While social determinants such as institutional racism, access to care, and wealth play a part in the differences in death rates, this brief focuses on geospatial and racial differences in multigenerational households. Households with multiple generations are particularly susceptible to intra-household COVID-19 spread. Difficulty in maintaining proper distancing for suspected or confirmed cases puts members of these households at a higher risk of becoming seriously ill or dying as a result of COVID-19.

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).

Research has shown that higher rates of appropriate Diabetes preventive care can lead to better health outcomes, fewer hospitalizations, and lower spending (Gray et al., 2012; Kralewski et al., 2013). Our previous work has demonstrated that geographic variation exists for Medicare spending, hospitalizations, and preventive care, while also identifying priority regions for improving care (Topmiller, 2016). However, little is known about the strategies that lead to higher rates of preventive care and why rates vary so much across geographic regions. Finding the “bright spots,” regions with higher than expected rates of appropriate Diabetes preventive care, can assist researchers and policy makers in identifying successful strategies for producing higher rates. This brief utilizes a two-step geospatial approach for identifying regions that are appropriate Diabetes preventive care “bright spots.”

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.