Nancy Krieger (Harvard School of Public Health) spoke on the use of vital statistics and related data to monitor health inequities in the United States—studies of trends in health and health care as they are related to socioeconomic position, ethnicity, and gender. Her remarks summarized findings from her Public Health Disparities Geocoding Project. Detailed information on the project and related publications are available online at http://www.hsph.harvard.edu/thegeocodingproject (April 2009).
The project’s objective is to augment data in public health surveillance systems, including the birth and death certificate data, with additional socioeconomic covariate information; the resulting constructs are termed area-based socioeconomic measures (ABSMs). The methodology links geocoded vital statistics and U.S. census data at the block group, census tract, and ZIP code tabulation area levels of geography. Ultimately, the intended goal is to develop a valid, robust, easy-to-construct, and easy-to-interpret ABSM that can be readily used by any U.S. state health department or health researcher for public health monitoring and for studying any health outcome from birth to death for any age, gender, or racial or ethnic group. The project started in 1998, making use of data from the Massachusetts Department of Public Health and the Rhode Island Department of Health; the data were for a set of years centered around the 1990 census, and the socioeconomic data in the ABSMs made use of information from that census.
To test robustly whether choice of ABSM and geographic level matters, Krieger said that she focused on a wide variety of health outcomes, including mortality (all cause and cause specific), birth (specifically, low birth weight) and also cancer incidence (all sites and site specific), childhood lead poisoning, sexually transmitted infections, tuberculosis, and nonfatal weapons-related injuries. Each outcome was analyzed in relation to 19 different AB-SMs, capturing diverse aspects of socioeconomic position. Eleven of the measures were single-variable measures (e.g., percent working class, percent crowded household) and eight were composites (e.g., deprivation indices developed in previous research). Analyses were performed for the total population and also stratified by race, ethnicity, and gender.
Krieger summarized four key findings from the geocoding project. First, measures of economic deprivation were most sensitive to the expected socioeconomic gradients in health. Second, census-tract-level analyses yielded the most consistent results, with maximal geocoding, compared to the block group and ZIP code data. Third, these findings held for separate analyses conducted for white, black, and Hispanic men and women; they also held for those outcomes that could be meaningfully analyzed among the