The Impact of Medicaid Accountable Care Organizations on Health Care Utilization, Quality Measures, Health Outcomes and Costs from 2012 to 2023: A Scoping Review.

Most of the evidence regarding the success of ACOs is from the Medicare program. This review evaluates the impacts of ACOs within the Medicaid population. We identified 32 relevant studies published between 2012 and 2023 which analyzed the association of Medicaid ACOs and health care utilization (n = 21), quality measures (n = 18), health outcomes (n = 10), and cost reduction (n = 3). The results of our review regarding the effectiveness of Medicaid ACOs are mixed. Significant improvements included increased primary care visits, reduced admissions, and reduced inpatient stays. Cost reductions were reported in a few studies, and savings were largely dependent on length of attribution and years elapsed after ACO implementation. Adopting the ACO model for the Medicaid population brings some different challenges from those with the Medicare population, which may limit its success, particularly given differences in state Medicaid programs.

Medical financial hardship between young adult cancer survivors and matched individuals without cancer in the United States.

BACKGROUND: Young adult cancer survivors face medical financial hardships that may lead to delaying or forgoing medical care. This study describes the medical financial difficulties young adult cancer survivors in the United States experience in the post-Patient Protection and Affordable Care Act period. METHOD: We identified 1009 cancer survivors aged 18 to 39 years from the National Health Interview Survey (2015-2022) and matched 963 (95%) cancer survivors to 2733 control individuals using nearest-neighbor matching. We used conditional logistic regression to examine the association between cancer history and medical financial hardship and to assess whether this association varied by age, sex, race and ethnicity, and region of residence. RESULTS: Compared with those who did not have a history of cancer, young adult cancer survivors were more likely to report material financial hardship (22.8% vs 15.2%; odds ratio = 1.65, 95% confidence interval = 1.50 to 1.81) and behavior-related financial hardship (34.3% vs 24.4%; odds ratio = 1.62, 95% confidence interval = 1.49 to 1.76) but not psychological financial hardship (52.6% vs 50.9%; odds ratio = 1.07, 95% confidence interval = 0.99 to 1.16). Young adult cancer survivors who were Hispanic or lived in the Midwest and South were more likely to report psychological financial hardship than their counterparts. CONCLUSIONS: We found that young adult cancer survivors were more likely to experience material and behavior-related financial hardship than young adults without a history of cancer. We also identified specific subgroups of young adult cancer survivors that may benefit from targeted policies and interventions to alleviate medical financial hardship.

Enrollment Patterns of Medicare Advantage Beneficiaries by Dental, Vision, and Hearing Benefits.

