Course Description¶

Title¶

Introduction to Public Health

Course Overview¶

Public health is the science and practice of protecting and improving the health of entire populations through prevention, policy, education, research, and data-driven decision-making. This course introduces the foundational concepts, frameworks, and computational skills that define the discipline — from measuring disease in communities to simulating the dynamic systems that drive health outcomes.

Students gain fluency in the five core domains required by CEPH-accredited public health programs (epidemiology, biostatistics, environmental health, health policy and management, and social and behavioral sciences), as well as seven cross-cutting competency areas: systems thinking, data science, simulation and modeling, health equity, ethics, communication, and prevention science.

A distinctive feature of this course is its emphasis on systems thinking and computational simulation. Public health problems — the opioid epidemic, COVID-19, chronic disease, health disparities — are complex adaptive systems with nonlinear dynamics, feedback loops, and emergent behaviors that defy simple cause-and-effect analysis. Students learn to build and interpret causal loop diagrams, stock-and-flow models, and agent-based simulations as tools for policy analysis and intervention design.

The COVID-19 pandemic is used as a recurring master case study throughout the course. It illustrates epidemiological modeling failures, data infrastructure weaknesses, health communication breakdowns, equity disasters, and systems- level policy resistance — making it the most instructive public health event of the modern era.

Audience¶

Undergraduate and graduate students in their first public health course; pre-professional students in medicine, nursing, social work, or data science seeking public health literacy; and working professionals entering accredited public health degree programs who need quantitative and systems-analytical foundations.

Prerequisites¶

None. Basic comfort reading data tables and graphs is helpful. Students who have taken introductory statistics will move more quickly through the biostatistics module but it is not required.

Topics Covered¶

1. Epidemiology¶

Measures of disease frequency: incidence rate, cumulative incidence, prevalence proportion, mortality rate, case fatality ratio, infection fatality ratio
Measures of association: relative risk, odds ratio, hazard ratio, attributable risk, population attributable fraction
Epidemiological study designs: cross-sectional, case-control, prospective cohort, retrospective cohort, ecological, randomized controlled trial, natural experiment
Causality frameworks: Bradford Hill criteria, directed acyclic graphs (DAGs), counterfactual model, sufficient-component cause model
Epidemic dynamics: basic reproduction number (R₀), effective reproduction number (Rₜ), serial interval, generation time, doubling time, epidemic curve shapes
Disease surveillance systems: passive vs. active surveillance, sentinel surveillance, syndromic surveillance, reportable conditions
Outbreak investigation: case definition, index case identification, attack rate calculation, epidemic curve analysis, source hypothesis testing, case-control studies in outbreaks
Screening programs: sensitivity, specificity, positive and negative predictive value, receiver operating characteristic (ROC) curves
COVID-19 epidemiology case study: estimating Rₜ in real time, variant emergence and immune escape, excess mortality calculation, limitations of confirmed case counts vs. true incidence

2. Biostatistics¶

Descriptive statistics: measures of central tendency and dispersion, frequency distributions, box plots, histograms
Probability foundations: conditional probability, Bayes' theorem, common distributions (normal, binomial, Poisson, negative binomial for overdispersed count data)
Sampling methods: simple random, stratified, cluster, systematic; non-probability sampling and its limitations
Hypothesis testing: null and alternative hypotheses, Type I and Type II errors, statistical power, sample size estimation
Confidence intervals: construction, interpretation, practical vs. statistical significance
Common inferential tests: chi-square, Fisher's exact, t-test, Mann-Whitney, ANOVA, Kruskal-Wallis
Regression in public health: linear regression, logistic regression, Poisson regression for rates, survival analysis (Kaplan-Meier, Cox proportional hazards)
Multiple comparisons, confounding, and effect modification
Meta-analysis and systematic reviews: pooling effect sizes, heterogeneity, forest plots, funnel plots
COVID-19 biostatistics case study: vaccine effectiveness calculation, test-negative design, excess mortality, Simpson's paradox in age-stratified data, p-hacking in pandemic-era research

