinically and biochemically) for evaluating metabolic syndrome in cases and controls. If we use different criteria to measure the metabolic syndrome, then it may cause information bias.

Types of Controls

We can select controls from a variety of groups. Some of them are: General population; relatives or friends; or hospital patients.

Hospital controls

An important source of controls is patients attending the hospital for diseases other than the outcome of interest. These controls are easy to recruit and are more likely to have similar quality of medical records.

However, we have to be careful while recruiting these controls. In the above example of metabolic syndrome and psoriasis, we recruit psoriasis patients from the Dermatology department of the hospital as controls. We recruit patients who do not have psoriasis and present to the Dermatology as controls. Some of these individuals have presented to the Dermatology department with tinea pedis. Do we recruit these individuals as controls for the study? What is the problem if we recruit these patients? Some studies have suggested that diabetes mellitus and obesity are predisposing factors for tinea pedis. As we know, fasting plasma glucose of >100 mg/dl and raised trigylcerides (>=150 mg/dl) are criteria for diagnosis of metabolic syndrome. Thus, it is quite likely that if we recruit many of these tinea pedis patients, the exposure of interest may turn out to be similar in cases and controls; this exposure may not reflect the truth in the population.

Relative and friend controls

Relative controls are relatively easy to recruit. They can be particularly useful when we are interested in trying to ensure that some of the measurable and non-measurable confounders are relatively equally distributed in cases and controls (such as home environment, socio-economic status, or genetic factors).

Another source of controls is a list of friends referred by the cases. These controls are easy to recruit and they are also more likely to be similar to the cases in socio-economic status and other demographic factors. However, they are also more likely to have similar behaviours (alcohol use, smoking etc.); thus, it may not be prudent to use these as controls if we want to study the effect of these exposures on the outcome.

Population controls

These controls can be easily conducted the list of all individuals is available. For example, list from state identity cards, voter's registration list, etc., In the Tanning and melanoma study, the researchers used population controls. They were identified from Minnesota state driver's list.

We may have to use sampling methods (such as random digit dialing or multistage sampling methods) to recruit controls from the population. A main advantage is that these controls are likely to satisfy the ‘study-base’ principle (described above) as suggested by Wacholder and colleagues. However, they can be expensive and time consuming. Furthermore, many of these controls will not be inclined to participate in the study; thus, the response rate may be very low.

Matching in a Case-Control Study

Matching is often used in case-control control studies to ensure that the cases and controls are similar in certain characteristics. For example, in the smoking and lung cancer study, the authors selected controls that were similar in age and sex to carcinoma cases. Matching is a useful technique to increase the efficiency of study.

’Individual matching’ is one common technique used in case-control study. For example, in the above mentioned metabolic syndrome and psoriasis, we can decide that for each case enrolled in the study, we will enroll a control that is matched for sex and age (+/- 2 years). Thus, if 40 year male patient with psoriasis is enrolled for the study as a case, we will enroll a 38-42 year male patient without psoriasis (and who will not be excluded for other reason) as controls.

If the study has used ‘individual matching’ procedures, then the data should also reflect the same. For instance, if you have 45 males among cases, you should also have 45 males among controls. If you show 60 males among controls, you should explain the discrepancy.

Even though matching is used to increase the efficiency in case-control studies, it may have its own problems. It may be difficult to fine the exact matching control for the study; we may have to screen many potential enrollees before we are able to recruit one control for each case recruited. Thus, it may increase the time and cost of the study.

Nonetheless, matching may be useful to control for certain types of confounders. For instance, environment variables may be accounted for by matching controls for neighbourhood or area of residence. Household environment and genetic factors may be accounted for by enrolling siblings as controls.

If we use controls from the past (time period when cases did not occur), then the controls are sometimes referred to historic controls. Such controls may be recruited from past hospital records.

Strengths of a Case-Control Study

• Case-Control studies can usually be conducted relatively faster and are inexpensive – particularly when compared with cohort studies (prospective)
• It is useful to study rare outcomes and outcomes with long latent periods. For example, if we wish to study the factors associated with melanoma in India, it will be useful to conduct a case-control study. We will recruit cases of melanoma as cases in one study site or multiple study sites. If we were to conduct a cohort study for this research question, we may to have follow individuals (with the exposure under study) for many years before the occurrence of the outcome
• It is also useful to study multiple exposures in the same outcome. For example, in the metabolic syndrome and psoriasis study, we can study other factors such as Vitamin D levels or genetic markers
• Case-control studies are useful to study the association of risk factors and outcomes in outbreak investigations. For instance, Freeman and colleagues (2015) in a study published in 2015 conducted a case-control study to evaluate the role of proton pump inhibitors in an outbreak of non-typhoidal salmonellosis.

