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.

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.

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.

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.

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.

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.

Practical considerations for reinterpretation of individual genetic variants

With the growing use of genetic testing in medicine, the question of when genetic findings should be reinterpreted in light of new data has become inescapable. The generation of population and disease-specific data, development of computational tools, and new understandings of the relationship of specific genes to disorders can all trigger changes in variant classification that may have important implications for patients and the clinicians caring for them. This is a particular concern for patients from groups underrepresented in current reference datasets, since they have higher rates of uncertain findings. Here we identify the challenges to implementing a systematic approach to variant reinterpretation and propose solutions. In particular, we address (a) the infrastructure needed to support implementation of systematic variant reinterpretation, (b) the issues around obtaining consent from patients for reinterpretation, (c) the process for triggering reinterpretation, (d) pathways for the flow of reinterpreted data, (e) considerations for how to cover the costs of reinterpretation, and (f) practical issues related to implementation of processes and policies that address these issues, including the importance of a fixed duration during which there is an expectation that variants will be reinterpreted.

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.

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.

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.

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.

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.

Trends in Reported Health Care Affordability for Men and Women With Employer-Sponsored Health Insurance Coverage in the US, 2000 to 2020

This study uses National Health Interview Survey data from 2000 to 2020 to examine reported differences between US men and women aged 19 to 64 years with employer-sponsored insurance in obtaining affordable health care.

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.

Geographical and Temporal Analysis of Tweets Related to COVID-19 and Cardiovascular Disease in the US

The COVID-19 pandemic has resulted in more than 600 million confirmed cases worldwide since December 2021. Cardiovascular disease (CVD) is both a risk factor for COVID-19 mortality and a complication that many COVID-19 patients develop. This study uses Twitter data to identify the spatiotemporal patterns and correlation of related tweets with daily COVID-19 cases and deaths at the national, regional, and state levels. We collected tweets mentioning both COVID-19 and CVD-related words from February to July 2020 (Eastern Time) and geocoded the tweets to the state level using GIScience techniques. We further proposed and validated that the Twitter user registration state can be a feasible proxy of geotags. We applied geographical and temporal analysis to investigate where and when people talked about COVID-19 and CVD. Our results indicated that the trend of COVID-19 and CVD-related tweets is correlated to the trend of COVID-19, especially the daily deaths. These social media messages revealed widespread recognition of CVD's important role in the COVID-19 pandemic, even before the medical community started to develop consensus and theory supports about CVD aspects of COVID-19. The second wave of the pandemic caused another rise in the related tweets but not as much as the first one, as tweet frequency increased from February to April, decreased till June, and bounced back in July. At the regional level, four regions (Northeast, Midwest, North, and West) had the same trend of related tweets compared to the country as a whole. However, only the Northeast region had a high correlation (0.8-0.9) between the tweet count, new cases, and new deaths. For the second wave of confirmed new cases, the major contributing regions, South and West, did not ripple as many related tweets as the first wave. Our understanding is that the early news attracted more attention and discussion all over the U.S. in the first wave, even though some regions were not impacted as much as the Northeast at that time. The study can be expanded to more geographic and temporal scales, and with more physical and socioeconomic variables, with better data acquisition in the future.

State Paid Sick Leave and Paid Sick-Leave Preemption Laws Across 50 U.S. States, 2009-2020

INTRODUCTION

Paid sick leave is associated with lower mortality risks and increased use of health services. Yet, the U.S. lacks a national law, and not all employers offer paid leave, especially to low-wage workers. States have enacted paid sick-leave laws or preemption laws that prohibit local governments from enacting paid sick-leave requirements.

METHODS

In 2019 and 2021, state paid sick-leave laws and preemption laws in effect in 2009-2020 were retrieved from Lexis+, coded, and analyzed for coverage and other features. Data from the U.S. Bureau of Economic Analysis were used to estimate the jobs covered by state paid sick-leave laws in 2009-2019.

