Key Measures of Disease Frequency

Why This Matters

Every epidemiological study you'll encounter—whether analyzing an outbreak, evaluating a treatment, or identifying risk factors—relies on these core measures. You're being tested on your ability to select the right measure for the right question: When do you use incidence versus prevalence? Why would a case-control study report odds ratios instead of relative risk? Understanding these distinctions separates students who memorize formulas from those who can actually apply epidemiological reasoning to real-world scenarios.

These measures fall into distinct categories: frequency measures (how often disease occurs), mortality measures (how deadly it is), and association measures (how exposures relate to outcomes). Don't just memorize definitions—know what each measure tells you, what study design it belongs to, and when you'd choose one over another. Master the relationships between these concepts, and you'll be ready for any calculation or interpretation question thrown your way.

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Frequency Measures: Counting Cases

These measures answer the fundamental question: how much disease exists in a population? The key distinction is whether you're counting new cases (incidence) or all existing cases (prevalence)—a difference that determines everything from study design to resource allocation.

Incidence

• Measures new cases only—the number of people who develop a disease during a specific time period, capturing disease risk
• Expressed as a rate (e.g., 50 cases per 100,000 person-years), which accounts for both population size and time at risk
• Essential for prevention evaluation—if incidence drops after an intervention, you know fewer people are getting sick in the first place

Prevalence

• Captures total disease burden—all existing cases (new and old) at a single point in time or over a period
• Expressed as a proportion or percentage of the population, providing a snapshot for healthcare planning
• Influenced by three factors: incidence rate, disease duration, and mortality—a disease with high incidence but quick recovery will have lower prevalence than one with moderate incidence but chronic course

Attack Rate

• A special incidence measure for outbreaks—the proportion of an at-risk population that develops illness during a defined epidemic period
• Calculated as $\frac{\text{new cases}}{\text{population at risk}} \times 100$, typically expressed as a percentage
• Critical for outbreak investigation—comparing attack rates across groups (e.g., those who ate potato salad vs. those who didn't) identifies the exposure source

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Mortality Measures: Quantifying Death

These measures assess how deadly a disease is, but they answer different questions. Mortality rate tells you about death in the population; case fatality rate tells you about death among those already diagnosed.

Mortality Rate

• Deaths per population over time—typically expressed as deaths per 1,000 or 100,000 individuals per year
• Enables population comparisons—you can compare mortality across countries, time periods, or demographic groups
• Drives public health priority-setting—diseases with high mortality rates command resources and policy attention

Case Fatality Rate (CFR)

• Proportion of diagnosed cases who die—calculated as $\frac{\text{deaths from disease}}{\text{total cases of disease}} \times 100$
• Reflects disease severity and treatment quality—a high CFR indicates either a lethal disease, poor healthcare access, or both
• Varies dramatically by context—Ebola CFR ranges from 25-90% depending on outbreak and healthcare infrastructure; COVID-19 CFR varied by age, vaccination status, and hospital capacity

Standardized Mortality Ratio (SMR)

• Compares observed vs. expected deaths—calculated as $\frac{\text{observed deaths}}{\text{expected deaths}} \times 100$, where expected deaths come from a reference population
• Adjusts for confounders—controls for age, sex, or other demographic differences that would otherwise skew comparisons
• Interpretation is straightforward: SMR > 100 means more deaths than expected; SMR < 100 means fewer deaths than expected

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Association Measures: Linking Exposure to Outcome

These measures quantify the relationship between an exposure (risk factor) and an outcome (disease). The measure you use depends entirely on your study design—this is a high-yield testing concept.

Relative Risk (RR)

• Compares incidence between exposed and unexposed groups—calculated as $RR = \frac{\text{incidence in exposed}}{\text{incidence in unexposed}}$
• Used in cohort studies where you follow groups over time and can calculate true incidence rates
• Interpretation: RR = 1 means no association; RR > 1 means exposure increases risk; RR < 1 means exposure is protective

Odds Ratio (OR)

• Compares odds of exposure between cases and controls—calculated as $OR = \frac{(a/c)}{(b/d)}$ or equivalently $\frac{ad}{bc}$ from a 2×2 table
• Required for case-control studies—because you select participants based on disease status, you cannot calculate true incidence or relative risk
• Approximates relative risk when the disease is rare (under ~10% prevalence), making interpretation similar

Attributable Risk (AR)

• The excess risk due to exposure—calculated as $AR = \text{incidence in exposed} - \text{incidence in unexposed}$
• Answers a different question than RR—tells you the absolute increase in disease that can be blamed on the exposure
• Directly informs intervention impact—if you eliminate the exposure, AR tells you how many cases you'd prevent in the exposed group

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Population-Level Impact Measures

These measures extend individual risk to the entire population, helping policymakers decide where to focus resources for maximum public health benefit.

Population Attributable Risk (PAR)

• Estimates disease burden attributable to exposure in the whole population—accounts for both the risk associated with exposure and how common the exposure is
• Calculated as $PAR = \text{incidence in total population} - \text{incidence in unexposed}$, or using the formula $PAR = \frac{P_e(RR-1)}{1 + P_e(RR-1)}$ where $P_e$ is exposure prevalence
• Guides public health priorities—a modest risk factor that's extremely common (like physical inactivity) may have higher PAR than a strong risk factor that's rare

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Quick Reference Table

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Self-Check Questions

1. A chronic disease has moderate incidence but patients live with it for decades. Would you expect prevalence to be higher or lower than incidence, and why?

2. You're investigating a foodborne outbreak at a wedding. Which measure would you calculate to compare illness rates between guests who ate the shrimp and those who didn't?

3. Compare and contrast relative risk and odds ratio: In what study designs is each appropriate, and under what conditions does OR approximate RR?

4. A risk factor doubles disease risk (RR = 2) but only 5% of the population is exposed. Another risk factor has RR = 1.3 but 60% of the population is exposed. Which likely has higher population attributable risk, and what does this tell you about intervention priorities?

5. An FRQ presents mortality data from two countries with very different age distributions. Which measure would allow a valid comparison, and what does it adjust for?