Importance: Most Medicare beneficiaries now choose to enroll in Medicare Advantage (MA) plans. Racial and ethnic minority group and low-income beneficiaries are increasingly enrolling in MA plans. Objective: To examine whether dental, vision, and hearing supplemental benefits offered in MA plans are associated with the plan choices of traditionally underserved Medicare beneficiaries. Design, Setting, and Participants: This exploratory observational cross-sectional study used data from the 2018 to 2020 Medicare Current Beneficiary Survey linked to MA plan benefits. The nationally representative sample comprised primarily community-dwelling MA beneficiaries enrolled in general enrollment MA plans. Data analysis was performed between April and October 2023. Exposures: Beneficiary self-identified race and ethnicity and combined individual and spouse income and educational attainment. Main Outcomes and Measures: Binary indicators were developed to determine whether beneficiaries were enrolled in a plan offering any dental, comprehensive dental, any vision, eyewear, any hearing, or hearing aid benefit. Mixed-effects logistic regression models were estimated to report average marginal effects adjusted for beneficiary-level demographic and health characteristics, plan attributes, and plan availability. Results: This study included 8139 (weighted N = 31 million) eligible MA beneficiaries, with a mean (SD) age of 77.7 (7.5) years. More than half of beneficiaries (54.9%) were women; 9.8% self-identified as Black, 2.0% as Hispanic, 83.9% as White, and 4.2% as other or multiple races or ethnicities. Plan choices by dental benefits were examined among 7516 beneficiaries who were not enrolled in any dental standalone plan, by vision benefits for 8026 beneficiaries not enrolled in any vision standalone plan, and by hearing benefits for 8131 beneficiaries not enrolled in any hearing standalone plan. Black beneficiaries were more likely to enroll in plans with any dental benefit (9.0 percentage points [95% CI, 3.4-14.4]; P < .001), any comprehensive dental benefit (11.2 percentage points [95% CI, 5.7-16.7]; P < .001), any eye benefit (3.0 percentage points [95% CI, 1.0 to 5.0]; P = .004), or any eyewear benefit (6.0 percentage points [95% CI, 0.6-11.5]; P = .03) compared with White beneficiaries. Lower-income individuals (earning ≤200% of the federal poverty level) were more likely to enroll in a plan with a comprehensive dental benefit (4.4 percentage-point difference [95% CI, 0.1-7.9]; P = .01) compared with higher-income beneficiaries. Beneficiaries without a college degree were more likely to enroll in a plan with a comprehensive dental benefit (4.7 percentage-point difference [95% CI, 1.4-8.0]; P = .005) compared with those with higher educational attainment. Conclusions and Relevance: The results of this study suggest that racial and ethnic minority individuals and those with lower income or educational attainment are more likely to choose MA plans with dental or vision benefits. As the federal government prepares to adjust MA plan star ratings for health equity, implements MA payment cuts, and allows increasing flexibility in supplemental benefit offerings, these findings may inform benefit monitoring for MA.

Cost-Effectiveness of the Second COVID-19 Booster Vaccination in the USA.

OBJECTIVE: To assess the cost effectiveness of the second COVID-19 booster vaccination with different age groups. METHODS: We developed a decision-analytic Susceptible-Exposed-Infected-Recovered (SEIR)-Markov model by five age groups (0-4 years, 5-11 years 12-17 years, 18-49 years, and 50+ years) and calibrated the model by actual mortality in each age group in the USA. We conducted five scenarios to evaluate the cost effectiveness of the second booster strategy and incremental benefits if the strategy would expand to 18-49 years and 12-17 years, from a health care system perspective. The analysis was reported according to the Consolidated Health Economic Evaluation Reporting Standards 2022 statement. RESULTS: Implementing the second booster strategy for those aged ≥ 50 years cost $823 million but reduced direct medical costs by $1166 million, corresponding to a benefit-cost ratio of 1.42. Moreover, the strategy also resulted in a gain of 2596 quality-adjusted life-years (QALYs) during the 180-day evaluation period, indicating it was dominant. Further, vaccinating individuals aged 18-49 years with the second booster would result in an additional gain of $1592 million and 8790 QALYs. Similarly, expanding the vaccination to individuals aged 12-17 years would result in an additional gain of $16 million and 403 QALYs. However, if social interaction between all age groups was severed, vaccination expansion to ages 18-49 and 12-17 years would no longer be dominant but cost effective with an incremental cost-effectiveness ratio (ICER) of $37,572 and $26,705/QALY gained, respectively. CONCLUSION: The second booster strategy was likely to be dominant in reducing the disease burden of the COVID-19 pandemic. Expanding the second booster strategy to ages 18-49 and 12-17 years would remain dominant due to their social contacts with the older age group.

Rural-urban disparities in the availability of hospital-based screening, medications for opioid use disorder, and addiction consult services.

