Health centers provide health care services to the nation’s most vulnerable populations, including behavioral health care. Given health centers’ experience providing integrative behavioral health (IBH) and the evidence that IBH models increase access to care and improve outcomes, it is important to identify successful models within health centers that can be replicated to improve care. This study describes an approach for identifying high-performing (bright spot) Health Resources and Services Administration (HRSA)-supported health centers for in-depth study of IBH models. We also compare the geographic, organizational, and patient characteristics of bright spots with low-performing health centers (cold spots).

In this research, we create a proxy for the availability of integrated behavioral health (IBH) by using a workforce co-location methodology to explore the geospatial distribution of primary care practices and behavioral health providers. We explore the distribution of these practices relative to primary care and mental health care Health Professional Shortage Areas (HPSAs), Medically Underserved Areas/Populations (MUA/Ps), and urban/rural designation. This brief outlines the above, as well as an innovative methodology, the IBH potential score, which explores the distribution of likely-integrated behavioral health locations.

Preliminary analyses suggest that regions with high levels of poor mental health and larger minority populations have less access to mental health services. This is consistent with literature on disparities in mental health access for minority populations who also have less access to substance use treatment services. Minority populations are also less likely to have health insurance, face higher barriers to accessing primary health care services, and have fewer behavioral health professionals practicing in close proximity to primary care providers in their neighborhoods. This research identifies priority areas for addressing racial disparities in access to behavioral health care. 

Annual vaccination rates for influenza and pneumonia vary significantly across the U.S., with some areas having much higher rates than others. Further, a lack of public health infrastructure and barriers related to social determinants of health limit the capacity of some areas to effectively vaccinate their populations. Stakeholders need to identify areas that may have higher rates of COVID-19 vaccine-hesitancy or refusal, areas that may lack capacity for large-scale vaccine distribution, and areas that have the most at-risk populations for COVID-19 hospitalizations and mortality. The purpose of this research is to explore the geographic variation of flu vaccine deficits across the U.S. and identify priority areas within each state for expanding COVID-19 vaccination efforts, with Virginia and Florida as examples.

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).