Limitations of a Case-control Study

• The design, in general, is not useful to study rare exposures. It may be prudent to conduct a cohort study for rare exposures

Since the investigator chooses the number of cases and controls, the proportion of cases may not be representative of the proportion in the population. For instance if we choose 50 cases of psoriasis and 50 controls, the prevalence of proportion of psoriasis cases in our study will be 50%. This is not true prevalence. If we had chosen 50 cases of psoriasis and 100 controls, then the proportion of the cases will be 33%.

• The design is not useful to study multiple outcomes. Since the cases are selected based on the outcome, we can only study the association between exposures and that particular outcome
• Sometimes the temporality of the exposure and outcome may not be clearly established in case-control studies
• The case-control studies are also prone to certain biases

If the cases and controls are not selected similarly from the study base, then it will lead to selection bias.

• Odds Ratio: We are able to calculate the odds ratios (OR) from a case-control study. Since we are not able to measure incidence data in case-control study, an odds ratio is a reasonable measure of the relative risk (under some assumptions). Additional details about OR will be discussed in the biostatistics section.

The OR in the above study is 3.5. Since the OR is greater than 1, the outcome is more likely in those exposed (those who are diagnosed with metabolic syndrome) compared with those who are not exposed (those who do are not diagnosed with metabolic syndrome). However, we will require confidence intervals to comment on further interpretation of the OR (This will be discussed in detail in the biostatistics section).

• Other analysis : We can use logistic regression models for multivariate analysis in case-control studies. It is important to note that conditional logistic regressions may be useful for matched case-control studies.

Calculating an Odds Ratio (OR)

Hypothetical study of metabolic syndrome and psoriasis

Additional Points in A Case-Control Study

How many controls can i have for each case.

The most optimum case-to-control ratio is 1:1. Jewell (2004) has suggested that for a fixed sample size, the chi square test for independence is most powerful if the number of cases is same as the number of controls. However, in many situations we may not be able recruit a large number of cases and it may be easier to recruit more controls for the study. It has been suggested that we can increase the number of controls to increase statistical power (if we have limited number of cases) of the study. If data are available at no extra cost, then we may recruit multiple controls for each case. However, if it is expensive to collect exposure and outcome information from cases and controls, then the optimal ratio is 4 controls: 1 case. It has been argued that the increase in statistical power may be limited with additional controls (greater than four) compared with the cost involved in recruiting them beyond this ratio.

I have conducted a randomised controlled trial. I have included a group which received the intervention and another group which did not receive the intervention. Can I call this a case-control study?

A randomised controlled trial is an experimental study. In contrast, case-control studies are observational studies. These are two different groups of studies. One should not use the word case-control study for a randomised controlled trial (even though you have a control group in the study). Every study with a control group is not a case-control study. For a study to be classified as a case-control study, the study should be an observational study and the participants should be recruited based on their outcome status (some have the disease and some do not).

Should I call case-control studies prospective or retrospective studies?

In ‘The Dictionary of Epidemiology’ by Porta (2014), the authors have suggested that even though the term ‘retrospective’ was used for case-control studies, the study participants are often recruited prospectively. In fact, the study on risk factors for erysipelas (Pitché et al ., 2015) was a prospective case case-control study. Thus, it is important to remember that the nature of the study (case-control or cohort) depends on the sampling method. If we sample the study participants based on exposure and move towards the outcome, it is a cohort study. However, if we sample the participants based on the outcome (some with outcome and some do not) and study the exposures in both these groups, it is a case-control study.

In case-control studies, participants are recruited on the basis of disease status. Thus, some of participants have the outcome of interest (referred to as cases), whereas others do not have the outcome of interest (referred to as controls). The investigator then assesses the exposure in both these groups. Case-control studies are less expensive and quicker to conduct (compared with prospective cohort studies at least). The measure of association in this type of study is an odds ratio. This type of design is useful for rare outcomes and those with long latent periods. However, they may also be prone to certain biases – selection bias and recall bias.

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Case-control studies: research in reverse

2002, Lancet

Epidemiologists benefit greatly from having case-control study designs in their research armamentarium. Case-control studies can yield important scientific findings with relatively little time, money, and effort compared with other study designs. This seemingly quick road to research results entices many newly trained epidemiologists. Indeed, investigators implement case-control studies more frequently than any other analytical epidemiological study. Unfortunately, case-control designs also tend to be more susceptible to biases than other comparative studies. Although easier to do, they are also easier to do wrong. Five main notions guide investigators who do, or readers who assess, case-control studies. First, investigators must explicitly define the criteria for diagnosis of a case and any eligibility criteria used for selection. Second, controls should come from the same population as the cases, and their selection should be independent of the exposures of interest. Third, invest...