INTRODUCTION: Hospitals are an ideal setting to stage opioid-related interventions with patients who are hospitalized due to overdose or other substance use-related complications. Transitional opioid programs-which initiate care and provide linkages upon discharge, such as screening, initiation of medications for opioid use disorder, and addiction consult services-have become the gold standard, but implementation has been uneven. The purpose of this study was to assess disparities in the availability of hospital-based transitional opioid programs, across rural and urban hospital settings in the United States. METHODS: Using hospital administrative data paired with county-level demographic data, we conducted bivariate and regression analyses to assess rural-urban differences in the availability of transitional opioid services including screening, addiction consult services, and MOUD in U.S general medical centers, controlling for hospital- and community-level factors. Our sample included 2846 general medical hospitals that completed the 2021 American Hospital Association (AHA) Annual Survey of Hospitals. Our primary outcomes were five self-reported measures: whether the hospital provided screening in the ED; provided screening in the inpatient setting; whether the hospital provided addiction consult services in the ED; provided addiction consult services in the inpatient setting; and whether the hospital provided medications for opioid use disorder. RESULTS: Rural hospitals did not have lower odds of screening for OUD or other SUDs than urban hospitals, but both micropolitan rural counties and noncore rural counties had significantly lower odds of having addiction consult services in either the ED (OR: 0.74, 95 % CI: 0.58, 0.95; OR: 0.68, 95 % CI: 0.50, 0.91) or inpatient setting (OR: 0.76, 95 % CI: 0.59, 0.97; OR: 0.68, 95 % CI: 0.50, 0.93), respectively, or of offering MOUD (OR: 0.69, 95 % CI: 0.52, 0.90; OR: 0.52, 95 % CI: 0.37, 0.74). CONCLUSIONS: Our study suggests that evidence-based interventions, such as medications for opioid use disorder and addiction consult services, are less often available in rural hospitals, which may contribute to rural-urban disparities in health outcomes secondary to OUD. A priority for population health improvement should be developing implementation strategies to support rural hospital adoption of transitional opioid programs.

Stakeholder Perspectives on Data-Driven Solutions to Address Cardiovascular Disease and Health Equity in New York City.

Introduction: There is growing recognition of the importance of addressing the social determinants of health in efforts to improve health equity. In dense urban environments such as New York City, disparities in chronic health conditions (e.g., cardiovascular disease) closely mimic inequities in social factors such as income, education, and housing. Although there is a wealth of data on these social factors in New York City, little is known about how to rapidly use available data sources to address health disparities. Methods: Semistructured interviews were conducted with key stakeholders (N=11) from across the public health landscape in New York City (health departments, healthcare delivery systems, and community-based organizations) to assess perspectives on how social determinants of health data can be used to address cardiovascular disease and health equity, what data-driven tools would be useful, and challenges to using these data sources and developing tools. A matrix analysis approach was used to analyze the interview data. Results: Stakeholders were optimistic about using social determinants of health data to address health equity by delivering holistic care, connecting people with additional resources, and increasing investments in under-resourced communities. However, interviewees noted challenges related to the quality and timeliness of social determinants of health data, interoperability between data systems, and lack of consistent metrics related to cardiovascular disease and health equity. Conclusions: Future research on this topic should focus on mitigating the barriers to using social determinants of health data, which includes incorporating social determinants of health data from other sectors. There is also a need to assess how data-driven solutions can be implemented within and across communities and organizations.

Rural-urban disparities in health outcomes, clinical care, health behaviors, and social determinants of health and an action-oriented, dynamic tool for visualizing them.

While rural-urban disparities in health and health outcomes have been demonstrated, because of their impact on (and intervenability to improve) health and health outcomes, we sought to examine cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and social determinants of health (SDOH) between rural and non-rural counties in the pre-pandemic era (2015 to 2019), and to present a Health Equity Dashboard that can be used by policymakers and researchers to facilitate examining such disparities. Therefore, using data obtained from 2015-2022 County Health Rankings datasets, we used analysis of variance to examine differences in 33 county level attributes between rural and non-rural counties, calculated the change in values for each measure between 2015 and 2019, determined whether rural-urban disparities had widened, and used those data to create a Health Equity Dashboard that displays county-level individual measures or compilations of them. We followed STROBE guidelines in writing the manuscript. We found that rural counties overwhelmingly had worse measures of SDOH at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures. When rural-urban gaps narrowed, it tended to be in measures wherein rural counties were outperforming urban ones in the earlier period. In conclusion, our findings highlight the need for policymakers to prioritize rural settings for interventions designed to improve health outcomes, likely through improving health behaviors, clinical care, social and environmental factors, and physical environment attributes. Visualization tools can help guide policymakers and researchers with grounded information, communicate necessary data to engage relevant stakeholders, and track SDOH changes and health outcomes over time.

