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Chapters

This textbook is organized into 20 chapters covering 500 concepts.

Chapter Overview

  1. Public Health Foundations — Core definitions, functions, prevention levels, and workforce infrastructure that underpin all public health practice.
  2. Epidemiology: Disease Measurement — Quantitative measures of disease frequency and association, foundational study designs, and causal inference frameworks.
  3. Epidemiology: Study Design and Causal Inference — Bias, confounding, surveillance systems, outbreak investigation, epidemic dynamics, and screening metrics.
  4. Biostatistics: Statistical Foundations — Descriptive statistics, probability theory, sampling, hypothesis testing, and common inferential tests.
  5. Biostatistics: Regression and Advanced Methods — Regression methods, survival analysis, meta-analysis, standardization, and interrupted time-series analysis.
  6. Environmental Health — Environmental risk assessment, air and water quality, toxicology, built environment, climate change, and environmental justice.
  7. Social and Behavioral Health — Health behavior theories, the social-ecological model, behavioral economics, health literacy, cultural humility, and structural racism.
  8. Health Policy and Management — US health system structure, policy development, healthcare financing, public health law, program planning, and quality improvement.
  9. Global Health — Global burden of disease, epidemiological transitions, universal health coverage, SDGs, pandemic governance, and humanitarian health.
  10. Health Equity and Social Determinants — Health equity frameworks, income and education gradients, racial disparities, historical trauma, intersectionality, and place-based interventions.
  11. Public Health Ethics — Bioethical principles, the stewardship model, research ethics, mandatory intervention ethics, and justice frameworks.
  12. Public Health Communication — Risk communication, CERC framework, audience segmentation, health misinformation, prebunking, and infodemic management.
  13. Prevention Science — IOM prevention spectrum, evidence-based practice, immunization, chronic disease prevention, harm reduction, and implementation science.
  14. Systems Thinking: Foundations and Causal Diagrams — Stocks, flows, feedback loops, causal loop diagrams, system archetypes, and stock-and-flow modeling tools.
  15. Systems Thinking: Modeling and Networks — SIR/SEIR compartmental models, agent-based modeling, group model building, model validation, leverage points, and network analysis.
  16. Data Science for Public Health: Foundations — Python and R environments, reproducible research, major public health data sources, and data cleaning methods.
  17. Data Science for Public Health: Advanced Analytics — GIS and spatial epidemiology, time-series analysis, machine learning, NLP for surveillance, and novel digital data sources.
  18. Simulation Design for Public Health — MicroSim design principles and interactive simulation implementation using p5.js, Chart.js, Plotly, and vis-network.
  19. COVID-19 as a Master Case Study — The COVID-19 pandemic as an integrative case study spanning epidemiology, data science, equity, communication, systems thinking, and policy.
  20. Health Fraud and Misinformation — DSHEA regulation, the supplement industry, MLM dynamics, digital health fraud, consumer protection, and policy reform.

How to Use This Textbook

Chapters are sequenced so that every concept appears after all its prerequisites. Work through chapters in order if this is your first exposure to public health. Students with background in specific domains (for example, biostatistics or environmental health) may skip ahead, but should verify they have the foundational concepts from Chapters 1–3 before doing so.

Each chapter includes a list of concepts covered, links to prerequisite chapters, and interactive MicroSims where available.

Note: Each chapter index includes a concept list used by the content generation workflow. Do not remove or reorder the concept lists.