3. Environmental Health¶

Environmental risk assessment framework: hazard identification, dose-response assessment, exposure assessment, risk characterization
Air quality: criteria pollutants (PM₂.₅, ozone, NO₂, SO₂, CO, lead), health effects by pollutant, Air Quality Index (AQI), NAAQS standards
Water safety: contamination pathways (biological, chemical, radiological), Safe Drinking Water Act, water treatment processes, surveillance
Toxicology principles: dose-response curves, LD₅₀, NOAEL, LOAEL, bioaccumulation, biomagnification, endocrine disruption
Built environment and health: urban heat islands, walkability, food deserts, green space, transportation infrastructure, land use and respiratory health
Climate change and health: heat-related illness, vector-borne disease expansion (Lyme, dengue, West Nile), wildfires and air quality, flooding and waterborne disease, climate displacement
Environmental justice: cumulative exposure burden in low-income and minority communities, EJScreen tool, legacy contamination, environmental permitting disparities
Causal loop modeling of environmental-health systems: air pollution feedback loops, agricultural runoff and water quality dynamics

Major health behavior theories: Health Belief Model (perceived susceptibility, severity, benefits, barriers, cues to action, self-efficacy); Transtheoretical Model (precontemplation, contemplation, preparation, action, maintenance, relapse); Social Cognitive Theory (self-efficacy, observational learning, reciprocal determinism); Theory of Planned Behavior (attitudes, subjective norms, perceived behavioral control)
Social-ecological model: individual, interpersonal, community, organizational, and policy levels; multilevel interventions
Social determinants of health: income and wealth, education, housing, food security, employment, transportation, social support networks, early childhood development
Health literacy: functional, communicative, critical; plain language principles; teach-back method; low-literacy communication design
Behavioral economics in public health: nudge theory, default effects, loss aversion, social norming, framing effects
Structural racism and health: redlining and neighborhood health effects, differential treatment in healthcare, criminal justice intersections, weathering hypothesis
Cultural humility and culturally responsive health practice: CLAS standards, community health workers, promotoras model
COVID-19 behavioral case study: mask compliance dynamics, vaccine hesitancy feedback loops, pandemic fatigue, misinformation adoption models

5. Health Policy and Management¶

US health system structure: public sector (federal, state, local), private sector, nonprofit, safety-net providers (FQHCs, Ryan White, Title X)
Health policy development cycle: agenda-setting (Kingdon's streams model), formulation, adoption, implementation, evaluation, termination
Healthcare financing: employer-sponsored insurance, individual market, Medicaid, Medicare, CHIP, ACA provisions and coverage expansion, uninsurance and underinsurance, high-deductible plans
Public health law: police powers, quarantine and isolation authority, mandatory reporting, Jacobson v. Massachusetts, preemption, public health emergency declarations
Public health administration: governmental public health infrastructure, accreditation (PHAB), workforce development, public health funding streams
Program planning models: logic models, PRECEDE-PROCEED, MAPP (Mobilizing for Action through Planning and Partnerships), MATCH model
Health economics: cost-effectiveness analysis (cost per QALY), cost-benefit analysis, return on investment for prevention, willingness-to-pay thresholds
Quality improvement: PDSA cycle, Lean/Six Sigma in public health, performance management, Dashboard KPIs
COVID-19 policy case study: emergency use authorization, vaccine distribution decision-making, mask mandate political dynamics, FEMA coordination failures, economic stimulus and health outcomes

6. Global Health¶

Global burden of disease: DALYs, YLLs, YLDs; the GBD study methodology; leading causes of premature death by income group and region
Epidemiological and demographic transitions: Omran's transition theory, double burden of disease in LMICs, aging populations
Universal health coverage: definition, measurement (financial protection, service coverage index), financing mechanisms (insurance vs. tax-based), Abuja Declaration, catastrophic health expenditure
Sustainable Development Goals: SDG 3 (Good Health and Well-Being) targets and indicators; health-relevant dimensions of SDGs 1, 2, 4, 6, 10, 13, 16
Pandemic preparedness and response: International Health Regulations (IHR 2005), WHO emergency declaration process, PHEIC criteria, Global Health Security Index, APSED framework
Neglected tropical diseases: burden, affected populations, NTD control strategies (preventive chemotherapy, vector control, WASH)
Global health governance: WHO structure and limitations, World Bank health financing, bilateral aid (PEPFAR, PMI), philanthropic actors (Gates Foundation), global health initiatives (GAVI, Global Fund)
Health in humanitarian settings: Sphere standards, refugee health priorities, complex emergencies, mental health in displaced populations
COVID-19 global case study: COVAX failures and vaccine nationalism, global surveillance gaps, WHO emergency declaration timeline critique, variant emergence from under-vaccinated populations

