Our previous geospatial briefs have shown the potential of
geospatial hot-spotting methods to identify priority areas of need (e.g.,
Topmiller, 2016). Geospatial methods can also be integrated with Bayesian
approaches to account for spatial variation and variance instability in regards
to population. This brief illustrates the use of a spatial empirical Bayes
approach to identify high-need areas based on low-income populations not served
by the federally-funded Health Center Program (HCP).
The HCP is part of the health care safety net and includes
approximately 1,400 grantees operating more than 9,800 clinic sites to meet the
needs of the medically underserved population in the US (HRSA, 2016). The HCP
experienced substantial growth under both the George W. Bush and Obama
administrations, with recent literature demonstrating the positive impacts of
this expansion (Iglehart 2008; Han et al., 2017; Cole et al., 2017). In an
uncertain health care climate, where health centers (HCs) are seen as important
cornerstones of the health care safety net, it is important to understand where
HCs are currently serving patients and where there may be continued or growing need.
Using 2015 data from the Uniform Data System (UDS) Mapper
(UDS, 2015), we utilized a variety of geospatial approaches to identify ZIP
Code Tabulation Areas (ZCTAs)[i] most in
need based on low-income penetration rates and the number of low-income
population not being served[ii].
Because health centers serve patients living outside of the ZCTA in which they
are located, it is necessary to create weighted or smoothed estimates of
penetration rates which incorporate need in contiguous ZCTAs. Smoothed
low-income penetration rates were estimated using a spatial empirical Bayes
approach[iii], which
adjusts or smooths the rates for each ZCTA based on its low-income population
and the local mean, which is the average of contiguous ZCTAs’ rates; ZCTAs with
smaller low-income populations will have their rates adjusted more towards the local
mean (Cromley & McLafferty, 2012). We also created spatially-weighted estimates
for the total number of low-income population by calculating the average of
each ZCTA and its contiguous neighbors.
High-need ZCTAs were identified by (1) removing all ZCTAs
that had zero low-income population; (2) creating smoothed low-income
penetration rates[iv]
, low-income rates, and poverty rates using a spatial empirical Bayes approach;
(3) creating spatially-weighted estimates for the number of low income
population not being served by health centers (weighted average of ZCTA and its
neighbors); (4) identifying all ZCTAs with smoothed low-income penetration
rates of less than 1 percent; (5) finally, focusing only on those ZCTAs with smoothed
low-income penetration rates of less than 1 percent, we identified the top 500 ZCTAs[v] ranked
by the highest number of spatially-weighted low-income population not being
served by health centers.
Smoothed
& Weighted Characteristics of High-Need ZCTAs
All ZCTAs^
(N=32,166)
|
All High-Need (N=500)
|
High-Need Urban (N=381)
|
High-Need Large Rural
(N=76)
|
High-Need Small or Isolated
Rural (N=15)
|
High-Need
Puerto Rico (N=28)
|
|
Unserved
Low-Income Population (% of High-Need)
|
84,303,659
|
3,378,283
|
2,355,967
(69.7%)
|
385,493
(11.4%)
|
64,975
(1.9%)
|
571,848
(16.9%)
|
Poverty
Rates (Below 100% FPL)
|
14.9%
|
16.5%
|
14.2%
|
18.8%
|
20.0%
|
38.5%
|
Low-Income
Rates (Below 200% FPL)
|
34.5%
|
35.8%
|
32.0%
|
41.8%
|
47.4%
|
65.1%
|
Sources: UDS, 2015; American Community Survey, 2010-2014
^Not weighted
The weighted unserved low-income population for the top 500
ZCTAs ranges from 3,497 to 39,455, with a total of almost 3.4 million people
living in these ZCTAs. More than one-quarter of these 3.4 million people live
in the top 50 ZCTAs in need, where almost half (24) of the ZCTAs are located in
Puerto Rico, and more than one-fourth (13) are located in Texas. More than
one-half of the high-need population is located in just four states or
territories, with Texas (21.5%), Puerto Rico (16.9%), Pennsylvania (8.6%), and
North Carolina (7.6%) having the largest populations in high-need areas. Fifteen
states have no high-need ZCTAs based on our criteria. Less than 20% of the
high-need ZCTAs and about 13% of the high-need population are located in rural
areas, where low-income rates exceed 40%, compared to 32% for urban high-need
ZCTAs, and 34.5% for all ZCTAs. Poverty and low-income rates are highest in
small and isolated rural ZCTAs, as well as in Puerto Rico. About 60% of
high-need ZCTAs are located in Medically Underserved Areas/Population (MUA/Ps)
and more than half (276) are being served by at least one health center, with
almost 25% (119) already being served by at least two health centers. The map
below displays the number of high-need ZCTAs by State.
