Primary Care Access in Areas of the United States With Aging Populations.

The percentage of U.S. residents 65 years and older was 17% in 2020, and this number is expected to rise due to the aging of the baby boomer generation.1Although life expectancy fell between 2020 and 2021, the proportion of U.S. residents older than 65 years continues to increase.2This age group often has more medical comorbidities and prescription medications, increasing the demand for primary care access. Domestic migration (U.S. residents moving within the country) of this retirement-aged population further strains the primary care workforce in underserved areas.

Identifying health centers in areas with low COVID-19 vaccination rates & high rates of vaccine hesitancy.

Context: Large numbers of US adults are vaccinated, but COVID-19 vaccine hesitancy remains high. Health centers funded by the Health Resources and Services Administration (HRSA) have played a major role in COVID-19 vaccinations and have the potential to vaccinate even larger numbers of people. Objective: To identify U.S. counties with low COVID-19 vaccination rates and high rates of vaccine hesitancy, explore the characteristics of these counties and health center presence in these areas, and identify priority health centers for targeted vaccine outreach. Study Design: Cross-sectional geospatial analysis of county-level COVID-19 vaccination rates and COVID-19 vaccination hesitancy. Bivariate Local Moran's I using GeoDa software to identify clusters of counties with low COVID-19 vaccination rates and high rates of COVID-19 vaccine hesitancy. Geographic Information Systems (GIS) mapping to overlay health centers with county-level data. Setting or Dataset: U.S. counties; vaccine hesitancy data from U.S. Department of Health & Human Services Office of the Assistant Secretary for Planning and Evaluation (ASPE); vaccination rates from the Centers for Disease Control and Prevention (CDC); and data on Health Center Program awardees from the HRSA. Population studied: U.S. Counties (n=2,825) for which data on COVID-19 vaccination and COVID-19 vaccine hesitancy are available; and HRSA-funded health centers, excluding Puerto Rico and Pacific Islands (n=1,559). Outcome Measures: COVID-19 vaccine hesitancy and COVID-19 vaccination rates. Results: We identified 219 counties that are part of clusters of high rates of vaccine hesitancy and low COVID-19 vaccination rates. In general, these counties have higher rates of poverty, larger percentages of black and Hispanic populations, and are located in the Southeast (Alabama, Georgia) and West Virginia. Sixty health center awardees are located within these counties, serving almost 700,000 patients. Conclusions: While almost one-half of US adults have been vaccinated, younger adults have much lower rates of vaccination and large numbers are still unvaccinated. Further, vaccine rates vary by race and ethnicity, with less than one-fifth of Hispanic and black adults having been vaccinated. Targeting areas with high rates of vaccine hesitancy and low vaccination rates supports strategic planning, optimizes finite resources, and better assists health centers in creating culturally competent outreach addressing vaccine hesitancy.

Using State All-Payer Claims Data to Identify the Active Primary Care Workforce: A Novel Study in Virginia.

PURPOSE: Primary care is the foundation of the health care workforce and the only part that extends life and improves health equity. Previous research on the geographic and specialty distribution of physicians has relied on the American Medical Association's Masterfile, but these data have limitations that overestimate the workforce. METHODS: We present a pragmatic, systematic, and more accurate method for identifying primary care physicians using the National Plan and Provider Enumeration System (NPPES) and the Virginia All-Payer Claims Database (VA-APCD). Between 2015 and 2019, we identified all Virginia physicians and their specialty through the NPPES. Active physicians were defined by at least 1 claim in the VA-APCD. Specialty was determined hierarchically by the NPPES. Wellness visits were used to identify non-family medicine physicians who were providing primary care. RESULTS: In 2019, there were 20,976 active physicians in Virginia, of whom 5,899 (28.1%) were classified as providing primary care. Of this primary care physician workforce, 52.4% were family medicine physicians; the remaining were internal medicine physicians (18.5%), pediatricians (16.8%), obstetricians and gynecologists (11.8%), and other specialists (0.5%). Over 5 years, the counts and relative percentages of the workforce made up by primary care physicians remained relatively stable. CONCLUSIONS: Our novel method of identifying active physicians with a primary care scope provides a realistic size of the primary care workforce in Virginia, smaller than some previous estimates. Although the method should be expanded to include advanced practice clinicians and to further delineate the scope of practice, this simple approach can be used by policy makers, payers, and planners to ensure adequate primary care capacity.

Distribution of Spanish-Speaking Family Physicians, 2013-2019.

Language concordance between physician and patient is important in providing high-quality care. Many counties in the United States have a disparity between the number of patients speaking Spanish and the number of family physicians who are able to provide care in Spanish. Family medicine training institutions should consider how to modify curricula and recruitment of medical students to meet the language needs of their local populations.

Identifying Bright Spot Counties for Appropriate Diabetes Preventive Care: A Geospatial, Positive Deviance Approach.