Dahlgren-Whitehead rainbow model: layers of influence from fixed individual characteristics to macroeconomic and environmental conditions
WHO Commission on Social Determinants of Health: key findings, three overarching recommendations (daily living conditions, power/money/resources, measurement/evidence/training)
Race, ethnicity, and health outcomes: infant mortality, maternal mortality, cardiovascular disease, diabetes, life expectancy gaps; structural vs. individual explanations
Income and wealth gradients: gradient effect across the full socioeconomic spectrum, wealth as distinct from income, intergenerational poverty traps
Educational attainment as a health determinant: pathways through health behaviors, income, social capital, stress, cognitive reserve
Neighborhood conditions: housing quality (lead, mold, crowding), food access, green space, toxic exposures, neighborhood poverty concentration effects
Historical trauma: intergenerational effects of slavery, Native American boarding schools, Japanese internment; epigenetic mechanisms
Intersectionality: overlapping identities (race × gender × class × disability) and compounding disadvantage; Crenshaw's framework applied to health
Place-based interventions: HUD Moving to Opportunity, Promise Neighborhoods, community development finance, upstream policy levers
COVID-19 equity case study: excess mortality by race and occupation, essential worker exposure disparities, vaccine access inequities, long COVID burden in low-income communities

8. Public Health Ethics¶

Principles of bioethics: autonomy, beneficence, non-maleficence, justice (Beauchamp and Childress four-principles framework)
Population ethics frameworks: utilitarian (greatest good for greatest number), egalitarian (prioritize the worst-off), libertarian (minimize coercion), communitarian (social solidarity)
Stewardship model of public health ethics: Nuffield Council ladder of interventions, proportionality, evidence requirement, accountability
Research ethics: Belmont Report (respect for persons, beneficence, justice), informed consent, IRB review, research with vulnerable populations, community advisory boards
Ethics of mandatory measures: vaccination policy, fluoridation, quarantine authority, seat belt laws, sugar taxes — autonomy vs. population benefit
Data ethics in public health: consent for secondary data use, re-identification risks, algorithmic bias in risk prediction models, differential privacy
Justice frameworks: distributive (allocating benefits/burdens), procedural (fair processes), recognition (respecting diverse identities), restorative (repairing historical harms)
Global health ethics: power asymmetries in North-South research partnerships, standard-of-care debates in clinical trials, brain drain from LMICs
COVID-19 ethics case study: ventilator allocation frameworks, vaccine prioritization decisions, mandatory vaccination for healthcare workers, privacy tradeoffs in digital contact tracing

9. Community Health¶

Community definitions: geographic, relational, identity-based; assets vs. deficits framing
Community health assessment methods: windshield survey, key informant interviews, focus groups, town halls, secondary data analysis, community surveys
Community health improvement planning: MAPP framework (four assessments, prioritization, strategic issues, goals and strategies)
Community-based participatory research: principles (community co-ownership, bi-directional learning, action orientation), power-sharing structures, data governance
Health education program design: needs and capacity assessment, SMART objectives, curriculum development, facilitator training, fidelity monitoring
Coalition building: stages (forming, storming, norming, performing), governance structures, memoranda of understanding, managing conflict, sustaining membership
Ottawa Charter health promotion: five action areas (healthy public policy, supportive environments, community action, personal skills, reorientation of health services)
Community resilience and asset-based community development (ABCD): social capital, bonding vs. bridging capital, Putnam's civic engagement research
Faith-based and community organization partnerships: trust mechanisms, culturally grounded outreach, place-based reach
Evaluation designs for community programs: logic model alignment, process and outcome evaluation, RE-AIM framework, contribution vs. attribution

