The problem
A lack of socioeconomic data in most US public health surveillance systems.
Why is this a problem?
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 (Healthy People 2010 overarching objective #2).
Possible solution
Geocoding public health surveillance data and using census-derived area-based socioeconomic measures (ABSMs) to characterize both the cases and population in the catchment area, thereby enabling computation of rates stratified by the area-based measure of socioeconomic position.
Knowledge gaps
Unknown which ABSMs, at which level of geography, would be most apt for monitoring US socioeconomic inequalities in health, overall and within diverse racial/ethnic-gender groups.
Methodologic study: The Public Health Disparities Geocoding Project
We accordingly launched the Public Health Disparities Geocoding Project to ascertain which 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.
Key findings
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.
Recommendation
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.
State Health Departments that have issued reports using the methodology of the Public Health Disparities Geocoding Project
- “The Health of Washington State Supplement: a statewide assessment addressing health disparities by race, ethnic group, poverty and education.” September 2004.
- The 2008 Virginia Health Equity Report.
- For a related Canadian analyses, based on 1991 Census of Canada data and deaths from June 4, 1991 to December 31, 2001: Pampalon R, Hamel D, Gamache P. A comparison of individual and area-based socio-economic data for monitoring social inequalities in health.
