Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data.

BACKGROUND: Two propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes. METHODS: We used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE. RESULTS: In the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0-0.02 vs. FS: 0.22-3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07-0.29), relative bias (homogeneity: 4.04-56.20 vs. 20-61.63; heterogeneity: 7.85-57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332-1.308 vs. 0.385-1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0-80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases. CONCLUSIONS: These findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Utilization, quality, and spending for pediatric Medicaid enrollees with primary care in health centers vs non-health centers.

BACKGROUND: Limited research has explored the performance of health centers (HCs) compared to other primary care settings among children in the United States. We evaluated utilization, quality, and expenditures for pediatric Medicaid enrollees receiving care in HCs versus non-HCs. METHODS: This national cross-sectional study utilized 2012 Medicaid Analytic eXtract (MAX) claims to examine children 0-17 years with a primary care visit, stratified by whether majority (> 50%) of primary care visits were at HCs or non-HCs. Outcome measures include utilization (primary care visits, non-primary care outpatient visits, prescription claims, Emergency Department (ED) visits, hospitalizations) and quality (well-child visits, avoidable ED visits, avoidable hospitalizations). For children enrolled in fee-for-service Medicaid, we also measured expenditures. Propensity score-based overlap weighting was used to balance covariates. RESULTS: A total of 2,383,270 Medicaid-enrolled children received the majority of their primary care at HCs, while 18,540,743 did at non-HCs. In adjusted analyses, HC patients had 20% more primary care visits, 15% less non-primary care outpatient visits, and 21% less prescription claims than non-HC patients. ED visits were similar across the two groups, while HC patients had 7% lower chance of hospitalization than non-HC. Quality of care outcomes favored HC patients in main analyses, but results were less robust when excluding managed care beneficiaries. Total expenditures among the fee-for-service subpopulation were lower by $239 (8%) for HC patients. CONCLUSIONS: In this study of nationwide claims data to evaluate healthcare utilization, quality, and spending among Medicaid-enrolled children who receive primary care at HCs versus non-HCs, findings suggest primary care delivery in HCs may be associated with a more cost-effective model of healthcare for children.

Impact of 2009 American Recovery and Reinvestment Act (ARRA) health center investments on disadvantaged neighborhoods after recession.

BACKGROUND: Federally qualified health centers (FQHCs) are integral to the U.S. healthcare safety net and uniquely situated in disadvantaged neighborhoods. The 2009 American Recovery and Reinvestment Act (ARRA) invested $2 billion in FQHC stimulus during the Great Recession; but it remains unknown whether this investment was associated with extended benefits for disadvantaged neighborhoods. METHODS: We used a propensity-score matched longitudinal design (2008-2012) to examine whether the 2009 ARRA FQHC investment was associated with local jobs and establishments recovery in FQHC neighborhoods. Job change data were obtained from the Longitudinal Employer-Household Dynamics (LEHD) survey and calculated as an annual rate per 1,000 population. Establishment change data were obtained from the National Neighborhood Data Archive (NaNDA) and calculated as an annual rate per 10,000 population. Establishment data included 4 establishment types: healthcare services, eating/drinking places, retail establishments, and grocery stores. Fixed effects were used to compare annual rates of jobs and establishments recovery between ARRA-funded FQHC census tracts and a matched control group. RESULTS: Of 50,381 tracts, 2,223 contained ≥ 1 FQHC that received ARRA funding. A higher proportion of FQHC tracts had an extreme poverty designation (11.6% vs. 5.4%), high unemployment rate (45.4% vs. 30.3%), and > 50% minority racial/ethnic composition (48.1% vs. 36.3%). On average, jobs grew at an annual rate of 3.84 jobs per 1,000 population (95% CI: 3.62,4.06). In propensity-score weighted models, jobs in ARRA-funded tracts grew at a higher annual rate of 4.34 per 1,000 (95% CI: 2.56,6.12) relative to those with similar social vulnerability. We observed persistent decline in non-healthcare establishments (-1.35 per 10,000; 95% CI: -1.68,-1.02); but did not observe decline in healthcare establishments. CONCLUSIONS: Direct funding to HCs may be an effective strategy to support healthcare establishments and some jobs recovery in disadvantaged neighborhoods during recession, reinforcing the important multidimensional roles HCs play in these communities. However, HCs may benefit from additional investments that target upstream determinants of health to mitigate uneven recovery and neighborhood decline.

Ensuring Equitable Care in Diabetes Management Among Patients of Health Resources & Services Administration-Funded Health Centers in the United States.