Spatiotemporal Optimization for the Placement of Automated External Defibrillators Using Mobile Phone Data.

With over 350,000 cases occurring each year, out-of-hospital cardiac arrest (OHCA) remains a severe public health concern in the United States. The correct and timely use of automated external defibrillators (AEDs) has been widely acknowledged as an effective measure to improve the survival rate of OHCA. While general guidelines have been provided by the American Heart Association (AHA) for AED deployment, the lack of detailed instructions hindered the adoption of such guidelines under dynamic scenarios with various time and space distributions. Formulating the AED deployment as a location optimization problem under budget and resource constraints, we proposed an overlayed spatio-temporal optimization (OSTO) method, which accounted for the spatiotemporal heterogeneity of potential OHCAs. To highlight the effectiveness of the proposed model, we applied the proposed method to Washington DC using user-generated anonymized mobile device location data. The results demonstrated that optimization-based planning provided an improved AED coverage level. We further evaluated the effectiveness of adding additional AEDs by analyzing the cost-coverage increment curve. In general, our framework provides a systematic approach for municipalities to integrate inclusive planning and budget-limited efficiency into their final decision-making. Given the high practicality and adaptability of the framework, the OSTO is highly amenable to different healthcare facilities' deployment tasks with flexible demand and resource restraints.

An observational, sequential analysis of the relationship between local economic distress and inequities in health outcomes, clinical care, health behaviors, and social determinants of health.

BACKGROUND: Socioeconomic status has long been associated with population health and health outcomes. While ameliorating social determinants of health may improve health, identifying and targeting areas where feasible interventions are most needed would help improve health equity. We sought to identify inequities in health and social determinants of health (SDOH) associated with local economic distress at the county-level. METHODS: For 3,131 counties in the 50 US states and Washington, DC (wherein approximately 325,711,203 people lived in 2019), we conducted a retrospective analysis of county-level data collected from County Health Rankings in two periods (centering around 2015 and 2019). We used ANOVA to compare thirty-three measures across five health and SDOH domains (Health Outcomes, Clinical Care, Health Behaviors, Physical Environment, and Social and Economic Factors) that were available in both periods, changes in measures between periods, and ratios of measures for the least to most prosperous counties across county-level prosperity quintiles, based on the Economic Innovation Group's 2015-2019 Distressed Community Index Scores. RESULTS: With seven exceptions, in both periods, we found a worsening of values with each progression from more to less prosperous counties, with least prosperous counties having the worst values (ANOVA p < 0.001 for all measures). Between 2015 and 2019, all except six measures progressively worsened when comparing higher to lower prosperity quintiles, and gaps between the least and most prosperous counties generally widened. CONCLUSIONS: In the late 2010s, the least prosperous US counties overwhelmingly had worse values in measures of Health Outcomes, Clinical Care, Health Behaviors, the Physical Environment, and Social and Economic Factors than more prosperous counties. Between 2015 and 2019, for most measures, inequities between the least and most prosperous counties widened. Our findings suggest that local economic prosperity may serve as a proxy for health and SDOH status of the community. Policymakers and leaders in public and private sectors might use long-term, targeted economic stimuli in low prosperity counties to generate local, community health benefits for vulnerable populations. Doing so could sustainably improve health; not doing so will continue to generate poor health outcomes and ever-widening economic disparities.

A Call to Increase Health Data Availability in US Territories-Not Too Small to Count.

This Viewpoint discusses gaps in collection systems for health care data in US territories.

Substance Use Disorder Program Availability in Safety-Net and Non-Safety-Net Hospitals in the US.

