Biostatistics Concentration

This core course examines social, psychological and cultural factors that have an impact on public health in community, national and global contexts. These factors may include: population characteristics (social class, age, gender, culture, race/ethnicity), individual beliefs and behaviors, and socio-political systems and policies that affect public health problems and their solutions. Theories and perspectives drawn from sociology, anthropology, and psychology are applied to critical issues in global public health including the AIDs epidemic, mental illness, chronic disease, community violence, war and natural disaster trauma as well as behavioral health problems such as smoking and substance abuse.

This course covers basic probability, descriptive and inferential statistics, and the role of biostatistics in the practice of public health. Specific attention will be given to common probability distributions in public health and medicine, t-tests, Analysis of Variance, multiple linear and logistic regression, categorical data analysis, and nonparametric statistics. Statistical topics are presented conceptually with little derivation, and applications are demonstrated using common statistical software.

Epidemiology is the study of the distribution and determinants of health and disease in different human populations and the application of methods to improve disease outcomes. As such, epidemiology is the basic science of public health. This course is designed to introduce students in all fields of public to the background, basic principles and methods of public health epidemiology. Topics covered include: measures of disease frequency; epidemiologic study designs, both experimental and non-experimental; understanding bias; and measures of effect and association. In addition, students will develop skills to read, interpret and evaluate health information from published epidemiological studies and mass media sources.

This course introduces MPH students with minimal formal training in biology
to the biological and molecular context of public health. The course
provides an overview of: a) basic biological principles and mechanisms
relevant to public health practice; and b) biomedical technology as applied
in public health. The course covers basic principles of genetics,
immunology, microbiology, and cell biology in the context of global public
health. Areas covered include infectious diseases, genetic and chronic
diseases, allostatic load, environmental factors affecting health, and
prevention and treatment strategies.

This course is designed to meet the data analytic needs of the doctoral students whose dissertation relies on the analysis of quantitative data. Procedures important to the data analyst are covered including data entry and definition, treating missing data, detecting outliers, and transforming distributions. First term topics include multiple regression, analysis of covariance, repeated measures analysis of variance, and multivariate analysis of variance and covariance. Second term topics emphasize categorical data analysis, odds, rations, standardization, log linear models, logistic regression. Other topics include multinominal logistic models, survival analysis, principle components, and factor analysis. The approach is conceptual with heavy reliance on computer software packages. Appropriate for doctoral students desiring specialized knowledge beyond the introductory sequence.

Public Health Informatics is a new field that is concerned with the systematic application of information and computer sciences to practice, research and learning. This course is created to ensure that graduates of the program have (1) basic familiarity with the issues of technology in computers, communications and genomics in global health; (2)

This course is a second year course in advanced statistical techniques that covers useful quantitative tools in public health research. This course focuses on data analysis that utilizes general linear regression models for continuous, categorical, discrete or limited outcomes that are commonly seen in health and policy studies. Examples are drawn from broad areas of public health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses.

Public health practice typically demands competencies in identifying,
extracting, analyzing and interpreting information from large survey and
administrative data sets (e.g., Demographic and Health Surveys), government
reports, qualitative studies, and other data sources. This course will
develop those competencies through a rigorous evaluation of existing
resources, their strengths and limitations, and best practices in data
utilization for situational assessment, monitoring, policy and strategy
development, and surveillance of health outcomes through real-world case
studies and practices. The course will also provide students with skills in
data analysis and data visualization using Microsoft Excel and the online
mapping software CartoDB.

*Students will examine the principles of measurement and testing as applied
to public health research and practice, including the technical
interpretation of test scores using the classical test model. Content of
the course will consider individual measures of constructs and behaviors
measured in public health research and practice. Students will examine and
deconstruct principles and techniques used in psychometric studies to
establish levels of reliability and validity and will utilize statistical
software to conduct analyses. *

This course is designed to meet the data analytic needs of the doctoral students whose dissertation relies on the analysis of quantitative data. Procedures important to the data analyst are covered including data entry and definition, treating missing data, detecting outliers, and transforming distributions. First term topics include multiple regression, analysis of covariance, repeated measures analysis of variance, and multivariate analysis of variance and covariance. Second term topics emphasize categorical data analysis, odds, rations, standardization, log linear models, logistic regression. Other topics include multinominal logistic models, survival analysis, principle components, and factor analysis. The approach is conceptual with heavy reliance on computer software packages. Appropriate for doctoral students desiring specialized knowledge beyond the introductory sequence

This course covers selected analytic and design issues that are relevant to policy research and program evaluation. The course is not a comprehensive or exhaustive review of the field of policy-relevant research or program evaluation, nor is it a course in how to evaluate a program. The focus is on impact analysis (rather than process evaluation, performance monitoring, cost effectiveness analysis, or evaluation synthesis) To that end, there is a substantial amount of data analysis both in and out of class. There is also a significant amount of new statistical material presented. All of this is done using real world examples, to solidify the base as you build your career as a practitioner and consumer of the research that informs public policy.

Understanding geographic relationships between people, land use, and resources is fundamental to planning. Urban planners routinely use spatial analysis to inform decision-making. This course will introduce students to Geographic Information Systems (GIS), a tool to analyze and visualize spatial data. The course will emphasize the core functions of GIS: map making, data management, and spatial analysis. Students will learn cartographic best practices, how to find and create spatial data, spatial analysis methodology, and how to approach problem solving from a geographic perspective. Throughout the course, students will build a portfolio of professional quality maps and data visualizations.

The purpose of this course is to provide students with an understanding of how surveys and interviews can be used to generate knowledge. This course will cover the design and implementation of survey and interview protocols, the data collection, analysis, and interpretation process, and the presentation of results. Students will learn how to design and implement these instruments for a variety of purposes and for different settings in support of their capstone projects or other research projects.

This course will develop an understanding of epidemiologic concepts and methods that will be a backbone to in depth training in specialty areas. It will provide a technical and conceptual training in study design, multivariant analysis, sample size calculations and other key epidemiologic techniques. It will build on the basic core course.

Review of research and original writings related to public health. Application of research methodology to problems in public health.

This is a course on models for multi-level growth curve data. These data arise in longitudinal designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this settings, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for longitudinal designs (these are often called growth curve models). The course assignments will use state of the art statistical software to explore, fit and interpret the models.

This course (part of a two-semester series; Thesis I and Thesis II)
introduces the thesis as the culminating experience for MPH candidates in
the Epidemiology (EPI), Public Health Policy and Management (PHPM), and
Social and Behavioral Science (SBS) tracks, and allows students to develop
skills in conducting public health research, analyzing and interpreting
data and presenting study findings. The thesis is intended to reflect the
training students have received in the MPH program and demonstrate their
ability to integrate, synthesize, and apply the knowledge and skills from
coursework and practicum experiences to a real world public health problem
or issue that is relevant to their major field of interest. In Thesis I
students will also have the opportunity to reflect on their practice
experiences, most often in the site from which the thesis is drawn. The
course provides students with the knowledge and skills to develop and
refine research questions, conduct a comprehensive review and analysis of
the literature relevant to the topic of interest, select a theory or
organizing framework, outline the methods, formulate a plan for data
collection and analysis, and develop an annotated outline of the project.