10. Public Health Communication¶

Health literacy: functional, interactive, critical levels; National Action Plan to Improve Health Literacy; plain language guidelines (6th-grade reading level, active voice, familiar words)
Risk communication principles: mental models research, numeracy and health decisions, relative vs. absolute risk framing, uncertainty communication
Crisis and emergency risk communication (CERC): six principles (be first, be right, be credible, express empathy, promote action, show respect), five stages (pre-crisis, initial, maintenance, resolution, evaluation)
Audience segmentation: demographic, psychographic, behavioral, health literacy-based segmentation; tailoring vs. targeting
Communication channels: mass media (television, radio, print), digital media (websites, email, SMS), social media platforms, community health workers, peer educators
Social media in public health: platform-specific strategies, influencer partnerships, listening and monitoring tools, infoveillance, Twitter/X epidemiology
Countering health misinformation: prebunking vs. debunking, inoculation theory, SIFT method (Stop, Investigate, Find better coverage, Trace claims), platform fact-checking partnerships
Cultural tailoring: formative research methods, back-translation, CLAS standards, community review processes
Campaign evaluation: reach, frequency, awareness, attitude change, behavior change; EPPM (Extended Parallel Process Model) as an evaluation framework
COVID-19 communication case study: infodemic dynamics, WHO infodemic management framework, CDC communication failures in early messaging, vaccine communication successes and failures by demographic

11. Prevention Science¶

Prevention classification: primordial (prevent risk factor development), primary (prevent disease onset), secondary (early detection and treatment), tertiary (reduce disability and complications)
IOM prevention spectrum: universal, selective, and indicated interventions; application to mental health and substance use
Evidence-based practice foundations: hierarchy of evidence (RCTs, cohort studies, case-control, expert opinion), systematic reviews, The Community Guide, ClinicalTrials.gov, What Works Clearinghouse
Vaccine-preventable diseases: immunization schedule, herd immunity threshold calculation, breakthrough infections, cold chain requirements, vaccine hesitancy measurement and intervention
Chronic disease prevention: modifiable risk factors (tobacco, physical inactivity, poor diet, excessive alcohol, obesity), dose-response relationships, brief motivational interviewing
Injury and violence prevention: Haddon Matrix (host, agent, environment × pre-event, event, post-event), public health approach to violence, falls prevention among older adults
Substance use prevention: social influence model, normative feedback interventions, naloxone distribution, syringe service programs, harm reduction philosophy
Screening program design: Wilson-Jungner criteria, population vs. risk-based screening, overdiagnosis tradeoffs, USPSTF evidence grading
Implementation science: fidelity vs. adaptation tension, EPIS framework (Exploration, Preparation, Implementation, Sustainment), scale-up strategies, de-implementation of ineffective practices

12. Systems Thinking and Causal Modeling¶

Fundamental systems concepts: stocks (accumulations), flows (rates of change), feedback loops, time delays, nonlinearity, emergence, path dependence
Feedback loop types: reinforcing loops (R, positive feedback — exponential growth, vicious cycles, virtuous cycles), balancing loops (B, negative feedback — goal-seeking, oscillation)
Causal loop diagram (CLD) construction: identifying variables, drawing causal links, assigning polarities (same S / opposite O or + / −), identifying loop polarity, naming loops, documenting assumptions
System archetypes and their public health manifestations:
- Limits to Growth (capacity constraints in healthcare surge)
- Shifting the Burden (opioid prescribing replacing pain management)
- Tragedy of the Commons (antibiotic resistance, shared water resources)
- Fixes that Fail (short-term symptom relief with long-term amplification)
- Escalation (arms race dynamics in drug-resistant pathogens)
- Eroding Goals (normalizing health disparities over time)
- Success to the Successful (resource concentration in well-funded systems)
- Addiction (dependence on a solution that undermines intrinsic capacity)
Stock-and-flow (SFD) modeling: translating CLDs into quantitative models, level equations, rate equations, auxiliary variables, initial conditions
Simulation tools: InsightMaker (free, web-based, suitable for coursework), Vensim PLE (free, industry-standard), Stella Architect, AnyLogic (multi- method), NetLogo (agent-based)
SIR/SEIR compartmental models: susceptible, exposed, infected, recovered compartments; equations for births/deaths, transmission rate β, recovery rate γ, R₀ = β/γ; model extensions (SEIRD, SEIRV with vaccination)
Agent-based modeling concepts: agents, rules, emergence, spatial grids, network topologies; example: epidemic spread on a social network
Network analysis in public health: nodes (people, organizations, places), edges (contacts, flows), degree distribution, clustering coefficient, betweenness centrality, super-spreader identification, network interventions
Group model building: facilitated CLD construction with stakeholders, boundary decisions, variable elicitation, shared mental model development
Model validation: face validity, sensitivity analysis, extreme condition testing, historical reproduction, cross-validation
Policy analysis with simulation: policy levers, leverage points (Meadows hierarchy: parameters → feedback structure → goals → paradigm → transcend), unintended consequences, policy resistance
COVID-19 systems modeling case study: SEIR model failures and successes in projecting wave dynamics, healthcare surge capacity modeling (ICU beds as stock with delayed inflow), vaccine rollout equity feedback loops, mask adoption as a tipping-point system, long COVID as a stock accumulating from cumulative infection flows, misinformation as a reinforcing loop