Importance: Safety-net hospitals (SNHs) are ideal sites to deliver addiction treatment to patients with substance use disorders (SUDs), but the availability of these services within SNHs nationwide remains unknown. Objective: To examine differences in the delivery of different SUD programs in SNHs vs non-SNHs across the US and to determine whether these differences are increased in certain types of SNHs depending on ownership. Design, Setting, and Participants: This cross-sectional analysis used data from the 2021 American Hospital Association Annual Survey of Hospitals to examine the associations of safety-net status and ownership with the availability of SUD services at acute care hospitals in the US. Data analysis was performed from January to March 2022. Main Outcomes and Measures: This study used 2 survey questions from the American Hospital Association survey to determine the delivery of 5 hospital-based SUD services: screening, consultation, inpatient treatment services, outpatient treatment services, and medications for opioid use disorder (MOUD). Results: A total of 2846 hospitals were included: 409 were SNHs and 2437 were non-SNHs. The lowest proportion of hospitals reported offering inpatient treatment services (791 hospitals [27%]), followed by MOUD (1055 hospitals [37%]), and outpatient treatment services (1087 hospitals [38%]). The majority of hospitals reported offering consultation (1704 hospitals [60%]) and screening (2240 hospitals [79%]). In multivariable models, SNHs were significantly less likely to offer SUD services across all 5 categories of services (screening odds ratio [OR], 0.62 [95% CI, 0.48-0.76]; consultation OR, 0.62 [95% CI, 0.47-0.83]; inpatient services OR, 0.73 [95% CI, 0.55-0.97]; outpatient services OR, 0.76 [95% CI, 0.59-0.99]; MOUD OR, 0.6 [95% CI, 0.46-0.78]). With the exception of MOUD, public or for-profit SNHs did not differ significantly from their non-SNH counterparts. However, nonprofit SNHs were significantly less likely to offer all 5 SUD services compared with their non-SNH counterparts (screening OR, 0.52 [95% CI, 0.41-0.66]; consultation OR, 0.56 [95% CI, 0.44-0.73]; inpatient services OR, 0.45 [95% CI, 0.33-0.61]; outpatient services OR, 0.58 [95% CI, 0.44-0.76]; MOUD OR, 0.61 [95% CI, 0.46-0.79]). Conclusions and Relevance: In this cross-sectional study of SNHs and non-SNHs, SNHs had significantly lower odds of offering the full range of SUD services. These findings add to a growing body of research suggesting that SNHs may face additional barriers to offering SUD programs. Further research is needed to understand these barriers and to identify strategies that support the adoption of evidence-based SUD programs in SNH settings.

Trends in the Prioritization and Implementation of Substance Use Programs by Nonprofit Hospitals: 2015-2021.

OBJECTIVES: Hospitalizations are an important opportunity to address substance use through inpatient services, outpatient care, and community partnerships, yet the extent to which nonprofit hospitals prioritize such services across time remains unknown. The objective of this study is to examine trends in nonprofit hospitals' prioritization and implementation of substance use disorder (SUD) programs. METHODS: We assessed trends in hospital prioritization of substance use as a top five community need and hospital implementation of SUD programing at nonprofit hospitals between 2015 and 2021 using two waves (wave 1: 2015-2018; wave 2: 2019-2021) by examining hospital community benefit reports. We utilized t or χ 2 tests to understand whether there were significant differences in the prioritization and implementation of SUD programs across waves. We used multilevel logistic regression to evaluate the relation between prioritization and implementation of SUD programs, hospital and community characteristics, and wave. RESULTS: Hospitals were less likely to have prioritized SUD but more likely to have implemented SUD programs in the most recent 3 years compared, even after adjusting for the local overdose rate and hospital- and community-level variables. Although most hospitals consistently prioritized and implemented SUD programs during the 2015-2021 period, a 11% removed and 15% never adopted SUD programs at all, despite an overall increase in overdose rates. CONCLUSIONS: Our study identified gaps in hospital SUD infrastructure during a time of elevated need. Failing to address this gap reflects missed opportunities to engage vulnerable populations, provide linkages to treatment, and prevent complications of substance use.

The Health and Economic Impact of Expanding Home Blood Pressure Monitoring.