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Fatal Traffic Risks With a Total Solar Eclipse in the US

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
• 6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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What the data says about gun deaths in the U.S.

More Americans died of gun-related injuries in 2021 than in any other year on record, according to the latest available statistics from the Centers for Disease Control and Prevention (CDC). That included record numbers of both gun murders and gun suicides. Despite the increase in such fatalities, the rate of gun deaths – a statistic that accounts for the nation’s growing population – remained below the levels of earlier decades.

Here’s a closer look at gun deaths in the United States, based on a Pew Research Center analysis of data from the CDC, the FBI and other sources. You can also read key public opinion findings about U.S. gun violence and gun policy .

This Pew Research Center analysis examines the changing number and rate of gun deaths in the United States. It is based primarily on data from the Centers for Disease Control and Prevention (CDC) and the Federal Bureau of Investigation (FBI). The CDC’s statistics are based on information contained in official death certificates, while the FBI’s figures are based on information voluntarily submitted by thousands of police departments around the country.

For the number and rate of gun deaths over time, we relied on mortality statistics in the CDC’s WONDER database covering four distinct time periods:  1968 to 1978 ,  1979 to 1998 ,  1999 to 2020 , and 2021 . While these statistics are mostly comparable for the full 1968-2021 period, gun murders and suicides between 1968 and 1978 are classified by the CDC as involving firearms  and  explosives; those between 1979 and 2021 are classified as involving firearms only. Similarly, gun deaths involving law enforcement between 1968 and 1978 exclude those caused by “operations of war”; those between 1979 and 2021 include that category, which refers to gun deaths among military personnel or civilians  due to war or civil insurrection in the U.S . All CDC gun death estimates in this analysis are adjusted to account for age differences over time and across states.

The FBI’s statistics about the types of firearms used in gun murders in 2020 come from the bureau’s  Crime Data Explorer website . Specifically, they are drawn from the expanded homicide tables of the agency’s  2020 Crime in the United States report . The FBI’s statistics include murders and non-negligent manslaughters involving firearms.

How many people die from gun-related injuries in the U.S. each year?

In 2021, the most recent year for which complete data is available, 48,830 people died from gun-related injuries in the U.S., according to the CDC. That figure includes gun murders and gun suicides, along with three less common types of gun-related deaths tracked by the CDC: those that were accidental, those that involved law enforcement and those whose circumstances could not be determined. The total excludes deaths in which gunshot injuries played a contributing, but not principal, role. (CDC fatality statistics are based on information contained in official death certificates, which identify a single cause of death.)

What share of U.S. gun deaths are murders and what share are suicides?

Though they tend to get less public attention than gun-related murders, suicides have long accounted for the majority of U.S. gun deaths . In 2021, 54% of all gun-related deaths in the U.S. were suicides (26,328), while 43% were murders (20,958), according to the CDC. The remaining gun deaths that year were accidental (549), involved law enforcement (537) or had undetermined circumstances (458).

What share of all murders and suicides in the U.S. involve a gun?

About eight-in-ten U.S. murders in 2021 – 20,958 out of 26,031, or 81% – involved a firearm. That marked the highest percentage since at least 1968, the earliest year for which the CDC has online records. More than half of all suicides in 2021 – 26,328 out of 48,183, or 55% – also involved a gun, the highest percentage since 2001.

How has the number of U.S. gun deaths changed over time?

The record 48,830 total gun deaths in 2021 reflect a 23% increase since 2019, before the onset of the coronavirus pandemic .

Gun murders, in particular, have climbed sharply during the pandemic, increasing 45% between 2019 and 2021, while the number of gun suicides rose 10% during that span.

The overall increase in U.S. gun deaths since the beginning of the pandemic includes an especially stark rise in such fatalities among children and teens under the age of 18. Gun deaths among children and teens rose 50% in just two years , from 1,732 in 2019 to 2,590 in 2021.

How has the rate of U.S. gun deaths changed over time?

While 2021 saw the highest total number of gun deaths in the U.S., this statistic does not take into account the nation’s growing population. On a per capita basis, there were 14.6 gun deaths per 100,000 people in 2021 – the highest rate since the early 1990s, but still well below the peak of 16.3 gun deaths per 100,000 people in 1974.

The gun murder rate in the U.S. remains below its peak level despite rising sharply during the pandemic. There were 6.7 gun murders per 100,000 people in 2021, below the 7.2 recorded in 1974.