13. Data Science and Computational Public Health¶

Note on language choice: Python is the primary computing language for this course. R is covered for historical context — many public health datasets, tutorials, and published analyses were written in R, and students will encounter it in the literature — but new analyses are done in Python.

Statistical computing environments: Python (pandas, NumPy, SciPy, statsmodels, scikit-learn, geopandas) is the primary tool; R (tidyverse: dplyr, ggplot2, tidyr, purrr; survival: survminer; spatial: sf, tmap) is introduced for historical context and reading existing code
Reproducible research: R Markdown and Quarto documents, Jupyter notebooks, Git/GitHub version control for analysis code, containerization concepts (Docker for reproducibility)
Public health data sources: CDC WONDER, BRFSS, NHANES, SEER cancer registry, HCUP (hospital data), vital statistics (birth and death certificates), ACS and decennial census, NIH All of Us, Global Burden of Disease study
Data cleaning and wargaming: missing data mechanisms (MCAR, MAR, MNAR), imputation methods, outlier detection, record linkage, data dictionary development
Geographic Information Systems (GIS) and spatial epidemiology: choropleth maps, dot density maps, kernel density estimation, spatial autocorrelation (Moran's I), spatial clustering (SaTScan, local indicators of spatial association), interpolation, geocoding
Disease mapping and surveillance dashboards: ArcGIS Online, QGIS, R Leaflet and tmap, Tableau Public, R Shiny for interactive dashboards
Time-series analysis in public health: decomposition (trend, seasonality, residual), ARIMA models, interrupted time-series analysis for policy evaluation, nowcasting
Machine learning applications: supervised learning for disease risk prediction (random forests, gradient boosting), unsupervised learning for population segmentation (k-means, hierarchical clustering), cross-validation, performance metrics (AUC, sensitivity, specificity, calibration)
Natural language processing (NLP) for public health: sentiment analysis of social media for infoveillance, named entity recognition for disease surveillance from news feeds, topic modeling (LDA) for public health reports
Digital and novel data sources: social media signals, mobility data (SafeGraph, Veraset), wastewater epidemiology (WBE), wearable sensor data, satellite imagery for environmental health, Google Trends syndromic surveillance
Data quality, bias, and ethics: selection bias in digital data, algorithmic fairness in risk prediction, differential privacy, de-identification standards (HIPAA Safe Harbor, Expert Determination)
COVID-19 data science case study: JHU, CDC, and state dashboard comparisons, wastewater surveillance as leading indicator, excess mortality estimation with R, Rt.live methodology, variant tracking with genomic surveillance

14. Simulation Design for Public Health Education¶

MicroSim design principles: one concept per simulation, interactive controls, real-time feedback, visual encoding of system dynamics
p5.js for public health simulations: agent-based epidemic spread, network visualization, animated epidemic curves, demographic pyramids
Chart.js and Plotly for data visualization: interactive epidemic dashboards, risk factor charts, comparative population health profiles
vis-network for network diagrams: contact tracing networks, healthcare referral networks, causal loop diagram visualization
SIR/SEIR interactive simulations: sliders for β (transmission rate), γ (recovery rate), vaccination coverage; real-time R₀ display; phase portraits
Causal loop diagram simulations: interactive CLDs where users adjust assumptions and observe system behavior changes
Health equity simulations: interactive visualization of social determinants and differential health outcomes across population groups
Healthcare surge capacity simulations: ICU bed stock-and-flow models with policy levers (admission rate, discharge rate, bed expansion)
Behavioral intervention simulations: PRECEDE-PROCEED model visualization, theory of change animations
Environmental health simulations: pollution dispersion models, dose-response curve explorers, cumulative exposure burden maps