Positive deviance approaches, which have been used to identify and study high performers (bright spots) and translate their successes to poorer performers, offer great potential for chronic disease management. However, there are few examples of applying positive deviance approaches across different geographic contexts. Building on prior research that developed a new measure for appropriate diabetes preventive care (DMPrevCare) and identified priority counties for this strategy, we introduce a geospatial approach for identifying bright spot counties and case matching them to priority counties that need improvement. We used the Local Moran's I tool to identify DMPrevCare spatial outliers, which are counties with larger percentages of Medicare beneficiaries receiving appropriate diabetes preventive care (DMPrevCare) surrounded by counties with smaller percentages of Medicare beneficiaries receiving DMPrevCare. We define these spatial outliers as bright spots. The Robert Wood Johnson Foundation County Health Rankings Peer Counties tool was used to link bright spot counties to previously identified priority counties. We identified 25 bright spot counties throughout the southern and mountain western United States. Bright spot counties were linked to 45 priority counties, resulting in 23 peer (bright/priority) county groups. A geospatial approach was shown to be effective in identifying peer counties across the United States that had either poor or strong metrics related to DMPrevCare, but were otherwise similar in terms of demographics and socioeconomic characteristics. We describe a framework for the next steps in the positive deviance process, which identifies potential factors in bright spot counties that positively impact diabetes care and how they may be applied to their peer priority counties.

Partnering with youth to map their neighborhood environments: a multilayered GIS approach.

Mapping approaches offer great potential for community-based participatory researchers interested in displaying youth perceptions and advocating for change. We describe a multilayered approach for gaining local knowledge of neighborhood environments that engages youths as coresearchers and active knowledge producers. By integrating geographic information systems with environmental audits, an interactive focus group, and sketch mapping, the approach provides a place-based understanding of physical activity resources from the situated experience of youths. Youths report safety and a lack of recreational resources as inhibiting physical activity. Maps reflecting youth perceptions aid policy makers in making place-based improvements for youth neighborhood environments.

Family physicians provide maternity care in and around the maternity care shortage areas, particularly rural.

PURPOSE: This study examined demographic, practice, and area-level characteristics associated with family physicians' (FP) provision of maternity care. METHODS: Using the American Board of Family Medicine Certification examination application survey data, we investigated the relationship between FPs' maternity care service provision and (1) demographic (gender, years in practice, race/ethnicity), (2) practice characteristics (size, ownership, rurality), and (3) county-level factors (percentage of reproductive-age women, the number of obstetrician-gynecologists (OBGYNs) and certified nurse midwives (CNMs) per 100,000 reproductive-age women). We performed summary statistics and multivariate logistic regression analyses. RESULTS: Of the 59,903 FPs in the sample, 7.5% provided maternity care. FPs practicing in rural were 2.5 times more likely to provide maternity care than those practicing in urban areas. FPs in academic (odds ratio [OR] 4.6, 95% confidence interval [CI] 4.1-5.1) and safety-net settings (OR 1.9, 1.7-2.1) had greater odds of providing maternity care. FPs in the bottom quintile with no or fewer OBGYNs and CNMs had a higher likelihood of maternity care provision (OR 2.1, 1.8-2.3) than those in the top quintile, with more OBGYNs and CNMs. CONCLUSIONS: FPs in high-needs areas, such as rural and safety net settings, and areas with fewer CNMs or OBGYNs are more likely to provide maternity care, demonstrating the importance of FPs in meeting the needs of women with limited maternity care access. Our study findings highlight the importance of considering the contributions of FPs to maternity care as the organizations prioritize resource allocation to areas of highest need.

Quantifying Population Characteristics Within and Outside a 30-Minute Drive-Time to Health Resources and Services Administration-Supported Health Centers.

The Health Resources and Services Administration's (HRSA) Health Center Program provides health care to vulnerable persons across the US, regardless of their ability to pay for health care. We examined characteristics of populations living within and outside a 30-minute drive-time to HRSA-supported health centers to establish a baseline to better understand the differences in these populations. Using a descriptive, cross-sectional study design and geographic information systems, we found that 94% of persons in the US live within a 30-minute drive-time of a health center. Of those outside a 30-minute drive-time to a health center, 11.7 million (60.11%) are rural and over 1.5 million households (20.32%) lack broadband internet access.

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.

Expanding Opioid Use Disorder Treatment in Priority Areas: The Role of Certified Nurse-Midwives.

