The Public Health Disparities Geocoding Project Monograph

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Institution: Harvard School of Public Health

Description: The problem: A lack of socioeconomic data in most US public health surveillance systems. Absent these data, we cannot: (a) monitor socioeconomic inequalities in US health; (b) ascertain their contribution to racial/ethnic and gender inequalities in health; and (c) galvanize public concern, debate, and action concerning how we, as a nation, can achieve the vital goal of eliminating social disparities in health. We accordingly launched the Public Health Disparities Geocoding Project to ascertain which area-based socioeconomic measures [ABSMs], at which geographic level (census block group [BG], census tract [CT], or ZIP Code [ZC]), would be suitable for monitoring US socioeconomic inequalities in the health. Drawing on 1990 census data and public health surveillance systems of 2 New England states, Massachusetts and Rhode Island, we analyzed data for: (a) 7 types of outcomes: mortality (all cause and cause-specific), cancer incidence (all-sites and site-specific), low birth weight, childhood lead poisoning, sexually transmitted infections, tuberculosis, and non-fatal weapons-related injuries, and (b) 18 different ABSMs. We conducted these analyses for both the total population and diverse racial/ethnic-gender groups, at all 3 geographic levels. Our key methodologic finding was that the ABSM most apt for monitoring socioeconomic inequalities in health was the census tract (CT) poverty level, since it: (a) consistently detected expected socioeconomic gradients in health across a wide range of health outcomes, among both the total population and diverse racial/ethnic-gender groups, (b) yielded maximal geocoding and linkage to area-based socioeconomic data (compared to BG and ZC data), and (c) was readily interpretable to and could feasibly be used by state health department staff. Using this measure, we were able to provide evidence of powerful socioeconomic gradients for virtually all the outcomes studied, using a common metric, and further demonstrated that: (a) adjusting solely for this measure substantially reduced excess risk observed in the black and Hispanic compared to the white population, and (b) for half the outcomes, over 50% of cases overall would have been averted if everyone’s risk equaled that of persons in the least impoverished CT, the only group that consistently achieved Healthy People 2000 goals a decade ahead of time. US public health surveillance data should be geocoded and routinely analyzed using the CT-level measure “percent of persons below poverty,” thereby enhancing efforts to track—and improve accountability for addressing—social disparities in health.