This brief highlights innovative geospatial approaches for
identifying priority areas in the context of the federally-funded Health Center
Program. These methods could be used as
a starting point for identifying priority areas for future health center
funding, as they incorporate need across contiguous ZCTAs and account for
variations in population. This research is subject to several limitations. First,
we defined our high-need criteria based on low-income penetration rates; there
are many other ways we could have defined high-need ZCTAs, such as excluding
ZCTAs with relatively low rates of low-income population or focusing only on
ZCTAs that are located in MUA/Ps. In addition, the UDS data do not include
patients being served by non-HC providers, such as free clinics or private
physicians. Thus, it is possible that some of the high-need areas that we
identified are being served by other providers. Ideas for future research
include using other criteria and exploring differences among high-need areas
and comparing the location of high-need areas with the actual locations of new
health centers.
Michael Topmiller, PhD, Health GIS Research
Specialist
Jennifer Rankin, PhD, Director of Research and
Product Services
References
Cole, Megan B., Omar Galarraga,
Ira B. Wilson, Brad Wright, & Amal N. Trivedi. 2017. At Federally Funded
Health Centers, Medicaid Expansion Was Associated with Improved Quality of
Care. Health Affairs 36(1): 40-48.
Cromley, Ellen K., & Sara L.
McLafferty. 2012. GIS and Public Health.
New York, NY. Guilford Press.
Han, Xinxin, Qian Luo, &
Leighton Ku. 2017. Medicaid Expansion and Grant Funding Increases Helped
Improve Community Health Center Capacity. Health
Affairs 36(1): 49-56.
Health
Resources and Services Administration (HRSA), 2016. The Health Center
Program.
Iglehart,
John K. 2008. Spreading the Safety Net — Obstacles to the Expansion of
Community Health Centers. New England Journal of Medicine 358:1321-1323.
Lu,
Yao, David Slusky. 2016. Impact of Women’s Health Clinic Closures on Preventive
Care. Applied Economics 8(3):
100-124.
Topmiller,
Michael, 2016. “Where are “Hot Spots” of Medicare Spending and Preventable
Hospitalizations and “Cold Spots” for Preventive Care?” HealthLandscape Geospatial Research Brief #2.
Accessed at http://www.healthlandscape.org/Geospatial-Analysis.cfm
Uniform
Data System (UDS), 2015. Available at http://www.udsmapper.org.
[i] Health Centers report data by ZIP Code, but the data are converted to ZCTAs in order to compare health center patient data to area demographic data. ZCTAs are created for the decennial census approximating the area covered by a ZIP Code.
[ii] Low-income includes those people who live below 200% of the Federal Poverty Level. This population is used by the Health Center Program as an approximation of the medically underserved population.
[iii] Spatially smoothed rates were calculated using GeoDa (Anselin, et al., 2006).
[iv] Health centers do not report patients by ZIP Code by income level, therefore low-income penetration rates are calculated by dividing the number of health center patients from a ZCTA divided by the total low-income population living in a ZCTA. Nationally in 2015, 93% of all health center patients were low income. For more information, see http://www.udsmapper.org/about.cfm.
[v] The HC Program has added between 400 and 700 new health centers annually over the past seven years.
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