4124.0 Statistical Modeling in Public Health I

Tuesday, November 9, 2010: 10:30 AM - 12:00 PM
The purpose of this session is to provide a forum for public health researchers to present results of their investigations in the statistical modeling of public health data. The relevance and importance of the session is to educate attendees about the attendant statistical methods in modeling data arising in a number of public health settings: Linear Mixed Models for Assessing Longitudinal Mediation; Negative Binomial Mixed Regression Modeling of Corona Atherosclerosis Hospitalizations; Markov Modeling Techniques for Selecting Morbidity; and Spatial Modeling of the Health Care Workforce in Georgia.
Session Objectives: Describe several statistical models applicable to public health problems Explain how statistical models are applied to public health data Demonstrate the application of several statistical models to public health data
Din Chen, PhD

Linear Mixed Models for Assessing Longitudinal Mediation
T. Mark Beasley, PhD and Yu-Mei Schoenberger, PhD
Markov Modeling Techniques for Selecting Morbidity
Hamisu Salihu, MD, PhD, Alfred Mbah, PhD and Heather Clayton, MPH
Spatial Modeling of Health Care Workforce in Georgia
Imam M. Xierali, PhD, Robert Phillips, MD, MPH and Andrew Bazemore, MD, MPH

See individual abstracts for presenting author's disclosure statement and author's information.

Organized by: Statistics
Endorsed by: Epidemiology, Social Work

CE Credits: Medical (CME), Health Education (CHES), Nursing (CNE), Public Health (CPH)

See more of: Statistics