15. COVID-19 as a Public Health Master Case Study¶

Pandemic origins and early detection failures: wet market hypothesis, early genomic sequencing, WHO notification timeline, Wuhan lockdown decision
Epidemiological modeling in real time: early R₀ estimates, IFR vs. CFR confusion, modeling team disagreements, Imperial College vs. IHME projections
Data infrastructure failures: fragmented US surveillance (state vs. federal), hospital capacity reporting gaps, race/ethnicity data undercollection, PCR vs. antigen test conflation
Non-pharmaceutical interventions (NPIs): evidence base for masking, social distancing, school closure; compliance dynamics; NPI fatigue as a systems phenomenon
Vaccine development and deployment: mRNA platform biology, Operation Warp Speed logistics, EUA process, ACIP prioritization decisions, cold chain requirements, last-mile delivery challenges
Health equity failures: BIPOC excess mortality, essential worker exposure, vaccine deserts, long COVID burden, mental health toll in caregivers
Communication failures and successes: CDC mask guidance reversals, hydroxychloroquine amplification, infodemic dynamics, trusted messenger strategies that worked (Black barbershop model, faith-based outreach)
Systems analysis of COVID: applying SEIR modeling, CLD for misinformation feedback loops, healthcare surge capacity model, political-economic system dynamics of pandemic response
Long COVID: prevalence estimates, organ systems affected, post-acute sequelae, disability implications, healthcare system burden accumulation

16. Health Fraud, Nutritional Misinformation, and the Supplement Industry¶

Regulatory landscape: Dietary Supplement Health and Education Act (DSHEA 1994) — why supplements require no pre-market safety or efficacy proof; FDA vs. FTC jurisdictional split; burden-of-proof inversion compared to pharmaceuticals
Scale of the problem: US supplement market size (~$50B/year), injury reports (FDA CFSAN Adverse Event Reporting System), hospitalizations attributable to supplements, economic fraud burden on consumers
Common fraud archetypes: miracle cure claims, detox and cleansing pseudoscience, proprietary blend opacity, endorsement by fake credentials, conspiracy framing ("doctors don't want you to know"), before-and-after testimonials
Multi-level marketing (MLM) as a public health system: distributor recruitment feedback loops, product health claim propagation through social networks, financial predation targeting vulnerable populations, FTC pyramid scheme enforcement criteria
Digital marketing of health fraud: social media influencer promotion, affiliate link monetization, manufactured social proof (fake reviews), algorithmic amplification of health misinformation, YouTube and TikTok supplement culture
Science of nutritional supplements: what randomized trial evidence actually shows (vitamins A, E, beta-carotene, selenium); null results from major trials (SELECT, CARET, AREDS2 revisions); distinction between deficiency correction and supplementation in replete populations
Vulnerable populations and predatory targeting: older adults and anti-aging claims, cancer patients and alternative therapy abandonment, weight loss seekers and stimulant-containing products, athletes and contaminated sports supplements, low-income consumers and financial exploitation
COVID-related supplement fraud case study: ivermectin and hydroxychloroquine promotion, vitamin D mega-dosing claims, "immune boosting" product surge, FTC and FDA enforcement actions during the pandemic, social media role in amplifying dangerous advice
Consumer protection tools: FTC Health Claims resources, QuackWatch (Barrett methodology), ConsumerLab independent testing, NSF and USP certification marks, National Institutes of Health Office of Dietary Supplements (ODS) evidence summaries
Detection and counter-measures: SIFT method applied to supplement claims, prebunking misinformation inoculation theory, fact-checking collaborations, health literacy as a protective factor, social network analysis of MLM recruitment chains
Systems thinking applied to supplement fraud: CLD of MLM recruitment and product claim propagation, reinforcing loop of testimonial sharing and social proof, balancing loop of regulatory enforcement, leverage points (DSHEA reform, platform content policy, health literacy)
Policy responses and reform debates: DSHEA reform proposals, FTC enforcement actions (Herbalife, AdvoCare settlements), state attorney general actions, proposed mandatory adverse event reporting, EU regulatory comparison (stricter pre-market notification requirements)

Topics Not Covered¶

This course intentionally excludes the following areas, which are treated in separate specialized courses:

