Course Descriptions - Week 1

The course is designed to provide the student with a broad introduction to key concepts in cancer epidemiology and biology, and the major methodological issues related to cancer epidemiology. Topics covered in the course include: basic principles of cancer biology; the relation between experimental and epidemiological data with regard to carcinogenesis; the relation between various environmental exposures and selected forms of cancer; genetic influences on cancer risk; and the importance of gene-environmental interactions.

This course will provide an overview of methods for the analysis of complex sample surveys, using the Ohio Family Health Survey as a case study. Topics will include estimation of means, proportions, ratios and regression coefficients under simple, stratified and complex sampling designs, as well as variance estimation and non-response adjustment. This course will include an introduction to computer software capable of analyzing complex survey data. Following this course, students should be able to appropriately analyze data from the Ohio Family Health Survey, as well as other publicly available sample survey data.

This course introduces participants to the assessment of the value of scientific information and its relevance to the formation of public health decisions. Topics include: the definition of information needs; appraisal of sources of documents; and appraisal of scientific articles reporting original studies on performance of measurement tools, impact of interventions, and on literature reviews.

Illustration of critical reading techniques (choosing a reference framework, checking sources, adapting appraisal forms or check lists and reporting conclusions) will be done through the analysis of information related to implementing a screening program. The course is designed for professionals with field experience in epidemiology or public health programs.

This course explores the use of the logistic regression model in medical and epidemiologic research. Topics include estimation and interpretation of the coefficients in the logistic regression model, confounding and effect modification, stratified analysis via logistic regression, assessing the scale of variables in the logistic regression model, numerical problems, logistic regression diagnostics, conditional logistic regression, polychotomous and ordinal logistic regression. Model building strategies and methods to evaluate model performance will be covered. Relevant statistical software packages will be discussed.

This course provides a comprehensive introduction to the use of geographic information systems in public health. Topics include geographic data, data acquisition, and metadata; mapping population and health outcome data as points and areas, including the use of Census data; spatial analytical methods for defining neighborhoods including network analysis, selected spatial statistical techniques and spatial sampling, integrating data layers using overlay operations, and map design and on-line GIS. In addition to lectures, hands-on laboratory sessions will involve students in working through a series of exercises to build health-related GIS applications using GIS software and publicly available geospatial databases.

This course will introduce the student to the principles of infectious disease epidemiology. It will include a practical overview of host factors, environmental factors, and microbiological factors that influence this dynamic fi eld of study. Through lectures and exercises, students will be introduced to infectious disease surveillance, diagnostic tools, outbreak investigations, vaccine trials, public health interventions, biodefense, emerging infectious diseases, and analytical approaches as they pertain to infectious disease prevention and control. Students will be introduced to a wide array of reference material (much of it public) that will help them in the practical application of course material. The course is designed for students and non-physician professionals in public health.