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CM7.1-11 | Epidemiology Methods — Practice Quiz
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Which of the following best captures what distinguishes epidemiology from clinical medicine?
Correct. Epidemiology (from Last's Dictionary) is the study of distribution and determinants of health-related states in populations — it always operates at the population level. Clinical medicine addresses the individual.
Park defines epidemiology as the study of distribution and determinants of health-related states in specified populations and the application of this study to control health problems. The unit of analysis is always the population.
Epidemiology's defining feature is its population focus. Clinical medicine addresses individual patients; epidemiology asks 'how much disease, in whom, and why?' across a defined population.
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In India's Integrated Disease Surveillance Programme (IDSP), the 'P-form' is completed by:
Correct. The P-form (community/peripheral-level form) is completed by peripheral health workers on a weekly basis, capturing syndromes before laboratory confirmation — enabling faster outbreak detection.
A key strength of the P-form is that it captures syndromes (e.g., acute watery diarrhoea, fever with rash) before laboratory results are available, allowing an epidemic alert within days rather than weeks.
The IDSP uses three form types: P-form (peripheral/community, syndrome-based, weekly), L-form (laboratory), and S-form (sub-district/district). The P-form is critical for early signal detection.
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CLINICAL SCENARIO
In January 2025, a district in Rajasthan with a mid-year population of 200,000 reported 400 new cases of dengue fever. By December, 50 additional new cases occurred. Fifteen patients with dengue died during the year. The district's vital registration recorded 1,800 total deaths that year.
Answer the following questions based on the scenario above.
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What is the annual incidence rate of dengue fever per 1,000 population in this district?
Correct. Total new cases = 400 + 50 = 450. Incidence rate = 450 / 200,000 × 1,000 = 2.25 per 1,000.
Incidence = (all new cases in the time period) / (mid-year population at risk) × multiplier. New cases = 400 + 50 = 450; rate = 450/200,000 × 1,000 = 2.25 per 1,000.
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What is the case fatality rate (CFR) for dengue in this district?
Correct. CFR = (Deaths from disease / Cases of disease) × 100 = 15/450 × 100 = 3.33%.
CFR denominator is the number of CASES (not total population or deaths). CFR = 15/450 × 100 = 3.33%.
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A researcher wants to study whether tobacco chewing increases the risk of oral cancer. She identifies 500 confirmed oral cancer cases and 500 age-sex-matched controls without oral cancer, then asks about tobacco chewing history. This is best described as:
Correct. The design starts with cases (oral cancer) and controls (no oral cancer), then looks back at exposure (tobacco chewing) — this is a case-control study. The measure of association is the odds ratio.
Case-control studies are efficient for rare diseases (like oral cancer). They recruit on the basis of outcome, then ascertain exposure history. Cohort studies recruit on exposure status and follow forward to outcomes.
The key is the direction of inquiry: starting with disease status and looking back at exposure = case-control. The appropriate measure is odds ratio, not relative risk.
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Statement 1 (Assertion):
In a case-control study of hypertension and stroke (stroke prevalence 5%), the odds ratio closely approximates the relative risk.
BECAUSE
Statement 2 (Reason):
The odds ratio approximates the relative risk only when the disease is rare (prevalence < 10%) in the source population.
Select the correct relationship:
Correct. Stroke prevalence of 5% meets the rare disease criterion (< 10%). The reason accurately explains the assertion: OR ≈ RR when the disease is rare because the odds ≈ probability when probability is small.
OR = (a×d)/(b×c); RR = (a/(a+b))/(c/(c+d)). When the disease is rare, a << b and c << d, so a/(a+b) ≈ a/b and c/(c+d) ≈ c/d, making OR ≈ RR. At high prevalence (e.g., 30%), OR systematically overestimates RR.
At 5% prevalence, the rare disease assumption holds (threshold < 10%). Therefore OR approximates RR — both assertion and reason are true and causally related.
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A study finds that coffee drinking is associated with lung cancer (OR 2.0). However, coffee drinkers are more likely to smoke, and smoking is a known cause of lung cancer. Smoking is best described as a:
Correct. Smoking meets all three criteria for a confounder: (1) it is associated with the exposure (coffee drinking), (2) it is an independent cause of the outcome (lung cancer), and (3) it is not an intermediary in the causal pathway between coffee and lung cancer.
Three criteria for confounding: (1) associated with the exposure, (2) independently associated with the outcome, (3) not an intermediate step. Control methods: restriction, matching, stratification, multivariate analysis.
A confounder is a variable that is associated with both the exposure and the outcome, and is not in the causal pathway. Smoking fits all three criteria here.
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A district plans to screen all adults for Type 2 diabetes using a fasting blood glucose threshold. The medical officer wants to minimise missed cases (false negatives). To achieve this, the threshold should be:
Correct. Lowering the threshold means more people who actually have diabetes will test positive (fewer false negatives), thereby increasing sensitivity. The trade-off is more false positives (lower specificity).
Sensitivity = TP/(TP+FN). Lowering the threshold captures more true cases but increases false positives. In mass screening where the goal is not to miss disease, high sensitivity is prioritised; confirmatory tests handle false positives.
