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.

Differences in Health Care Utilization of High-Need and High-Cost Patients of Federally Funded Health Centers Versus Other Primary Care Providers.

BACKGROUND: Primary care providers (PCP) differ in their ability to address the needs and reduce use of costly services among complex Medicaid beneficiaries. Among PCPs, Health Resources and Services Administration (HRSA)-funded health centers (HCs) are shown to provide high-value care. OBJECTIVE: We compared health care utilization of complex Medicaid managed care beneficiaries whose PCPs were HCs versus 3 other groups. RESEARCH DESIGN: Cross-sectional study using propensity score matching comparing health care use by provider type, controlling for demographics, health status, and other covariates. SUBJECTS: California Medicaid administrative data for complex adult managed care beneficiaries with at least 1 primary care visit in 2018. MEASURES: Primary and specialty care evaluation & management visits and services; emergency department (ED) visits; and hospitalizations. PCPs included HCs, clinics not funded by HRSA, solo, and group practice providers. RESULTS: HRSA-funded HCs had lower predicted rates of specialty evaluation & management and other services than all others; lower predicted probability of any ED visits than clinics not funded by HRSA [54% (95% CI: 53%-55%) vs. 56% (95% CI: 55%-57%)] and group practice providers [51% (95% CI: 51%-52%) vs. 52% (95% CI: 52%-53%)]; and lower PP of any hospitalizations than solo [20% (95% CI: 19%-20%) vs. 23% (95% CI: 22%-24%)] and group practice providers [21% (95% CI: 20%-21%) vs. 24% (95% CI: 23%-24%)]. CONCLUSIONS: Differences in HC care delivery and practices were associated with lower use of specialty, ED, and hospitalization visits compared with other PCPs for complex Medicaid managed care beneficiaries. Understanding the underlying reasons for these utilization differences may promote better outcomes among these patients.

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.

Weight management practices of health center providers in the United States.

BACKGROUND: We examined weight management counseling practices of Health Resources and Services Administration-funded health center (HC) providers for patients with overweight (POW) and obesity (POB) status, focusing on weight-related conditions, risk factors, and health care utilization. METHOD: We used a nationally representative cross-sectional survey of HC patients and multilevel generalized structural equation logistic regression models to assess the association of provider counseling practices for POW and POB and by three obesity classes. Dependent variables included being told by the HC provider that weight was a problem, receiving a diet or exercise recommendation, referral to a nutritionist, or receiving weight loss prescriptions. Independent variables included weight-related conditions such as diabetes and hypertension, risk factors such as smoking, and health service utilization such as five or more primary care visits. RESULTS: All POB classes had higher odds of receiving all five counseling interventions than POW. Patients with diabetes and high cholesterol had higher odds of diet recommendations (OR = 1.8) and nutritionist referrals (OR = 2.3), while patients with cardiovascular disease had higher odds of nutritionist referral (OR = 2.0) and receiving weight loss prescriptions (OR = 2.6). Respondents with POB class III and diabetes had higher odds of receiving exercise recommendations (OR = 3.4), while POB class 1 and had hypertension had lower odds of nutritionist referral (OR = 0.3). CONCLUSIONS: Variations in HC primary care providers' weight management counseling practices between POW and POB present missed opportunities for consistent practice and early intervention. Assessing providers' counseling practices for patients with comorbid conditions is essential to the successful management of the obesity crisis.

Factors associated with frequent emergency department visits among health centre patients receiving primary care.

