Cohort studies — MCQs

Q: A researcher wants to determine whether there is an association between CRP values and the risk of MI or cancer. Four relative risk (RR) values were plotted $(0.5,1.5,1.7,1.8)$ with respect to CRP levels. What conclusion can be drawn?

CRP increases disease/cancer risk. ***CRP increases disease/cancer risk*** - A **relative risk (RR)** greater than 1 indicates an increased risk of the outcome (MI or cancer) in the exposed group (higher CRP levels) compared to the unexposed group. - The plots show RRs of 1.5, 1.7, and 1.8, all of which are greater than 1, consistently indicating that higher CRP levels are associated with an elevated risk for MI or cancer. - The overall trend across the four intervals demonstrates a positive association between CRP and disease risk. *CRP has no relationship* - This conclusion is incorrect because three of the four plotted RR values (1.5, 1.7, 1.8) are above 1, indicating a positive association or increased risk. - An RR of 1 signifies no relationship, but the majority of values clearly deviate from 1, showing a definite association. *CRP decreases & disease decreases* - While one RR value (0.5) suggests a decreased risk, the majority of the given RRs (1.5, 1.7, 1.8) are greater than 1, indicating an increased risk. - This option would only be true if all or most RR values were less than 1, implying a protective effect, which is not the overall trend here. *No association in first interval* - The first interval shows an RR of 0.5. An RR of 1 indicates no association, while an RR of 0.5 actually indicates a **decreased risk or protective effect**, rather than no association. - Therefore, stating "no association" for the first interval is inaccurate given the definition of relative risk. *CRP shows protective effect in first interval* - While the first interval RR of 0.5 does suggest a protective effect in isolation, this option fails to capture the **overall conclusion** from all four data points. - When interpreting multiple RR values together, the predominant pattern (three values >1) indicates an overall increased risk, making this a misleading conclusion for the study as a whole.

Q: A researcher wants to study the carcinogenic effects of a food additive. From the literature, he finds that 7 different types of cancers have been linked to the consumption of this food additive. He wants to study all 7 possible outcomes. He conducts interviews with people who consume food containing these additives and people who do not. He then follows both groups for several years to see if they develop any of these 7 cancers or any other health outcomes. Which of the following study models best represents this study?

Cohort study . ***Cohort study*** - This study design involves selecting a group based on their **exposure status** (consumers vs. non-consumers of the food additive) and **following them forward in time** to observe the incidence of outcomes (cancers). - It is ideal for studying **multiple potential outcomes** from a single exposure and for establishing the **temporal relationship** between exposure and disease. *Case-control study* - This design starts by identifying individuals with a particular **outcome (cases)** and comparing them to individuals without the outcome (controls) to look back for **past exposures**. - It is efficient for studying **rare diseases** or when multiple exposures are suspected for a single outcome, which is inverse to the scenario described. *Cross-sectional study* - This study measures both **exposure and outcome simultaneously** at a single point in time, providing a snapshot of prevalence. - It cannot establish a **temporal relationship** between exposure and outcome and is less suitable for studying incident diseases that develop over time. *Randomized clinical trial* - This design involves **randomly assigning participants** to an intervention group or a control group and following them for outcomes. - It is primarily used to evaluate the **efficacy of interventions** or treatments, not to study the carcinogenic effects of naturally occurring exposures. *Crossover study* - In a crossover design, participants **receive all interventions** in a specific sequence, making each subject serve as their own control. - This design is generally used for evaluating **short-term effects of treatments** in chronic, stable conditions and is unsuitable for observing the development of diseases like cancer over extended periods.

Q: A 52-year-old man comes to the physician for a follow-up examination 1 year after an uncomplicated liver transplantation. He feels well but wants to know how long he can expect his donor graft to function. The physician informs him that the odds of graft survival are 90% at 1 year, 78% at 5 years, and 64% at 10 years. At this time, given that the graft has already survived 1 year, the probability of the patient's graft surviving to 10 years after transplantation is closest to which of the following?

71%. ***71%*** - This question tests understanding of **conditional probability** in survival analysis. - The patient is currently at 1 year post-transplant with a functioning graft. We need to calculate the probability of surviving to 10 years **given survival to 1 year**. - Using the conditional probability formula: P(survive to 10 years | survived to 1 year) = P(S10) / P(S1) = 64% / 90% = 0.711 ≈ **71%** - This represents the probability that a graft that has already "made it" through the first year will continue functioning until year 10. - In **Kaplan-Meier survival analysis**, conditional probabilities are crucial for counseling patients at different timepoints post-procedure. *64%* - This represents the **absolute probability** of 10-year graft survival measured from the time of transplantation (time zero). - However, the question asks "at this time" (1 year post-transplant) for the conditional probability, not the absolute probability from transplantation. - This would be correct if asking a patient at time zero what their 10-year survival odds are. *82%* - This does not represent any valid calculation from the given survival data. - It may result from incorrect manipulation of the probabilities (e.g., incorrectly adding or averaging values). *58%* - This is not derived from proper statistical calculation of the given survival probabilities. - It does not represent either absolute or conditional probability for any relevant timepoint. *45%* - This is incorrect and does not correspond to any valid calculation. - It might arise from incorrectly multiplying probabilities (e.g., 0.90 × 0.50) but has no basis in survival analysis.

Q: A population is studied for risk factors associated with testicular cancer. Alcohol exposure, smoking, dietary factors, social support, and environmental exposure are all assessed. The researchers are interested in the incidence and prevalence of the disease in addition to other outcomes. Which pair of studies would best assess the 1. incidence and 2. prevalence?

1. Prospective cohort study 2. Cross sectional study. ***1. Prospective cohort study 2. Cross sectional study*** - A **prospective cohort study** is ideal for measuring **incidence** (new cases over time) because it follows a group of individuals forward in time to observe who develops the disease. - A **cross-sectional study** is suitable for measuring **prevalence** (existing cases at a specific point in time) as it surveys a population at one moment to determine the proportion with the disease. *1. Prospective cohort study 2. Retrospective cohort study* - A **retrospective cohort study** assesses past exposures and outcomes and can measure incidence, but it is not the primary choice for prevalence. - While a prospective cohort study is appropriate for incidence, a retrospective cohort study is less suited for determining current prevalence. *1. Cross sectional study 2. Retrospective cohort study* - A **cross-sectional study** measures prevalence, not incidence, as it captures disease status at a single point in time. - A **retrospective cohort study** looks back in time to identify past exposures and subsequent outcomes, which is not the best method for current prevalence. *1. Case-control study 2. Prospective cohort study* - A **case-control study** compares exposures between individuals with a disease (cases) and those without (controls) and is best for studying rare diseases and estimating odds ratios, not incidence or prevalence directly. - A **prospective cohort study** is suitable for incidence, but a case-control study is not for incidence or prevalence. *1. Clinical trial 2. Cross sectional study* - A **clinical trial** is an experimental study designed to test the efficacy of interventions and is not primarily used to measure disease incidence or prevalence in a general population. - While a cross-sectional study is appropriate for prevalence, a clinical trial is not designed for incidence measurement.

Q: A researcher is designing an experiment to examine the toxicity of a new chemotherapeutic agent in mice. She splits the mice into 2 groups, one of which she exposes to daily injections of the drug for 1 week. The other group is not exposed to any intervention. Both groups are otherwise raised in the same conditions with the same diet. One month later, she sacrifices the mice to check for dilated cardiomyopathy. In total, 52 mice were exposed to the drug, and 50 were not exposed. Out of the exposed group, 13 were found to have dilated cardiomyopathy on necropsy. In the unexposed group, 1 mouse was found to have dilated cardiomyopathy. Which of the following is the relative risk of developing cardiomyopathy with this drug?

12.5. ***Correct Option: 12.5*** - The **relative risk (RR)** is calculated as the **risk in the exposed group divided by the risk in the unexposed group**: RR = [a/(a+b)] / [c/(c+d)] - **Risk in exposed group** = 13/52 = 0.25 (25%) - **Risk in unexposed group** = 1/50 = 0.02 (2%) - **RR = 0.25 / 0.02 = 12.5** - This indicates that mice exposed to the chemotherapeutic agent are **12.5 times more likely** to develop dilated cardiomyopathy compared to unexposed mice - An **RR > 1** indicates increased risk with exposure, supporting the drug's cardiotoxicity *Incorrect Option: 25.0* - This value results from **miscalculating the unexposed group risk** (e.g., using 0.01 instead of 0.02 as the denominator) - If the unexposed risk was halved incorrectly: 0.25 / 0.01 = 25.0 - This overestimates the relative risk by a factor of 2 *Incorrect Option: 13.7* - This value does not result from the correct **relative risk formula** - May arise from an **arithmetic error** or confusion with other epidemiological measures - The correct calculation of 13/52 ÷ 1/50 does not yield this result *Incorrect Option: 16.3* - This might result from **miscounting the number of subjects** in either group or confusing **relative risk with odds ratio** - The **odds ratio** would be calculated as (13/39) / (1/49) = 16.3 - Remember: **Relative risk uses total exposed/unexposed as denominators**, while odds ratio uses non-diseased counts *Incorrect Option: 23.0* - This value suggests a **fundamental error** in applying the relative risk formula - Could result from using incorrect numerators or denominators (e.g., 13/1 instead of proper risk calculation) - Significantly overestimates the true relative risk of 12.5

Q: A research group designs a study to investigate the epidemiology of syphilis in the United States. After a review of medical records, the investigators identify patients who are active cocaine users but did not have a history of syphilis as of one year ago. Another group of similar patients with no history of cocaine use or syphilis infection is also identified. The investigators examine the medical charts to determine whether the group of patients who are actively using cocaine was more likely to have developed syphilis over a 6-month period. The investigators ultimately found that the rate of syphilis was 30% higher in patients with active cocaine use compared to patients without cocaine use. This study is best described as which of the following?

Retrospective cohort study. ***Retrospective cohort study*** - This study design examines **past data** (medical records) to identify groups based on exposure status (cocaine use) and then follows them forward in time from pre-existing data to determine the incidence of an outcome (syphilis). - The investigators looked back one year to identify patients without syphilis and then observed if they developed syphilis over a subsequent 6-month period using already recorded information. *Prospective cohort study* - Involves identifying exposure groups (cocaine users vs. non-users) **at the present time** and then following them into the future to observe the development of an outcome. - This study used existing medical records to define past exposure and outcomes, rather than recruiting participants and following them going forward. *Case-control study* - This design starts by identifying individuals with the **outcome (syphilis cases)** and a control group without the outcome, then looks **retrospectively** to determine past exposures (cocaine use) that might have contributed. - The study described here, however, started with exposure status (cocaine use) and looked forward in time for outcomes, which is characteristic of a cohort study. *Case series* - A descriptive study that reports on the characteristics of a group of patients with a particular **disease or exposure**, without a comparison group. - This study included a comparison group of patients without cocaine use, which is beyond the scope of a simple case series. *Meta-analysis* - A systematic review that statistically **combines the results of multiple independent studies** to provide a more precise estimate of an effect. - This study describes a single observational study design, not a synthesis of other studies.

