Health centres and social determinants of health: an analysis of enabling services provision and clinical quality.

OBJECTIVE: It is well known that social determinants of health (SDOH), including poverty, education, transportation and housing, are important predictors of health outcomes. Health Resources and Services Administration (HRSA)-funded health centres serve a patient population with high vulnerability to barriers posed by SDOH and are required to provide services that enable health centre service utilisation and assist patients in navigating barriers to care. This study explores whether health centres with higher percentages of patients using these enabling services experience better clinical performance and outcomes. DESIGN AND SETTING: The analysis uses organisational characteristics, patient demographics and clinical quality measures from HRSA's 2018 Uniform Data System. Health centres (n=875) were sorted into quartiles with quartile 1 (Q1) representing the lowest utilisation of enabling services and quartile 4 (Q4) representing the highest. The researchers calculated a service area social deprivation score weighted by the number of patients for each health centre and used ordinary least squares to create adjusted values for each of the clinical quality process and outcome measures. Analysis of variance was used to test differences across enabling services quartiles. RESULTS: After adjusting for patient characteristics, health centre size and social deprivation, authors found statistically significant differences for all clinical quality process measures across enabling services quartiles, with Q4 health centres performing significantly better than Q1 health centres for several clinical process measures. However, these Q4 health centres performed poorer in outcome measures, including blood pressure and haemoglobin A1c control. CONCLUSION: These findings emphasise the importance of how enabling services (eg, translation services, transportation) can address unmet social needs, improve utilisation of health services and reaffirm the challenges inherent in overcoming SDOH to improve health outcomes.

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.

How Engaged Are Family Physicians in Addressing the Social Determinants of Health? A Survey Supporting the American Academy of Family Physician's Health Equity Environmental Scan.

Purpose: Public health leaders have advocated for clinical and population-based interventions to address the social determinants of health (SDoH). The American Academy of Family Physicians has worked to support family physicians with addressing the SDoH. However, the extent that family physicians are engaged and the factors that influence this are unknown. Methods: A survey was used to identify actions family physicians had taken to address the SDoH and perceived barriers. Physician and community characteristics were linked. Ordinal logistic regression was used to identify factors associated with engagement in clinical and population-based actions, separately. Results: There were 434 (8.7%) responses. Among respondents, 81.1% were engaged in at least one clinical action, and 43.3% were engaged in at least one population-based action. Time (80.0%) and staffing (64.5%) were the most common barriers. Physician experience was associated with higher levels of clinical engagement, lower median household income was associated with higher levels of population-based engagement, and working for a federally qualified health center (FQHC) was associated with both. Conclusions: The study provides preliminary information suggesting that family physicians are engaged in addressing the SDoH through clinical and population-based actions. Newer family physicians and those working in FQHCs may be good targets for piloting clinical actions to address SDoH and family physician advocates may be more likely to come from an FQHC or in a lower socioeconomic neighborhood. The study also raises questions about the value family physicians serving disadvantaged communities place on clinical interventions to address the SDoH.

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.

Identifying Priority Areas for Increasing the Supply of Medication-Assisted Treatments for Opioid Use Disorder: A Geospatial Approach.

BACKGROUND: The opioid epidemic has disproportionately affected several areas across the United States (US), with research indicating that these areas may be underserved and lack access to sufficient medication-assisted treatment (MAT) options. The objective of this study was to introduce a geospatial analytical framework for identifying sub-state priority areas to target federal allocation of MAT training and resources. METHODS: We used a geospatial analytical framework, which integrated multiple substance use measures and layers of geographic information. Measures included estimates of illicit drug dependence and unmet treatment need from the National Survey on Drug Use and Health (NSDUH), opioid-related admissions from the Treatment Episode Data Set: Admissions (TEDs-A), and Drug Enforcement Agency (DEA) waiver practitioner data from the Substance Abuse and Mental Health Services Administration (SAMHSA). Analyses included standard deviation outlier mapping, local indicators of spatial autocorrelation (LISA), and map overlays. RESULTS: We identified twenty-nine opioid dependence priority areas, eleven unmet treatment need priority areas, and seven low MAT capacity priority areas, located across the US, including southeastern Ohio, western Indiana, the District of Columbia, New England, and northern and southern California. CONCLUSIONS: This study identified several areas across the US that have unmet need for MAT. Targeting these areas will allow for the most effective deployment of cost-effective MAT resources to aid the greatest number of patients with opioid use disorders.