RATIONALE, AIMS AND OBJECTIVES: We sought to examine specific care-seeking behaviours and experiences, access indicators, and patient care management approaches associated with frequency of emergency department (ED) visits among patients of Health Resources and Services Administration-funded health centres that provide comprehensive primary care to low-income and uninsured patients. METHOD: We used cross-sectional data of a most recent nationally representative sample of health centre adult patients aged 18-64 (n = 4577) conducted between October 2014 and April 2015. These data were merged with the 2014 Uniform Data System to incorporate health centre characteristics. We measured care-seeking behaviours by whether the patient called the health centre afterhours, for an urgent appointment, or talked to a provider about a concern. Access to care indicators included health centre continuity of care and receipt of transportation or translation services. We included receipt of care coordination and specialist referral as care management indicators. We used a multilevel multinomial logistic regression model to identify the association of independent variables with number of ED visits (4 or more visits, 2-3 visits, 1 visit, vs. 0 visits), controlling for predisposing, enabling, and need characteristics. RESULTS: Calling the health centre after-hours (OR = 2.41) or for urgent care (OR = 2.53), and being referred to specialists (OR = 2.36) were associated with higher odds of four or more ED visits versus none. Three or more years of continuity with the health centre (OR = 0.32) was also associated with lower odds of four or more ED visits versus none. CONCLUSIONS: Findings underscore opportunities to reduce higher frequency of ED visits in health centres, which are primary care providers to many low-income populations. Our findings highlight the potential importance of improving patient retention, better access to providers afterhours or for urgent visits, and access to specialist as areas of care in need of improvement.

Staffing transformation following Patient-Centered Medical Home recognition among Health Resources & Services Administration-funded health centers.

INTRODUCTION: Patient-Centered Medical Home (PCMH) recognition is designed to promote whole-person team-based and integrated care. PURPOSE: Our goal was to assess changes in staffing infrastructure that promoted team-based and integrated care delivery before and after PCMH recognition in Health Resources & Services Administration (HRSA)-funded health centers (HCs). METHODOLOGY/APPROACH: We identified changes in staffing 2 years before and 3 years after PCMH recognition using 2010-2019 Uniform Data System data among three cohorts of HCs that received PCMH recognition in 2013 ( n = 346), 2014 ( n = 207), and 2015 ( n = 115). Our outcomes were team-based ratio (full-time equivalent medical and nonmedical providers and staff to one primary care physician) and a multidisciplinary staff ratio (allied medical and nonmedical staff to 1,000 patients). We used mixed-effects Poisson regression models. RESULTS: The earlier cohorts served fewer complex patients and were larger before PCMH recognition. Three years following recognition, the 2013 and 2014 cohorts had significantly larger team-based ratios, and all three cohorts had significantly larger multidisciplinary staff ratios. Cohorts varied, however, in the type of staff that drove this change. Both ratios increased in the longer term. CONCLUSION: Our study suggests that growth in team-based and multidisciplinary staff ratios in each cohort may have been due to a combination of HCs' perceptions of need for specific services, HRSA funding, and technical assistance opportunities. POLICY IMPLICATIONS: Further research is needed to understand barriers such as costs of employing a multidisciplinary staff, particularly those that cannot directly bill for services as well as whether such changes lead to practice transformation and improved quality of care.

A Systematic Literature Review of Health Center Efforts to Address Social Determinants of Health.

Health centers (HCs) play a crucial and integral role in addressing social determinants of health (SDOH) among vulnerable and underserved populations, yet data on SDOH assessment and subsequent actions is limited. We conducted a systematic review to understand the existing evidence of integration of SDOH into HC primary-care practices. Database searches yielded 3,516 studies, of which 41 articles met the inclusion criteria. A majority of studies showed that HCs primarily captured patient-level rather than community-level SDOH data. Studies also showed that HCs utilized SDOH in electronic health records but capabilities varied widely. A few studies indicated that HCs measured health-related outcomes of integrating SDOH data. The review highlighted that many knowledge gaps exist in the collection, use, and assessment of impact of these data on outcomes, and future research is needed to address this knowledge gap.

Health care use and spending for Medicaid patients diagnosed with opioid use disorder receiving primary care in Federally Qualified Health Centers and other primary care settings.

