Course Descriptions - Week 1
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Maternal and Child Health Epidemiology William M. Sappenfield, M.D., M.P.H.
This one-week course will cover the practice of MCH epidemiology at a state and local level. Topics will include practicing effective data use in public health, conducting surveillance using available state and local data sets, conducting epidemiology investigations, applying multivariate analysis to practice, analyzing vital records, performing Perinatal Periods of Risk analysis, searching and reading the evidence-base, and conducting basic needs assessment and program evaluation. Real life examples, exercises and case-studies will be used to reinforce course material and help assimilate knowledge and skills into practice. The course will reinforce the linkage of epidemiology in public health practice.
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Applied Logistic Regression Stanley Lemeshow, Ph.D.
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.
Basic knowledge of statistics required.
A computer laboratory will be held from 5:30 – 8:00 p.m. Monday through Thursday |
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Public Health Field Epidemiology Richard Dicker, M.D., M.S.
This course uses a series of classroom-based interactive case studies to teach the principles and practice of field epidemiology, ranging from descriptive epidemiology to surveillance to outbreak investigation to field surveys. The course focuses on the use of sound epidemiologic judgment, particularly when epidemiologic theory and practical considerations conflict. Following this course, the student will be familiar with the principles of epidemiology relevant to public health practitioners, and should be able to apply those principles to address acute public health problems in the community.
This course is a component of the Practice-Based Epidemiology Series
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Longitudinal Data Analysis David G. Kleinbaum, Ph.D.
This course covers recent developments in the analysis of correlated data, which includes longitudinal data as a special case. Topics to be discussed include: the data layout required; rationale for using methods for analysis of correlated data; matrix notation for modeling correlated data; types of models (marginal, transitional, and random effects); analysis of continuous correlated responses using SAS's MIXED procedure; generalized linear models and quasi-likelihood estimation; SAS’s GENMOD, GLMMIX and NLMIXED procedures for analyzing discrete outcomes; robust (i.e., empirical) estimation of variance; multi-level modeling methods and examples and comparison of procedures.
Advanced knowledge of statistics required, particularly logistic regression, Cox regression and Poisson regression modeling. Also, knowledge of intermediate epidemiology concepts and methods is required.
A computer laboratory will be held from 5:30 – 8:00 p.m. Monday through Thursday |
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Applied Survival Analysis David Hosmer, Jr. Ph.D.
This course focuses on applications of the analysis of time to event data. The first part of the course deals with methods for estimation, interpretation and comparison of survival functions. The second part of the course considers regression methods within the context of the semi-parametric proportional hazards model (Cox model). Topics covered in this section include: variable selection; scaling of continuous covariates; inclusion of interactions; assessment of model fit and diagnostics for the proportional hazards assumption and individual subject influence on the fitted model. Special emphasis is placed on the interpretation and presentation of the results. Examples are drawn primarily from medical studies.
Advanced knowledge of statistics required
A Computer Laboratory will be held from 5:30 – 8:00 p.m. Monday through Thursday |
Social Epidemiology J. Michael Oakes, Ph.D.
Social epidemiology is the branch of epidemiology that considers how social interactions and purposive human activity affect health. Community assessment is about the “which” and “how” community-level exposures should be measured and evaluated. Accordingly, this course considers the dynamic social relationships and human activities that ultimately locate toxic dumps in one community instead of another, make fresh produce available to some and not others, and permit some to enjoy resources such that they can purchase salubrious environments and excellent health care. We focus on conceptual models, measurement issues, and analytic approaches, including the strength and weaknesses of the multilevel model. |
| Introduction to Biostatistics Jack Barnette Ph.D.
The course covers basic statistical methods used in public health and other health sciences. Topics include measurement, graphic and tabular methods of displaying data, measures of central tendency, estimation of proportions and means, one and two group hypothesis testing of proportions and means, contingency table analysis, odds ratios and relative risk including confidence intervals, and bivariate correlation and linear regression. A general description of more advanced methods will be provided. Examples of how these methods are seen and used in public health literature will be presented. Stata and SPSS software applications will be discussed.
A computer laboratory will be held from 5:30 – 8:00 p.m. Monday through Thursday
This course is a component of the Practice-Based Epidemiology Series
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Environmental Epidemiology Harvey Checkoway, Ph.D.
This course will provide a summary of the epidemiologic study designs and methods for
investigating health hazards associated with environmental exposures. Related topics that will be covered include: case cluster investigation methods; approaches to characterizing environmental exposures; sources of and methods to minimize study bias; and applications of epidemiologic data, such as for environmental risk assessment. Methodological concepts will be illustrated with examples of research on environmental risk factors for cancer, neurological diseases, respiratory diseases, and adverse reproductive outcomes. Lectures will be supplemented by discussions of selected journal articles and with in-class exercises that emphasize the roles public health practitioners play in the conduct of environmental epidemiology.
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