Q: A cohort study was conducted to investigate the impact of post-traumatic stress disorder (PTSD) on asthma symptoms in a group of firefighters who worked at Ground Zero during the September 11, 2001 terrorist attacks in New York City and developed asthma in the attack's aftermath. The study compared patients who had PTSD with those who did not have PTSD in order to determine if PTSD is associated with worse asthma control. During a follow-up period of 12 months, the researchers found that patients with PTSD had a greater number of hospitalizations for asthma exacerbations (RR = 2.0, 95% confidence interval = 1.4–2.5) after adjusting for medical comorbidities, psychiatric comorbidities other than PTSD, and sociodemographic variables. Results are shown: ≥ 1 asthma exacerbation No asthma exacerbations PTSD 80 80 No PTSD 50 150 Based on these results, what proportion of asthma hospitalizations in patients with PTSD could be attributed to PTSD?

0.50. ***0.50*** - The **Attributable Risk Percent (AR%)** in the exposed group is calculated as (RR - 1) / RR. Given a Relative Risk (RR) of 2.0, AR% = (2.0 - 1) / 2.0 = 1 / 2.0 = 0.50. - This means that **50% of asthma hospitalizations** in the group with PTSD can be attributed to their PTSD status. *0.25* - This value is obtained by dividing 1 by 4, not relevant to the formula for **attributable risk percent** using the given relative risk. - Incorrectly applying the formula or misinterpreting the RR would lead to this value. *4.0* - This value is not derived from the **attributable risk percent formula** (AR% = (RR - 1) / RR) with the given RR of 2.0. - It might represent a misunderstanding of risk ratios or how to calculate attributable risk. *2.0* - This is the **Relative Risk (RR)** itself, not the proportion of asthma hospitalizations attributable to PTSD. - The RR compares the incidence of an outcome in the exposed group to the unexposed group. *3.0* - This value is not obtained through any standard epidemiological calculation for **attributable risk** given the relative risk of 2.0. - It likely results from an arbitrary calculation or an incorrect application of epidemiological formulas.

Q: A prospective cohort study was conducted to assess the relationship between LDL and the incidence of heart disease. The patients were selected at random. Results showed a 10-year relative risk (RR) of 3.0 for people with elevated LDL levels compared to individuals with normal LDL levels. The p-value was 0.04 with a 95% confidence interval of 2.0-4.0. According to the study results, what percent of heart disease in these patients can be attributed to elevated LDL?

67%. ***67%*** - The **attributable risk percent (AR%)** in the exposed group represents the proportion of disease in the exposed population that can be attributed to the exposure. - It is calculated using the formula: **AR% = [(RR - 1) / RR] × 100** - With an RR of 3.0: [(3.0 - 1) / 3.0] × 100 = (2.0 / 3.0) × 100 = 66.67%, which rounds to **67%** - This means that 67% of heart disease cases among those with elevated LDL can be attributed to the elevated LDL levels. *50%* - This value would result from an RR of 2.0: [(2.0 - 1) / 2.0] × 100 = 50% - The study reports an RR of 3.0, not 2.0, making this incorrect. *100%* - This would imply that all cases of heart disease in the exposed group are due solely to elevated LDL (RR approaching infinity). - An RR of 3.0 indicates elevated LDL increases risk threefold, but does not account for all cases. *33%* - This might result from incorrectly calculating 1/RR = 1/3.0 = 33.3% - The correct formula is (RR - 1)/RR, not 1/RR. *25%* - This would correspond to an RR of 1.33: [(1.33 - 1) / 1.33] × 100 ≈ 25% - The given RR of 3.0 yields a much higher attributable risk percent.

A researcher wants to study the carcinogenic effects of a food additive. From the literature, he finds that 7 different types of cancers have been linked to the consumption of this food additive. He wants to study all 7 possible outcomes. He conducts interviews with people who consume food containing these additives and people who do not. He then follows both groups for several years to see if they develop any of these 7 cancers or any other health outcomes. Which of the following study models best represents this study?

A 52-year-old man comes to the physician for a follow-up examination 1 year after an uncomplicated liver transplantation. He feels well but wants to know how long he can expect his donor graft to function. The physician informs him that the odds of graft survival are 90% at 1 year, 78% at 5 years, and 64% at 10 years. At this time, given that the graft has already survived 1 year, the probability of the patient's graft surviving to 10 years after transplantation is closest to which of the following?

The study is performed to examine the association between type 2 diabetes mellitus (DM2) and Alzheimer's disease (AD). Group of 250 subjects diagnosed with DM2 and a matched group of 250 subjects without DM2 are enrolled. Each subject is monitored regularly over their lifetime for the development of symptoms of dementia or mild cognitive impairment. If symptoms are present, an autopsy is performed after the patient's death to confirm the diagnosis of AD. Which of the following is most correct regarding this study?

A population is studied for risk factors associated with testicular cancer. Alcohol exposure, smoking, dietary factors, social support, and environmental exposure are all assessed. The researchers are interested in the incidence and prevalence of the disease in addition to other outcomes. Which pair of studies would best assess the 1. incidence and 2. prevalence?

A researcher is designing an experiment to examine the toxicity of a new chemotherapeutic agent in mice. She splits the mice into 2 groups, one of which she exposes to daily injections of the drug for 1 week. The other group is not exposed to any intervention. Both groups are otherwise raised in the same conditions with the same diet. One month later, she sacrifices the mice to check for dilated cardiomyopathy. In total, 52 mice were exposed to the drug, and 50 were not exposed. Out of the exposed group, 13 were found to have dilated cardiomyopathy on necropsy. In the unexposed group, 1 mouse was found to have dilated cardiomyopathy. Which of the following is the relative risk of developing cardiomyopathy with this drug?

A research group designs a study to investigate the epidemiology of syphilis in the United States. After a review of medical records, the investigators identify patients who are active cocaine users but did not have a history of syphilis as of one year ago. Another group of similar patients with no history of cocaine use or syphilis infection is also identified. The investigators examine the medical charts to determine whether the group of patients who are actively using cocaine was more likely to have developed syphilis over a 6-month period. The investigators ultimately found that the rate of syphilis was 30% higher in patients with active cocaine use compared to patients without cocaine use. This study is best described as which of the following?

A cohort study was conducted to investigate the impact of post-traumatic stress disorder (PTSD) on asthma symptoms in a group of firefighters who worked at Ground Zero during the September 11, 2001 terrorist attacks in New York City and developed asthma in the attack's aftermath. The study compared patients who had PTSD with those who did not have PTSD in order to determine if PTSD is associated with worse asthma control. During a follow-up period of 12 months, the researchers found that patients with PTSD had a greater number of hospitalizations for asthma exacerbations (RR = 2.0, 95% confidence interval = 1.4–2.5) after adjusting for medical comorbidities, psychiatric comorbidities other than PTSD, and sociodemographic variables. Results are shown: ≥ 1 asthma exacerbation No asthma exacerbations PTSD 80 80 No PTSD 50 150 Based on these results, what proportion of asthma hospitalizations in patients with PTSD could be attributed to PTSD?

A prospective cohort study was conducted to assess the relationship between LDL and the incidence of heart disease. The patients were selected at random. Results showed a 10-year relative risk (RR) of 3.0 for people with elevated LDL levels compared to individuals with normal LDL levels. The p-value was 0.04 with a 95% confidence interval of 2.0-4.0. According to the study results, what percent of heart disease in these patients can be attributed to elevated LDL?

Q10

A recently published prospective cohort study of 1,000 men reports that smoking is significantly associated with higher rates of esophageal cancer. The next week, however, the journal publishes a letter to the editor in which a re-analysis of the study's data when accounting for the confounding effects of alcohol usage found no association between smoking and esophageal cancer. Which of the following statements is both necessary and sufficient to explain the change in result?

Cohort studies US Medical PG Practice Questions and MCQs

Question 1: A researcher wants to determine whether there is an association between CRP values and the risk of MI or cancer. Four relative risk (RR) values were plotted $(0.5,1.5,1.7,1.8)$ with respect to CRP levels. What conclusion can be drawn?

A. CRP has no relationship
B. CRP decreases & disease decreases
C. CRP increases disease/cancer risk (Correct Answer)
D. No association in first interval
E. CRP shows protective effect in first interval

Explanation: ***CRP increases disease/cancer risk*** - A **relative risk (RR)** greater than 1 indicates an increased risk of the outcome (MI or cancer) in the exposed group (higher CRP levels) compared to the unexposed group. - The plots show RRs of 1.5, 1.7, and 1.8, all of which are greater than 1, consistently indicating that higher CRP levels are associated with an elevated risk for MI or cancer. - The overall trend across the four intervals demonstrates a positive association between CRP and disease risk. *CRP has no relationship* - This conclusion is incorrect because three of the four plotted RR values (1.5, 1.7, 1.8) are above 1, indicating a positive association or increased risk. - An RR of 1 signifies no relationship, but the majority of values clearly deviate from 1, showing a definite association. *CRP decreases & disease decreases* - While one RR value (0.5) suggests a decreased risk, the majority of the given RRs (1.5, 1.7, 1.8) are greater than 1, indicating an increased risk. - This option would only be true if all or most RR values were less than 1, implying a protective effect, which is not the overall trend here. *No association in first interval* - The first interval shows an RR of 0.5. An RR of 1 indicates no association, while an RR of 0.5 actually indicates a **decreased risk or protective effect**, rather than no association. - Therefore, stating "no association" for the first interval is inaccurate given the definition of relative risk. *CRP shows protective effect in first interval* - While the first interval RR of 0.5 does suggest a protective effect in isolation, this option fails to capture the **overall conclusion** from all four data points. - When interpreting multiple RR values together, the predominant pattern (three values >1) indicates an overall increased risk, making this a misleading conclusion for the study as a whole.

Question 2: A researcher wants to study the carcinogenic effects of a food additive. From the literature, he finds that 7 different types of cancers have been linked to the consumption of this food additive. He wants to study all 7 possible outcomes. He conducts interviews with people who consume food containing these additives and people who do not. He then follows both groups for several years to see if they develop any of these 7 cancers or any other health outcomes. Which of the following study models best represents this study?

