Epidemiology — MCQs

Epidemiology Indian Medical PG Practice Questions and MCQs

Question 1691: A 60-year-old man presents with a chronic cough and is found to have a lung lesion on chest X-ray. What is the significance of high sensitivity in a diagnostic test for lung cancer?

A. It correctly identifies those with the disease (Correct Answer)
B. It incorrectly identifies those without the disease
C. It indicates the proportion of true positives among all positive results
D. It indicates the proportion of false negatives among all actual positives

Explanation: ***It correctly identifies those with the disease*** - A test with **high sensitivity** is good at ruling out disease when the result is negative because it has a low chance of missing true cases. - In lung cancer screening, high sensitivity helps ensure that individuals who actually have lung cancer are **identified**, reducing false negatives. *It incorrectly identifies those without the disease* - This statement describes a test with **low specificity**, where many healthy individuals are misidentified as having the disease (many false positives). - A high sensitivity test focuses on identifying true positives, not on incorrectly identifying healthy individuals. *It indicates the proportion of false negatives among all actual positives.* - The proportion of **false negatives among all actual positives** is the complement of sensitivity (1 - sensitivity). - High sensitivity implies a **low false-negative rate**, meaning few true cases are missed. *It indicates the proportion of true positives among all positive results.* - This describes **positive predictive value (PPV)**, which is the probability that a positive test result is truly positive. - While related to test utility, PPV is influenced by disease prevalence, whereas sensitivity is an intrinsic property of the test.

Question 1692: A researcher calculates the relative risk (RR) of developing a disease among exposed individuals compared to non-exposed individuals and finds it to be 2. What does this indicate?

A. The exposure does not affect disease risk
B. The exposure doubles the risk of disease (Correct Answer)
C. The exposure halves the risk of disease
D. The exposure is protective against the disease

Explanation: ***The exposure doubles the risk of disease*** - A **relative risk (RR)** of 2 means that the incidence of the disease in the exposed group is **twice** that in the unexposed group. - This indicates a **two-fold increase** in the likelihood of developing the disease due to the exposure. *The exposure does not affect disease risk* - This would be indicated by an **RR of 1**, meaning the risk in the exposed group is the same as in the unexposed group. - An RR of 2 clearly shows a **difference** in risk between the two groups. *The exposure halves the risk of disease* - This would be indicated by an RR of **0.5 (or 1/2)**, meaning the incidence in the exposed group is half that of the unexposed group. - A value of 2 signifies an **increase**, not a decrease, in risk. *The exposure is protective against the disease* - A protective effect would result in an RR **less than 1**, indicating a lower risk in the exposed group. - An RR of 2 demonstrates an **increased risk**, which is the opposite of a protective effect.

Question 1693: In a community-based intervention, the incidence of a disease is found to decrease after vaccination. What study design is most appropriate to confirm vaccine efficacy?

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

Explanation: ***Randomized controlled trial*** - A **randomized controlled trial (RCT)** is the most appropriate study design to confirm vaccine efficacy because it allows for a robust comparison between a vaccinated group and a control group (placebo or no intervention) by **randomly assigning participants** to each. - This design minimizes bias and allows researchers to establish a direct **cause-and-effect relationship** between the vaccine and the prevention of the disease by controlling for confounding factors. *Cohort study* - A **cohort study** observes groups over time, but participants are not randomized to receive the intervention (vaccine), which can introduce **selection bias** if those who choose to be vaccinated differ from those who do not. - While useful for studying prognosis or the effectiveness of interventions in real-world settings, it is less ideal for definitively establishing efficacy compared to an RCT due to the lack of **randomized assignment**. *Case-control study* - A **case-control study** compares individuals with a disease (cases) to those without the disease (controls) and looks back retrospectively for exposure to a risk factor (e.g., lack of vaccination). - This design is prone to **recall bias** and is suitable for rare outcomes, but it cannot directly measure vaccine efficacy or incidence in the same way an RCT can. *Cross-sectional study* - A **cross-sectional study** assesses exposure and outcome simultaneously at a single point in time, providing a snapshot of prevalence. - This design cannot establish temporality (which came first, exposure or outcome) and is therefore unsuitable for confirming vaccine efficacy or a **cause-and-effect relationship**.

Question 1694: Admission rate bias is?

A. Reporting bias
B. Response bias
C. Berksonian bias (Correct Answer)
D. None of the options

Explanation: ***Berksonian bias*** - **Berksonian bias** is a form of selection bias, also known as **admission rate bias**, that occurs when different rates of admission to a hospital or clinical setting distort the association between diseases or between a disease and a risk factor. - This bias arises because the hospitalized population may not be representative of the general population, leading to spurious associations or masking real ones. *Reporting bias* - **Reporting bias** is a type of information bias where the outcome or exposure information is reported inaccurately, often due to social desirability or recall issues. - It does not specifically refer to distortions stemming from hospital admission rates. *Response bias* - **Response bias** occurs when participants in a study alter their answers or behavior from what is true due to factors like leading questions, social desirability, or acquiescence. - This is an issue related to data collection, not an unrepresentative study population due to hospital admission protocols. *None of the options* - Berksonian bias directly corresponds to the definition of admission rate bias, making this option incorrect.

