Clinicians' Overestimation of Their Geographic Service Area.

In this study, we evaluated family physicians' ability to estimate the service area of their patient panel-a critical first step in contextual population-based primary care. We surveyed 14 clinicians and administrators from 6 practices. Participants circled their estimated service area on county maps that were compared with the actual service area containing 70% of the practice's patients. Accuracy was ascertained from overlap and the amount of estimated census tracts that were not part of the actual service area. Average overlap was 75%, but participants overestimated their service area by an average of 166 square miles. Service area overestimation impedes implementation of targeted community interventions by practices.

Improving Access to Treatment for Opioid Use Disorder in High-Need Areas: The Role of HRSA Health Centers.

Introduction: Despite the opioid epidemic adversely affecting areas across the U.S. for more than two decades and increasing evidence that medication-assisted treatment (MAT) is effective for patients with opioid use disorder (OUD), access to treatment is still limited. The limited access to treatment holds true in the Appalachia region despite being disproportionately affected by the crisis, particularly in rural, central Appalachia. Purpose: This research identifies opportunities for health centers located in high-need areas based on drug poisoning mortality to better meet MAT care gaps. We also provide an in-depth look at health center MAT capacity relative to need in the Appalachia region. Methods: The analysis included county-level drug poisoning mortality data (2013-2015) from the National Center for Health Statistics (NCHS) and Health Center Program Awardee and Look-Alike data (2017) on the number of providers with a DATA waiver to provide medication-assisted treatment (MAT) and the number of patients receiving MAT for the U.S. Several geospatial methods were used including an Empirical Bayes approach to estimate drug poisoning mortality, excess risk maps to identify outliers, and the Local Moran's I tool to identify clusters of high drug poisoning mortality counties. Results: High-need counties were disproportionately located in the Appalachia region. More than 6 in 10 health centers in high-need counties have the potential to expand MAT delivery to patients. Implications: The results indicate an opportunity to increase health center capacity for providing treatment for opioid use disorder in high-need areas, particularly in central and northern Appalachia.

Identifying Priority and "Bright-Spot" Counties for Diabetes Preventive Care in Appalachia: An Exploratory Analysis.

Introduction: Type 2 diabetes mellitus (T2DM) prevalence and mortality in Appalachian counties is substantially higher when compared to non-Appalachian counties, although there is significant variation within Appalachia. Purpose: The objectives of this research were to identify low-performing (priority) and high-performing (bright spot) counties with respect to improving T2DM preventive care. Methods: Using data from the Centers for Medicare and Medicaid (CMS), the Dartmouth Atlas of Health Care, and the Appalachia Regional Commission, conditional maps were created using county-level estimates for T2DM prevalence, mortality, and annual hemoglobin A1c (HbA1c) testing rates. Priority counties were identified using the following criteria: top 33rd percentile for T2DM mortality; top 33rd percentile for T2DM prevalence; bottom 50th percentile for A1c testing rates. Bright spot counties were identified as counties in the bottom 33rd percentile for T2DM mortality, the top 33rd percentile for T2DM prevalence; and the top 50th percentile for HbA1c testing rates. Results: Forty-one priority counties were identified (those with high T2DM mortality, high T2DM prevalence, and low HbA1c testing rates), which were located primarily in Central and North Central Appalachia; and 17 bright spot counties were identified (high T2DM prevalence, low T2DM mortality, and high HbA1c testing rates), which were scattered throughout Appalachia. Eight of the 17 bright spot counties were adjacent to priority counties. Implications: By employing conditional mapping to T2DM, multiple variables can be summarized into a single, easily interpretable map. This could be valuable for T2DM-prevention programs seeking to prioritize diagnostic and intervention resources for the management of T2DM in Appalachia.

Living in "Cold Spot" Communities Is Associated with Poor Health and Health Quality.

PURPOSE: Little is known about incorporating community data into clinical care. This study sought to understand the clinical associations of cold spots (census tracts with worse income, education, and composite deprivation). METHODS: Across 12 practices, we assessed the relationship between cold spots and clinical outcomes (obesity, uncontrolled diabetes, pneumonia vaccination, cancer screening-colon, cervical, and prostate-and aspirin chemoprophylaxis) for 152,962 patients. We geocoded and linked addresses to census tracts and assessed, at the census tract level, the percentage earning less than 200% of the Federal Poverty Level, without high school diplomas, and the social deprivation index (SDI). We labeled those census tracts in the worst quartiles as cold spots and conducted bivariate and logistic regression. RESULTS: There was a 10-fold difference in the proportion of patients in cold spots between the highest (29.1%) and lowest practices (2.6%). Except for aspirin, all outcomes were influenced by cold spots. Fifteen percent of low-education cold-spot patients had uncontrolled diabetes compared with 13% of noncold-spot patients (P < .05). In regression, those in poverty, low education, and SDI cold spots were less likely to receive colon cancer screening (odds ratio [CI], 0.88 [0.83-0.93], 0.87 [0.82-0.92], and 0.89 [0.83-0.95], respectively) although cold-spot patients were more likely to receive cervical cancer screening. CONCLUSION: Living in cold spots is associated with worse chronic conditions and quality for some screening tests. Practices can use neighborhood data to allocate resources and identify those at risk for poor outcomes.