Clinical medicine — diagnosis, treatment protocols, pharmacotherapy, and clinical decision-making at the individual patient level
Nursing practice — patient care delivery, nursing assessment, clinical nursing interventions
Advanced health informatics — electronic health record systems, clinical decision support, HL7/FHIR standards, clinical IT architecture (population- level surveillance data use is covered)
Occupational health and safety — individual workplace exposure limits, OSHA standards, industrial hygiene, workers' compensation (population-level environmental exposures are covered)
Precision and genomic medicine — individual genetic testing, pharmacogenomics, clinical genetics (population genetics and GWAS in health disparities context are introduced but not in depth)
Hospital and health system administration — operations management, staffing models, supply chain, revenue cycle management
Mental health treatment — psychotherapy modalities, psychiatric medication management, clinical mental health practice (social determinants of mental health and population-level behavioral health are covered)
Oral and dental public health — covered in specialized dental public health courses
Pharmaceutical research and drug development — clinical trials for therapeutic agents, FDA drug approval process beyond emergency use
Veterinary medicine — clinical animal care (One Health zoonotic and ecosystem dimensions are covered under Global Health and Systems Thinking)
Advanced machine learning and deep learning — neural networks, computer vision, large language models (introductory ML applications to public health data are covered; advanced ML methods require a separate course)

Learning Outcomes¶

After completing this course, students will be able to:

Remember¶

List and describe each of the 10 Essential Public Health Services and the three categories (Assessment, Policy Development, Assurance) they belong to.
Define core epidemiological measures: incidence rate, prevalence proportion, relative risk, odds ratio, hazard ratio, R₀, Rₜ, and serial interval.
Name the five CEPH-accredited curricular domains and give at least two representative concepts from each.
Recall the six levels of prevention from primordial through tertiary and give a COVID-19 example at each level.
Identify the major public health data sources (BRFSS, NHANES, CDC WONDER, SEER, GBD, vital statistics) and describe what each measures.
List the components of a causal loop diagram: variables, links, polarities, and feedback loop types (reinforcing R, balancing B).
Recall the names and defining features of the eight major system archetypes and identify at least one public health example of each.
List the key provisions of the Dietary Supplement Health and Education Act (DSHEA 1994) and explain why it shifted the burden of proof away from manufacturers.
Name the stages of the Transtheoretical Model and the Health Belief Model constructs.

Understand¶

Explain the social-ecological model and describe how determinants at each level (individual through policy) interact to produce population health outcomes, using COVID-19 as an example.
Describe the Bradford Hill criteria and explain how they are used to move from observed association to causal inference.
Summarize why compartmental models (SIR/SEIR) captured some COVID-19 dynamics well and failed on others (behavioral adaptation, heterogeneous mixing, variant emergence).
Classify epidemiological study designs by evidence hierarchy and explain the tradeoffs between internal validity, external validity, and feasibility.
Explain the difference between a reinforcing feedback loop and a balancing feedback loop, and describe how each shapes epidemic dynamics.
Describe at least three mechanisms by which structural racism produces measurable health disparities.
Explain how behavioral economics concepts (defaults, loss aversion, social norms) can be applied to public health intervention design.
Summarize the WHO social determinants of health commission's three overarching recommendations and explain why upstream interventions are often more cost-effective than downstream ones.

Apply¶

Calculate incidence rates, attack rates, relative risk, odds ratios, and vaccine effectiveness from a two-by-two contingency table or outbreak dataset.
Build a causal loop diagram for a public health problem (e.g., opioid epidemic, COVID-19 vaccination uptake, childhood obesity) using standard CLD notation.
Run an interactive SEIR simulation, adjust β and γ parameters, and explain the resulting changes in epidemic curve shape, peak timing, and final attack rate.
Apply the PRECEDE-PROCEED model to plan a community health intervention, identifying predisposing, reinforcing, and enabling factors.
Conduct a community health needs assessment using publicly available secondary data sources, producing a data summary with maps and visualizations.
Use Python (and R for reading existing analyses) to calculate descriptive statistics, create an epidemic curve, and produce a choropleth map from public health surveillance data.
Apply CERC principles to draft a public communication message for a simulated public health emergency involving conflicting evidence.
Use the Community Guide to identify evidence-based interventions for a specified health problem and population.