RESULTS

In 2009, no state had a paid sick-leave law, and 1 state had preemption. By 2020, a total of 12 states had paid sick-leave laws, with a form of preemption (n=9) or no preemption (n=3), and 18 additional states solely preempted local laws without requiring coverage, creating a regulatory vacuum in those states. Although all state paid sick-leave laws covered private employers and required care for children and spouses, some laws exempted small or public employers or did not cover additional family members. The percentage of U.S. jobs covered by state-required paid sick leave grew from 0% in 2009 to 27.6% in 2019.

CONCLUSIONS

Variation in state paid sick-leave laws, preemption, and lack of employer provision of paid sick leave to low-wage workers creates substantial inequities nationally. The federal government should enact a national paid sick-leave law.

Generational differences in beliefs about COVID-19 vaccines

Vaccine uptake variation across demographic groups remains a public health barrier to overcome the coronavirus pandemic despite substantial evidence demonstrating the effectiveness of COVID-19 vaccines against severe illness and death. Generational cohorts differ in their experience with historical and public health events, which may contribute to variation in beliefs about COVID-19 vaccines. Nationally representative longitudinal data (December 20, 2020 to July 23, 2021) from the Understanding America Study (UAS) COVID-19 tracking survey (N = 7279) and multilevel logistic regression were used to investigate whether generational cohorts differ in COVID-19 vaccine beliefs. Regression models adjusted for wave, socioeconomic and demographic characteristics, political affiliation, and trusted source of information about COVID-19. Birth-year cutoffs define the generational cohorts: Silent (1945 and earlier), Boomer (1946-1964), Gen X (1965-1980), Millennial (1981-1996), and Gen Z (1997-2012). Compared to Boomers, Silents had a lower likelihood of believing that COVID-19 vaccines have many known harmful side effects (OR = 0.52, 95%CI = 0.35-0.74) and that they may lead to illness and death (OR = 0.53, 95%CI = 0.37-0.77). Compared to Boomers, Silents had a higher likelihood of believing that the vaccines provide important benefits to society (OR = 2.27, 95%CI = 1.34-3.86) and that they are useful and effective (OR = 1.97, 95%CI = 1.17-3.30). Results for Gen Z are similar to those reported for Silents. Beliefs about COVID-19 vaccines markedly differ across generations. This is consistent with the idea of generational imprinting-the idea that some beliefs may be resistant to change through adulthood. Policy strategies other than vaccine education may be needed to overcome this pandemic and future public health challenges.

Transforming Primary Care in New York Through Patient-Centered Medical Homes: Findings From Qualitative Research

Background:

The patient-centered medical home (PCMH) model, an important component of healthcare transformation in the United States, is an approach to primary care delivery with the goal of improving population health and the patient care experience while reducing costs. PCMH research most often focuses on system level indicators including healthcare use and cost; descriptions of patient and provider experience with PCMH are relatively sparse and commonly limited in scope. This study, part of a mixed-methods evaluation of a multi-year New York State initiative to refine and expand the PCMH model, describes patient and provider experience with New York State PCMH and its key components.

Methods:

The qualitative component of the evaluation included focus groups with patients of PCMH practices in 5 New York State counties (n = 9 groups and 67 participants) and interviews with providers and practice administrators at New York State PCMH practices (n = 9 interviews with 10 participants). Through these focus groups and interviews, we elicited first-person descriptions of experiences with, as well as perspectives on, key components of the New York State PCMH model, including accessibility, expanded use of electronic health records, integration of behavioral health care, and care coordination.

Results:

There was evident progress and some satisfaction with the PCMH model, particularly regarding integrated behavioral health and, to some extent, expanded use of electronic health records. There was less evident progress with respect to improved access and reasonable wait times, which caused patients to continue to use urgent care or the emergency department as substitutes for primary care.

Conclusions:

It is critical to understand the strengths and limitations of the PCMH model, so as to continue to improve upon and promote it. Strengths of the model were evident to participants in this study; however, challenges were also described. It is important to note that these challenges are difficult to separate from wider healthcare system issues, including inadequate incentives for value-based care, and carry implications for PCMH and other models of healthcare delivery.

