Preventive Medicine and Public Health Flashcards

A cohort study follows 4,000 non-smokers and 2,000 smokers for 10 years. At the end, 40 non-smokers and 60 smokers develop lung cancer. What is the population attributable risk percent (PAR%) for smoking, assuming a smoking prevalence of 30% in the general population?

Answer: 37.5%

Incidence in smokers = 60/2000 = 3%. Incidence in non-smokers = 40/4000 = 1%. RR = 3. PAR% = [Pe(RR-1)] / [Pe(RR-1) + 1] × 100, where Pe = 0.30. Numerator = 0.30 × (3-1) = 0.60. Denominator = 0.60 + 1 = 1.60. PAR% = 0.60/1.60 × 100 = 37.5%. This reflects the proportion of lung cancer in the total population attributable to smoking.

A public health department implements a new screening program for colorectal cancer using fecal immunochemical testing (FIT). Among 10,000 screened individuals, 200 test positive; of these, 50 actually have colorectal cancer. Among those who test negative, 10 actually have colorectal cancer. Which statement best describes the program's positive predictive value (PPV) and its primary determinant in this population?

Answer: PPV = 25%; primarily determined by the low prevalence of colorectal cancer in the screened population

PPV = True Positives / All Positives = 50/200 = 25%. The total disease cases = 50 + 10 = 60 out of 10,000, giving a prevalence of 0.6%. In low-prevalence populations, even a highly specific test yields a low PPV because the base rate of disease is so small that false positives outnumber true positives. This is the primary determinant — disease prevalence — not sensitivity alone.

A randomized controlled trial of a vaccine shows an efficacy of 80%. In a subsequent outbreak, the attack rate among unvaccinated individuals is 25%, while the attack rate among vaccinated individuals is 10%. However, 70% of the affected community is vaccinated. What is the vaccine effectiveness under these field conditions, and which phenomenon best explains the discrepancy from the trial efficacy?

Answer: Field VE = 60%; explained by healthy vaccinee bias and cold-chain failure reducing real-world protection

Field VE = 1 − (Attack Rate Vaccinated / Attack Rate Unvaccinated) = 1 − (10/25) = 1 − 0.4 = 60%. This is lower than the trial efficacy of 80%. In field conditions, healthy vaccinee bias (healthier individuals more likely to be vaccinated) can paradoxically inflate observed benefit in trials, while cold-chain failures, immunosenescence, and variant drift reduce real-world protection. The most operationally addressable explanation in outbreak investigations is cold-chain compromise alongside healthy vaccinee bias.

A meta-analysis of 15 randomized trials examining beta-blocker use post-MI produces a funnel plot that is markedly asymmetric, with small studies clustered on one side showing larger effect sizes. The Egger's test is statistically significant (p = 0.03). Which of the following represents the MOST methodologically rigorous next step to address this finding?

Answer: Apply the trim-and-fill method to impute missing studies and recalculate the pooled effect size, then assess whether the adjusted estimate remains clinically significant

Funnel plot asymmetry with a significant Egger's test strongly suggests publication bias (though heterogeneity and true small-study effects are also possible causes). The trim-and-fill method is the standard next step: it imputes the theoretically missing studies on the opposite side of the funnel and recalculates the pooled estimate. If the adjusted estimate remains clinically meaningful, the conclusion is more robust. Simply excluding small studies introduces selection bias; accepting the result ignores potential overestimation; subgroup analysis by quality addresses heterogeneity but not publication bias directly.

During a multistate foodborne outbreak investigation, epidemiologists use a case-control study because the source is unknown. Cases are matched 1:2 to controls by age and sex. The matched odds ratio for exposure to a particular brand of bagged spinach is 8.4 (95% CI: 2.1–33.6). Traceback investigation reveals the implicated lots were harvested from fields irrigated with water from a source also used by a nearby cattle farm. Which analytic approach was necessary for the case-control study given the matched design, and what does the OR indicate about causation?

Answer: Conditional logistic regression was required; OR of 8.4 suggests strong association but does not by itself establish causation — Bradford Hill criteria must be applied

In matched case-control studies, using unconditional logistic regression ignores the matched structure and yields biased estimates — conditional logistic regression (or McNemar's for simple 1:1 pairs, but for 1:2 matching, conditional logistic regression is appropriate) accounts for the matched sets. An OR of 8.4 with CI excluding 1.0 demonstrates a strong, statistically significant association, but association does not equal causation; the Bradford Hill criteria (strength, consistency, specificity, temporality, biologic gradient, plausibility, coherence, experiment, analogy) must be applied. The traceback adds coherence and specificity but full causal inference requires the full framework.

A state health department is evaluating whether to implement a mandatory iodine fortification program for table salt to address subclinical iodine deficiency (median urinary iodine 85 µg/L) in a population where 22% of pregnant women have iodine levels below 50 µg/L. An independent assessment finds that the current voluntary fortification program covers only 40% of salt sold. Using the PRECEDE-PROCEED model, which phase is being addressed by quantifying the urinary iodine levels and pregnancy-specific burden, and what is the MOST critical barrier to address next?

Answer: Epidemiological assessment phase; the most critical barrier is enabling factors — specifically, the inadequate market penetration of voluntarily fortified salt

In the PRECEDE-PROCEED model, quantifying disease burden and at-risk subgroups (median urinary iodine, % of pregnant women deficient) constitutes the Epidemiological Assessment phase (Phase 2). The data show that voluntary fortification reaches only 40% of salt — this is an enabling factor (availability and accessibility of the health-promoting product), not a knowledge deficit or policy gap. Enabling factors are environmental and resource conditions that make healthy behavior possible. The most actionable next step — shifting to mandatory fortification or dramatically expanding distribution — directly targets this enabling barrier. Predisposing (knowledge/attitudes) and reinforcing (social support/clinician counseling) factors are secondary when product availability itself is the bottleneck.