Measuring Disease Occurrence: Prevalence, Incidence, and Age Standardisation

Epidemiology basics

Definition: Epidemiology is the study of the occurrence and distribution of health-related events, states or processes in specified populations. Source: Porta M. A dictionary of epidemiology, 6th ed.

Core questions: Who? What? Why? When? Where?
Actions: Defining and measuring the problem; describing causes and consequences; developing and evaluating interventions; disseminating effective policy and practice.

What: The proportion of a population who have the disease at a point in time.
Calculation: \text{Prevalence} = \frac{\text{Number with disease at a given point in time}}{\text{Total population at that point in time}}
Reporting: e.g., The prevalence of asthma in POPH192 class on 18 Aug 2025 was 10%.
Limitations:
- Influenced by duration of disease; reflects existing cases
- Difficult to infer incident risk and disease onset
Influence of duration: Longer duration increases prevalence for a given incidence
Recap note: Prevalence is a snapshot of existing disease burden, not new cases

The occurrence of new cases of an outcome in a population during a follow-up period
Subtypes:
- Incidence proportion (IP) — cumulative incidence
- Incidence rate (IR) — rate using person-time

What: Proportion of an outcome-free population that develops the outcome during a specified period
Calculation: \text{IP} = \frac{\text{Number of new cases during period}}{\text{Population at risk at start}}
At risk: those without the outcome at start; who can develop the outcome
Reporting: e.g., The incidence proportion of low back pain in nurses in 12 months was 35%
Limitations:
- Assumes a closed population (no entrants or losses)
- Depends on the length of the period (longer period -> higher IP)
Example: 35/100 = 0.35

What: The rate at which new cases occur in a population; captures speed of disease development
Calculation: \text{IR} = \frac{\text{Number of new cases during period}}{\text{Sum of person-years at risk}}
Person-time concept: follow-up time contributed by each person at risk
Example: 2 cases over 4 person-years → \text{IR} = \frac{2}{4} = 0.5\ \text{per person-year} = 50\ \text{per 100 person-years}
At-risk changes: cases, loss to follow-up, end of follow-up
Reporting: e.g., The incidence rate of glandular fever was 50 per 100 person-years
Limitations:
- Requires accurate person-time data; more complex to compute

Incidence proportion: new cases – risk; depends on time; assumes closed population
Prevalence: existing cases – burden; influenced by duration
Incidence rate: new cases – speed; uses person-time
Relationship reminder: P \approx I \times D (P = prevalence, I = incidence, D = average duration); note this is an approximation, not a strict formula

P ~ I × D; reflects that higher incidence or longer duration increases prevalence
Do not treat as a precise identity; context matters

Purpose: remove effects of differing age structures when comparing populations
When to use: if age structures differ AND disease risk varies by age
How: apply age-specific rates to a standard population (e.g., WHO standard population)
Example context: crude rates may differ; age-standardised rates provide a fair comparison (e.g., comparisons between The Gambia and Germany)

All key formulas above use LaTeX notation where indicated to support quick recall and exam-ready reference