The Impact of Expanding Telehealth-Delivered Dietary Interventions on Long-Term Cardiometabolic Health

A healthy diet is an important protective factor to prevent cardiometabolic disease. Traditional face-to-face dietary interventions are often episodic, expensive, and may have limited effectiveness, particularly among older adults and people living in rural areas. Telehealth-delivered dietary interventions have proven to be a low-cost and effective alternative approach to improve dietary behaviors among adults with chronic health conditions. In this study, we developed a validated agent-based model of cardiometabolic health conditions to project the impact of expanding telehealth-delivered dietary interventions among older adults in the state of Georgia, a state with a large rural population. We projected the incidence of major cardiometabolic health conditions (type 2 diabetes, hypertension, and high cholesterol) with the implementation of telehealth-delivered dietary interventions versus no intervention among all older adults and 3 subpopulations (older adults with diabetes, hypertension, and high cholesterol, separately). The results showed that expanding telehealth-delivered dietary interventions could avert 22,774 (95% confidence interval [CI]: 22,091-23,457) cases of type 2 diabetes, 19,732 (19,145-20,329) cases of hypertension, and 18,219 (17,672-18,766) cases of high cholesterol for 5 years among older adults in Georgia. The intervention would have a similar effect in preventing cardiometabolic health conditions among the 3 selected subpopulations. Therefore, expanding telehealth-delivered dietary interventions could substantially reduce the burden of cardiometabolic health conditions in the long term among older adults and those with chronic health conditions.

The role of good governance in the race for global vaccination during the COVID-19 pandemic

Governments have developed and implemented various policies and interventions to fight the COVID-19 pandemic. COVID-19 vaccines are now being produced and distributed globally. This study investigated the role of good governance and government effectiveness indicators in the acquisition and administration of COVID-19 vaccines at the population level. Data on six World Bank good governance indicators for 172 countries for 2019 and machine-learning methods (K-Means Method and Principal Component Analysis) were used to cluster countries based on these indicators and COVID-19 vaccination rates. XGBoost was used to classify countries based on their vaccination status and identify the relative contribution of each governance indicator to the vaccination rollout in each country. Countries with the highest COVID-19 vaccination rates (e.g., Israel, United Arab Emirates, United States) also have higher effective governance indicators. Regulatory Quality is the most important indicator in predicting COVID-19 vaccination status in a country, followed by Voice and Accountability, and Government Effectiveness. Our findings suggest that coordinated global efforts led by the World Health Organization and wealthier nations may be necessary to assist in the supply and distribution of vaccines to those countries that have less effective governance.

"Hey, We Can Do This Together": Findings from an Evaluation of a Multi-sectoral Community Coalition

Multi-sectoral coalitions focused on systemic health inequities are commonly promoted as important mechanisms to facilitate changes with lasting impacts on population health. However, the development and implementation of such initiatives present significant challenges, and evaluation results are commonly inconclusive. In an effort to add to the evidence base, we conducted a mixed-methods evaluation of the Claremont Healthy Village Initiative, a multi-sectoral partnership based in the Bronx, New York City. At an organizational level, there were positive outcomes with respect to expanded services, increased access to resources for programs, improved linkages, better coordination, and empowerment of local leaders-all consistent with a systemic, community building approach to change. Direct impacts on community members were more difficult to assess: perceived access to health and other services improved, while community violence and poor sanitation, which were also priorities for community members, remained important challenges. Findings suggest significant progress, as well as continued need.

Reimbursement for genetic variant reinterpretation: five questions payers should ask

Reaching the goals set by the Health Care Payment and Learning Action Network requires an unyielding and unrelenting focus on encouraging providers to adopt advanced alternative payment models (APMs). Many of these models will continue to be voluntary because they either are in early stages or have not yet proven their effectiveness. The models that have proven their effectiveness should become permanent, comprising the new way that providers are paid in the Medicare program. Either way, getting today's high performers into those programs and keeping them engaged to continue to innovate and set new benchmarks is as important as attracting and improving the performance of poorer performers. That will require a shift in Medicare's policy on pricing and evaluating APMs.

Aligning Health Care and Social Services to Reduce Hospitalizations and Emergency Department Visits: An Evaluation of the Community Care Connections Program

BACKGROUND

Integration of social services in health care delivery is increasingly recognized as a potential strategy for improving health and reducing the use of acute care services. Collaborative models that provide older adults with case management, linkages to social services, and assistance with health care navigation have emerged as promising strategies.