A. Cohort study (Correct Answer)
B. Case-control study
C. Cross-sectional study
D. Randomized clinical trial
E. Crossover study

Explanation: ***Cohort study*** - This study design involves selecting a group based on their **exposure status** (consumers vs. non-consumers of the food additive) and **following them forward in time** to observe the incidence of outcomes (cancers). - It is ideal for studying **multiple potential outcomes** from a single exposure and for establishing the **temporal relationship** between exposure and disease. *Case-control study* - This design starts by identifying individuals with a particular **outcome (cases)** and comparing them to individuals without the outcome (controls) to look back for **past exposures**. - It is efficient for studying **rare diseases** or when multiple exposures are suspected for a single outcome, which is inverse to the scenario described. *Cross-sectional study* - This study measures both **exposure and outcome simultaneously** at a single point in time, providing a snapshot of prevalence. - It cannot establish a **temporal relationship** between exposure and outcome and is less suitable for studying incident diseases that develop over time. *Randomized clinical trial* - This design involves **randomly assigning participants** to an intervention group or a control group and following them for outcomes. - It is primarily used to evaluate the **efficacy of interventions** or treatments, not to study the carcinogenic effects of naturally occurring exposures. *Crossover study* - In a crossover design, participants **receive all interventions** in a specific sequence, making each subject serve as their own control. - This design is generally used for evaluating **short-term effects of treatments** in chronic, stable conditions and is unsuitable for observing the development of diseases like cancer over extended periods.

Question 3: A 52-year-old man comes to the physician for a follow-up examination 1 year after an uncomplicated liver transplantation. He feels well but wants to know how long he can expect his donor graft to function. The physician informs him that the odds of graft survival are 90% at 1 year, 78% at 5 years, and 64% at 10 years. At this time, given that the graft has already survived 1 year, the probability of the patient's graft surviving to 10 years after transplantation is closest to which of the following?

A. 82%
B. 58%
C. 71% (Correct Answer)
D. 64%
E. 45%

Explanation: ***71%*** - This question tests understanding of **conditional probability** in survival analysis. - The patient is currently at 1 year post-transplant with a functioning graft. We need to calculate the probability of surviving to 10 years **given survival to 1 year**. - Using the conditional probability formula: P(survive to 10 years | survived to 1 year) = P(S10) / P(S1) = 64% / 90% = 0.711 ≈ **71%** - This represents the probability that a graft that has already "made it" through the first year will continue functioning until year 10. - In **Kaplan-Meier survival analysis**, conditional probabilities are crucial for counseling patients at different timepoints post-procedure. *64%* - This represents the **absolute probability** of 10-year graft survival measured from the time of transplantation (time zero). - However, the question asks "at this time" (1 year post-transplant) for the conditional probability, not the absolute probability from transplantation. - This would be correct if asking a patient at time zero what their 10-year survival odds are. *82%* - This does not represent any valid calculation from the given survival data. - It may result from incorrect manipulation of the probabilities (e.g., incorrectly adding or averaging values). *58%* - This is not derived from proper statistical calculation of the given survival probabilities. - It does not represent either absolute or conditional probability for any relevant timepoint. *45%* - This is incorrect and does not correspond to any valid calculation. - It might arise from incorrectly multiplying probabilities (e.g., 0.90 × 0.50) but has no basis in survival analysis.

Question 4: The study is performed to examine the association between type 2 diabetes mellitus (DM2) and Alzheimer's disease (AD). Group of 250 subjects diagnosed with DM2 and a matched group of 250 subjects without DM2 are enrolled. Each subject is monitored regularly over their lifetime for the development of symptoms of dementia or mild cognitive impairment. If symptoms are present, an autopsy is performed after the patient's death to confirm the diagnosis of AD. Which of the following is most correct regarding this study?

A. It is a retrospective observational study.
B. Relative risk cannot be determined from this study.
C. It is a prospective observational study. (Correct Answer)
D. It can provide proof of causation between DM2 and AD.
E. It is a case-control study.

Explanation: ***It is a prospective observational study.*** - The study enrolls subjects and then follows them forward in time ("**monitored regularly over their lifetime**") to observe the development of an outcome (AD), which defines a **prospective study**. - Since the researchers are observing and not actively intervening (e.g., administering a treatment), it is an **observational study**. *It is a retrospective observational study.* - A **retrospective study** looks back in time to examine outcomes that have already occurred, which is contrary to the description of following subjects over their lifetime. - In a retrospective study, data on exposures and outcomes are collected from past records or participant recall. *Relative risk cannot be determined from this study.* - This study design, a **prospective cohort study**, allows for the calculation of **relative risk** because it follows groups defined by their exposure (DM2 vs. no DM2) to determine the incidence of the outcome (AD) in each group. - Relative risk compares the incidence rate of an outcome in an exposed group to the incidence rate in an unexposed group. *It can provide proof of causation between DM2 and AD.* - **Observational studies** like this can identify **associations** and suggest potential causal links, but they generally cannot **prove causation** due to the possibility of confounding variables. - While it can strengthen the hypothesis of a causal link, randomized controlled trials are often considered the gold standard for establishing causation. *It is a case-control study.* - A **case-control study** begins by identifying individuals with an outcome (cases, e.g., AD patients) and comparing them to individuals without the outcome (controls) to determine past exposures, which is different from following exposed and unexposed groups forward. - This study design defines groups based on their exposure (DM2 status) at the beginning, not based on the presence or absence of the outcome.

Question 5: A population is studied for risk factors associated with testicular cancer. Alcohol exposure, smoking, dietary factors, social support, and environmental exposure are all assessed. The researchers are interested in the incidence and prevalence of the disease in addition to other outcomes. Which pair of studies would best assess the 1. incidence and 2. prevalence?

A. 1. Prospective cohort study 2. Cross sectional study (Correct Answer)
B. 1. Prospective cohort study 2. Retrospective cohort study
C. 1. Cross sectional study 2. Retrospective cohort study
D. 1. Case-control study 2. Prospective cohort study
E. 1. Clinical trial 2. Cross sectional study

Explanation: ***1. Prospective cohort study 2. Cross sectional study*** - A **prospective cohort study** is ideal for measuring **incidence** (new cases over time) because it follows a group of individuals forward in time to observe who develops the disease. - A **cross-sectional study** is suitable for measuring **prevalence** (existing cases at a specific point in time) as it surveys a population at one moment to determine the proportion with the disease. *1. Prospective cohort study 2. Retrospective cohort study* - A **retrospective cohort study** assesses past exposures and outcomes and can measure incidence, but it is not the primary choice for prevalence. - While a prospective cohort study is appropriate for incidence, a retrospective cohort study is less suited for determining current prevalence. *1. Cross sectional study 2. Retrospective cohort study* - A **cross-sectional study** measures prevalence, not incidence, as it captures disease status at a single point in time. - A **retrospective cohort study** looks back in time to identify past exposures and subsequent outcomes, which is not the best method for current prevalence. *1. Case-control study 2. Prospective cohort study* - A **case-control study** compares exposures between individuals with a disease (cases) and those without (controls) and is best for studying rare diseases and estimating odds ratios, not incidence or prevalence directly. - A **prospective cohort study** is suitable for incidence, but a case-control study is not for incidence or prevalence. *1. Clinical trial 2. Cross sectional study* - A **clinical trial** is an experimental study designed to test the efficacy of interventions and is not primarily used to measure disease incidence or prevalence in a general population. - While a cross-sectional study is appropriate for prevalence, a clinical trial is not designed for incidence measurement.

Question 6: A researcher is designing an experiment to examine the toxicity of a new chemotherapeutic agent in mice. She splits the mice into 2 groups, one of which she exposes to daily injections of the drug for 1 week. The other group is not exposed to any intervention. Both groups are otherwise raised in the same conditions with the same diet. One month later, she sacrifices the mice to check for dilated cardiomyopathy. In total, 52 mice were exposed to the drug, and 50 were not exposed. Out of the exposed group, 13 were found to have dilated cardiomyopathy on necropsy. In the unexposed group, 1 mouse was found to have dilated cardiomyopathy. Which of the following is the relative risk of developing cardiomyopathy with this drug?

A. 12.5 (Correct Answer)
B. 25.0
C. 13.7
D. 16.3
E. 23.0

Explanation: ***Correct Option: 12.5*** - The **relative risk (RR)** is calculated as the **risk in the exposed group divided by the risk in the unexposed group**: RR = [a/(a+b)] / [c/(c+d)] - **Risk in exposed group** = 13/52 = 0.25 (25%) - **Risk in unexposed group** = 1/50 = 0.02 (2%) - **RR = 0.25 / 0.02 = 12.5** - This indicates that mice exposed to the chemotherapeutic agent are **12.5 times more likely** to develop dilated cardiomyopathy compared to unexposed mice - An **RR > 1** indicates increased risk with exposure, supporting the drug's cardiotoxicity *Incorrect Option: 25.0* - This value results from **miscalculating the unexposed group risk** (e.g., using 0.01 instead of 0.02 as the denominator) - If the unexposed risk was halved incorrectly: 0.25 / 0.01 = 25.0 - This overestimates the relative risk by a factor of 2 *Incorrect Option: 13.7* - This value does not result from the correct **relative risk formula** - May arise from an **arithmetic error** or confusion with other epidemiological measures - The correct calculation of 13/52 ÷ 1/50 does not yield this result *Incorrect Option: 16.3* - This might result from **miscounting the number of subjects** in either group or confusing **relative risk with odds ratio** - The **odds ratio** would be calculated as (13/39) / (1/49) = 16.3 - Remember: **Relative risk uses total exposed/unexposed as denominators**, while odds ratio uses non-diseased counts *Incorrect Option: 23.0* - This value suggests a **fundamental error** in applying the relative risk formula - Could result from using incorrect numerators or denominators (e.g., 13/1 instead of proper risk calculation) - Significantly overestimates the true relative risk of 12.5

Question 7: A research group designs a study to investigate the epidemiology of syphilis in the United States. After a review of medical records, the investigators identify patients who are active cocaine users but did not have a history of syphilis as of one year ago. Another group of similar patients with no history of cocaine use or syphilis infection is also identified. The investigators examine the medical charts to determine whether the group of patients who are actively using cocaine was more likely to have developed syphilis over a 6-month period. The investigators ultimately found that the rate of syphilis was 30% higher in patients with active cocaine use compared to patients without cocaine use. This study is best described as which of the following?

