How many new cases occur?
Incidence tracks the flow of new events over a defined population and time.
Side 105
Public Health
A study of health at population scale. Public health measures patterns, identifies causes, prevents avoidable harm and evaluates interventions whose effects depend on behavior, institutions, environment and distribution.
population→pattern→cause→intervention→evaluation
06study lenses
36working concepts
V0working model
105Side
Public health changes the unit of analysis from patient to population.
Rates, denominators and subgroup structure matter because counts alone can mislead.
How many people currently have the condition?
Prevalence reflects both incidence and duration.
Who dies, from what, and when?
Cause-specific and all-cause mortality answer different questions.
How much health is lost?
Burden measures combine frequency, severity or duration but depend on value choices.
Which groups carry the burden?
Population averages can conceal large inequalities.
Compared with what population?
A rate is only interpretable when the population at risk is defined correctly.
Prevention acts at different points in the causal pathway.
The earlier the intervention, the broader its reach and the less specific its target may be.
Prevent risk conditions from arising.
Urban design, food systems and regulation can shape exposure before individual risk factors appear.
Prevent disease onset.
Vaccination, protective equipment and risk reduction act before illness begins.
Detect early disease.
Screening is useful only when earlier detection improves outcomes enough to justify harms and costs.
Reduce complications.
Treatment, rehabilitation and recurrence prevention limit downstream burden.
Shift the whole distribution.
Small risk reductions across many people can exceed large reductions in a small high-risk group.
Target those most exposed.
Precision can improve efficiency but miss structural causes.
Public health needs timely observation of changing conditions.
Surveillance trades speed, completeness, representativeness and privacy.
What counts as an event?
Changing definitions can change trends without changing underlying disease.
How do events enter the system?
Passive reporting is cheap but incomplete; active systems are richer but expensive.
Watch selected sites closely.
Sentinel systems can detect movement before full population data arrive.
Measure shared signals.
Environmental sampling can capture community-level changes without individual diagnosis.
Who is missing from the data?
Testing access, care-seeking and reporting behavior affect observed rates.
Population information can still identify people.
Data minimization and governance remain part of surveillance design.
Health is produced by more than medical care.
Income, housing, education, work, environment and social position shape exposure and resilience.
Resources shape risk.
Housing quality, nutrition, transport and workplace safety alter daily exposure.
Care must be reachable and usable.
Insurance, geography, language, trust and time can all block nominal availability.
Air, water, heat and hazards matter.
Exposure is often spatially patterned rather than randomly distributed.
Choices occur inside constraints.
Individual behavior is real but shaped by price, defaults, norms and opportunity.
Health can propagate socially.
Information, norms, support and contagion all move through relationships.
Rules alter population risk.
Taxes, standards, zoning and benefits can change exposure without individual clinical encounters.
Screening is a decision problem under uncertainty.
Finding disease earlier is beneficial only when test performance and treatment pathways justify the downstream consequences.
How many true cases are detected?
Higher sensitivity reduces missed cases but may increase false positives depending on threshold.
How many unaffected people test negative?
Low specificity can produce large follow-up burdens in low-prevalence populations.
Prevalence changes predictive value.
Even accurate tests can yield many false positives when the condition is rare.
Earlier diagnosis can appear to extend survival.
Survival from diagnosis is not the same as living longer.
Some detected disease would never cause harm.
Detection can create treatment burden without improving health.
A screening program is a pathway, not a test.
Confirmatory testing, treatment capacity and adherence determine real benefit.
Population interventions require evidence about both effect and implementation.
Randomized trials, natural experiments, cohorts and surveillance each answer different questions.
Did the intervention change the outcome?
Design and assumptions determine whether association supports causal inference.
Will the effect travel?
Population, setting, delivery system and baseline risk can change results.
Benefits are not the only outcomes.
Behavioral, economic and equity harms belong in evaluation.
Who benefits first and most?
An effective intervention can widen disparities if access is uneven.
What health is gained per resource used?
Economic evaluation makes trade-offs explicit but depends on perspective and valuation.
Can the program operate at scale?
Fidelity, adaptation, staffing and logistics determine whether efficacy becomes impact.