INTRODUCTION: Home blood pressure monitoring is more convenient and effective than clinic-based monitoring in diagnosing and managing hypertension. Despite its effectiveness, there is limited evidence of the economic impact of home blood pressure monitoring. This study aims to fill this research gap by assessing the health and economic impact of adopting home blood pressure monitoring among adults with hypertension in the U.S. METHODS: A previously developed microsimulation model of cardiovascular disease was used to estimate the long-term impact of adopting home blood pressure monitoring versus usual care on myocardial infarction, stroke, and healthcare costs. Data from the 2019 Behavioral Risk Factor Surveillance System and the published literature were used to estimate model parameters. The averted cases of myocardial infarction and stroke and healthcare cost savings were estimated among the U.S. adult population with hypertension and in subpopulations defined by sex, race, ethnicity, and rural/urban area. The simulation analyses were conducted between February and August 2022. RESULTS: Compared with usual care, adopting home blood pressure monitoring was estimated to reduce myocardial infarction cases by 4.9% and stroke cases by 3.8% as well as saving an average of $7,794 in healthcare costs per person over 20 years. Non-Hispanic Blacks, women, and rural residents had more averted cardiovascular events and greater cost savings related to adopting home blood pressure monitoring compared with non-Hispanic Whites, men, and urban residents. CONCLUSIONS: Home blood pressure monitoring could substantially reduce the burden of cardiovascular disease and save healthcare costs in the long term, and the benefits could be more pronounced in racial and ethnic minority groups and those living in rural areas. These findings have important implications in expanding home blood pressure monitoring for improving population health and reducing health disparities.

Use of Telehealth to Address Depression and Anxiety in Low-income US Populations: A Narrative Review.

Symptoms of anxiety and depressive disorders have been increasing substantially among adults in the United States (US) during the COVID-19 pandemic, particularly for low-income populations. Under-resourced communities have difficulties accessing optimal treatment for anxiety and depression due to costs as well as the result of limited access to health care providers. Telehealth has been growing as a digital strategy to treat anxiety and depression across the country but it is unclear how best to implement telehealth interventions to serve low-income populations. A narrative review was conducted to evaluate the role of telehealth in addressing anxiety and depression in low-income groups in the US. A PubMed database search identified a total of 14 studies published from 2012 to 2022 on telehealth interventions that focused on strengthening access to therapy, coordination of care, and medication and treatment adherence. Our findings suggest that telehealth increases patient engagement through virtual therapy and the use of primarily telephone communication to treat and monitor anxiety and depression. Telehealth seems to be a promising approach to improving anxiety and depressive symptoms but socioeconomic and technological barriers to accessing mental health services are substantial for low-income US populations.

Utilizing Publicly Available Community Data to Address Social Determinants of Health: A Compendium of Data Sources.

To compile a compendium of data sources representing different areas of social determinants of health (SDOH) in New York City. We conducted a PubMed search of the peer-reviewed and gray literature using the terms "social determinants of health" and "New York City," with the Boolean operator "AND." We then conducted a search of the "gray literature," defined as sources outside of standard bibliographic databases, using similar terms. We extracted publicly available data sources containing NYC-based data. In defining SDOH, we used the framework outlined by the CDC's Healthy People 2030, which uses a place-based framework to categorize 5 domains of SDOH: (1) healthcare access and quality; (2) education access and quality; (3) social and community context; (4) economic stability; and (5) neighborhood and built environment. We identified 29 datasets from the PubMed search, and 34 datasets from the gray literature, resulting in 63 datasets related to SDOH in NYC. Of these, 20 were available at the zip code level, 18 at the census tract-level, 12 at the community-district level, and 13 at the census block or specific address level. Community-level SDOH data are readily attainable from many public sources and can be linked with health data on local geographic-levels to assess the effect of social and community factors on individual health outcomes.

Social Determinants of Cardiovascular Health: A Longitudinal Analysis of Cardiovascular Disease Mortality in US Counties From 2009 to 2018.