A. Prospective cohort study
B. Case-control study
C. Retrospective cohort study (Correct Answer)
D. Case series
E. Meta-analysis

Explanation: ***Retrospective cohort study*** - This study design examines **past data** (medical records) to identify groups based on exposure status (cocaine use) and then follows them forward in time from pre-existing data to determine the incidence of an outcome (syphilis). - The investigators looked back one year to identify patients without syphilis and then observed if they developed syphilis over a subsequent 6-month period using already recorded information. *Prospective cohort study* - Involves identifying exposure groups (cocaine users vs. non-users) **at the present time** and then following them into the future to observe the development of an outcome. - This study used existing medical records to define past exposure and outcomes, rather than recruiting participants and following them going forward. *Case-control study* - This design starts by identifying individuals with the **outcome (syphilis cases)** and a control group without the outcome, then looks **retrospectively** to determine past exposures (cocaine use) that might have contributed. - The study described here, however, started with exposure status (cocaine use) and looked forward in time for outcomes, which is characteristic of a cohort study. *Case series* - A descriptive study that reports on the characteristics of a group of patients with a particular **disease or exposure**, without a comparison group. - This study included a comparison group of patients without cocaine use, which is beyond the scope of a simple case series. *Meta-analysis* - A systematic review that statistically **combines the results of multiple independent studies** to provide a more precise estimate of an effect. - This study describes a single observational study design, not a synthesis of other studies.

Question 8: A cohort study was conducted to investigate the impact of post-traumatic stress disorder (PTSD) on asthma symptoms in a group of firefighters who worked at Ground Zero during the September 11, 2001 terrorist attacks in New York City and developed asthma in the attack's aftermath. The study compared patients who had PTSD with those who did not have PTSD in order to determine if PTSD is associated with worse asthma control. During a follow-up period of 12 months, the researchers found that patients with PTSD had a greater number of hospitalizations for asthma exacerbations (RR = 2.0, 95% confidence interval = 1.4–2.5) after adjusting for medical comorbidities, psychiatric comorbidities other than PTSD, and sociodemographic variables. Results are shown: ≥ 1 asthma exacerbation No asthma exacerbations PTSD 80 80 No PTSD 50 150 Based on these results, what proportion of asthma hospitalizations in patients with PTSD could be attributed to PTSD?

A. 0.25
B. 4.0
C. 0.50 (Correct Answer)
D. 2.0
E. 3.0

Explanation: ***0.50*** - The **Attributable Risk Percent (AR%)** in the exposed group is calculated as (RR - 1) / RR. Given a Relative Risk (RR) of 2.0, AR% = (2.0 - 1) / 2.0 = 1 / 2.0 = 0.50. - This means that **50% of asthma hospitalizations** in the group with PTSD can be attributed to their PTSD status. *0.25* - This value is obtained by dividing 1 by 4, not relevant to the formula for **attributable risk percent** using the given relative risk. - Incorrectly applying the formula or misinterpreting the RR would lead to this value. *4.0* - This value is not derived from the **attributable risk percent formula** (AR% = (RR - 1) / RR) with the given RR of 2.0. - It might represent a misunderstanding of risk ratios or how to calculate attributable risk. *2.0* - This is the **Relative Risk (RR)** itself, not the proportion of asthma hospitalizations attributable to PTSD. - The RR compares the incidence of an outcome in the exposed group to the unexposed group. *3.0* - This value is not obtained through any standard epidemiological calculation for **attributable risk** given the relative risk of 2.0. - It likely results from an arbitrary calculation or an incorrect application of epidemiological formulas.

Question 9: A prospective cohort study was conducted to assess the relationship between LDL and the incidence of heart disease. The patients were selected at random. Results showed a 10-year relative risk (RR) of 3.0 for people with elevated LDL levels compared to individuals with normal LDL levels. The p-value was 0.04 with a 95% confidence interval of 2.0-4.0. According to the study results, what percent of heart disease in these patients can be attributed to elevated LDL?

A. 67% (Correct Answer)
B. 50%
C. 100%
D. 33%
E. 25%

Explanation: ***67%*** - The **attributable risk percent (AR%)** in the exposed group represents the proportion of disease in the exposed population that can be attributed to the exposure. - It is calculated using the formula: **AR% = [(RR - 1) / RR] × 100** - With an RR of 3.0: [(3.0 - 1) / 3.0] × 100 = (2.0 / 3.0) × 100 = 66.67%, which rounds to **67%** - This means that 67% of heart disease cases among those with elevated LDL can be attributed to the elevated LDL levels. *50%* - This value would result from an RR of 2.0: [(2.0 - 1) / 2.0] × 100 = 50% - The study reports an RR of 3.0, not 2.0, making this incorrect. *100%* - This would imply that all cases of heart disease in the exposed group are due solely to elevated LDL (RR approaching infinity). - An RR of 3.0 indicates elevated LDL increases risk threefold, but does not account for all cases. *33%* - This might result from incorrectly calculating 1/RR = 1/3.0 = 33.3% - The correct formula is (RR - 1)/RR, not 1/RR. *25%* - This would correspond to an RR of 1.33: [(1.33 - 1) / 1.33] × 100 ≈ 25% - The given RR of 3.0 yields a much higher attributable risk percent.

Question 10: A recently published prospective cohort study of 1,000 men reports that smoking is significantly associated with higher rates of esophageal cancer. The next week, however, the journal publishes a letter to the editor in which a re-analysis of the study's data when accounting for the confounding effects of alcohol usage found no association between smoking and esophageal cancer. Which of the following statements is both necessary and sufficient to explain the change in result?

A. Men who smoke are more likely to drink
B. Men who smoke are more likely to get esophageal cancer
C. Men who drink are more likely to get esophageal cancer
D. The change in result is impossible even after adjusting for the confounding effects of alcohol intake
E. Men who drink are both more likely to smoke and more likely to develop esophageal cancer (Correct Answer)

Explanation: ***Men who drink are both more likely to smoke and more likely to develop esophageal cancer*** - This statement explains why **alcohol is a confounder**: it is associated with the exposure (**smoking**) and independently affects the outcome (**esophageal cancer**). - When **alcohol usage** (the confounder) is accounted for in the analysis, the apparent association between smoking and esophageal cancer disappears, indicating that the initial association was misleading. *Men who smoke are more likely to drink* - This statement describes one necessary condition for **confounding** (association between exposure and confounder) but does not include the confounding effect of alcohol on the outcome. - It does not explain fully why adjusting for alcohol would completely negate the association between smoking and esophageal cancer. *Men who smoke are more likely to get esophageal cancer* - This is the initial observation from the study before **confounding** was considered. - It doesn't explain why the association disappears after adjusting for alcohol, as it only describes the initial raw association. *Men who drink are more likely to get esophageal cancer* - This statement describes another necessary condition for **confounding** (association between confounder and outcome) but does not include the association between alcohol and smoking. - It does not fully illustrate the confounding mechanism where alcohol influences both the exposure and the disease. *The change in result is impossible even after adjusting for the confounding effects of alcohol intake* - This statement is incorrect because the scenario explicitly states that the re-analysis, after accounting for **confounding effects**, led to a change in the result. - Confounding is a well-established phenomenon in epidemiology that can indeed alter study results when not properly addressed.

Question 11: Two separate investigators have conducted cohort studies to calculate the risk of lymphoma in rheumatoid arthritis patients taking anti-TNF alpha medications. They each followed patients with rheumatoid arthritis for a number of years and tracked the number of patients who were diagnosed with lymphoma. The results of the two studies are summarized in the table. Number of patients Follow-up period Number of new cases of lymphoma Study 1 3000 10 years 30 Study 2 300 30 years 9 Based on these results, which of the following statements about the risk of lymphoma is most accurate?

A. The risk is higher in study 1, with an incidence rate of 30 cases per 10 person-years
B. The risks are equivalent, with a prevalence of 39 cases per 3300 persons
C. The risks are equivalent, with an incidence rate of 1 case per 1000 person-years (Correct Answer)
D. The risk is higher in study 1, with a prevalence of 30 cases per 3000 patients
E. The risk is higher in study 2, with a cumulative incidence of 9 cases per 300 patients

Explanation: **The risks are equivalent, with an incidence rate of 1 case per 1000 person-years** - To calculate the **incidence rate**, divide the number of new cases by the total person-time at risk. - For Study 1: 30 cases / (3000 persons × 10 years) = 30 / 30,000 = 0.001 cases per person-year, or **1 case per 1000 person-years**. - For Study 2: 9 cases / (300 persons × 30 years) = 9 / 9,000 = 0.001 cases per person-year, or **1 case per 1000 person-years**. - Both studies yield the same incidence rate, indicating equivalent risk. *The risk is higher in study 1, with an incidence rate of 30 cases per 10 person-years* - The calculation "30 cases per 10 person-years" incorrectly uses the follow-up period instead of the total person-time at risk across all participants. - This calculation does not accurately reflect the **incidence rate** as it doesn't account for the number of individuals in the study. *The risks are equivalent, with a prevalence of 39 cases per 3300 persons* - **Prevalence** refers to existing cases at a specific point in time, not new cases over a period. - This calculation incorrectly combines data from two separate studies as if it were a single point prevalence, which is methodologically incorrect. *The risk is higher in study 1, with a prevalence of 30 cases per 3000 patients* - This statement uses a calculation that resembles **cumulative incidence** (new cases divided by initial population) for Study 1, but incorrectly labels it as prevalence. - A cumulative incidence of 30/3000 = 0.01 (or 1%) for Study 1 does not account for the duration of follow-up, making direct comparison with Study 2 (which has a 30-year follow-up) inappropriate. *The risk is higher in study 2, with a cumulative incidence of 9 cases per 300 patients* - Study 2 has a **cumulative incidence** of 9/300 = 0.03 (or 3%) over 30 years. - Comparing cumulative incidence directly between studies with different follow-up durations (10 years vs. 30 years) is not appropriate for assessing the underlying risk rate, as it does not account for the time component.

Question 12: A prospective cohort study was conducted to assess the relationship between LDL and the incidence of heart disease. The patients were selected at random. Results showed a 10-year relative risk of 2.3 for people with elevated LDL levels compared to individuals with normal LDL levels. The 95% confidence interval was 1.05-3.50. This study is most likely to have which of the following p values?

A. 0.20
B. 0.06
C. 0.08
D. 0.04 (Correct Answer)
E. 0.10

Explanation: ***0.04*** - A 95% confidence interval that **does not include 1 (one)** suggests a **statistically significant** association, meaning the p-value is likely to be **less than 0.05**. - The given CI of 1.05-3.50 for the relative risk (RR) is entirely above 1, indicating a significant positive association, and therefore, a p-value less than 0.05. *0.20* - A p-value of 0.20 is **greater than 0.05**, which would imply the finding is **not statistically significant**. - If the p-value were 0.20, the 95% confidence interval would likely **include 1**, suggesting no significant difference in risk. *0.06* - A p-value of 0.06 is **greater than 0.05**, indicating that the association is **not statistically significant at the conventional alpha level**. - If the p-value were 0.06, the 95% confidence interval would likely **include 1**, or be very close to including it, contradicting the given CI of 1.05-3.50. *0.08* - A p-value of 0.08 is **greater than 0.05**, indicating that the finding is **not statistically significant**. - If the p-value were 0.08, the 95% confidence interval would almost certainly **include 1**, which is inconsistent with the provided interval. *0.10* - A p-value of 0.10 is **greater than 0.05**, which signifies that the finding is **not statistically significant**. - If the p-value were 0.10, the 95% confidence interval for the relative risk would typically **include 1**, contradicting the given confidence interval.