OBJECTIVE

The objective of this study was to evaluate the Community Care Connections (CCC) program, a cross-sector collaboration designed to align social and health care services for older adults.

RESEARCH DESIGN

We compared hospitalizations and emergency department (ED) visits 90 days after enrollment with a propensity score-matched group of non-CCC patients. Subgroup analyses were also conducted for adults with hypertension, diabetes, and high cholesterol.

SUBJECTS

A total of 1004 patients enrolled in CCC between June 1, 2016, and November 15, 2018, and 1004 matched patients from the same metropolitan area.

MEASURES

Mean hospitalizations and ED visits per patient 90 days after CCC enrollment.

RESULTS

Mean hospitalizations were lower among CCC patients 90 days after enrollment than among non-CCC adults [difference=-0.039, 95% confidence interval (CI): -0.077 to -0.001, P=0.044]. They were also lower among CCC patients with hypertension (difference=-0.057, 95% CI: -0.103 to -0.010, P=0.017). However, 90 days after enrollment mean ED visits were higher among CCC patients relative to non-CCC adults (difference=0.238, 95% CI: 0.195-0.281, P<0.001).

CONCLUSIONS

Connecting older adults to social services while being served by the health care system may lead to decreases in hospitalizations. Cross-sector partnerships that address social and economic needs may reduce the use of costly health care services.

How Patient-Centered Medical Homes Integrate Dental Services Into Primary Care: A Scoping Review

Integrated care delivery is at the core of patient-centered medical homes (PCMHs). The extent of integration of dental services in PCMHs for adults is largely unknown. We first identified dental-medical integrating processes from the literature and then conducted a scoping review using PRISMA guidelines to evaluate their implementation among PCMHs. Processes were categorized into workforce, information-sharing, evidence-based care, and measuring and monitoring. After screening, 16 articles describing 21 PCMHs fulfilled the inclusion criteria. Overall, the implementation of integrating processes was limited. Less than half of the PCMHs reported processes for information exchange across medical and dental teams, referral tracking, and standardized protocols for oral health assessments by medical providers. Results highlight significant gaps in current implementation of adult dental integration in PCMHs, despite an increasing policy-level recognition of and support for dental-medical integration in primary care. Understanding and addressing associated barriers is important to achieve comprehensive patient-centered primary care.

Rurality and Origin-Destination Trajectories of Medical School Application and Matriculation in the United States

Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin-destination (O-D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural-urban gap in medical school applications, and reduce physician shortages in rural areas.

Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use

BACKGROUND

Multiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon.

METHODS

We developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration.

RESULTS

Without a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8-4 million infections and 15,000-240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55-94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32-57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48-78% or a strong vaccine (100% effective) with coverage of 33-58% would be required to suppress the epidemic. Delaying vaccination rollout for 1-2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained.

CONCLUSIONS

The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study

There is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723-126,312) COVID-19 infections and 7978 (5692-10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9017 (6446-11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7540-13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality.

Aligning social and health care services: The case of Community Care Connections

The Community Care Connections (CCC) program aims to align social and healthcare services to improve health outcomes in older adults with complex medical and social needs. This study assessed changes in healthcare utilization before and after CCC program participation. Between June 2016 and March 2019, 1214 adults with complete data who provided informed consent participated in the CCC program. CCC client data were linked with data on hospitalizations, emergency department (ED) visits, and observation stays 90 days before and after program start. Data analysis examined changes in health care utilization 90 days after program start, compared to 90 days before. Hospitalizations decreased by 30% (Change = -0.029, 95% Confidence Interval (CI) = -0.053, -0.005), ED visits decreased by 29% (Change = -0.114, 95% CI = -0.163, -0.066), and observation stays decreased by 23% (Change = -0.041, 95% CI = -0.073, -0.009) during the post period. ED visits decreased by 37% (Change = -0.140, 95% CI = -0.209, -0.070) for those with hypertension and by 30% (Change = -0.109, 95% CI = -0.199, -0.020) for those with high cholesterol, while observation stays decreased by 46% (Change = -0.118, 95% CI = -0.185, -0.052) for those with diabetes and by 44% (Change = -0.082, 95% CI = -0.150, -0.014) for those with high cholesterol during the post period. Connecting older adults with social services through the healthcare delivery system may lead to decreases in hospitalizations, ED visits, and observation stays. Implementation of cross-sector partnerships that address non-clinical factors that impact the health of older adults may reduce the use of costly healthcare services.