The gun suicide rate, on the other hand, is now on par with its historical peak. There were 7.5 gun suicides per 100,000 people in 2021, statistically similar to the 7.7 measured in 1977. (One caveat when considering the 1970s figures: In the CDC’s database, gun murders and gun suicides between 1968 and 1978 are classified as those caused by firearms and explosives. In subsequent years, they are classified as deaths involving firearms only.)

Which states have the highest and lowest gun death rates in the U.S.?

The rate of gun fatalities varies widely from state to state. In 2021, the states with the highest total rates of gun-related deaths – counting murders, suicides and all other categories tracked by the CDC – included Mississippi (33.9 per 100,000 people), Louisiana (29.1), New Mexico (27.8), Alabama (26.4) and Wyoming (26.1). The states with the lowest total rates included Massachusetts (3.4), Hawaii (4.8), New Jersey (5.2), New York (5.4) and Rhode Island (5.6).

The results are somewhat different when looking at gun murder and gun suicide rates separately. The places with the highest gun murder rates in 2021 included the District of Columbia (22.3 per 100,000 people), Mississippi (21.2), Louisiana (18.4), Alabama (13.9) and New Mexico (11.7). Those with the lowest gun murder rates included Massachusetts (1.5), Idaho (1.5), Hawaii (1.6), Utah (2.1) and Iowa (2.2). Rate estimates are not available for Maine, New Hampshire, Vermont or Wyoming.

The states with the highest gun suicide rates in 2021 included Wyoming (22.8 per 100,000 people), Montana (21.1), Alaska (19.9), New Mexico (13.9) and Oklahoma (13.7). The states with the lowest gun suicide rates were Massachusetts (1.7), New Jersey (1.9), New York (2.0), Hawaii (2.8) and Connecticut (2.9). Rate estimates are not available for the District of Columbia.

How does the gun death rate in the U.S. compare with other countries?

The gun death rate in the U.S. is much higher than in most other nations, particularly developed nations. But it is still far below the rates in several Latin American countries, according to a 2018 study of 195 countries and territories by researchers at the Institute for Health Metrics and Evaluation at the University of Washington.

The U.S. gun death rate was 10.6 per 100,000 people in 2016, the most recent year in the study, which used a somewhat different methodology from the CDC. That was far higher than in countries such as Canada (2.1 per 100,000) and Australia (1.0), as well as European nations such as France (2.7), Germany (0.9) and Spain (0.6). But the rate in the U.S. was much lower than in El Salvador (39.2 per 100,000 people), Venezuela (38.7), Guatemala (32.3), Colombia (25.9) and Honduras (22.5), the study found. Overall, the U.S. ranked 20th in its gun fatality rate that year .

How many people are killed in mass shootings in the U.S. every year?

This is a difficult question to answer because there is no single, agreed-upon definition of the term “mass shooting.” Definitions can vary depending on factors including the number of victims and the circumstances of the shooting.

The FBI collects data on “active shooter incidents,” which it defines as “one or more individuals actively engaged in killing or attempting to kill people in a populated area.” Using the FBI’s definition, 103 people – excluding the shooters – died in such incidents in 2021 .

The Gun Violence Archive, an online database of gun violence incidents in the U.S., defines mass shootings as incidents in which four or more people are shot, even if no one was killed (again excluding the shooters). Using this definition, 706 people died in these incidents in 2021 .

Regardless of the definition being used, fatalities in mass shooting incidents in the U.S. account for a small fraction of all gun murders that occur nationwide each year.

How has the number of mass shootings in the U.S. changed over time?

The same definitional issue that makes it challenging to calculate mass shooting fatalities comes into play when trying to determine the frequency of U.S. mass shootings over time. The unpredictability of these incidents also complicates matters: As Rand Corp. noted in a research brief , “Chance variability in the annual number of mass shooting incidents makes it challenging to discern a clear trend, and trend estimates will be sensitive to outliers and to the time frame chosen for analysis.”

The FBI found an increase in active shooter incidents between 2000 and 2021. There were three such incidents in 2000. By 2021, that figure had increased to 61.

Which types of firearms are most commonly used in gun murders in the U.S.?

In 2020, the most recent year for which the FBI has published data, handguns were involved in 59% of the 13,620 U.S. gun murders and non-negligent manslaughters for which data is available. Rifles – the category that includes guns sometimes referred to as “assault weapons” – were involved in 3% of firearm murders. Shotguns were involved in 1%. The remainder of gun homicides and non-negligent manslaughters (36%) involved other kinds of firearms or those classified as “type not stated.”

