Health Education & Preventable Disease Incidence

Population & Program

Cohort size (each group) 25

Community baseline risk 40%

Education effectiveness 50%

Simulation speed 1.0 yr/s

Outcomes

Years elapsed

Risk reduction

—

Control incidence

0.0%

Educated incidence

0.0%

Healthy, low risk factor load

Healthy, elevated risk factors

Developed preventable disease

How it works

Each agent carries three lifestyle risk factors — smoking (yes/no), exercise level and diet quality (0–1). They combine into one risk load:

R = 0.4·smoking + 0.3·(1-exercise) + 0.3·(1-diet)   (0 ≤ R ≤ 1)

Every simulated year, each healthy agent faces a chance of developing a preventable chronic condition (e.g. type 2 diabetes, coronary disease), drawn from a logistic-style annual hazard:

hazard(R) = 0.01 + 0.15·R   (1%/yr at R=0, 16%/yr at R=1)
P(disease this year) = hazard(R) · 1 year

The control cohort keeps its starting risk factors fixed all simulation. The education cohort gets a chance each year — scaled by the effectiveness slider — to improve its worst factor: a smoker quits, or exercise/diet improves by a step. This mirrors real lifestyle-intervention trials such as the Diabetes Prevention Program, where structured education and coaching measurably lowered risk-factor prevalence and cut incident disease versus a control arm.

Community baseline risk sets how unhealthy the starting population is (higher = more smokers, less exercise/diet quality on average).
Education effectiveness controls how fast the right-hand cohort's behaviour improves per year.
Spheres sink and darken the year an agent develops disease; colour tracks live risk load from green (low) to amber (high).

The coefficients above are simplified, illustrative values chosen for a clear pedagogical effect, not a clinical risk calculator — real epidemiological models (e.g. Framingham, ASCVD) use many more variables fitted to cohort data.

Control — no programHealth Education Program

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