UPSC CMSE Preventive and Social Medicine Syllabus

Preventive and Social Medicine syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Preventive and Social Medicine in UPSC CMSE, not a summary of it.

1. 1

   Epidemiology

   6 topics

   • Study Designs
   • Measures of Disease Frequency
   • Screening for Diseases
   • Outbreak Investigation
   • Epidemiological Surveillance
   • Bias and Confounding
2. 2

   Biostatistics

   6 topics

   • Descriptive Statistics
   • Inferential Statistics
   • Probability
   • Hypothesis Testing
   • Regression Analysis
   • Survival Analysis
3. 3

   Health Programs and Policies

   6 topics

   • National Health Mission
   • Immunization Programs
   • Maternal and Child Health Programs
   • Communicable Disease Control Programs
   • Non-Communicable Disease Control Programs
   • Health Policy and Planning

Preventive and Social Medicine flashcards for UPSC CMSE

24 of 51 cards from the Preventive and Social Medicine deck — real questions with worked answers.

1. In epidemiology, what is a cohort (prospective) study and what key measure does it directly yield?

   An observational study that follows exposed and unexposed groups forward in time to compare incidence of an outcome. It directly yields incidence and allows calculation of relative risk (and attributable risk).

2. How does a case-control study work, and why can't it directly measure risk?

   It starts with people who have the disease (cases) and those without (controls), then looks backward at past exposure. It cannot measure incidence/risk because it starts from outcome status, so it estimates association using the odds ratio.

3. What is the defining feature of a randomized controlled trial (RCT) and why is it the gold standard for causation?

   Participants are randomly allocated to intervention vs control. Randomization balances known and unknown confounders between groups, minimizing bias and allowing strong causal inference.

4. Rank these study designs from strongest to weakest evidence for causation: case series, cohort, RCT, case-control, ecological.

   RCT > cohort > case-control > ecological > case series (with systematic reviews/meta-analyses of RCTs above all).

5. What is an ecological study and its main limitation (the ecological fallacy)?

   It examines exposure-outcome associations at the population/group level rather than individuals. The ecological fallacy is wrongly inferring individual-level relationships from group-level data.

6. Define incidence rate and give its formula.

   Incidence rate = number of NEW cases of a disease during a period / total person-time at risk. It measures the rate of occurrence of new disease.

7. Define point prevalence and give its formula.

   Point prevalence = number of EXISTING (old + new) cases at a point in time / total population at that time. It measures the burden of disease.

8. State the relationship between prevalence, incidence, and disease duration.

   In a steady state, Prevalence ≈ Incidence × average Duration (P = I × D).

9. How do you calculate the crude death rate?

   Crude death rate = (number of deaths in a year / mid-year population) × 1000.

10. Define case fatality rate (CFR) and give its formula.

    CFR = (number of deaths from a disease / number of diagnosed cases of that disease) × 100. It measures the killing power/virulence of a disease.

11. What is the infant mortality rate (IMR) and why is it an important indicator?

    IMR = (deaths under 1 year of age in a year / live births in same year) × 1000. It is a sensitive index of the overall health and socioeconomic status of a community.

12. Define sensitivity and specificity of a screening test.

    Sensitivity = ability to correctly identify those WITH disease = TP/(TP+FN). Specificity = ability to correctly identify those WITHOUT disease = TN/(TN+FP).

13. What do positive predictive value (PPV) and negative predictive value (NPV) measure, and what affects them?

    PPV = TP/(TP+FP) = probability a positive test truly has disease. NPV = TN/(TN+FN) = probability a negative test is truly disease-free. Both depend on disease prevalence (PPV rises as prevalence rises).

14. What are the Wilson and Jungner criteria themes for a condition suitable for screening?

    The condition should be an important health problem with a recognizable latent/early stage and known natural history; there must be a suitable, acceptable, valid test; an accepted, available treatment; and screening should be cost-effective and continuous.

15. Distinguish lead-time bias from length-time bias in screening.

    Lead-time bias: screening detects disease earlier, so survival APPEARS longer though death is unchanged. Length-time bias: screening preferentially detects slow-growing, less aggressive cases, falsely improving apparent survival.

16. List the key steps of an outbreak investigation.

    Verify the diagnosis and confirm the existence of an outbreak; define a case and count cases; describe by time, place, and person; formulate and test hypotheses; implement control/prevention measures; and communicate findings.

17. What is an epidemic curve and what does its shape tell you?

    A histogram of case counts over time. A single sharp peak suggests a point-source (common-source) outbreak; multiple peaks or a prolonged curve suggest propagated (person-to-person) or continuous common-source spread.

18. How is the attack rate calculated in an outbreak?

    Attack rate = (number of people who became ill / number of people at risk) × 100, over the outbreak period.

19. Define epidemiological surveillance and distinguish active from passive surveillance.

    Surveillance is the continuous, systematic collection, analysis, and interpretation of health data for action. Passive: providers report routinely to health authorities. Active: health authorities actively seek out case reports (more complete but resource-intensive).

20. What is sentinel surveillance?

    A system using selected reporting sites/institutions (sentinels) that provide high-quality data on specific conditions to monitor trends, rather than attempting full population coverage.

21. Define selection bias and give a common example.

    Systematic error from how participants are selected or retained, so the sample is not representative. Examples: Berksonian (hospital admission) bias, healthy worker effect, non-response bias.

22. Define information (measurement) bias and name two subtypes.

    Systematic error in measuring exposure or outcome. Subtypes include recall bias (cases remember exposures differently) and observer/interviewer bias.

23. What is confounding and what three criteria define a confounder?

    Distortion of an exposure-outcome association by a third variable. A confounder must: (1) be associated with the exposure, (2) be an independent risk factor for the outcome, and (3) NOT be on the causal pathway between exposure and outcome.

24. Name methods to control for confounding in study design and analysis.

    Design: randomization, restriction, matching. Analysis: stratification (e.g., Mantel-Haenszel) and multivariable regression/standardization.

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