It’s important to note that the FBI’s statistics do not capture the details on all gun murders in the U.S. each year. The FBI’s data is based on information voluntarily submitted by police departments around the country, and not all agencies participate or provide complete information each year.

Note: This is an update of a post originally published on Aug. 16, 2019.

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Using reverse mentoring to support inclusivity

Antonia Aluko, a student in Greek and Latin, worked with Dr Mazal Oaknin to reverse mentor a group of educators working in Gender Studies on how to make their teaching more inclusive.

15 April 2024

Only got a minute? Jump straight to Antonia's and Mazal's top tips

I worked with Dr Mazal Oaknin, Associate Professor (Teaching) in Spanish Language and Literature and Gender Studies, to formulate this project in response to our growing understanding of the non-inclusive teaching models in the Faculty of Arts and Humanities.

Our project “Decolonising Gender Studies Through Reverse Mentoring in the Faculty of Arts and Humanities” took place within the 2023-4 academic year. 

The aim this reverse mentoring project was to inform and equip teaching staff in our faculty on how to be more considerate of the multi-faceted experiences and interests of their students. 

Facilitated by students

We facilitated three sessions on inclusive teaching:

• Session 1: we explored some of the legislation regarding inclusivity and engagement in the classroom, as well as introducing participants to key scholars that have not always been included in traditional Gender Studies reading lists.
• Session 2: we focused on the benefits of inclusive teaching for students and how this increased student satisfaction and attainment
• Session 3: we shared techniques and methods of inclusive teaching so that participants could have inclusive teaching resources to use in their everyday teaching experiences

Together, the group discussed their own personal experiences in classrooms as educators and students. They used these conversations to form opinions and strategies on how to approach the modules that they are teaching with intersectionality, diversity, and inclusion in mind . 

Each session included a lunch or coffee break where participants had time to interact and socialise. 

Our approach and preparation

Intersectionality was at the forefront of our approach. We ensured that our project answered questions in regard to disability, race, gender, class as well as other protected characteristics.

Over a month, Dr Mazal Oaknin offered training and support to equip me with the tools to facilitate the sessions with confidence and ease .  

I collated a questionnaire for prospective mentees and shortlisted candidates who were selected out of UCL’s teaching staff. I also prepared handouts and presentations for the sessions, to give mentees a sense of an agenda and structure.  

We were delighted with the feedback from staff after the sessions. 100% of the attendees found the sessions very helpful. All attendees all said they would recommend the project to their colleagues and felt they left with practical ideas for making their classes more inclusive.  

Antonia's top tips:

• Always adapt sessions so participants are shown examples relevant to them. 
• Use a range of materials, including videos, Mentimeters and interactive activities. 
• Plan in tasks that get people talking, allowing there to be a space for open communication and discussion. 

Mazal's top tips and how staff can benefit from reverse mentoring:

• Staff mentees are exposed to new ideas to make modules more diverse and inclusive . Be ready to apply the lessons learnt to the modules you are teaching.
• Through participative discussions, professional friendships are developed between participants with different levels of seniority. Encourage a social atmosphere in the sessions and if possible offer food and drinks. Time for mingling is a must!
• Staff mentees gain new perspectives, skills, and insights from the student mentor: Make sure you listen to your mentor. Offer your help, but make sure not to take the lead .
• Staff mentees become more aware of different biases and more committed to escalating complaints.  Show cultural humility, become an ally, acknowledge power imbalances and practice self-reflection .
• Participants can promote and share good practices with the rest of participants, and also with other colleagues in and beyond UCL. Share the lessons learnt in your departmental meetings, committees, conferences, townhalls. 

Antonia Aluko: 

“ I think it makes the case for the benefit of reverse mentoring and how it can benefit the experiences of teaching staff at UCL by being educated on the student experience by their students. Additionally, I think it combats issues of un-inclusive experiences as a student within UCL by considering the nuanced experiences and various backgrounds of students and their educators.  I think it makes the case for the benefit of reverse mentoring and how it can benefit the experiences of teaching staff at UCL by being educated on the student experience by their students. Additionally, I think it combats issues of un-inclusive experiences as a student within UCL by considering the nuanced experiences and various backgrounds of students and their educators. 

Anonymous attendee: 

It was very useful to hear practical suggestions from Antonia and colleagues, and to exchange experiences in different scenarios in a safe, friendly environment.” I think it makes the case for the benefit of reverse mentoring and how it can benefit the experiences of teaching staff at UCL by being educated on the student experience by their students. Additionally, I think it combats issues of un-inclusive experiences as a student within UCL by considering the nuanced experiences and various backgrounds of students and their educators. 

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