Fall 2025

Overview of behavioral theory and research in disease prevention and health promotion and in adaptation to chronic disease, including an introduction to measures of outcomes. Prerequisite: Admission to HBR Ph.D. Program or permission of instructor.

Overview and application of behavioral theories to the field of health education and promotion. Examines the determinants of health behavior and strategies for change at the individual, group and community level.

Overview of public health concepts and issues, including population health; ethics; health disparities, determinants, and systems of care; global health; policy; health promotion; environmental health.

Overview of program implementation challenges in global health contexts, including best practices for establishing effective cross-cultural collaborations, working with key leaders, and operationalizing complex interventions.

Historical development of the American health care system; determinants of health care utilization; role of health care providers; health policy; public health services; and health care finance.

Concepts of biostatistics; appropriate uses and common misuses of health statistics; practice in the application of statistical procedures; introduction to statistical software including EXCEL, SPSS, nQuery. Laboratory.

Major parametric and non-parametric statistical tools used in biomedical research, computer packages including SAS. Includes laboratory. Lecture, 2 hours; laboratory, 2 hours.

Statistical methods for analysis of categorical data including dichotomous, ordinal, multinomial and count data, using Stata package. Includes laboratory.

Methods and applications for modeling longitudinal, time-to-event and multi- level data. Includes laboratory using R package. Lecture 2 hrs; laboratory 2 hrs. Prerequisites: PM-511b.

Terminology/uses of epidemiology and demography; sources/uses of population data; types of epidemiologic studies; risk assessment; common sources of bias in population studies; principles of screening. Recommended preparation: algebra

An overview of the tools used by statisticians for solving statistical problems. Introduces students to the field of biostatistical consulting.

Study design, ascertainment of study objects, questionnaire development, various methodological issues in study design and interpretation including bias, measurement error, confounding and effect modification.

a: Density distribution and hazard functions; normal, chi-square, student's t and F distributions; and sampling procedures for single factor and multiple factor designs, distributions. Recommended preparation: working knowledge of multivariable calculus and familiarity with linear algebra.

International variations in health status with a focus on the impact of socioeconomic status, politics, environment, education and gender in etiology of illness, access to health care, progression of disease, and recovery.

An overview of environmental health, identifying issues in assessing effects of exposure on health and potential interventions for reducing adverse health risks. Prerequisite: PM-512

Familial aggregation, segregation analysis, linkage analysis, association, regressive models, gene-environment interactions, genetic heterogeneity and linkage disequilibrium.

Overview of concepts, tools, data collection, analysis methods and designs used to evaluate health promotion programs. Examples from substance abuse prevention, family planning and reproductive health programs.

Overview of causative factors and demographic distribution of major chronic diseases in the western world. Epidemiologic concepts, methods and research design as applied to chronic disease prevention will be emphasized. Prerequisite: PM 512.

Examine epidemiologic methods, concepts, and statistical approaches; case-study seminars with structured critiques of current literature on human-environment interactions affecting public health. Recommended preparation: PM-510 and PM-512.

The contents include: R objects, control structure, data input and output, subsetting objects, data manipulation and aggregations, character manipulation, graphics, and writing user-defined functions. Recommended preparation: Knowledge of one programming language other than R or a half-year experience programming in R.

Survey of effective community organizing and mobilization efforts in the U.S. and abroad, using participatory, organizational, community empowerment and public-private partnership models.

Introduction to leadership and management concepts, tools, and practices in the context of domestic and global public health and healthcare delivery settings.

Current public health issues and research topics relating to 21st century challenges and threats. Lessons learned and best practices to strengthen public health systems and enhance public health readiness and preparedness.

An introduction to the toolsets needed to create workable and reproducible datasets, conduct exploratory analysis and visualizations, learn from data, summarize and communicate analytic results.

Definition and chronology of natural and man-made disasters and their effects on the global community. Structure and organization of disaster management systems and the role of humanitarian organizations.

Ethical principles in the distribution of health resources, conduct of global public health research, and implementation of public health initiatives across different nations, cultures, religions.

An introduction to statistical methods for analyzing and interpreting spatially referenced data topics include: geostatistics, areal data, point pattern data and visualization.

Research leading to the master's degree. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Provides a rigorous introduction to statistical methods for analyzing data with a focus on regression modeling for continuous and binary outcomes.

Credit on acceptance of thesis. Graded IP/CR/NC.

Credit on acceptance of thesis. Graded IP/CR/NC.

Credit on acceptance of thesis. Graded IP/CR/NC.

Part-time or full-time, practical work experience in the students field of study. The internship must be located at an off-campus facility. Students are individually supervised by faculty.

Applied practical experience through field placement in federal, state, and/or local public health agencies/organizations, including community-based organizations; research and school-based settings.

Provides the culminating, integrative learning experience for students enrolled in the Master of Public Health program.

Special topics of current interest to provide background for research in biostatistics and epidemiology. Based largely on student dissertation research. Graded CR/NC.

First of three courses in CTR, a discipline that fosters multidirectional integration of basic, patient-oriented and population-based research with long-term goal of improving public health. Recommended preparation: PM 510.

Concepts and major methods in artificial neural networks and deep learning, including backpropagation, CNN, RNN, GAN.

Introduces students to modern causal inference methods for epidemiological research including both theoretical background and practical applications.

Theory of statistics behind most commonly used statistical inference and machine learning methods in biomedical applications.

Independent research at an advanced level on a problem in the field of Health Behavior. Recommended preparation PM 604. Graded CR/NC.

Independent research at an advanced level on a problem in the field of Health Behavior. Graded CR/NC.

Independent research at an advanced level on a problem in the field of Health Behavior. Graded CR/NC.

Independent research at an advanced level on a problem in the field of Health Behavior.

Independent research at an advanced level on a problem in the field of health behavior. Graded CR/NC.

Short seminar presentations and discussions on issues accompanying the development of the field of health behavior and implementation of research in this field. Graded CR/NC.

Research applicable to the doctorate. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of dissertation. Graded IP/CR/NC.

Credit on acceptance of dissertation. Graded IP/CR/NC.

Credit on acceptance of dissertation. Graded IP/CR/NC.

Credit on acceptance of dissertation. Graded IP/CR/NC.

Credit on acceptance of dissertation. Graded IP/CR/NC.