INTRODUCTION: This nationwide study builds on prior research, which suggests that Federally Qualified Health Centers (FQHCs) and other primary care providers are associated with increased access to opioid use disorder (OUD) treatment. We compare health care utilization, spending, and quality for Medicaid patients diagnosed with OUD who receive primary care at FQHCs and Medicaid patients who receive most primary care in other settings, such as physician offices (non-FQHCs). We hypothesized that the integrated care model of FQHCs would be associated with greater access to medication for opioid use disorder (MOUD) and/or behavioral health therapy and lower rates of potentially inappropriate co-prescribing. METHODS: This cross-sectional study examined 2012 Medicaid Analytic eXtract files for patients diagnosed with OUD receiving most (>50%) primary care at FQHCs (N = 37,142) versus non-FQHCs (N = 196,712) in all 50 states and Washington DC. We used propensity score overlap weighting to adjust for measurable confounding between patients who received care at FQHCs versus non-FQHCs and increase generalizability of findings given variation in Medicaid programs and substance use policies across states. RESULTS: FQHC patients displayed higher primary care utilization and fee-for-service spending, and similar or lower utilization and fee-for-service spending for other health service categories. Contrary to our hypotheses, non-FQHC patients were more likely to receive timely (≤90 days) MOUD (buprenorphine, methadone, naltrexone, or suboxone) (Relative Risk [RR] = 1.10, 95% CI: 1.07, 1.12) and more likely be retained in medication treatment (>180 days) (RR = 1.12, 95% CI: 1.09, 1.14). However, non-FQHC patients were less likely to receive behavioral health therapy (mental health or substance use therapy) (RR = 0.90, 95% CI: 0.88, 0.92) and less likely to remain in behavioral health treatment (RR = 0.92, 95% CI: 0.89, 0.94). Non-FQHC patients were more likely to fill potentially inappropriate prescriptions of benzodiazepines and opioids after OUD diagnosis (RR = 1.35, 95% CI: 1.30, 1.40). CONCLUSIONS: Observed patterns suggest that Medicaid patients diagnosed with OUD who obtained primary care at FQHCs received more integrated care compared to non-FQHC patients. Greater care integration may be associated with increased access to behavioral health therapy and quality of care (lower potentially inappropriate co-prescribing) but not necessarily greater access to MOUD.

Health Centers and Value-Based Payment: A Framework for Health Center Payment Reform and Early Experiences in Medicaid Value-Based Payment in Seven States.

Policy Points As essential access points to primary care for almost 29 million US patients, of whom 47% are Medicaid enrollees, health centers are positioned to implement the population health management necessary in value-based payment (VBP) contracts. Primary care payment reform requires multiple payment methodologies used together to provide flexibility to care providers, encourage investments in infrastructure and new services, and offer incentives for achieving better health outcomes. State policy and significant financial incentives from Medicaid agencies and Medicaid managed care plans will likely be required to increase health center participation in VBP, which is consistent with broader state efforts to expand investment in primary care. CONTEXT: Efforts are ongoing to advance value-based payment (VBP), and health centers serve as essential access points to comprehensive primary care services for almost 29 million people in the United States. Therefore, it is important to assess the levels of health center participation in VBP, types of VBP contracts, characteristics of health centers participating in VBP, and variations in state policy environments that influence VBP participation. METHODS: This mixed methods study combined qualitative research on state policy environments and health center participation in VBP with quantitative analysis of Uniform Data System and health center financial data in seven vanguard states: Oregon, Washington, California, Colorado, New York, Hawaii, and Kentucky. VBP contracts were classified into three layers: base payments being transformed from visit-based to population-based (Layer 1), infrastructure and care coordination payments (Layer 2), and performance incentive payments (Layer 3). FINDINGS: Health centers in all seven states participated in Layer 2 and Layer 3 VBP, with VBP participation growing from 35% to 58% of all health centers in these states from 2013 to 2017. Among participating health centers, the average percentage of Medicaid revenue received as Layer 2 and Layer 3 VBP rose from 6.4% in 2013 to 9.1% in 2017. Oregon and Washington health centers participating in Layer 1 payment reforms received most of their Medicaid revenue in VBP. In 2017, VBP participation was associated with larger health center size in four states (P <.05), and higher average number of days cash on hand (P <.05) in three states. CONCLUSIONS: A multilayer payment model is useful for implementing and monitoring VBP adoption among health centers. State policy, financial incentives from Medicaid agencies and Medicaid managed plans, and health center-Medicaid collaboration under strong primary care association and health center leadership will likely be required to increase health center participation in VBP.