Question 1695: Which observational study design is most appropriate for analyzing the long-term effects of an exposure or treatment over a specified timeline?

A. Cohort Study (Correct Answer)
B. Cross sectional study
C. Randomized Control Trials
D. Interventional Studies

Explanation: ***Cohort Study*** - A **cohort study** tracks a group of individuals (cohort) who share a common characteristic or exposure over a period, making it ideal for observing **long-term effects** and outcomes. - Researchers follow participants forward in time to see who develops the outcome of interest, providing strong evidence for **causality** in observational settings. *Cross-sectional study* - A **cross-sectional study** assesses exposure and outcome simultaneously at a single point in time, failing to establish temporality or long-term effects. - This design is suitable for determining **prevalence** but not for analyzing changes over time or causal relationships. *Randomized Control Trials* - **Randomized controlled trials (RCTs)** are interventional studies where participants are randomly assigned to treatment or control groups, providing the strongest evidence for **causality** in the short to medium term. - While excellent for assessing treatment efficacy, RCTs are often impractical or unethical for very long-term follow-up due to cost, logistical challenges, and participant retention issues. *Interventional Studies* - **Interventional studies** involve researchers actively manipulating an intervention (e.g., a treatment) and observing its effects. - This broad category includes RCTs, but simply being an "interventional study" does not inherently make it the most appropriate for analyzing **long-term effects** over an extended timeline, which is better suited for the observational follow-up of a cohort study.

Question 1696: All are true regarding Japanese encephalitis except which of the following?

A. Humans are dead-end hosts
B. Caused by flavivirus
C. Transmitted by culex
D. Horses are amplifier hosts (Correct Answer)

Explanation: ***Horses are amplifier hosts*** - **Horses** can act as **sentinel animals** and develop severe neurological disease, but they are generally considered **dead-end hosts** for Japanese encephalitis virus, meaning they do not amplify the virus to a level sufficient to transmit it further. - The primary **amplifier hosts** for Japanese encephalitis are **pigs and wading birds**, which maintain the transmission cycle. *Caused by flavivirus* - Japanese encephalitis is indeed caused by a **flavivirus**, specifically the **Japanese encephalitis virus (JEV)**, which belongs to the Flaviviridae family. - This characteristic is consistent with the general epidemiology and pathogenesis of other well-known flaviviruses like Dengue and West Nile virus. *Humans are dead-end hosts* - **Humans** are considered **dead-end hosts** for Japanese encephalitis because the **viremia** (virus levels in the blood) in infected humans is typically too low to infect mosquitoes that feed on them. - This means that humans do not contribute to the ongoing transmission cycle of the virus between mosquitoes and amplifier hosts. *Transmitted by culex* - The primary vectors for Japanese encephalitis virus are mosquitoes of the **Culex genus**, particularly **Culex tritaeniorhynchus**. - These mosquitoes typically breed in rice paddies and other agricultural wetlands, which are common in regions where the disease is endemic.

Question 1697: Nosocomial infections are defined as infections that develop after how many hours of hospital admission?

A. A) 48 hours (Correct Answer)
B. B) 72 hours
C. C) 24 hours
D. D) 50 hours

Explanation: ***A) 48 hours*** - Nosocomial infections, also known as **hospital-acquired infections (HAI)**, are defined as infections that develop **48 hours or more** after hospital admission. - This is the **standard definition** used by the **CDC, WHO**, and major medical textbooks including **Park's Textbook of Preventive and Social Medicine**. - The 48-hour threshold helps differentiate infections acquired during hospitalization from those that were **incubating at the time of admission** (typical incubation periods for most common infections are less than 48 hours). - Infections can also be classified as nosocomial if they occur **within 3 days after discharge** or **within 30 days after surgery**. *B) 72 hours* - While **72 hours** is occasionally mentioned in some contexts or specific institutional protocols, it is **not the standard definition** for nosocomial infections. - Using 72 hours would be too restrictive and could miss true hospital-acquired infections that manifest between 48-72 hours. - The universally accepted standard remains **48 hours**. *C) 24 hours* - An infection developing within **24 hours** is very likely to have been **present or incubating prior to admission**. - This timeframe is too short to establish that the infection was acquired during hospitalization. - Most common bacterial and viral infections have incubation periods longer than 24 hours. *D) 50 hours* - This is **not a standard threshold** for defining nosocomial infections. - The conventional definitions use **48 hours** as the cutoff point, which is based on typical incubation periods and epidemiological evidence.