To minimise false negatives (missed cases), increase sensitivity. This is achieved by lowering the threshold — more cases are caught, but at the cost of more false positives.
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CLINICAL SCENARIO
A screening test for pulmonary tuberculosis has a sensitivity of 90% and specificity of 95%. It is applied to a rural population where the prevalence of active TB is 1% (10 per 1,000). Of 10,000 screened persons:
Answer the following questions based on the scenario above.
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How many true positives are expected?
Correct. Disease cases = 1% × 10,000 = 100. True positives = 90% × 100 = 90.
True positives = Sensitivity × Total cases = 0.90 × 100 = 90.
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What is the approximate Positive Predictive Value (PPV) of this test in this population?
Correct. FP = 5% × 9,900 = 495. PPV = TP/(TP+FP) = 90/(90+495) = 90/585 ≈ 15.4%. This illustrates the PPV paradox in low-prevalence settings.
PPV = TP / (TP + FP). FP = 0.05 × 9,900 = 495. PPV = 90/585 ≈ 15.4% — only 15 in every 100 positive screens actually have TB.
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During a cholera outbreak in a district, the epidemic curve shows cases appearing in successive waves with gradually increasing peak heights over 4 weeks. The modal interval between successive peaks is approximately 5 days (consistent with the incubation period of cholera). This pattern is most consistent with:
Correct. Successive waves separated by one incubation period, with increasing amplitude, are the hallmark of a propagated epidemic (person-to-person spread). Each wave represents one generation of transmission.
Epidemic curve shapes: (1) Point source: single sharp peak, exposure within 1 incubation period. (2) Propagated: successive waves each separated by incubation period, growing amplitude. (3) Intermittent common source: irregular multiple peaks. (4) Continuous common source: plateau pattern.
Successive waves at intervals matching the incubation period, each larger than the last, indicate propagated transmission. A point source produces a single steep peak; intermittent common source shows irregular multi-peak patterns without progressive growth.
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Which of the following statements are TRUE regarding cohort studies?
- a. They can calculate incidence rates directly.
- b. The measure of association produced is the Odds Ratio.
- c. They are efficient for studying rare diseases.
- d. Both prospective and retrospective cohort designs are possible.
- e. They are superior to case-control studies for establishing temporality of exposure before outcome.
Correct. (a) TRUE — cohort studies track new cases prospectively, so incidence rates can be directly computed. (d) TRUE — prospective (follow forward) and retrospective (using historical records) cohorts are both valid. (e) TRUE — exposure is ascertained before the outcome occurs, making temporality clear.
Key cohort study features: (1) Exposure ascertained at baseline, disease-free enrollment. (2) Produces incidence rates and Relative Risk. (3) Efficient for common outcomes, rare exposures. (4) Both prospective and retrospective subtypes. (5) Best for establishing temporal sequence.
Statement b is false (cohort studies produce Relative Risk, not Odds Ratio). Statement c is false (cohort studies are inefficient for rare diseases; case-control studies are preferred). Statements a, d, and e are true.
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A medical officer at a PHC receives a report that 12 children in the same village developed acute watery diarrhoea within 48 hours. The officer suspects a common-source water contamination. What should be the FIRST step in the investigation?
Correct. Step 1 of epidemic investigation (Park) is to confirm the existence of an epidemic — verifying that observed cases exceed normally expected rates for that place and time. Without this, you may respond to spurious clustering.
The 10 steps of epidemic investigation (Park): (1) Confirm epidemic exists, (2) Confirm diagnosis, (3) Define a case, (4) Count cases and construct epidemic curve, (5) Describe in terms of person/place/time, (6) Formulate hypothesis, (7) Test hypothesis, (8) Implement control measures, (9) Evaluate, (10) Communicate findings.
Epidemic investigation follows a defined sequence. Step 1: confirm existence of an epidemic (observed > expected for time and place). Steps 2-3: confirm diagnosis, define a case definition. Intervention (chlorination) should be implemented early but in parallel with investigation, not before confirming an epidemic exists.
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A randomised trial of a new antihypertensive drug shows a mean SBP reduction of 2 mmHg compared to placebo (p = 0.001, 95% CI 1.5–2.5 mmHg). A family physician concludes: 'This is a highly significant result — I should switch all my hypertensive patients to this drug.' This conclusion is:
Correct. Statistical significance (p = 0.001) means the result is unlikely to be due to chance. However, a 2 mmHg SBP reduction is not clinically meaningful — guideline-level benefit from antihypertensives requires ≥10 mmHg reductions to reduce cardiovascular events. Large trials can produce statistically significant but clinically trivial results.
Critical appraisal requires distinguishing statistical significance (p-value: probability of results if null hypothesis is true) from clinical significance (magnitude of effect relative to what is meaningful in practice). Always interpret effect size and confidence intervals alongside p-values.
Statistical significance (low p-value) reflects sample size and precision, not clinical importance. A 2 mmHg SBP reduction with p = 0.001 means the small effect is reliably measured — not that it is clinically important.
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