Early experience of the quality improvement award program in federally funded health centers.

OBJECTIVES: To describe the Health Resources and Services Administration's Quality Improvement Award (QIA) program, award patterns, and early lessons learned. STUDY SETTING: 1413 health centers were eligible for QIA from 2014 to 2018. STUDY DESIGN: We assessed cumulative QIA funding earned and modified funding excluding payments for per-patient bonuses, electronic health record (EHR) use, patient-centered medical home (PCMH) accreditation, and health information technology. We compared health centers on rural/urban location, PCMH accreditation, EHR reporting, and size. DATA COLLECTION: Organizational and quality measures are reported in the Uniform Data System, QIA program data. PRINCIPAL FINDINGS: Average cumulative funding was higher for health centers that were not rural (USD 380,387 [± USD 233,467] vs. USD 303,526 [± USD 164,272]), had PCMH accreditation (USD 401,675 [± USD 218,246] vs. USD 250,784 [± USD 144,404]), used their EHR for quality reporting (USD 374,214 (± USD 222,866) vs. USD 331,150 (± USD 198,689)), and were large (USD 435,473 (± USD 238,193) vs. USD 270,681 (± USD 114,484) an USD 231,917 (± USD 97,847) for small and medium centers, respectively). There were similar patterns, with smaller differences, for average modified payments. CONCLUSIONS: QIA is an important feasible initiative to introduce value-based payment principles to health centers. Early lessons for program design include announcing award criteria in advance and focusing on a smaller number of priority targets.

Factors associated with federally qualified health center financial performance.

OBJECTIVES: To understand factors associated with federally qualified health center (FQHC) financial performance. STUDY DESIGN: We used multivariate linear regression to identify correlates of health center financial performance. We examined six measures of health center financial performance across four domains: margin (operating margin), liquidity (days cash on hand [DCOH], current ratio), solvency (debt-to-equity ratio), and others (net patient accounts receivable days, personnel-related expenses). We examined potential correlates of financial performance, including characteristics of the patient population, health center organization, and location/geography. DATA SOURCES: We use 2012-2017 Uniform Data System (UDS) files, financial audit data from Capital link, and publicly available data. DATA COLLECTION/EXTRACTION METHODS: We focused on health centers in the 50 US states and District of Columbia, which reported information to UDS for at least 1 year between 2012 and 2017 and had Capital link financial audit data. PRINCIPAL FINDINGS: FQHC financial performance generally improved over the study period, especially from 2015 to 2017. In multivariate regression models, a higher percentage of Medicaid patients was associated with better margins (operating margin: 0.06, p < 0.001), liquidity (DCOH: 0.67, p < 0.001; current ratio: 0.28, p = 0.001), and solvency (debt-to equity ratio: -0.08, p = 0.004). Moreover, a staffing mix comprised of more nonphysician providers was associated with better margin (operating margin: 0.21, p = 0.001) and liquidity (current ratio: 1.12, p < 0.001) measures. Patient-centered medical home (PCMH) recognition was also associated with better liquidity (DCOH: 19.01, p < 0.001; current ratio: 4.68, p = 0.014) and solvency (debt-to-equity ratio: -2.03, p < 0.001). CONCLUSIONS: The financial health of FQHCs improved with provisions of the Affordable Care Act, which included significant Medicaid expansion and direct funding support for health centers. FQHC financial health was also associated with key staffing and operating characteristics of health centers. Maintaining the financial health of FQHCs is critical to their ability to continuously provide affordable and high-quality care in medically underserved areas.

