The 131st Annual Meeting (November 15-19, 2003) of APHA

Abstract #59991

Suzanne E. Proctor, MSPH and Richard J. Klein, MPH. Office of Analysis, Epidemiology and Health Promotion, National Center for Health Statistics, CDC, 3311 Toledo Road, Mailstop 6320, Hyattsville, MD 20782, (301) 458-4186, SProctor@cdc.gov

The Leading Health Indicators (LHIs) are a set of ten public health priorities that focus on key health behaviors, physical and social environmental factors, and health system issues affecting the health of individuals and communities in the United States. A cornerstone of the Healthy People 2010 initiative, the LHIs provide a snapshot of the health of the nation. Progress on the LHIs is tracked by 22 specific measures selected from the Healthy People 2010 objectives. National- and State-level data from sample surveys or surveillance systems are available for each of the 22 measures. However, sub-State data (e.g., city, county-level data) are often not available for these measures. As a result, tracking the LHIs can be a substantial challenge for local public health officials. One methodology that can be used to generate sub-State estimates for the LHIs is synthetic estimation. Using State-level data from a sample survey (e.g., Behavioral Risk Factor Surveillance System) and sub-State demographic data (e.g., Census data), multivariate regression models can be used to generate estimates for the LHIs at various sub-State geographic levels. This presentation demonstrates the application of this methodology to generate synthetic estimates for the LHIs for Anne Arundel County, MD for the year 2001. Because Anne Arundel County conducted a special county-level Behavioral Risk Factor Surveillance System study in 2001, this case study provides a unique opportunity to compare synthetic estimates with actual estimates obtained from a sample survey. Key assumptions and limitations of synthetic estimation are discussed in reference to the case study.

Learning Objectives:

Following this presentation, participants should be able to: (
• Identify the Leading Health Indicators (LHIs) and some of the Healthy People 2010 measures used to track their progress. (
• Appreciate the difficulty of tracking the LHIs at sub-State geographic levels and understand how synthetic estimation can address this issue. (
• Identify situations when it would be appropriate and advantageous to generate synthetic estimates for health indicators for a sub-State geographic area. (
• Describe the types of data needed to produce synthetic estimates for a sub-State geographic area. (
• Understand they key steps in formulating, fitting, and using a multivariate regression model to produce synthetic estimates for a sub-State geographic area. (
• State the underlying assumptions and limitations of synthetic estimation.

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.