Foundations of Health Science
Domain 1: Foundations of Health Science
This domain tests the conceptual toolkit of health science: how health is defined and measured, which factors shape it, how epidemiologists describe disease, and how behavior-change models explain and guide intervention. Expect application items-interpret a rate, choose the model that fits a scenario, or spot a flawed inference-not just definitions.
Health, wellness, and determinants
- WHO (1948): health is a state of complete physical, mental, and social well-being, not merely the absence of disease or infirmity. Wellness is an active, multidimensional process (physical, emotional, intellectual, social, spiritual, occupational, environmental).
- Determinants cluster into biological/genetic, behavioral, social, environmental, and health-service/policy categories. The ecological model nests individual factors inside interpersonal, organizational, community, and policy levels.
Measuring disease
- Incidence = new cases ÷ population at risk ÷ time. Prevalence = all existing cases ÷ total population at a point or period. For a stable condition, prevalence ≈ incidence × average duration.
- Morbidity is illness; mortality is death. Case fatality = deaths from a disease ÷ diagnosed cases. Crude rates are unadjusted; age-adjusted rates allow fair comparison across populations with different age structures.
- Sensitivity = true positives ÷ all diseased. Specificity = true negatives ÷ all nondiseased. Positive predictive value = true positives ÷ all test positives and depends heavily on prevalence.
Behavior-change models
- Health Belief Model: perceived susceptibility, severity, benefits, barriers, cues to action, self-efficacy.
- Transtheoretical Model stages: precontemplation, contemplation, preparation, action, maintenance (termination).
- Social Cognitive Theory: reciprocal determinism, observational learning, self-efficacy, outcome expectations.
- Theory of Planned Behavior: attitudes, subjective norms, perceived behavioral control → intention → behavior.
Worked example
A town of 10,000 has 500 people with diagnosed diabetes on January 1. During the year, 100 new cases are diagnosed. Prevalence on January 1 = 500 ÷ 10,000 = 5.0%. Incidence during the year = 100 ÷ (10,000 − 500) = 1.05% per year. Reasoning: prevalence counts everyone who already has the condition, while incidence counts only new onsets among those still at risk, so the 500 existing cases are removed from the denominator.
Traps that cost marks
- Using the total population as the incidence denominator (inflates or deflates the rate).
- Treating a prevalence drop as proof a program worked without a comparison group; secular trends, migration, and self-report bias can explain it.
- Confusing sensitivity with predictive value; a highly sensitive test can have low PPV when prevalence is low.
- Mixing up contemplation (thinking about change) with preparation (taking small steps).
- Inferring causation from cross-sectional association.