Question 13: An investigator is studying the efficacy of preventative measures to reduce pesticide poisonings among Central American farmers. The investigator evaluates the effect of a ban on aldicarb, an especially neurotoxic pesticide of the carbamate class. The ban aims to reduce pesticide poisonings attributable to carbamates. The investigator followed 1,000 agricultural workers residing in Central American towns that banned aldicarb as well as 2,000 agricultural workers residing in communities that continued to use aldicarb over a period of 5 years. The results show: Pesticide poisoning No pesticide poisoning Total Aldicarb ban 10 990 1000 No aldicarb ban 100 1900 2000 Which of the following values corresponds to the difference in risk attributable to the ban on aldicarb?

A. 0.04 (Correct Answer)
B. 0.19
C. 0.8
D. 90
E. 0.2

Explanation: ***0.04*** - The **risk in the ban group** (unexposed to aldicarb) is 10/1000 = 0.01 - The **risk in the no-ban group** (exposed to aldicarb) is 100/2000 = 0.05 - The **attributable risk (risk difference)** is calculated as: Risk in no-ban group - Risk in ban group = 0.05 - 0.01 = **0.04** - This represents the absolute risk reduction achieved by implementing the ban, meaning the ban prevented 4 additional cases per 100 workers *0.19* - This value does not correspond to any standard epidemiological measure calculated from this data - Not the risk difference, relative risk, or odds ratio *0.8* - This represents the **relative risk reduction**: (0.05 - 0.01)/0.05 = 0.8 or 80% - While this shows the ban reduced risk by 80% relative to baseline, the question specifically asks for the **difference in risk** (attributable risk), not the proportional reduction *90* - This represents the **absolute number of excess cases** in the no-ban group that could have been prevented - Calculated as: (100 cases in no-ban group) - (20 cases that would be expected if ban-group rate applied to 2000 workers) = 80, or comparing absolute numbers: 100 - 10 = 90 - However, attributable risk is expressed as a **rate or proportion**, not as an absolute count *0.2* - This value does not match any standard calculation from the provided data - May result from an error in calculation or confusion with other epidemiological measures

Question 14: A clinical study is performed to examine the effect of smoking on the development of pulmonary hypertension (PAH) in a sample of 40-year-old women. A group of 1,000 matched healthy subjects (500 controls; 500 smokers) were monitored for the development of (PAH) from enrollment to death. The data from the study are shown in the table below: Group\PAH Yes No Smokers 35 465 Controls 20 480 Which of the following is correct regarding the risk of developing PAH from this study?

A. The lifetime absolute risk increase of developing PAH in female smokers is 3%. (Correct Answer)
B. The lifetime absolute risk of developing PAH in healthy non-smoking women is 3%.
C. The increase in the absolute risk of developing PAH by quitting smoking is 75%.
D. The absolute risk of developing PAH in smokers versus controls is 1.75.
E. The lifetime absolute risk of developing PAH in healthy nonsmoking women is 5.5%.

Explanation: ***The lifetime absolute risk increase of developing PAH in female smokers is 3%.*** - The **absolute risk reduction** (ARR) is the difference in risk between the exposed group (smokers) and the unexposed group (controls). For smokers, the risk of PAH is 35/500 = 0.07 (7%). For controls, the risk is 20/500 = 0.04 (4%). The absolute risk increase for smokers is 7% - 4% = **3%**. - This value represents the additional risk of developing PAH attributable to smoking in this population. *The lifetime absolute risk of developing PAH in healthy non-smoking women is 3%.* - The **absolute risk** of developing PAH in **healthy non-smoking women (controls)** is 20/500, which equals **0.04 or 4%**. - The 3% presented in this option is incorrect; it actually represents the **absolute risk increase** for smokers. *The increase in the absolute risk of developing PAH by quitting smoking is 75%.* - This statement implies a calculation of **relative risk reduction** or similar, but the value of 75% is not directly obtained from the provided data as an increase in absolute risk by quitting. - Quitting smoking would lead to a reduction in risk, not an increase, and 75% does not correspond to a direct calculation of risk difference or ratio. *The absolute risk of developing PAH in smokers versus controls is 1.75.* - An absolute risk value cannot be 1.75, as risk is typically expressed as a **proportion or percentage (0 to 1 or 0% to 100%)**. - This value of 1.75 might be the **relative risk**, calculated as (Risk in smokers / Risk in controls) = (0.07 / 0.04) = 1.75. However, this is a **relative risk**, not an absolute risk. *The lifetime absolute risk of developing PAH in healthy nonsmoking women is 5.5%.* - The **absolute risk** of developing PAH in **healthy non-smoking women (controls)** is 20/500, which equals **0.04 or 4%**. - The value of 5.5% is incorrect for the control group's absolute risk.

Question 15: In recent years, psoriasis has been identified as a risk factor for cardiovascular disease. A researcher conducted a study in which he identified 200 patients with psoriasis and 200 patients without psoriasis. The patients were followed for 10 years. At the end of this period, participants' charts were reviewed for myocardial infarction during this time interval. Myocardial infarction No myocardial infarction Total Psoriasis 12 188 200 No psoriasis 4 196 200 Total 16 384 400 What is the 10-year risk of myocardial infarction in participants with psoriasis?

A. 0.75
B. 0.04
C. 0.5
D. 0.06 (Correct Answer)
E. 0.02

Explanation: ***0.06*** - The **risk of myocardial infarction** in participants with psoriasis is calculated by dividing the number of psoriasis patients who had a myocardial infarction by the total number of psoriasis patients. - This calculation is 12 (myocardial infarctions in psoriasis group) / 200 (total psoriasis patients) = **0.06 or 6%**. - This represents the **cumulative incidence** or **absolute risk** in the exposed cohort over 10 years. *0.75* - This value represents the **proportion of all MI cases that occurred in the psoriasis group**: 12/16 = 0.75. - This is not the same as risk, which requires the denominator to be the total at-risk population (all psoriasis patients), not just those with the outcome. *0.04* - This value represents the **risk of myocardial infarction in the control group** (no psoriasis): 4/200 = 0.02, not 0.04. - However, 0.04 could represent 2 × 0.02, which has no meaningful epidemiological interpretation for this study. *0.5* - This value does not correspond to any standard epidemiological measure from the given data. - It might represent a miscalculation or confusion with other statistical concepts. *0.02* - This value represents the **risk of myocardial infarction in the unexposed group** (no psoriasis): 4/200 = 0.02 or 2%. - The question specifically asks for the risk in the psoriasis group, not the control group.

Question 16: Researchers are studying a farming community with a high incidence of acute myelogenous leukemia (AML). A retrospective cohort study is performed looking at the relationship between exposure to a certain pesticide chemical and the risk of developing AML. In 84 patients who developed AML, 17 had exposure to the pesticide chemical. In the control group of 116 patients, 2 had exposure to the chemical. What is the relative risk of developing AML upon exposure to the pesticide in this study group?

A. Number of exposed with AML (17) divided by the total number of AML cases (84)
B. Total number of cases (84) divided by the total number of study participants (200)
C. Probability of AML among exposed (17/19) divided by probability of AML among unexposed (67/181) (Correct Answer)
D. Odds of exposure in the cases (17/67) divided by odds of exposure in the controls (2/114)
E. Prevalence of cases (84/200) divided by prevalence of controls (116/200)

Explanation: ***Probability of AML among exposed (17/19) divided by probability of AML among unexposed (67/181)*** - This calculation correctly applies the formula for **relative risk** in a cohort study. It compares the **incidence of AML in the exposed group** to the **incidence of AML in the unexposed group**. - The number **17** represents the exposed individuals who developed AML, and the total exposed population is **19** (17 cases + 2 controls). The number **67** represents the unexposed individuals who developed AML (84 total AML cases - 17 exposed AML cases), and the total unexposed population is **181** (116 total controls + 67 unexposed AML cases - 2 exposed controls). *Number of exposed with AML (17) divided by the total number of AML cases (84)* - This calculation represents the **proportion of AML cases that were exposed**, not the relative risk. - It does not account for the **total number of exposed or unexposed individuals** in the study population. *Total number of cases (84) divided by the total number of study participants (200)* - This calculation gives the **overall incidence of AML** in the entire study population, not the relative risk associated with pesticide exposure. - Relative risk specifically compares **risk between exposed and unexposed groups**. *Odds of exposure in the cases (17/67) divided by odds of exposure in the controls (2/114)* - This calculation determines the **odds ratio**, which is used in **case-control studies**, not the relative risk used in cohort studies. - The odds ratio estimates relative risk when the outcome is rare, but it is not a direct measure of relative risk. *Prevalence of cases (84/200) divided by prevalence of controls (116/200)* - This calculation is incorrect for determining relative risk. It compares the **overall proportion of cases to the overall proportion of controls** from the entire study population. - It does not differentiate the incidence of disease between the **exposed and unexposed groups**.

Question 17: A 45-year-old man comes to the clinic concerned about his recent exposure to radon. He heard from his co-worker that radon exposure can cause lung cancer. He brings in a study concerning the risks of radon exposure. In the study, there were 300 patients exposed to radon, and 18 developed lung cancer over a 10-year period. To compare, there were 500 patients without radon exposure and 11 developed lung cancer over the same 10-year period. If we know that 0.05% of the population has been exposed to radon, what is the attributable risk percent for developing lung cancer over a 10 year period after radon exposure?

A. 3.8%
B. 0.31%
C. 2.2%
D. 6.0%
E. 63.3% (Correct Answer)

Explanation: ***63.3%*** - The **attributable risk percent (ARP)** quantifies the proportion of disease in the exposed group that is attributable to the exposure. It is calculated as [(Incidence in exposed - Incidence in unexposed) / Incidence in exposed] * 100. - In this case, **Incidence in exposed (radon)** = 18/300 = 0.06 or 6%. **Incidence in unexposed** = 11/500 = 0.022 or 2.2%. Therefore, ARP = [(0.06 - 0.022) / 0.06] * 100 = (0.038 / 0.06) * 100 = **63.3%**. *3.8%* - This value represents the difference in the **absolute risk** or incidence between the exposed and unexposed groups (6% - 2.2% = 3.8%). - It does not represent the proportion of disease in the exposed group that is due to the exposure. *0.31%* - This value is not derived from the given data using standard epidemiological formulas for attributable risk percent. - It is possibly a miscalculation or an irrelevant measure in this context. *2.2%* - This value represents the **incidence of lung cancer in the unexposed group** (11/500 = 0.022 or 2.2%). - It is a component of the ARP calculation but not the ARP itself. *6.0%* - This value represents the **incidence of lung cancer in the radon-exposed group** (18/300 = 0.06 or 6%). - It is used in the numerator and denominator for calculating the attributable risk percent but is not the final ARP.