Intersection of complexity and high utilization among health center patients aged 18 to 64 years.

OBJECTIVES: Existing literature indicates that multimorbidity, mental health (MH) conditions, substance use disorders (SUDs), and social determinants of health are hallmarks of high-need, high-cost patients. Health Resources and Services Administration-funded health centers (HCs) provide care to nearly 30 million patients, but data on their patients' complexity and utilization patterns are limited. We identified subgroups of HC patients based on latent concepts of complexity and utilization. STUDY DESIGN: We used cross-sectional national data from the 2014 Health Center Patient Survey and latent class analyses to identify distinct and homogenous groups of complex high-utilizing patients aged 18 to 64 years. METHODS: We included indicators of chronic conditions (CCs), MH, SUD risk, and health behavior to measure complexity. We used number of outpatient and emergency department visits in the past year to measure utilization. RESULTS: HC patients were separated in 9 distinct groups based on 3 complexity latent classes (MH, multiple CCs, and low risk) and 3 utilization classes (low, high, and superutilizers). Conditions associated with each subgroup differed. The highest prevalence of bipolar disorder (45%) and high SUD risk (6%) was observed among MH superutilizers, whereas the highest prevalence of cardiovascular disease (48%) and obesity (96%) was seen among CC superutilizers. Most MH superutilizer patients concurrently had MH conditions and obesity and were smokers, but most CC superutilizer patients concurrently had hypertension, obesity, and cardiovascular disease. CONCLUSIONS: Our examination of complexity and utilization indicated distinct HC patient populations. Managing the care of each group may require different targeted intervention approaches such as multidisciplinary care teams that include MH providers or specialists.

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.

Mental Health Staffing at HRSA-Funded Health Centers May Improve Access to Care.

OBJECTIVE: The study objective was to examine the association between mental health staffing at health centers funded by the Health Resources and Services Administration (HRSA) and patients' receipt of mental health treatment. METHODS: Data were from the 2014 HRSA-funded Health Center Patient Survey and the 2013 Uniform Data System. Colocation of any mental health staff, including psychiatrists, psychologists, and other licensed staff, was examined. The outcomes of interest were whether a patient received any mental treatment and received any such treatment on site (at the health center). Analyses were conducted with multilevel generalized structural equation logistic regression models for 4,575 patients ages 18-64. RESULTS: Patients attending health centers with at least one mental health full-time equivalent (FTE) per 2,000 patients had a higher predicted probability of receiving mental health treatment (32%) compared with those attending centers with fewer than one such FTE (24%) or no such staffing (22%). Among patients who received this treatment, those at health centers with no staffing had a significantly lower predicted probability of receiving such treatment on site (28%), compared with patients at health centers with fewer than one such FTE (49%) and with at least one such FTE (65%). The predicted probability of receiving such treatment on site was significantly higher if there was a colocated psychiatrist versus no psychiatrist (58% versus 40%). CONCLUSIONS: Colocating mental health staff at health centers increases the probability of patients' access to such treatment on site as well as from off-site providers.

The role of dentist supply, need for care and long-term continuity in Health Resources and Services Administration-funded health centres in the United States.

