This required course for GHP students explores how we study the distribution and determinants of disease, introducing methods for measuring health status, disease occurrence, the association between risk factors and health outcomes, probing evidence for causality, and characterizing how ecology and evolution shapes human health. Emphasis on: study design and sampling, bias and confounding, the generalizability of research, identifying causality, infectious disease dynamics, global health, and one health.

This course is a doctoral seminar in modern demographic methods and theory, with a focus on application in dissertation research. We cover non-stable population methods, including tempo and quantum as well as variable-r methods; population projection; modern multistate life table methods; multivariate and functional decomposition; event history (survival) models; indirect estimation; sample survey methods; the biology and demography of aging, including coverage of biomarkers, Alzheimer's disease, and quantitative models for aging; modeling heterogeneity and selection; imputation of missing data; and sources of data in population research.

This seminar explores important factors facing the field of global health today, as well as policy actions to address these factors. It examines demographic changes and rapid urbanization, climate change and its implications for global health, the increased importance of non-communicable diseases in low- and middle-income countries, the rise of social media and misinformation/disinformation, new health risk factors such as antimicrobial resistance, and the increased prominence of humanitarian emergencies due to conflicts, natural disasters, pandemics and other disease outbreaks.

This course combines a traditional public health course in epidemiology with a policy-oriented course on population health. Conventional topics include measurement of health and survival and impact of associated risk factors; techniques for design, analysis of epidemiologic studies; sources of bias and confounding; and causal inference. We also examine: models of infectious disease with an emphasis on COVID-19, inference and decision making based on large numbers of studies and contradictory information, the science underlying screening procedures, social inequalities in health, and ethical issues in medical research.