Question 18: A researcher has identified a chemical compound that she expects may contribute to the development of colorectal cancer. She designs an experiment where she exposes 70 mice to a diet containing this compound with another 50 mice in a control group that was fed a regular diet. After 9 months, the mice were evaluated for tumor development at necropsy. In total, 14 mice in the experimental group developed colorectal tumor burden, and 1 mouse in the control group developed tumors. Based on this experiment, what risk of colorectal cancer can be attributable to this chemical compound?

A. 22.0%
B. 2.0%
C. 12.5%
D. 18.0% (Correct Answer)
E. 20.0%

Explanation: ***18.0%*** - The **attributable risk (AR)** is calculated as the **incidence in the exposed group (Ie)** minus the **incidence in the unexposed group (Iu)**. - In this case, **Ie = 14/70 = 0.20** and **Iu = 1/50 = 0.02**. Therefore, **AR = 0.20 - 0.02 = 0.18**, or **18.0%**. *22.0%* - This value might result from an incorrect calculation or misinterpretation of the attributable risk formula. - It does not correctly represent the difference in risk between the exposed and unexposed groups. *2.0%* - This represents the **incidence of colorectal tumors in the control group (Iu)**, not the attributable risk. - The attributable risk accounts for the excess risk specifically due to the exposure. *12.5%* - This value is not derived from the standard formula for attributable risk using the provided data. - It might represent a misunderstanding of how to calculate risk difference from incidence rates. *20.0%* - This represents the **incidence of colorectal tumors in the experimental group (Ie)**, not the attributable risk. - The attributable risk needs to subtract the background risk observed in the unexposed group.

Question 19: A prospective cohort study is conducted to evaluate the risk of pleural mesothelioma in construction workers exposed to asbestos in Los Angeles. Three hundred construction workers reporting current occupational asbestos exposure were followed alongside 300 construction workers without a history of asbestos exposure. After 8 years of follow-up, no statistically significant difference in the incidence of pleural mesothelioma was observed between the two groups (p = 0.13), even after controlling for known mesothelioma risk factors such as radiation, age, and sex. Which of the following is the most likely explanation for the observed results of this study?

A. Berkson bias
B. Lead-time bias
C. Observer effect
D. Latency period (Correct Answer)
E. Length-time bias

Explanation: ***Latency period*** - Asbestos-related pleural mesothelioma has a **long latency period**, typically 20-50 years, between initial exposure and the development of clinical disease. - An 8-year follow-up period is likely too short to observe a **statistically significant incidence** of mesothelioma, even in an exposed cohort. *Berkson bias* - This is a form of **selection bias** that occurs in studies using hospital-based controls, where exposure rates may differ between hospital patients and the general population due to varying admission probabilities for different diseases. - The given study is a **prospective cohort study** of a specific occupational group, not a case-control study based in a hospital, making Berkson bias unlikely. *Lead-time bias* - This bias occurs when early detection of a disease (e.g., through screening) falsely appears to prolong survival due to the **earlier diagnosis**, not due to an actual improvement in the course of the disease. - The study is assessing the **incidence** of mesothelioma in exposed vs. unexposed groups, not comparing survival outcomes based on screening, so lead-time bias is not relevant here. *Observer effect* - Also known as the **Hawthorne effect**, this occurs when individuals modify an aspect of their behavior in response to their awareness of being observed. - The study is observing the development of a disease (mesothelioma), which is not subject to behavioral changes due to observation. *Length-time bias* - This bias arises in screening programs where individuals with **slowly progressing diseases** are more likely to be detected by screening because their disease is present for a longer "detectable" period. - The study is focused on the **incidence** of a disease in a cohort, not on the effectiveness or impact of a screening program, rendering length-time bias an irrelevant explanation.

Question 20: You have been entrusted with the task of finding the causes of low birth weight in infants born in the health jurisdiction for which you are responsible. In 2017, there were 1,500 live births and, upon further inspection of the birth certificates, 108 of these children had a low birth weight (i.e. lower than 2,500 g), while 237 had mothers who smoked continuously during pregnancy. Further calculations have shown that the risk of low birth weight in smokers was 14% and in non-smokers, it was 7%, while the relative risk of low birth weight linked to cigarette smoking during pregnancy was 2%. In other words, women who smoked during pregnancy were twice as likely as those who did not smoke to deliver a low-weight infant. Using this data, you are also asked to calculate how much of the excess risk for low birth weight, in percentage terms, can be attributed to smoking. What is the attributable risk percentage for smoking leading to low birth weight?

A. 40%
B. 30%
C. 20%
D. 10%
E. 50% (Correct Answer)

Explanation: ***50%*** - This value is calculated using the formula for **attributable risk percent (ARP)** in the exposed group: ARP = ((Risk in exposed - Risk in unexposed) / Risk in exposed) × 100. - Given that the risk of low birth weight in smokers (exposed) is 14% and in non-smokers (unexposed) is 7%, the calculation is ((0.14 - 0.07) / 0.14) × 100 = (0.07 / 0.14) × 100 = **0.50 × 100 = 50%**. *40%* - This percentage does not align with the provided risk values for low birth weight in smokers (14%) and non-smokers (7%). - A calculation of ((0.14 - 0.07) / 0.14) * 100 does not yield 40%. *30%* - This value is incorrect, as it would suggest a smaller difference in risk between the exposed and unexposed groups relative to the risk in the exposed group than what is presented in the problem. - The calculated attributable risk percent is higher than 30%. *20%* - This option is significantly lower than the true attributable risk percent derived from the given risk figures. - It would imply a much weaker association between smoking and low birth weight in terms of excess risk than what is calculated. *10%* - This value is substantially different from the correct calculation and would suggest a very minor attributable risk. - The attributable risk percent for smoking leading to low birth weight is much higher than 10% based on the provided data.

Question 21: Clinical study looks at the effect of childhood exposure of 2nd-hand smoking on the incidence of bronchogenic adenocarcinoma (BA). Study of 100 subjects (50 exposed to childhood 2nd-hand smoking and 50 healthy controls with no childhood exposure) involves monitoring the lifetime incidence of BA data from the study are shown in the table below: Group\BA Dx Yes No Exposed 18 32 Controls 7 43 Which of the following statements is correct regarding the number needed to harm (NNH) based on this study?

A. If the absolute risk in the exposed group increases, the NNH increases.
B. If the incidence of BA increases in the control group, the NNH will decrease.
C. The NNH is inversely correlated with the relative risk increase. (Correct Answer)
D. If the incidence of BA increases in the experimental group, the NNH will increase.
E. The NNH is approximately 4.5.

Explanation: ***The NNH is inversely correlated with the relative risk increase.*** - The **number needed to harm (NNH)** is defined as 1 divided by the **absolute risk increase (ARI)**. The formula is NNH = 1 / ARI. Since **relative risk increase (RRI)** is directly proportional to **absolute risk increase (ARI)** when baseline risk is constant, NNH is inversely correlated with RRI. - Mathematically, NNH is 1/(Incidence in exposed - Incidence in unexposed). A larger increase in relative risk implies a larger **absolute risk increase**, which would result in a smaller NNH. *The NNH is approximately 4.5.* - The **incidence** in the exposed group is 18/50 = 0.36. The **incidence** in the control group is 7/50 = 0.14. - The **absolute risk increase (ARI)** = Incidence (exposed) - Incidence (control) = 0.36 - 0.14 = 0.22. Therefore, NNH = 1/0.22 ≈ 4.5. This option is incorrect because it accurately calculates the NNH, making it a factually correct statement, but it is not the **best answer** to what the question asks. *If the absolute risk in the exposed group increases, the NNH increases.* - An increase in the **absolute risk in the exposed group** would lead to a larger **absolute risk increase (ARI)**, assuming the control group risk remains constant or increases less proportionally. - Since NNH = 1/ARI, a larger ARI would result in a **smaller NNH**, not an increase. *If the incidence of BA increases in the control group, the NNH will decrease.* - If the **incidence of BA in the control group** increases, the **absolute risk increase (ARI)** (Incidence in exposed - Incidence in control) would likely decrease (assuming the exposed incidence stays the same or increases less proportionally). - A decrease in ARI would lead to an **increase in NNH**, not a decrease, because NNH is inversely related to ARI. *If the incidence of BA increases in the experimental group, the NNH will increase.* - An increase in the **incidence of BA in the experimental (exposed) group** would lead to a larger **absolute risk increase (ARI)**, assuming the control group incidence remains constant. - A larger ARI would result in a **smaller NNH**, not an increase, as NNH is inversely proportional to ARI.

Question 22: You are attempting to quantify the degree of infectivity of a novel respiratory virus. You assess 1,000 patients who have been exposed to the virus and find that 500 ultimately are found positive for the virus within a 1-year follow up period. Conversely, from a 1,000 patient control group who has not been exposed to carriers of the virus, only 5 became positive over the same 1-year period. What is the relative risk of a contracting this virus if exposed?

A. (500 * 995) / (500 * 5)
B. [995 / (995 + 5)] / [500 / (500 + 500)]
C. (500 * 5) / (500 * 995)
D. [5 / (500 + 500)] / [5 / (995 + 995)]
E. (500/1000) / (5/1000) (Correct Answer)

Explanation: ***(500/1000) / (5/1000)*** - This formula correctly calculates the **relative risk (RR)**, which is the **risk in the exposed group divided by the risk in the unexposed group**. - The risk in the exposed group is 500 cases out of 1000 exposed individuals (500/1000 = 0.5 or 50%). - The risk in the unexposed group is 5 cases out of 1000 unexposed individuals (5/1000 = 0.005 or 0.5%). - RR = 0.5/0.005 = 100, indicating that exposed individuals are 100 times more likely to contract the virus. *(500 * 995) / (500 * 5)* - This calculation represents an incorrect attempt at calculating an **odds ratio**. - While odds ratio uses the formula (a×d)/(b×c) from a 2×2 table, this specific formulation does not correctly represent the odds ratio for this scenario. - Relative risk and odds ratio are different measures; this question specifically asks for relative risk. *[995 / (995 + 5)] / [500 / (500 + 500)]* - This formula incorrectly calculates the ratio of **non-event rates** rather than event rates and inverts the comparison groups. - It compares those who did NOT get infected in the unexposed group to the infection risk in the exposed group, which is not relative risk. *(500 * 5) / (500 * 995)* - This formula is an incorrect mathematical expression that does not represent any valid epidemiological measure. - It does not follow the proper structure for calculating relative risk, odds ratio, or any other standard risk measure. *[5 / (500 + 500)] / [5 / (995 + 995)]* - This formula uses **incorrect denominators** that do not represent the actual study populations. - The denominators (500+500=1000 and 995+995=1990) are mathematically wrong; the second denominator should be 1000, not 1990. - It also incorrectly uses 5 cases in both the numerator and denominator groups.