Aim: To explore whether there are racial/ethnic differences in diabetes management and outcomes among adult health center (HC) patients with type 2 diabetes. Methods: We analyzed data from the 2014 Health Center Patient Survey, a national sample of HC patients. We examined indicators of diabetes monitoring (A1C testing, annual foot/eye doctor visits, and cholesterol checks) and care management (specialist referrals, individual treatment plan, and receipt of calls/appointments/home visits). We also examined diabetes-specific outcomes (blood glucose levels, diabetes-related emergency department [ED] visits/hospitalizations, and diabetes self-management confidence) and general outcomes (number of doctor visits, ED visits, and hospitalizations). We used multilevel logistic regression models to examine racial/ethnic disparities by the above indicators. Results: We found racial/ethnic parity in A1C testing, eye doctor visits, and diabetes-specific outcomes. However, Hispanics/Latinos (odds ratio [OR] 0.26), non-Hispanic African Americans (OR 0.25), and Asians (OR 0.11) were less likely to receive a cholesterol check than Whites. Non-Hispanic African Americans (OR 0.43) were less likely to have frequent doctor visits, while Hispanic/Latino patients (OR 0.45) were less likely to receive an individual treatment plan. Conclusion: HCs largely provide equitable diabetes care but have room for improvement in some indicators. Tailored efforts such as culturally competent care and health education for some racial/ethnic groups may be needed to improve diabetes management and outcomes.

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.

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.

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.

Quality of Care for US Adults With Medicaid Insurance and Type 2 Diabetes in Federally Qualified Health Centers Compared With Other Primary Care Settings.

OBJECTIVE: The objective of this study was to evaluate indicators of diabetes quality of care for US nonelderly, adult Medicaid enrollees with type 2 diabetes and compare federally qualified health centers (FQHCs) versus non-FQHCs. RESEARCH DESIGN AND METHODS: We analyzed diabetes process measures and acute health services utilization with 2012 US fee-for-service and managed care Medicaid claims in all 50 states and DC. We compared FQHC (N=121,977) to non-FQHC patients (N=700,401) using propensity scores to balance covariates and generalized estimating equation models. RESULTS: Overall, laboratory-based process measures occurred more frequently (range, 65.7%-76.6%) than measures requiring specialty referrals (retinal examinations, 33.3%; diabetes education, 3.4%). Compared with non-FQHC patients, FQHC patients had about 3 percentage point lower rates of each process measure, except for higher rates of diabetes education [relative risk=1.09, 95% confidence interval (CI): 1.03-1.16]. FQHC patients had fewer overall [incident rate ratio (IRR)=0.87, 95% CI: 0.86-0.88] and diabetes-related hospitalizations (IRR=0.79, 95% CI: 0.77-0.81), but more overall (IRR=1.06, 95% CI: 1.05-1.07) and diabetes-related emergency department visits (IRR=1.10, 95% CI: 1.08-1.13). CONCLUSIONS: This national analysis identified opportunities to improve diabetes management among Medicaid enrollees with type 2 diabetes, especially for retinal examinations or diabetes education. Overall, we found slightly lower rates of most diabetes care process measures for FQHC patients versus non-FQHC patients. Despite having higher rates of emergency department visits, FQHC patients were significantly less likely to be hospitalized than non-FQHC patients. These findings emphasize the need to identify innovative, effective approaches to improve diabetes care for Medicaid enrollees, especially in FQHC settings.

The Relative Contribution of Social Determinants of Health Among Health Resources and Services Administration-Funded Health Centers.

Frameworks for identifying and assessing social determinants of health (SDOH) are effective for developing long-term societal policies to promote health and well-being, but may be less applicable in clinical settings. The authors compared the relative contribution of a specific set of SDOH indicators with several measures of health status among patients served by health centers (HCs). The 2014 Health Center Patient Survey was used to identify a sample of HC patient adults 18 years and older that reported the HC as their usual source of care (n = 5024). The authors examined the relationship between SDOH indicators organized in categories (health behaviors, access and utilization, social factors, economic factors, quality of care, physical environment) with health status measures (fair or poor health, diabetes, hypertension, cardiovascular disease, depression, or anxiety) using logistic regressions and predicted probabilities. Findings indicated that access to care and utilization indicators had the greatest relative contribution to all health status measures, but the relative contribution of other SDOH indicators varied. For example, access indicators had the highest predicted probability in the model with fair or poor health as the dependent variable (72.4%) and the model with hypertension as the dependent variable (47.4%). However, the second highest predicted probability was for social indicators (54.1%) in the former model and physical environment (44.7%) indicators in the latter model. These findings have implications for HCs that serve as the primary point of access to medical care in underserved communities and to mitigate SDOH particularly for patients with diabetes, depression, or anxiety.