Views on the need to implement restriction policies to be able to address COVID-19 in the United States

Several restriction policies implemented in many states in the United States have demonstrated their effectiveness in mitigating the spread of the coronavirus disease (COVID-19), but less is known about the differences in views on the restriction policies among different population segments. This study aimed to understand which different population groups of adults in the United States consider several key restriction policies as necessary to combat COVID-19. Survey data from Wave 64 (March 19-24, 2020) of the Pew Research Center's American Trends Panel (n=10,609) and logistic regression were used to evaluate the association between socioeconomic and demographic characteristics, employment status, political party affiliation, news exposure, census region, and opinions about COVID-19 restriction policies. The policies included restricting international travel, imposing business closures, banning large group gatherings, cancelling entertainment events, closing schools, limiting restaurants to carry-out only, and postponing state primary elections. Most survey respondents viewed COVID-19 restriction policies as necessary. Views on each restriction policy varied substantially across some population segments such as age, race, and ethnicity. Regardless of population segments, those who followed news closely or considered themselves Democrat/lean Democrat were more likely to consider all the policies as necessary than those not following the news closely or those who considered themselves Republican/lean Republican. The effectiveness of key COVID-19 restriction policies is likely to vary substantially across population groups given that views on the need to implement these policies vary widely. Tailored health messages may be needed for some population segments given divergent views on COVID-19 restriction policies.

Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model

IMPORTANCE

Knowledge about use of health care services (health care utilization) and expenditures among unauthorized immigrant populations is uncertain because of limitations in ascertaining legal status in population data.

OBJECTIVE

To examine health care utilization and expenditures that are attributable to unauthorized and authorized immigrants vs US-born individuals.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used the data on documentation status from the Los Angeles Family and Neighborhood Survey (LAFANS) to develop a random forest classifier machine learning model. K-fold cross-validation was used to test model performance. The LAFANS is a randomized, multilevel, in-person survey of households residing in Los Angeles County, California, consisting of 2 waves. Wave 1 began in April 2000 and ended in January 2002, and wave 2 began in August 2006 and ended in December 2008. The machine learning model was then applied to a nationally representative database, the 2016-2017 Medical Expenditure Panel Survey (MEPS), to predict health care expenditures and utilization among unauthorized and authorized immigrants and US-born individuals. A generalized linear model analyzed health care expenditures. Logistic regression modeling estimated dichotomous use of emergency department (ED), inpatient, outpatient, and office-based physician visits by immigrant groups with adjusting for confounding factors. Data were analyzed from May 1, 2019, to October 14, 2020.

EXPOSURES

Self-reported immigration status (US-born, authorized, and unauthorized status).

MAIN OUTCOMES AND MEASURES

Annual health care expenditures per capita and use of ED, outpatient, inpatient, and office-based physician care.

RESULTS

Of 47 199 MEPS respondents with nonmissing data, 35 079 (74.3%) were US born, 10 816 (22.9%) were authorized immigrants, and 1304 (2.8%) were unauthorized immigrants (51.7% female; mean age, 47.6 [95% CI, 47.4-47.8] years). Compared with authorized immigrants and US-born individuals, unauthorized immigrants were more likely to be aged 18 to 44 years (80.8%), Latino (96.3%), and Spanish speaking (95.2%) and to have less than 12 years of education (53.7%). Half of unauthorized immigrants (47.1%) were uninsured compared with 15.9% of authorized immigrants and 6.0% of US-born individuals. Mean annual health care expenditures per person were $1629 (95% CI, $1330-$1928) for unauthorized immigrants, $3795 (95% CI, $3555-$4035) for authorized immigrants, and $6088 (95% CI, $5935-$6242) for US-born individuals.