Question 23: A rheumatologist is evaluating the long-term risk of venous thromboembolism in patients with newly diagnosed rheumatoid arthritis by comparing two retrospective cohort studies. In study A, the hazard ratio for venous thromboembolism was found to be 1.7 with a 95% confidence interval of 0.89–2.9. Study B identified a hazard ratio for venous thromboembolism of 1.6 with a 95% confidence interval of 1.1–2.5. Which of the following statements about the reported association in these studies is most accurate?

A. The power of study B is likely smaller than the power of study A.
B. The p-value of study A is likely larger than the p-value of study B. (Correct Answer)
C. Study A likely had a larger sample size than study B.
D. The HR of study B is less likely to be statistically significant than the HR of study A.
E. The results of study B are less likely to be accurate than the results of study A.

Explanation: ***The p-value of study A is likely larger than the p-value of study B.*** * **Study A's 95% confidence interval (0.89-2.9) crosses the null value of 1**, indicating that the hazard ratio is **not statistically significant** at the 0.05 level, hence a p-value > 0.05. * **Study B's 95% confidence interval (1.1-2.5) does not cross the null value of 1**, indicating that the hazard ratio is **statistically significant** at the 0.05 level, hence a p-value < 0.05. Therefore, study A's p-value is likely larger. *The power of study B is likely smaller than the power of study A.* * Study B shows a **statistically significant result**, suggesting **adequate power** to detect an effect. * Study A's **non-significant result** (confidence interval crossing 1) could be due to **insufficient power** to detect a true effect, implying that its power might actually be smaller than study B's, or at least not larger. *Study A likely had a larger sample size than study B.* * A **wider confidence interval (Study A: 0.89-2.9)** often suggests a **smaller sample size** or greater variability, as smaller sample sizes lead to less precise estimates. * A **narrower confidence interval (Study B: 1.1-2.5)** typically indicates a **larger sample size** and more precision. *The HR of study B is less likely to be statistically significant than the HR of study A.* * **Study B's confidence interval (1.1-2.5) does not include 1**, meaning its hazard ratio is **statistically significant**. * **Study A's confidence interval (0.89-2.9) includes 1**, meaning its hazard ratio is **not statistically significant**. This statement is therefore incorrect. *The results of study B are less likely to be accurate than the results of study A.* * **Study B's results are statistically significant**, providing stronger evidence for an association given the data. * **Study A's results are not statistically significant**, suggesting a lack of sufficient evidence in that particular study for the reported association.

Question 24: A prospective cohort study was conducted to assess the relationship between LDL-C and the incidence of heart disease. The patients were selected at random. Results showed a 10-year relative risk (RR) of 2.30 for people with elevated LDL-C levels compared to individuals with normal LDL levels. The p value was 0.04. This study is most likely to have which of the following 95% confidence intervals?

A. 1.00-3.60
B. 0.07-3.30
C. 0.09-3.50
D. 1.01-3.70 (Correct Answer)
E. 0.08-3.40

Explanation: ***1.01-3.70*** - A **p-value of 0.04** indicates statistical significance, meaning the **95% confidence interval** for the relative risk (RR) should **not include 1.0**. - Given an RR of **2.30**, a confidence interval that is entirely above 1.0, such as **1.01-3.70**, is consistent with a statistically significant finding where the exposure (elevated LDL-C) is associated with an increased risk of heart disease. *1.00-3.60* - This interval includes the value **1.00**, which would suggest no statistically significant difference in risk, or a **p-value above 0.05**. - Since the p-value is **0.04** (which is less than 0.05), the lower bound of the confidence interval must be greater than 1.00. *0.07-3.30* - This confidence interval includes values far below **1.0**, indicating a statistically insignificant result and suggesting a possible protective effect or no association, which contradicts the given **p-value of 0.04** and an RR of 2.30. - An interval that spans both below and above 1.0 would have a **p-value greater than 0.05**. *0.09-3.50* - This confidence interval, similar to 0.07-3.30, encompasses values less than **1.0**, implying a non-significant finding (p > 0.05). - This contradicts the given **p-value of 0.04**, which indicates a statistically significant increased risk. *0.08-3.40* - This confidence interval also includes values less than **1.0**, suggesting a non-significant association between LDL-C and heart disease. - This is inconsistent with the provided **p-value of 0.04**, which indicates a statistically significant increased risk.

Question 25: A cohort study was done to assess the differential incidence of diabetes in patients consuming a typical western diet, versus those consuming a Mediterranean diet. A total of 600 subjects were included with 300 in each arm. Results are as follows: Diabetes development No-diabetes development Western diet 36 264 Mediterranean diet 9 291 What is the odds ratio of developing diabetes for a given subject consuming the western diet as compared to a subject who consumes the Mediterranean diet?

A. 4.4 (Correct Answer)
B. 6.7
C. 5.6
D. 3.2
E. 1.0

Explanation: ***4.4*** - The **Odds Ratio (OR)** is calculated as (odds of outcome in exposed group) / (odds of outcome in unexposed group). - For the western diet, the odds of developing diabetes are 36/264. For the Mediterranean diet, the odds are 9/291. Therefore, OR = (36/264) / (9/291) = 0.13636 / 0.03093 = **4.408**, which rounds to 4.4. *6.7* - This value would be obtained if there was an error in calculating the ratios or the division step. - An OR of 6.7 would imply a significantly higher association than the actual data suggests. *5.6* - This result is incorrect and would likely arise from an arithmetic mistake in the calculation. - It does not accurately reflect the ratio of the odds of developing diabetes between the two groups based on the provided data. *3.2* - This value is not derived from the correct application of the odds ratio formula to the given data. - It suggests a weaker association than what is truly represented by the numbers for diabetes development in each diet group. *1.0* - An odds ratio of 1.0 indicates **no association** between the exposure (western diet) and the outcome (diabetes). - The given data clearly shows a higher incidence of diabetes in the western diet group, so an OR of 1.0 is incorrect.

Question 26: In a community of 5,000 people, 40 people from 40 different households develop an infection with a new strain of influenza virus with an incubation period of 7 days. The total number of people in these households is 150. Ten days later, 90 new cases of the same disease are reported from these same households. Twenty-five more cases are reported from these households after a month. The total number of cases reported after a month from this community is 1,024. What is the secondary attack rate for this infection?

A. (115/150) × 100
B. (90/150) × 100
C. (115/1024) × 100
D. (90/110) × 100 (Correct Answer)
E. (90/5000) × 100

Explanation: ***(90/110) × 100*** - The **secondary attack rate** is calculated by dividing the number of new cases among contacts by the total number of susceptible contacts. - In this scenario, there were 40 primary cases, leaving 110 susceptible contacts (150 total household members - 40 primary cases). - The 90 new cases reported after 10 days represent **secondary transmission** within these households and occurred within a plausible timeframe (within 1-2 incubation periods of 7 days). - The additional 25 cases after a month represent **tertiary or later generation transmission** and are not included in the secondary attack rate calculation. *(115/150) × 100* - This calculation incorrectly includes the **primary cases** in the denominator (150 total household members), rather than only the susceptible contacts. - It also incorrectly sums the 90 secondary cases and 25 tertiary cases in the numerator (115 total), but secondary attack rate only measures the **first wave of transmission** from primary cases to their contacts. *(90/150) × 100* - This option incorrectly uses the total number of household members (150) as the denominator, failing to subtract the **primary cases** to determine the susceptible population. - The numerator (90 secondary cases) is correct, but the denominator must exclude those already infected. *(115/1024) × 100* - This calculation incorrectly mixes the **secondary attack rate** within households with the cumulative incidence in the entire community (1,024 cases), which are distinct epidemiological measures. - The numerator incorrectly combines secondary and tertiary cases (115), and the denominator represents the wrong population at risk. *(90/5000) × 100* - This formula represents the **attack rate** in the entire community (population of 5,000) for these 90 cases, not the secondary attack rate within households. - The secondary attack rate specifically measures transmission within a defined contact group (households), not the general population.

Question 27: A popular news outlet recently published an article that discussed the size of low-density lipoprotein (LDL) cholesterol particles: type A and type B. Type B is thought to be more harmful to arterial walls. A group of researchers wants to determine whether patients who have an elevated level of type B LDL cholesterol are more likely to develop cardiovascular events. A study is designed with 3418 adult participants. Initial levels of type B LDL are obtained and participants are separated into normal and elevated levels of type B LDL. Socio-demographics including age, gender, education level, and smoking status are also recorded. The primary outcome is incidence of cardiovascular events over 10 years. Secondary outcomes include all-cause death, death by cardiovascular events, stroke, and hospitalizations. For this study, which of the following analyses would be the most appropriate measure to determine the association between type B LDL and cardiovascular events?

A. Analysis of covariance
B. Fisher’s exact test
C. Likelihood ratios
D. Relative risk (Correct Answer)
E. Odds ratio

Explanation: ***Relative risk*** - **Relative risk** is the most appropriate measure for **cohort studies** to determine the likelihood of an event in an exposed group compared to an unexposed group. - This study prospectively follows participants with and without elevated type B LDL to observe the **incidence of cardiovascular events** over 10 years, which perfectly aligns with the calculation and interpretation of relative risk. *Analysis of covariance* - **ANCOVA** is used to compare means across groups while statistically controlling for the effects of one or more **covariates**. - While covariates like age and smoking status are collected, ANCOVA is not the primary measure for assessing the association between an exposure (Type B LDL) and the incidence of an outcome (cardiovascular events) in this **cohort study design**. *Fisher’s exact test* - This test is used for analyzing **categorical data** in **small sample sizes** or when expected cell counts are low, typically in 2x2 contingency tables. - Given the large sample size (3418 participants) and the prospective nature of the study, it would not be the most appropriate primary analytical tool for determining the risk association. *Likelihood ratios* - **Likelihood ratios** are used to assess the **diagnostic accuracy of a test**, indicating how much a positive or negative test result changes the probability of a disease. - This study is focused on the **prognostic association** between an exposure and an outcome, not the diagnostic performance of a test. *Odds ratio* - The **odds ratio** is primarily used in **case-control studies** or cross-sectional studies where the incidence of the outcome cannot be directly calculated. - While it can approximate relative risk when the outcome is rare, this is a **cohort study** where the **incidence** of cardiovascular events can be directly measured, making relative risk more suitable.

