Session: Multi Level Analysis for Evaluating Individual and Census Tract Characteristics on Preterm Delivery

4174.0: Tuesday, November 9, 2004: 12:30 PM-2:00 PM

Oral

Multi Level Analysis for Evaluating Individual and Census Tract Characteristics on Preterm Delivery

Using multilevel modeling techniques, various types of measures and data can be simultaneously analyzed to gain a better understanding of how multiple levels of influence are associated with predictors of health disparities and preterm birth. For example, hierarchical linear models can be used to estimate and examine the net effects of infant-level maternal characteristics (race, age, marital status and education), county-level contextual factors (e.g., median family income) and the cross-level interactions on preterm birth (PTB). The purpose of this session is to acquaint the participant with an important application of multi-level analysis techniques to health research. The session will focus specifically on health research which seeks to understand risks associated with pre-term birth. A number of different types of study designs are explored to enrich the student's appreciation and understanding of the material.

Learning Objectives: At the end of this session the participant should be able to: 1. Appreciate the usefulness of census data to approximate a community-level exposure for preterm birth 2. Understand the utility of multi-level modeling to preterm birth study analysis 3. Give an example of an application of multi-level analysis to studies of racial health disparities. 4. Relate aggregate individual health data to preterm delivery rates using multi-level modeling

Moderator(s):

Michael D. Kogan, PhD

12:30 PM

Introductory Remarks: Michael Kogan, HRSA

12:35 PM

Using Census Data to approximate a Community-Level Exposure for Preterm Birth