EXECUTIVE
SUMMARY
Painting
a Truer Picture of the Public's Health
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| |
| 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. http://www.doh.wa.gov/HWS
The
Virginia Department of Health Epidemiology Profile 2007. http://www.vdh.virginia.gov/epidemiology/DiseasePrevention/Profile2007.htm
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