CONCLUSIONS AND RELEVANCE

Contrary to much political discourse in the US, this cross-sectional study found no evidence that unauthorized immigrants are a substantial economic burden on safety net facilities such as EDs. This study illustrates the value of machine learning in the study of unauthorized immigrants using large-scale, secondary databases.

Community Health Needs Predict Population Health Partnerships Among U.S. Children's Hospitals

Cross-sector collaboration is critical to improving population health, but data on partnership activities by children's hospitals are limited, and there is a need to identify service delivery gaps for families. The aim of this study is to use public community benefit reports for all children's hospitals in the United States to assess the extent to which children's hospitals partner with external organizations to address five key health needs: health care access, chronic disease, social needs, mental health, and substance abuse. Strategies that involved partnering with community organizations were most common in addressing social needs and substance abuse. When adjusted for institutional and community characteristics hospitals in a multilevel regression model, hospitals had higher odds of partnering to address chronic illness and social needs. To encourage hospital engagement with complex social and behavioral health needs and promote health equity, support should be provided to help hospitals establish local population health networks.

Assessment of Changes in Rural and Urban Primary Care Workforce in the United States From 2009 to 2017

IMPORTANCE

Access to primary care clinicians, including primary care physicians and nonphysician clinicians (nurse practitioners and physician assistants) is necessary to improving population health. However, rural-urban trends in primary care access in the US are not well studied.

OBJECTIVE

To assess the rural-urban trends in the primary care workforce from 2009 to 2017 across all counties in the US.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study of US counties, county rural-urban status was defined according to the national rural-urban classification scheme for counties used by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Trends in the county-level distribution of primary care clinicians from 2009 to 2017 were examined. Data were analyzed from November 12, 2019, to February 10, 2020.

MAIN OUTCOMES AND MEASURES

Density of primary care clinicians measured as the number of primary care physicians, nurse practitioners, and physician assistants per 3500 population in each county. The average annual percentage change (APC) of the means of the density of primary care clinicians over time was calculated, and generalized estimating equations were used to adjust for county-level sociodemographic variables obtained from the American Community Survey.

RESULTS

The study included data from 3143 US counties (1167 [37%] urban and 1976 [63%] rural). The number of primary care clinicians per 3500 people increased significantly in rural counties (2009 median density: 2.04; interquartile range [IQR], 1.43-2.76; and 2017 median density: 2.29; IQR, 1.57-3.23; P < .001) and urban counties (2009 median density: 2.26; IQR. 1.52-3.23; and 2017 median density: 2.66; IQR, 1.72-4.02; P < .001). The APC of the mean density of primary care physicians in rural counties was 1.70% (95% CI, 0.84%-2.57%), nurse practitioners was 8.37% (95% CI, 7.11%-9.63%), and physician assistants was 5.14% (95% CI, 3.91%-6.37%); the APC of the mean density of primary care physicians in urban counties was 2.40% (95% CI, 1.19%-3.61%), nurse practitioners was 8.64% (95% CI, 7.72%-9.55%), and physician assistants was 6.42% (95% CI, 5.34%-7.50%). Results from the generalized estimating equations model showed that the density of primary care clinicians in urban counties increased faster than in rural counties (β = 0.04; 95% CI, 0.03 to 0.05; P < .001).

CONCLUSIONS AND RELEVANCE

Although the density of primary care clinicians increased in both rural and urban counties during the 2009-2017 period, the increase was more pronounced in urban than in rural counties. Closing rural-urban gaps in access to primary care clinicians may require increasingly intensive efforts targeting rural areas.

Timely Postpartum Visits for Low-Income Women: A Health System and Medicaid Payer Partnership

A health care system and a Medicaid payer partnered to develop an educational intervention and payment redesign program to improve timely postpartum visits for low-income, high-risk mothers in New York City between April 2015 and October 2016. The timely postpartum visit rate was higher for 363 mothers continuously enrolled in the program than for a control group matched by propensity score (67% [243/363] and 56% [407/726], respectively; P < .001). An innovative partnership between a health care system and Medicaid payer improved access to health care services and community resources for high-risk mothers.