INTRODUCTION: Perinatal opioid use disorder (OUD) is associated with maternal and neonatal morbidity, and treatment has been definitively shown to improve outcomes for both. As of 2018, certified nurse-midwives (CNMs) can prescribe buprenorphine for the treatment of OUD by obtaining a Drug Addiction Treatment Act waiver. This research aims to identify the number of CNMs who are waivered to prescribe buprenorphine for the treatment of OUD and who practice in priority areas for treatment expansion. METHODS: Through a descriptive study design, authors collected and analyzed publicly available data from August 2020 to January 2021. Using the software GeoDa, authors identified priority counties in the United States and the number of waivered and nonwaivered CNMs in these areas. Counties were designated as priority if they had drug-poisoning mortality rates in the top 20th percentile of all US counties and no health care providers waivered to treat OUD or had a waivered health care provider per population rate in the bottom 20th percentile. RESULTS: Analysis identified 141 priority counties in 23 states concentrated in rural areas throughout the Appalachian region of the United States. Tennessee had the most priority counties of any state. As of 2020, only 26 CNMs in the United States were waivered to prescribe buprenorphine, none of whom practiced in priority counties. Of the CNMs practicing in priority counties and without waivers, 59% (39 of 66) practiced in states where CNMs have independent practice authority. DISCUSSION: CNMs are uniquely positioned to treat perinatal OUD given their scope of practice as primary and reproductive health care providers, especially in rural, underserved areas. To increase the number of midwives prescribing buprenorphine and expand treatment access, researchers, educators, practice leaders, and policy makers must address barriers to and experiences of treating OUD.

Identifying priority and bright spot areas for improving diabetes care: a geospatial approach.

The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran's I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community.

Exploring the association of social determinants of health and clinical quality measures and performance in HRSA-funded health centres.

OBJECTIVE: This paper explores the impact of service area-level social deprivation on health centre clinical quality measures. DESIGN: Cross-sectional data analysis of Health Resources and Services Administration (HRSA)-funded health centres. We created a weighted service area social deprivation score for HRSA-funded health centres as a proxy measure for social determinants of health, and then explored adjusted and unadjusted clinical quality measures by weighted service area Social Deprivation Index quartiles for health centres. SETTINGS: HRSA-funded health centres in the USA. PARTICIPANTS: Our analysis included a subset of 1161 HRSA-funded health centres serving more than 22 million mostly low-income patients across the country. RESULTS: Higher levels of social deprivation are associated with statistically significant poorer outcomes for all clinical quality outcome measures (both unadjusted and adjusted), including rates of blood pressure control, uncontrolled diabetes and low birth weight. The adjusted and unadjusted results are mixed for clinical quality process measures as higher levels of social deprivation are associated with better quality for some measures including cervical cancer screening and child immunisation status but worse quality for other such as colorectal cancer screening and early entry into prenatal care. CONCLUSIONS: This research highlights the importance of incorporating community characteristics when evaluating clinical outcomes. We also present an innovative method for capturing health centre service area-level social deprivation and exploring its relationship to health centre clinical quality measures.

Disparities in Opioid Use Disorder Treatment Admissions.

OBJECTIVES: The Cincinnati region has been at the epicenter of the nation's unfolding opioid epidemic. The objectives of this study were twofold: (1) to compare the Cincinnati region to the United States in length of time to obtain treatment and planned medication-assisted therapy for the treatment for opioid use disorder (OUD); and (2) to assess racial disparities within the Cincinnati region in wait time and type of treatment. METHODS: The 2017 Treatment Episode Data Set: Admissions (TEDS-A) from the Substance Abuse and Mental Health Services Administration (SAMHSA) was used to identify a cohort of eligible individuals with a primary substance use of opioids, including opioid derivatives. Logistic regression models were performed to assess the differences for treatment wait time and type of planned treatment. Model covariates included patient demographics and socioeconomic characteristics. Three different models were performed to assess the influence of covariates of the outcomes. RESULTS: There were 678 766 US and 3298 Cincinnati region individuals admitted for OUD treatment in 2017. The rate per 1000 for treatment admissions was 2.08 and 1.51 (P value < 0.0001) for the United States and Cincinnati, respectively. The fully saturated regression results found that the odds of Cincinnati individuals receiving planned medication-assisted therapy were 0.497 (95% CI, 0.451-0.546; P value < 0.001). The odds of waiting longer for treatment in Cincinnati were higher than in the United States as a whole: 2.33 (95% CI, 2.19-2.48; P value < 0.001). In Cincinnati, there were 3102 Caucasian, 123 African American, and 73 Other admissions. The fully saturated model results found that Caucasians and Other had an increased likelihood of receiving planned medicationassisted therapy (OR 1.89, P value 0.039; OR 7.07, P value 0.002, respectively) compared to African Americans. Within Cincinnati, there was not a statistically significant difference in the likelihood of waiting time to receive treatment by race. CONCLUSION: Individuals seeking treatment for OUD in Cincinnati were less likely to receive planned medication-assisted therapy and were more likely to wait longer than individuals in the United States as a whole. These results suggest that the demand for treatment is greater than the supply in Cincinnati. Within Cincinnati, there does not appear to be a racial disparity in treatment type or length of time to receive treatment for OUD.