Analyze¶

Distinguish association from causation in a given epidemiological study, citing confounding, selection bias, information bias, and chance as alternative explanations.
Analyze a causal loop diagram to identify the dominant feedback loops driving a public health problem and the highest-leverage intervention points using Meadows' leverage point hierarchy.
Compare COVID-19 mortality rates across racial and socioeconomic groups, identify the structural determinants responsible, and distinguish them from behavioral explanations.
Analyze a public health policy proposal by mapping stakeholders, identifying feedback-driven unintended consequences, and assessing equity implications.
Break down the COVID-19 pandemic response into systems failures (data infrastructure, communication, equity, political dynamics) using CLD analysis.
Examine a disease outbreak investigation to identify the case definition, index case, transmission mode, attack rates, and recommended control measures, applying DAG methods to assess confounding.
Analyze a public health dataset in Python, identifying data quality issues, applying appropriate statistical tests, and interpreting results.
Compare the effectiveness of upstream (structural) vs. downstream (behavioral) interventions for a chronic disease using evidence from systematic reviews.

Evaluate¶

Assess the strength of evidence for a public health intervention using the GRADE framework or Community Guide systematic review standards, identifying evidence gaps.
Critique a public health simulation model for face validity, parameter sensitivity, historical fit, and policy relevance; identify assumptions that most affect conclusions.
Judge the ethical justification of a mandatory public health measure (e.g., vaccine mandate, quarantine, fluoridation) using the stewardship model and the ladder of interventions.
Appraise competing health policies for a defined problem using cost- effectiveness, feasibility, equity impact, and systems-level unintended consequences.
Evaluate the quality of a public health data source for completeness, representativeness, timeliness, and potential biases, with reference to COVID-19 surveillance data failures.
Critique the COVID-19 public health communication response (CDC, WHO, state health departments), identifying specific message failures, equity gaps, and lessons for future crises.
Appraise a machine learning risk prediction model for a public health application, evaluating algorithmic fairness, overfitting, and decision threshold tradeoffs.

Create¶

Design a causal loop diagram for a complex public health problem, including at least three feedback loops, delays, and a written narrative identifying dominant loops, system archetypes, and leverage points.
Build an interactive SEIR or stock-and-flow simulation using InsightMaker or a comparable tool, with adjustable policy levers and a policy analysis memo interpreting model results.
Develop a targeted public health communication campaign — including audience segmentation, channel selection, message testing strategy, and evaluation plan — addressing a specific health disparity.
Produce a spatial analysis of a public health problem using Python (geopandas, Folium) or QGIS, including choropleth maps, hotspot analysis, and a written interpretation of geographic patterns.
Construct a community emergency preparedness plan incorporating surveillance triggers, coordination roles, equity considerations for vulnerable populations, and a surge capacity stock-and-flow model.
Produce a policy brief that synthesizes epidemiological evidence, cost analysis, equity implications, systems dynamics, and stakeholder landscape to recommend a systems-level change.
Design a MicroSim (interactive simulation) for a public health concept using p5.js, Chart.js, or vis-network, incorporating user-adjustable parameters and real-time visual feedback.
Produce a consumer health fraud analysis: select a commercially available supplement, evaluate its claims against the peer-reviewed evidence base, identify DSHEA regulatory gaps that enable the marketing, and draft a plain-language warning for a targeted vulnerable population.
Capstone: Develop a comprehensive community health improvement plan for a real or simulated community facing a documented health disparity. The plan must include: (1) a data-driven needs assessment with maps and statistical analysis in R or Python; (2) a causal loop diagram identifying root causes and system archetypes; (3) selection of evidence-based interventions with GRADE ratings; (4) an equity impact analysis; (5) a stakeholder engagement strategy; (6) an implementation timeline with logic model; (7) a mixed- methods evaluation framework; and (8) a policy brief for a decision-maker audience. All quantitative analysis must be conducted in Python; R may be referenced where existing published code is cited.

Authoritative Frameworks¶

This course is structured around the competency frameworks of three complementary bodies: the Council on Education for Public Health (CEPH), which defines the five core domains and cross-cutting skills required of accredited U.S. public health programs; the Centers for Disease Control and Prevention (CDC), whose Essential Public Health Services, One Health initiative, and Public Health 101 framework anchor applied practice; and the World Health Organization (WHO), whose social determinants of health model, Sustainable Development Goals, and global burden of disease methodology ground the course in international evidence. Secondary influences include the National Institutes of Health (NIH), the American Public Health Association (APHA), the Society for Public Health Education (SOPHE), the Public Health Foundation (PHF), and the Agency for Healthcare Research and Quality (AHRQ). A full catalog of these organizations and their curriculum contributions is maintained in concept-list-authorities.md.