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Epidemiology and Biostatistics

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Epidemiology and Biostatistics — Key Facts for FMGE Core concept: Epidemiology studies the distribution and determinants of health states in populations; biostatistics provides tools to analyze health data High-yield point: Understand the difference between descriptive, analytical, and experimental epidemiology; know the key measures of disease frequency ⚡ Exam tip: Attack rates, attack rates secondary attack rates, and incidence/prevalence calculations are frequently tested in FMGE

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Epidemiology and Biostatistics — FMGE Study Guide

Measures of Disease Frequency

Prevalence

  • Proportion of existing cases at a point in time
  • P = Number of existing cases / Total population at that time
  • Expressed as a proportion (0-1) or percentage
  • Includes both old and new cases
  • Example: If 50 out of 1000 people have hypertension on Jan 1, prevalence = 5%

Incidence

  • New cases occurring in a time period
  • Cumulative incidence (CI): Risk of developing disease = New cases during period / Population at risk at start
  • Incidence rate (IR): New cases per unit of person-time = New cases / Total person-time at risk
  • Example: 30 new cases of TB in a population of 10,000 over 1 year; IR = 30/10,000 = 3 per 1000 per year

Attack Rate

  • Specific type of incidence rate used in outbreaks
  • AR = Number of cases / Total population at risk
  • Used for: Communicable diseases, food poisoning outbreaks

Secondary Attack Rate

  • Attack rate among contacts of known cases
  • SAR = New cases among contacts / Total contacts at risk
  • Important for: Person-to-person transmitted diseases
  • Excludes primary cases

Relationship

  • Prevalence ≈ Incidence × Average duration of disease
  • Acute diseases: Low prevalence, high incidence rate
  • Chronic diseases: High prevalence, lower incidence rate

Morbidity and Mortality Measures

Morbidity Rates

  • Morbidity: Any departure from health (illness, injury, disability)
  • Morbidity rate: Incidence or prevalence of illness in a defined population

Mortality Rates

Crude Death Rate (CDR):

  • CDR = Total deaths / Total population × 1000
  • Least specific mortality measure
  • Affected by age-sex distribution

Age-Specific Mortality Rate (ASMR):

  • ASMR = Deaths in specific age group / Population in that age group × 1000

Cause-Specific Mortality Rate:

  • Deaths from specific cause / Total population × 100,000

Proportionate Mortality Ratio (PMR):

  • Deaths from cause / Total deaths × 100
  • Describes proportion of deaths from a cause

Case Fatality Rate (CFR):

  • CFR = Deaths from disease / Total cases of disease × 100
  • Indicates severity of disease
  • Not a true rate (denominator is cases, not person-time)

Infant Mortality Rate (IMR):

  • IMR = Deaths under 1 year / Live births in that year × 1000
  • Key indicator of health status of a community
  • Components: Neonatal mortality rate (0-28 days) + Post-neonatal mortality rate (28-365 days)

Maternal Mortality Ratio (MMR):

  • MMR = Maternal deaths / Live births × 100,000
  • Maternal death: Death during pregnancy or within 42 days of termination

Perinatal Mortality Rate (PMR):

  • PMR = (Late fetal deaths + Early neonatal deaths) / (Live births + Late fetal deaths) × 1000
  • Late fetal death: ≥28 weeks gestation; Early neonatal: 0-7 days

Epidemiological Studies

Descriptive Studies

Case Reports/Case Series: Description of individual cases; generates hypotheses

Cross-sectional studies: Prevalence survey; data collected at one point in time; no follow-up; cannot establish temporality

Ecological studies: Population-level data; correlation between exposures and outcomes at group level; cannot infer individual-level relationships (ecological fallacy)

Analytical Studies

Case-control studies:

  • Start with disease status (cases vs controls)
  • Look backward for prior exposures
  • Odds ratio: OR = (a/c) / (b/d) = ad/bc
  • Retrospective: Good for rare diseases
  • Disadvantage: Prone to recall bias, selection bias

Cohort studies:

  • Start with exposure status (exposed vs unexposed)
  • Follow forward for disease development
  • Relative Risk (RR): RR = (Incidence in exposed) / (Incidence in unexposed)
  • Attributable Risk (AR): AR = Incidence in exposed - Incidence in unexposed
  • Prospective: Better for establishing temporality; can study multiple outcomes
  • Disadvantage: Expensive, long follow-up, not good for rare diseases

Experimental Studies

Randomized Controlled Trials (RCT):

  • Gold standard for therapeutic interventions
  • Randomization: Eliminates confounding and selection bias
  • Blinding: Single, double, triple (prevents observer and subject bias)
  • Parallel group design: Two or more groups receiving different interventions
  • Crossover design: Each subject receives all interventions in sequence

Community Trials:

  • Intervention applied to communities rather than individuals
  • Example: Fluoridation of water supply, vaccination campaigns

Screening and Diagnostic Tests

Validity

Sensitivity: Ability to correctly identify those WITH disease

  • Se = TP / (TP + FN) = True positives / All with disease
  • High sensitivity: Few false negatives (important when disease is serious and must not be missed - e.g., HIV screening)

Specificity: Ability to correctly identify those WITHOUT disease

  • Sp = TN / (TN + FP) = True negatives / All without disease
  • High specificity: Few false positives (important when test result has serious consequences - e.g., confirmatory test)

Predictive Values:

  • Positive Predictive Value (PPV): Probability of disease given positive test
  • Negative Predictive Value (NPV): Probability of no disease given negative test
  • PPV and NPV depend on prevalence (unlike Se and Sp)

Likelihood Ratios:

  • LR+ = Sensitivity / (1 - Specificity)
  • LR- = (1 - Sensitivity) / Specificity
  • LR+ > 1 increases post-test probability; LR- < 1 decreases it

Accuracy

  • Youden’s Index = Sensitivity + Specificity - 1
  • Perfect test would have Youden’s index of 1

ROC Curve

  • Plots True positive rate (sensitivity) vs False positive rate (1-specificity)
  • Area Under Curve (AUC): 0.5 = chance; 1.0 = perfect test
  • Higher curve = better test

Screening Criteria (Wilson and Jungner)

  • Important health problem
  • Acceptable test
  • Clear diagnostic criteria
  • Treatment available
  • Benefits of early detection outweigh costs

Statistical Concepts

Measures of Central Tendency

  • Mean: Arithmetic average (sensitive to extreme values)
  • Median: Middle value when data arranged (not sensitive to extremes)
  • Mode: Most frequent value

Measures of Dispersion

  • Range: Maximum - minimum
  • Variance: Average squared deviation from mean
  • Standard Deviation (SD): Square root of variance; most commonly used
  • Standard Error (SE): SD / √n; used for confidence intervals

Normal Distribution

  • Bell-shaped curve; mean = median = mode
  • 68% within 1 SD, 95% within 2 SD, 99.7% within 3 SD

Hypothesis Testing

  • Null hypothesis (H0): No difference/association
  • Alternative hypothesis (H1): There is difference/association
  • P-value: Probability of observing result if H0 is true; <0.05 typically significant
  • Type I error (α): Reject H0 when true (false positive)
  • Type II error (β): Accept H0 when false (false negative)

Confidence Intervals (CI)

  • Range within which true population value likely falls
  • 95% CI: 95% confident that true value lies within this range

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