Outcomes of Hospitalized COVID-19 Patients by Risk Factors: Results from a United States Hospital Claims Database.

BACKGROUND/OBJECTIVE: The primary objective was to quantify the role of the number of Centers of Disease Control and Prevention (CDC) risk factors on in-hospital mortality. The secondary objective was to assess the associated hospital length of stay (LOS), intensive care unit (ICU) bed utilization, and ICU LOS with the number of CDC risk factors. METHODS: A retrospective cohort study consisting of all hospitalizations with a confirmed COVID-19 diagnosis discharged between March 15, 2020 and April 30, 2020 was conducted. Data was obtained from 276 acute care hospitals across the United States. Cohorts were identified based upon the number of the CDC COVID-19 risk factors. Multivariable regression modeling was performed to assess outcomes and utilization. The odds ratio (OR) and incidence rate ratio (IRR) were reported. RESULTS: Compared with patients with no CDC risk factors, patients with risk factors were significantly more likely to die during the hospitalization: One risk factor (OR 2.08, 95% CI, 1.60-2.70; P < 0.001), two risk factors (OR 2.63, 95% CI, 2.00-3.47; P < 0.001), and three or more risk factors (OR 3.49, 95% CI, 2.53-4.80; P < 0.001). The presence of CDC risk factors was associated with increased ICU utilization, longer ICU LOS, and longer hospital LOS compared to those with no risk factors. Patients with hypertension (OR 0.77, 95% CI, 0.70-0.86; P < 0.001) and those administered statins were less likely to die (OR 0.54, 95% CI, 0.49-0.60; P < 0.001). CONCLUSIONS: Quantifying the role of CDC risk factors upon admission may improve risk stratification and identification of patients who may require closer monitoring and more intensive treatment.

Identifying diabetes management opportunity areas in the USA.

Using adherence to diabetes management guidelines as a case study, this paper applied a novel geospatial hot-spot and cold-spot methodology to identify priority counties to target interventions. Data for this study were obtained from the Dartmouth Atlas of Healthcare, the United States Census Bureau's American Community Survey and the University of Wisconsin County Health Rankings. A geospatial approach was used to identify four tiers of priority counties for diabetes preventive and management services: diabetes management cold-spots, clusters of counties with low rates of adherence to diabetes preventive and management services (Tier D); Medicare spending hot-spots, clusters of counties with high rates of spending and were diabetes management cold-spots (Tier C); preventable hospitalisation hot-spots, clusters of counties with high rates of spending and are diabetes management cold-spots (Tier B); and counties that were located in a diabetes management cold-spot cluster, preventable hospitalisation hot-spot cluster and Medicare spending hot-spot cluster (Tier A). The four tiers of priority counties were geographically concentrated in Texas and Oklahoma, the Southeast and central Appalachia. Of these tiers, there were 62 Tier A counties. Rates of preventable hospitalisations and Medicare spending were higher in Tier A counties compared with national averages. These same counties had much lower rates of adherence to diabetes preventive and management services. The novel geospatial mapping approach used in this study may allow practitioners and policy makers to target interventions in areas that have the highest need. Further refinement of this approach is necessary before making policy recommendations.

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.

Estimating the Prevalence of Opioid use Disorder in the Cincinnati Region using Probabilistic Multiplier Methods and Model Averaging.

BACKGROUND: Opioid use disorder (OUD) and its consequences have strained the resources of health, social, and criminal justice services in the Cincinnati region. However, understanding of the potential number of people suffering from OUD is limited. Little robust and reliable information quantifies the prevalence and there is often great variation between individual estimates of prevalence. In other fields such as meteorology, finance, sports, and politics, model averaging is commonly employed to improve estimates and forecasts. The objective of this study was to apply a model averaging approach to estimate the number of individuals with OUD in the Cincinnati region. METHODS: Three individual probabilistic simulation models were developed to estimate the number of OUD individuals in the Cincinnati Core Based Statistical Area (CBSA). The models used counts of overdose deaths, non-fatal overdoses, and treatment admissions as benchmark data. A systematic literature review was performed to obtain the multiplier data for each model. The three models were averaged to generate single estimate and confidence band of the prevalence of OUD. RESULTS: This study estimated 15 067 (SE 1556) individuals with OUD in the Cincinnati CBSA (2 165 139 total population). Based on these results, we estimate the prevalence of OUD to be between 13 507 (0.62% of population) and 16 620 (0.77% of population). CONCLUSIONS: The method proposed herein has been shown in diverse fields to mitigate some of the uncertainty associated with reliance on a single model. Further, the simplicity of the method described is easily replicable by community health centers, first-responders, and social services to estimate capacity needs supported by OUD estimates for the region they serve.

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.