OBJECTIVES: Health Resources and Services Administration-funded health centres (HCs) are an important source of dental services for low-income and vulnerable patients in the United States. About 82% of HCs in 2018 had dental workforce, but it is unclear whether this workforce meets the oral health needs of HC patients. Thus, we first examined (a) whether dental workforce was associated with any dental visits vs none and (b) whether HC patients with any visits were more likely to have a visit at the HC vs elsewhere. We then examined (c) if need for oral health care and long-term continuity at the HC were associated with dental visits and visits at the HC. METHODS: This study used the 2014 Health Center Patient Survey, a nationally representative study of US HC patients, and the 2013 Uniform Data System, an administrative dataset of HC characteristics. We also used the 2013 Area Health Resource File to measure the contribution of local supply of dentists. We included working-age adult patients (n = 5006) and used multilevel structural equation models with Poisson specification. RESULTS: Larger dental workforce at the HC was significantly associated with 1% higher likelihood (relative risk [RR]: 1.01, 1.00-1.02) of any visits and 10% higher likelihood of a visit at the HC among those with a visit (RR: 1.10, 1.06-1.14). Patient self-reported oral health need was positively associated with 157% higher likelihood of dental visits (RR: 2.57, 2.29-2.88), and 42% higher likelihood of dental visit at the HC vs elsewhere (RR: 1.42, 1.19-1.69). Long-term continuity with the HC was not significantly associated with likelihood of dental visits, but was associated with 26% higher likelihood of visits at the HC among those who had any visits (RR: 1.26, 1.02-1.56). DISCUSSION: The findings highlight the potential impact of increasing dental workforce at HCs to promote access; the high level of need for oral health care at HCs; and the increased effort required to promote access among newer patients who may be less familiar with the availability of oral health care at HCs. Together, these findings reinforce the importance of addressing barriers of use of oral health services among low-income and uninsured patients.

Assessing clinical quality performance and staffing capacity differences between urban and rural Health Resources and Services Administration-funded health centers in the United States: A cross sectional study.

BACKGROUND: In the United States, there are nearly 1,400 Health Resources and Services Administration-funded health centers (HCs) serving low-income and underserved populations and more than 600 of these HCs are located in rural areas. Disparities in quality of medical care in urban vs. rural areas exist but data on such differences between urban and rural HCs is limited in the literature. We examined whether urban and rural HCs differed in their performance on clinical quality measures before and after controlling for patient, organizational, and contextual characteristics. METHODS AND FINDINGS: We used the 2017 Uniform Data System to examine performance on clinical quality measures between urban and rural HCs (n = 1,373). We used generalized linear regression models with the logit link function and binomial distribution, controlling for confounding factors. After adjusting for potential confounders, we found on par performance between urban and rural HCs in all but one clinical quality measure. Rural HCs had lower rates of linking patients newly diagnosed with HIV to care (74% [95% CI: 69%, 80%] vs. 83% [95% CI: 80%, 86%]). We identified control variables that systematically accounted for eliminating urban vs. rural differences in performance on clinical quality measures. We also found that both urban and rural HCs had some clinical quality performance measures that were lower than available national benchmarks. Main limitations included potential discrepancy of urban or rural designation across all HC sites within a HC organization. CONCLUSIONS: Findings highlight HCs' contributions in addressing rural disparities in quality of care and identify opportunities for improvement. Performance in both rural and urban HCs may be improved by supporting programs that increase the availability of providers, training, and provision of technical resources.

Examining trends in substance use disorder capacity and service delivery by Health Resources and Services Administration-funded health centers: A time series regression analysis.

BACKGROUND: The opioid epidemic and subsequent mortality is a national concern in the U.S. The burden of this problem is disproportionately high among low-income and uninsured populations who are more likely to experience unmet need for substance use services. We assessed the impact of two Health Resources and Services Administration (HRSA) substance use disorder (SUD) service capacity grants on SUD staffing and service use in HRSA -funded health centers (HCs). METHODS AND FINDINGS: We conducted cross-sectional analyses of the Uniform Data System (UDS) from 2010 to 2017 to assess HC (n = 1,341) trends in capacity measured by supply of SUD and medication-assisted treatment (MAT) providers, utilization of SUD and MAT services, and panel size and visit ratio measured by the number of patients seen and visits delivered by SUD and MAT providers. We merged mortality and national survey data to incorporate SUD mortality and SUD treatment services availability, respectively. From 2010 to 2015, 20% of HC organizations had any SUD staff, had an average of one full-time equivalent SUD employee, and did not report an increase in SUD patients or SUD services. SUD capacity grew significantly in 2016 (43%) and 2017 (22%). MAT capacity growth was measured only in 2016 and 2017 and grew by 29% between those years. Receipt of both supplementary grants increased the probability of any SUD capacity by 35% (95% CI: 26%, 44%) and service use, but decreased the probability of SUD visit ratio by 680 visits (95% CI: -1,013, -347), compared to not receiving grants. CONCLUSIONS: The significant growth in HC specialized SUD capacity is likely due to supplemental SUD-specific HRSA grants and may vary by structure of grants. Expanding SUD capacity in HCs is an important step in increasing SUD access for low income and uninsured populations broadly and for patients of these organizations.

