Today, HealthLandscape
is releasing our fifth Geospatial Brief, “Where are Areas in Greatest Need of New HealthCenters? A Spatial Empirical Bayes Approach.” [MT1] Brief #5 uses 2015 data from the UDS Mapper
(www.udsmapper.org) to explore areas with high rates and
numbers of low-income population that are not being served by the Health Center
Program. Specifically, we use a spatial empirical Bayes approach to create
smoothed rates for low-income penetration and weighted estimates for the number
of low-income population not being served by a health center. This blog
discusses the importance of identifying priority areas of need and provides
more details about our approach.
Prioritizing areas in highest need of safety-net health
services and allocating resources in the most efficient way possible will be
increasingly important in the current political context. The American Health
Care Act (AHCA) and the Better Care Reconciliation Act (BRCA) drastically
reduce funding for Medicaid, while the president’s proposed budget slashes
funding for the Food and Drug Administration (FDA), the Centers for Disease
Control (CDC), and National Institutes of Health (NIH). Despite the growing
opioid epidemic, even the Substance Abuse and Mental Health Services
Administration (SAMHSA) budget is the target of proposed cuts (Jost, 2017). While
the funding for the federally-funded Health Center Program (HCP) appears to be
steady, the funding cuts in these other areas have the potential to be
particularly impactful on the HCP, which relies on the federal government and
Medicaid as important revenue sources (Han et al., 2017).
Health Centers (HCs) that are part of the HCP are a vital
part of the health care safety net. Established more than 50 years ago to
provide primary medical, dental and behavioral care, the approximately 1,400
HCs operate more than 9,800 clinic sites to meet the needs of the medically
underserved population in the US. The HCP serves roughly 25 million high-need
people annually. Research has shown that HCs have a positive impact in terms of
improved access to care, reduced avoidable hospitalizations, and better health
outcomes (e.g., Evans, et al., 2015). The Health Resources and Services
Administration (HRSA) and Bureau of Primary Health Care (BPHC) fund new sites
based on need within a community, established by the applicant based on
service-area based statistics.
In general, identifying priority areas of need presents
several challenges. First, one could choose from a number of different ways to
define need. Is poverty a sufficient measure to define need? What about a composite measure of social
deprivation? These are questions that
are still being sorted out. In the
context of the HCP, using low-income penetration or poverty rates will assure
you prioritize areas with highest rates of need, but doesn’t consider the
number of people in need. This issue could be dealt with by using the number of
low-income people that are not served; however, this would lead to only densely
populated urban areas being identified as areas in need, and would exclude
rural and isolated areas.
There is also the matter of attempting to define the
geography in need, which comes back to the age-old modifiable areal unit
problem (MAUP). Should neighborhoods be used as the unit of analysis? If so, is it appropriate to define
neighborhoods based on census tracts? In the health center world, ZIP Code tabulation
areas (ZCTAs) are typically used because of data availability (and that is how
data are reported in the UDS Mapper), but need may exist within particular
parts of a ZCTA or be spread across parts of multiple ZCTAs.
To address the issues above, we used a spatial empirical
Bayes approach to identify high-need ZCTAs. In general, empirical Bayesian
approaches help deal with the instability in population over large areas by
adjusting or smoothing rates based on population size toward the overall mean.
For example, using a Bayesian approach to estimate low-income penetration rates
would adjust ZCTAs with small low-income populations more towards the overall
mean, while high low-income population ZCTAs would be adjusted less.
While using a standard empirical Bayesian approach would
help smooth out ZCTAs with very small populations, the approach does not
account for regional variations, and would likely over-adjust small, isolated
rural areas with high need. For these reasons, we used a spatial (also called
local) empirical Bayes approach to estimate low-income penetration rates. The
spatial empirical Bayes approach still adjusts the rates based on the size of
the low-income population, but instead of adjusting based on the overall mean,
it adjusts the rates for ZCTAs based on a local mean, which is the average rate
of each ZCTA and its contiguous neighbors.
We defined high-need ZCTAs as those with a smoothed
penetration rate of less than one percent and ranked them based on a weighted
estimate of low-income population not served by health centers. The weighted
unserved low-income estimate was calculated by taking the average number
unserved for each ZCTA and its contiguous neighbors. The geospatial brief
focuses on the top 500 ZCTAs in need based on our criteria; we chose 500
because between 400 and 700 new HCs have been funded annually over the past
seven years.
Again, it is important to note that our approach for
defining high-need areas for the HCP is just one of many possibilities. It is
not intended to be used as a rule for funding allocations for the HCP, but only
as a starting point to help overcome the challenges of identifying areas most
in need. The primary objective of the geospatial brief is to highlight the
potential of geospatial and Bayesian methods for overcoming these challenges to
identify priority areas.
References
Evans, Christopher S., Sunny Smith, Leslie Kobayashi, and
David C. Chang. (2015). The Effect of Community Health Center (CHC) Density on
Preventable Hospital Admissions in Medicaid and Uninsured Patients. Journal of Health Care for the Poor and
Underserved 26(3): 839-851.
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.
Jost, Timothy. “Trump
Budget Proposes Big Health Cuts.” May 23, 2017. Health Affairs Blog
Available at http://healthaffairs.org/blog/2017/05/23/trump-budget-proposes-big-health-cuts/
[MT1]Link
to Brief
