IHP-515 focuses on research designs and methods to describe measures of disease occurrence and risk factor associations, utilizing quantitative information to ascertain whether relationships exist between risk or protective factors and diseases in a population. The course explores the role of chance, bias, confounding, and effect modification when looking at potential causal associations, and how these factors influence the design and interpretation of epidemiologic studies.
Measuring disease occurrence rigorously
The course establishes the quantitative measures — incidence, prevalence, risk ratios — used to describe how disease occurs across a population, building the foundational vocabulary for any epidemiologic analysis.
Distinguishing genuine causal association from chance, bias, and confounding
IHP-515 gives particular depth to the factors that can produce a misleading statistical association — chance, bias, confounding, effect modification — since correctly identifying these is what separates a scientifically sound epidemiologic conclusion from a spurious one.
Key topics in IHP515
- Measures of disease occurrence
- Risk factor and disease association studies
- Epidemiologic study design
- Chance, bias, and confounding
- Effect modification
- Interpreting population health data critically
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Worked example: confounding creating a misleading association
- Apparent association: A statistical link appears between two variables in population data
- Confounding revealed: Closer analysis shows a third, unaccounted-for factor actually explains both, making the original association spurious
- Lesson: IHP-515 teaches that recognizing and controlling for confounding is essential to avoid drawing false causal conclusions from population health data
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Frequently asked questions
Calculating a statistical association between a risk factor and a disease is only the first step — that association could be genuinely causal, or it could result from random chance, systematic bias in how data was collected, or a confounding variable that explains both factors without either causing the other, and failing to distinguish between these possibilities can lead to seriously flawed public health conclusions. IHP-515 emphasizes these factors because correctly ruling them out (or accounting for them) is what makes an epidemiologic finding scientifically credible.
IHP-525 Biostatistics builds the underlying statistical toolkit — probability theory, hypothesis testing, confidence intervals — while IHP-515 applies quantitative reasoning specifically to epidemiologic study design and the population health question of whether risk factors are genuinely associated with disease outcomes. A student typically draws on biostatistical methods to conduct the kind of rigorous epidemiologic analysis IHP-515 covers.