Background Disparities in cardiovascular disease (CVD) outcomes persist across the United States. Social determinants of health play an important role in driving these disparities. The current study aims to identify the most important social determinants associated with CVD mortality over time in US counties. Methods and Results The authors used the Agency for Healthcare Research and Quality's database on social determinants of health and linked it with CVD mortality data at the county level from 2009 to 2018. The age-standardized CVD mortality rate was measured as the number of deaths per 100 000 people. Penalized generalized estimating equations were used to select social determinants associated with county-level CVD mortality. The analytic sample included 3142 counties. The penalized generalized estimating equation identified 17 key social determinants of health including rural-urban status, county's racial composition, income, food, and housing status. Over the 10-year period, CVD mortality declined at an annual rate of 1.08 (95% CI, 0.74-1.42) deaths per 100 000 people. Rural counties and counties with a higher percentage of Black residents had a consistently higher CVD mortality rate than urban counties and counties with a lower percentage of Black residents. The rural-urban CVD mortality gap did not change significantly over the past decade, whereas the association between the percentage of Black residents and CVD mortality showed a significant diminishing trend over time. Conclusions County-level CVD mortality declined from 2009 through 2018. However, rural counties and counties with a higher percentage of Black residents continued to experience higher CVD mortality. Median income, food, and housing status consistently predicted higher CVD mortality.

Association of Medicaid expansion and 1115 waivers for substance use disorders with hospital provision of opioid use disorder services: a cross sectional study.

INTRODUCTION: Opioid-related hospitalizations have risen dramatically, placing hospitals at the frontlines of the opioid epidemic. Medicaid expansion and 1115 waivers for substance use disorders (SUDs) are two key policies aimed at expanding access to care, including opioid use disorder (OUD) services. Yet, little is known about the relationship between these policies and the availability of hospital based OUD programs. The aim of this study is to determine whether state Medicaid expansion and adoption of 1115 waivers for SUDs are associated with hospital provision of OUD programs. METHODS: We conducted a cross-sectional study of a random sample of hospitals (n = 457) from the American Hospital Association's 2015 American Hospital Directory, compiled with the most recent publicly available community health needs assessment (2015-2018). RESULTS: Controlling for hospital characteristics, overdose burden, and socio-demographic characteristics, both Medicaid policies were associated with hospital adoption of several OUD programs. Hospitals in Medicaid expansion states had significantly higher odds of implementing any program related to SUDs (OR: 1.740; 95% CI: 1.032-2.934) as well as some specific activities such as programs for OUD treatment (OR: 1.955; 95% CI: 1.245-3.070) and efforts to address social determinants of health (OR: 6.787; 95% CI: 1.308-35.20). State 1115 waivers for SUDs were not significantly associated with any hospital-based SUD activities. CONCLUSIONS: Medicaid expansion was associated with several hospital programs for addressing OUD. The differential availability of hospital-based OUD programs may indicate an added layer of disadvantage for low-income patients with SUD living in non-expansion states.

Building and Sustaining Community Partnerships: An Organizational Network Analysis in a Low-resource Neighborhood.

BACKGROUND: Launched in 2012, the Claremont Healthy Village Initiative (CHVI) is a partnership focused on fostering community collaboration, addressing the social determinants of health, and reducing health disparities. Partners include local community centers, schools, after-school programs, health care providers, a health insurer, city agencies, tenant associations, resident leaders, elected officials, and other stakeholders. OBJECTIVES: To understand the development and value of collaboration within the CHVI from the perspective of multiple partners. METHODS: Using a community-based participatory research approach, we worked collaboratively with the CHVI leaders, we used paper-and web-based surveys grounded in social network theory were administered in 2017 and 2018. Questions focused on relationships between organizations that are part of the coalition. Our analysis included responses from organizations that participated in the survey at both points in time. Network measures such as density, degree centrality, and node characteristics were used to understand information sharing, referral, and collaboration among the participating organizations. Additional data included stake-holder interviews. RESULTS: Coalition partners increased connectedness with one another over the course of the study, with significantly greater density of relationship and bi-directional partnerships in the follow-up survey. Of the three types of interactions, referrals showed a trend for highest density change. Trust levels were highest among organizations with a local physical presence. CONCLUSIONS: Social network analysis provided visual and quantitative information that helped reinforce relationships and identify opportunities to improve connectedness and collaboration among diverse community partners, helping to support the coalition's goals and objectives.