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).

When planning for the expansion of services and determining areas of need, estimating people’s ability to reach health care services is an important issue. In order to accurately identify the areas in need of additional health care providers, while avoiding service area overlap, it is necessary to understand the practical accessibility of other nearby providers. While there is a wealth of literature exploring definitions and measures of potential access (Apparicio et al, 2008, Topmiller, 2013), this brief illustrates the importance of local context in choosing the right measure by exploring the relationship between potential health care access and utilization.

Many healthcare reform efforts are underway that are working towards achieving the triple aim of better care, better health, and lower costs. However, questions still remain about how reforms take into account the significant geographic variation of healthcare spending and utilization. Recognizing the importance of geography, researchers have developed hot-spot and cold-spot approaches for targeting healthcare super-utilizers and high need communities, offering potential models for identifying priority regions where policymakers can target scarce resources. Hot-spotting and cold-spotting has also been used in the field of geospatial analysis, where hot spots are defined as clusters of high values and cold spots as clusters of low values. Thus, we could think of clusters of counties with low rates of preventive care as cold spots. This brief details an approach for identifying priority geographic regions for improving preventive care.

Complications from Diabetes are a major cause of hospitalizations and high Medicare spending, with about 27% of Medicare beneficiaries diagnosed with the disease (CMS, 2013). Appropriate Diabetes preventive care such as annual hemoglobin A1C tests, blood lipids LDL‐C tests, and eye exams have been shown to reduce complications with Diabetes and improve quality of life (Gray et al., 2012). Despite the evidence for positive outcomes associated with more preventive care, few studies have explored the relationship between Diabetes preventive care, utilization, and spending.
This brief explores the relationship of appropriate Diabetes preventive care to preventable hospitalization rates and Medicare spending per enrollee (age‐sex‐race adjusted).