Question 1698: The difference in incidence between exposed and non-exposed groups in epidemiological studies is best described as:

A. Measure of association between exposure and outcome
B. Total impact of exposure on disease occurrence in a population
C. Difference in incidence between exposed and non-exposed groups (Correct Answer)
D. Ratio of incidence in exposed versus non-exposed groups

Explanation: ***Difference in incidence between exposed and non-exposed groups*** - This is the **precise, correct definition** of **Attributable Risk (AR)** or **Risk Difference**. - Calculated as: **AR = Incidence in exposed - Incidence in unexposed** - This absolute difference represents the **excess risk** attributable to the exposure. - It is a fundamental epidemiological measure that quantifies the **additional incidence** of disease that can be attributed to the exposure. - Example: If incidence in exposed = 30% and in unexposed = 10%, then AR = 20% (the excess risk due to exposure). *Measure of association between exposure and outcome* - This is a **generic, umbrella term** that encompasses multiple measures including risk difference, relative risk, and odds ratio. - While technically correct that risk difference is "a" measure of association, this option is **too broad** and not the specific term for "difference in incidence." - When the question asks specifically about "the difference," the precise epidemiological term is needed, not a general category. *Total impact of exposure on disease occurrence in a population* - This describes **Population Attributable Risk (PAR)** or **Population Attributable Fraction**. - PAR considers both the **risk difference** AND the **prevalence of exposure** in the population: PAR = AR × Prevalence of exposure. - The simple difference between exposed and non-exposed groups does not account for how common the exposure is in the population. *Ratio of incidence in exposed versus non-exposed groups* - This describes **Relative Risk (RR)** or **Risk Ratio**. - Calculated as: **RR = Incidence in exposed / Incidence in unexposed** - This is a **ratio**, not a **difference** - it shows how many times more likely the exposed group is to develop the outcome compared to unexposed.

Question 1699: Which type of study determines the odds ratio?

A. Case control (Correct Answer)
B. Cohort
C. Cross sectional
D. RCT

Explanation: ***Case control*** - **Case-control studies** compare individuals with a disease (cases) to individuals without the disease (controls) and look back in time to identify previous exposures. - The **odds ratio** is the primary measure of association used in case-control studies, quantifying the odds of exposure among cases versus controls. *Cohort* - **Cohort studies** follow groups of individuals over time, some exposed to a risk factor and some not, to determine the incidence of a disease. - They typically determine **relative risk**, which is the ratio of incidence rates in exposed versus unexposed groups. *Cross sectional* - **Cross-sectional studies** assess the prevalence of disease and exposure at a single point in time. - They primarily measure **prevalence** and can be used to calculate a **prevalence odds ratio**, but they do not establish temporality between exposure and outcome. *RCT* - **Randomized controlled trials (RCTs)** are interventional studies where participants are randomly assigned to an intervention or control group to determine the effectiveness of a treatment or exposure. - The main measure of effect in RCTs is often the **relative risk reduction**, **absolute risk reduction**, or **number needed to treat**, rather than the odds ratio for observational exposure.

Question 1700: What is the expected percentage reduction in mortality risk from breast cancer with annual mammography screening after the age of 50?

A. 30-35%
B. 15-20% (Correct Answer)
C. 25-30%
D. 20-25%

Explanation: ***15-20%*** - Current evidence from **meta-analyses and systematic reviews** shows that annual mammography screening in women aged 50 and older reduces breast cancer mortality by approximately **15-20%**. - This estimate is supported by the **U.S. Preventive Services Task Force (USPSTF)** and **Canadian Task Force on Preventive Health Care** based on modern randomized controlled trials. - The benefit reflects **earlier detection** and treatment of breast cancer in this age group. *20-25%* - This range **slightly overestimates** the mortality reduction demonstrated in contemporary evidence-based reviews. - While some older studies reported benefits in this range, more rigorous modern analyses support a lower estimate. *25-30%* - This represents **older estimates** from earlier meta-analyses that have been revised downward with more recent data. - Contemporary evidence-based guidelines do **not support** this level of mortality reduction for routine mammography screening. - This figure may have included methodological biases present in older trials. *30-35%* - This range **significantly overestimates** the mortality benefit of mammography screening. - No current evidence-based guidelines support this level of mortality reduction for average-risk women aged 50 and above. - Such high estimates are not supported by modern randomized controlled trials.

Practice by Chapter

Principles of Epidemiology

Practice Questions

Measures of Disease Frequency

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Epidemiological Study Designs

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Descriptive Epidemiology

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Analytical Epidemiology

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Experimental Epidemiology

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Screening for Disease

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Surveillance Systems

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Investigation of an Epidemic

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Association and Causation

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Modern Epidemiological Methods

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Critical Appraisal of Epidemiological Studies

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