An introduction to methodological foundations. Seven steps toward an epidemiology of consequence.
Learn epidemiology by doing. Manipulate populations, observe associations, introduce bias, and see what happens. No formulas required to start — just curiosity.
Replace a lecture with a live demonstration. Use preset scenarios to show confounding, Simpson's paradox, or selection bias in real time. Assign interactive exercises tied to each chapter.
Epidemiology shapes policy, headlines, and daily life. Understand how we know what causes disease — and why it matters who we study and how we study them.
The book organizes epidemiologic thinking around seven foundational steps. Each builds on the last. Together, they constitute a complete framework for studies that matter.
Each tool maps directly to the book. Use them in class, assign them as exercises, or explore on your own. The print edition includes QR codes linking to each module.
Build a data-generating process. Add confounding, measurement error, and selection bias. See how they distort causal estimates. Five preset scenarios for classroom use.
Adjust disease and exposure in a population of Farrlandians. Watch prevalence, risk ratios, and risk differences update in real time.
Toggle causes for individual Farrlandians. Disease occurs when a sufficient cause is complete. Different people, different pathways, same disease.
Walk through all seven steps to design your own epidemiologic study of consequence.
1,000 Farrlandians with full profiles. Hover to meet them. Filter, stratify, and compute associations from the living dataset.
Compare causal effects across populations with different distributions of component causes. See why the same exposure can matter more in one place than another.
Adjust sensitivity, specificity, and prevalence. See how predictive values change. Encounter lead-time and length bias through animation.
Each chapter connects a methodological choice to a question of social responsibility.
How we define a health indicator determines who counts as a case. Who counts determines what risk factors we find. What we find determines what gets funded.
Consider depression. A clinical interview identifies fewer cases than a screening tool. Those missed are disproportionately people without access to clinical settings. The measurement choice is not neutral.
When you choose a measure, ask: whose health does this make visible, and whose does it obscure?
An inter-school competition for public health students. Investigate an outbreak in Farrlandia. Identify causes. Propose interventions. Compete against teams from schools across the country.
Get notified when new tools launch, when the Farrlandia Challenge opens, and when the second edition is available.