Question 28: A multicentric, ambidirectional cohort study (i.e. a study that combines elements of both prospective and retrospective cohort studies) was designed in order to evaluate the relationship between nasal colonization with methicillin-resistant Staphylococcus aureus (MRSA) and exposure to patients in intensive-care units of several tertiary hospital centers. The sample included 1,000 physicians who worked in the hospital environment and who willingly underwent swabbing of their nasal vestibule and nasopharynx for active surveillance. Data of their working location was obtained from hospital administrative services. Of those who worked in the intensive care unit, 350 were colonized with MRSA, while 250 were not. Whereas in those that worked in other hospital wards, 100 were colonized with MRSA, and 300 were not. What is the relative risk of MRSA colonization in relation to working in the intensive-care unit?

A. 3.22
B. 2.33 (Correct Answer)
C. 0.43
D. 1.66
E. 0.18

Explanation: ***2.33*** - The relative risk (RR) is calculated as the incidence of the outcome in the exposed group divided by the incidence of the outcome in the unexposed group. - In this case, the incidence of MRSA colonization in ICU workers is 350 / (350 + 250) = 350 / 600 = 0.5833. The incidence in non-ICU workers is 100 / (100 + 300) = 100 / 400 = 0.25. Therefore, the **RR = 0.5833 / 0.25 = 2.33**. *3.22* - This value is obtained if the calculation is performed incorrectly, for example, by misidentifying the exposed and unexposed groups or by incorrectly calculating the incidences. - It does not reflect the correct ratio of MRSA colonization rates between the two groups. *0.43* - This value represents the **inverse of the relative risk** (1/2.33) if the exposed and unexposed groups were swapped in the calculation. - It would suggest a protective effect of working in the ICU, which is not supported by the data. *1.66* - This value is a result of an incorrect calculation of the incidences in either the exposed or unexposed groups. - It significantly underestimates the actual relative risk of MRSA colonization associated with working in an ICU. *0.18* - This value would arise from a substantial error in the calculation, possibly by inverting the incidences or using an inappropriate formula. - It suggests a **strong protective effect** of ICU exposure, which is contrary to the observed data of higher colonization rates in ICU workers.

Question 29: The study is performed in an attempt to determine whether there is an association between maternal exposure to 2nd-hand smoke and low birth weight. A total of 1,000 women who have given birth to at least 1 child are placed into 1 of 2 groups according to the birth weight of their 1st child. Each group includes 500 women whose 1st child either weighed < 2,500 g (5.5 lb) or > 2,500 g (5.5 lb). In the 1st group, 250 subjects admitted to living with or being in close proximity to a smoker. In the 2nd group, 50 subjects admitted to living with or being in close proximity to a smoker. Which of the following is the strongest measure of association that can be calculated from this study?

A. Rate ratio
B. Odds ratio (Correct Answer)
C. Relative risk
D. Absolute risk
E. Risk difference

Explanation: ***Odds ratio*** - This study design is a **case-control study**, where individuals are selected based on their outcome (low birth weight vs. normal birth weight) and then exposure history is ascertained. - In case-control studies, the **odds ratio** is the primary and strongest measure of association as it estimates the relative odds of exposure among cases compared to controls. *Rate ratio* - **Rate ratios** are used in **cohort studies** or **experimental studies** where incidence rates can be calculated over a period of time. - This study does not provide information on incidence rates of low birth weight over time, making a rate ratio inappropriate. *Relative risk* - **Relative risk (RR)** is typically calculated in **cohort studies** where a direct measure of the incidence of an outcome in exposed versus unexposed groups is available. - This study is designed backwards from outcome to exposure, so RR cannot be directly calculated. *Absolute risk* - **Absolute risk** refers to the probability of an event occurring in a population over a specified period. - While absolute risk can be calculated for each group, it does not provide a measure of association between exposure and outcome in this study design. *Risk difference* - **Risk difference** is the difference in absolute risk between exposed and unexposed groups, primarily used in **cohort studies** or **randomized controlled trials**. - Like relative risk, it is not the most appropriate measure for a case-control study design.

Question 30: You have been asked to quantify the relative risk of developing bacterial meningitis following exposure to a patient with active disease. You analyze 200 patients in total, half of which are controls. In the trial arm, 30% of exposed patients ultimately contracted bacterial meningitis. In the unexposed group, only 1% contracted the disease. Which of the following is the relative risk due to disease exposure?

A. (30 * 99) / (70 * 1)
B. [30 / (30 + 70)] / [1 / (1 + 99)] (Correct Answer)
C. [70 / (30 + 70)] / [99 / (1 + 99)]
D. [[1 / (1 + 99)] / [30 / (30 + 70)]]
E. (70 * 1) / (30 * 99)

Explanation: ***[30 / (30 + 70)] / [1 / (1 + 99)]*** - This formula correctly calculates the **relative risk (RR)**. The numerator represents the **incidence rate in the exposed group** (30% of 100 exposed patients = 30 cases out of 100), and the denominator represents the **incidence rate in the unexposed group** (1% of 100 unexposed patients = 1 case out of 100). - Relative risk is the ratio of the **risk of an event** in an **exposed group** to the **risk of an event** in an **unexposed group**. *[(30 * 99) / (70 * 1)]* - This formula is for calculating the **odds ratio (OR)**, specifically using a 2x2 table setup where 30 represents exposed cases, 70 represents exposed non-cases, 1 represents unexposed cases, and 99 represents unexposed non-cases. - The odds ratio is a measure of association between an exposure and an outcome, representing the **odds of an outcome** given exposure compared to the odds of the outcome without exposure. *[70 / (30 + 70)] / [99 / (1 + 99)]* - This formula calculates the **relative risk of *not* developing the disease**, which is the inverse of what the question asks for. - It compares the proportion of exposed individuals who *do not* contract the disease to the proportion of unexposed individuals who *do not* contract the disease. *[[1 / (1 + 99)] / [30 / (30 + 70)]]* - This formula calculates the **inverse of the relative risk**, which is not what the question asks for. - It would represent the ratio of the incidence in the unexposed group to the incidence in the exposed group. *[(70 * 1) / (30 * 99)]* - This is an **incorrect variation** of the odds ratio calculation, with the terms in the numerator and denominator swapped compared to the standard formula. - Therefore, it does not represent the relative risk or a correctly calculated odds ratio.

Question 31: A new treatment for hemorrhagic stroke, which is a life-threatening clinical condition that occurs when a diseased blood vessel in the brain ruptures or leaks, was evaluated as soon as it hit the market by an international group of neurology specialists. In those treated with the new drug, a good outcome was achieved in 30%, while those treated with the current standard of care had a good outcome in just 10% of cases. The clinicians involved in this cohort study concluded that the newer drug is more effective and prompted for urgent changes in the guidelines addressing hemorrhagic stroke incidents. According to the aforementioned percentages, how many patients must be treated with the new drug to see 1 additional good outcome?

A. 5 (Correct Answer)
B. 30
C. 20
D. 15
E. 10

Explanation: ***Correct: 5*** - This is calculated using the concept of **Number Needed to Treat (NNT)**, which tells us how many patients need to receive the new treatment to see one additional good outcome compared to standard care. - The **Absolute Risk Reduction (ARR)** is the difference in good outcome rates: 30% - 10% = 20% (or 0.20 as a proportion). - **NNT = 1 / ARR = 1 / 0.20 = 5** - Therefore, treating 5 patients with the new drug will result in 1 additional patient with a good outcome compared to standard care. *Incorrect: 30* - This represents the **percentage of patients** who achieved a good outcome with the new drug, not the number needed to treat. - It does not account for the baseline effectiveness of standard treatment, which is essential for calculating the marginal benefit. - This is the absolute event rate in the treatment group, not a comparative measure. *Incorrect: 20* - This is the **Absolute Risk Reduction (ARR)** expressed as a percentage (30% - 10% = 20%). - While this is a key component in calculating NNT, it is not the NNT itself. - The NNT requires taking the reciprocal of the ARR when expressed as a proportion: 1/0.20 = 5. *Incorrect: 15* - This number does not correspond to any standard epidemiological or biostatistical measure in this context. - It is neither the ARR, NNT, relative risk, nor any other interpretable value from the given data. - This is an arbitrary distractor with no mathematical basis. *Incorrect: 10* - This represents the **percentage of patients** who achieved a good outcome with standard care (the control group). - It is the baseline event rate, not a measure of treatment effect or comparative effectiveness. - Like option 30, it does not reflect the additional benefit from the new treatment.

Question 32: A recent study attempted to analyze whether increased "patient satisfaction" driven healthcare resulted in increased hospitalization. In this hospital, several of the wards adopted new aspects of "patient satisfaction" driven healthcare, whereas the remainder of the hospital continued to use existing protocols. Baseline population characteristics and demographics were collected at the start of the study. At the end of the following year, hospital use was assessed and compared between the two groups. Which of the following best describes this type of study?

A. Prospective cohort (Correct Answer)
B. Cross-sectional study
C. Retrospective case-control
D. Prospective case-control
E. Retrospective cohort

Explanation: ***Prospective cohort*** - This study design involves following a group of **exposed individuals (patient satisfaction-driven healthcare)** and a group of **unexposed individuals (existing protocols)** forward in time to observe the development of an outcome (**hospitalization**). - The exposure status is determined at the **start of the study (baseline)**, and outcomes are measured prospectively over a period, which aligns with observing hospital use over the following year. *Cross-sectional study* - A **cross-sectional study** assesses both exposure and outcome simultaneously at a single point in time, providing a snapshot. - This study design involves follow-up over a year, making it unsuitable for a cross-sectional classification. *Retrospective case-control* - A **retrospective case-control study** begins with identifying individuals based on their outcome status (cases with the outcome and controls without the outcome) and then looks back in time to determine past exposures. - This study starts by defining exposure groups and then follows them to observe future outcomes, which is the opposite of a case-control design. *Prospective case-control* - A **prospective case-control study** is a less common and often refers to a nested case-control study within a cohort, where cases and controls are selected from a larger cohort that has been followed prospectively. - The described study directly follows entire exposed and unexposed groups without specifically selecting cases and controls from a pre-defined cohort. *Retrospective cohort* - A **retrospective cohort study** defines cohorts based on past exposures using existing data and then looks back in time to determine outcomes that have already occurred. - The study explicitly states that hospital use was assessed at the "end of the following year," indicating a forward-looking collection of outcome data, not a retrospective assessment of already recorded outcomes.

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Prospective cohort design

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Retrospective cohort design

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Exposure assessment

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Follow-up methods

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Loss to follow-up handling

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Time-to-event analysis

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Survival curves

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Cox proportional hazards model

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Competing risks

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Nested case-control studies

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Case-cohort studies

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Historical cohorts

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Strengths and limitations of cohort studies

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