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.
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/