Impact of health information technology optimization on clinical quality performance in health centers: A national cross-sectional study.

BACKGROUND: Delivery of preventive care and chronic disease management are key components of a high functioning primary care practice. Health Centers (HCs) funded by the Health Resources and Services Administration (HRSA) have been delivering affordable and accessible primary health care to patients in underserved communities for over fifty years. This study examines the association between health center organization's health information technology (IT) optimization and clinical quality performance. METHODS AND FINDINGS: Using 2016 Uniform Data System (UDS) data, we performed bivariate and multivariate analyses to study the association of Meaningful Use (MU) attestation as a proxy for health IT optimization, patient centered medical home (PCMH) recognition status, and practice size on performance of twelve electronically specified clinical quality measures (eCQMs). Bivariate analysis demonstrated performance of eleven out of the twelve preventive and chronic care eCQMs was higher among HCs attesting to MU Stage 2 or above. Multivariate analysis demonstrated that Stage 2 MU or above, PCMH status, and larger practice size were positively associated with performance on cancer screening, smoking cessation counseling and pediatric weight assessment and counseling eCQMs. CONCLUSIONS: Organizational advancement in MU stages has led to improved quality of care that augments HCs patient care capacity for disease prevention, health promotion, and chronic care management. However, rapid technological advancement in health care acts as a potential source of disparity, as considerable resources needed to optimize the electronic health record (EHR) and to undertake PCMH transformation are found more commonly among larger HCs practices. Smaller practices may lack the financial, human and educational assets to implement and to maintain EHR technology. Accordingly, targeted approaches to support small HCs practices in leveraging economies of scale for health IT optimization, clinical decision support, and clinical workflow enhancements are critical for practices to thrive in the dynamic value-based payment environment.

Racial/ethnic variations in weight management among patients with overweight and obesity status who are served by health centres.

This study sought to examine racial/ethnic variations in receipt of provider recommendations on weight loss, patient adherence, perception of weight, attempts at weight loss and actual weight loss among patients with overweight/obesity status at Health Resources and Services Administration-funded health centres (HC). We used a 2014 nationally representative survey of adult HC patients with overweight/obesity status (PwOW/OB) last year and reported the HC was their usual source of care (n = 3517). We used logistic regression models to assess the interaction of race/ethnicity and having obesity in (1) provider recommendations of diet or (2) exercise, (3) patient adherence to diet or (4) exercise, (5) perceptions of weight and (6) weight loss attempts. We used a multinomial regression model to examine (7) weight loss or gain vs no change and a linear regression model to evaluate (8) percent weight change. We found Black PwOW/OB (OR = 1.65) experienced greater odds of receiving diet recommendations than Whites. We found limited racial/ethnic disparities in adherence. Black (OR = 0.41), Hispanic/Latino (OR = 0.45), and American Indian/Alaska Native (OR = 0.41) PwOW/OB had lower odds of perceiving themselves as overweight. Black (OR = 1.68) and Hispanic (OR = 1.98) PwOW/OB had a greater odds of reporting weight gain, and Asian PwOW/OB (OR = 0.42) had lower odds of reporting weight loss than Whites. Disparities in provider diet recommendations among Blacks and Hispanics indicated the importance of personalized weight management recommendations. Understanding underlying reasons for discordance between self-perception and observed weight among different groups is needed. Overall increase in weight, despite current interventions, should be addressed through targeted racially/ethnically appropriate approaches.

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.