Trends and sociodemographic disparities in sugary drink consumption among adults in New York City, 2009-2017

Despite efforts to decrease sugary drink consumption, sugary drinks remain the largest single source of added sugars in diets in the United States. This study aimed to examine trends in sugary drink consumption among adults in New York City (NYC) over the past decade by key sociodemographic factors. We used data from the 2009-2017 NYC Community Health Survey to examine trends in sugary drink consumption overall, and across different age, gender, and racial/ethnic subgroups. We conducted a test of trend to examine the significance of change in mean sugary drink consumption over time. We also conducted multiple zero-inflated negative binomial regression to identify the association between different sociodemographic and neighborhood factors and sugary drink consumption. Sugary drink consumption decreased from 2009 to 2014 from 0.97 to 0.69 servings per day (p < 0.001), but then plateaued from 2014 to 2017 (p = 0.01). Although decreases were observed across all age, gender and racial/ethnic subgroups, the largest decreases over this time period were observed among 18-24 year old (1.75 to 1.22 servings per day, p < 0.001); men (1.12 to 0.86 servings per day, p < 0.001); Blacks (1.45 to 1.14 servings per day, p < 0.001); and Hispanics (1.26 to 0.86 servings per day, p < 0.001). Despite these decreases, actual mean consumption remains highest in these same sociodemographic subgroups. Although overall sugary drink consumption has been declining, the decline has slowed in more recent years. Further, certain age, gender and racial/ethnic groups still consume disproportionately more sugary drinks than others. More research is needed to understand and address the root causes of disparities in sugary drink consumption.

Measuring Efforts of Nonprofit Hospitals to Address Opioid Abuse After the Affordable Care Act

Objectives: To assess the strategies that nonprofit hospitals are adopting to address opioid abuse after requirements for community engagement expanded in the Affordable Care Act. Methods: We constructed a dataset of implementation activities for a 20% random sample of nonprofit hospitals in the United States. Using logistic regression, we assessed the extent to which strategies adopted are new, existing, or primarily partnerships. Using negative binomial regression, we assessed the total number of strategies adopted. We controlled for hospital and community characteristics as well as state policies related to opioid abuse. Results: Most strategies adopted by hospitals were new and clinical in nature and the most common number of strategies adopted was one. Hospitals in the Northeast were more likely to adopt a higher number of strategies and to partner with community-based organizations. Hospitals that partner with community-based organizations were more likely to adopt strategies that engage in harm reduction, targeted risk education, or focus on addressing social determinants of health. Conclusions: Community, institutional, and state policy characteristics predict hospital involvement in addressing opioid abuse. These findings underscore several opportunities to support hospital-led interventions to address opioid abuse.

Decoding Nonadherence to Hypertensive Medication in New York City: A Population Segmentation Approach

Objective: Nearly one-third of adults in New York City (NYC) have high blood pressure and many social, economic, and behavioral factors may influence nonadherence to antihypertensive medication. The objective of this study is to identify profiles of adults who are not taking antihypertensive medications despite being advised to do so. Methods: We used a machine learning–based population segmentation approach to identify population profiles related to nonadherence to antihypertensive medication. We used data from the 2016 NYC Community Health Survey to identify and segment adults into subgroups according to their level of nonadherence to antihypertensive medications. Results: We found that more than 10% of adults in NYC were not taking antihypertensive medications despite being advised to do so by their health care providers. We identified age, neighborhood poverty, diabetes, household income, health insurance coverage, and race/ethnicity as important characteristics that can be used to predict nonadherence behaviors as well as used to segment adults with hypertension into 10 subgroups. Conclusions: Identifying segments of adults who do not adhere to hypertensive medications has practical implications as this knowledge can be used to develop targeted interventions to address this population health management challenge and reduce health disparities.

Connecting healthcare professionals in Central America through management and leadership development: a social network analysis

Background

Leadership and management training has become increasingly important in the education of health care professionals. Previous research has shown the benefits that a network provides to its members, such as access to resources and information, but ideas for creating these networks vary. This study used social network analysis to explore the interactions among Central American Healthcare Initiative (CAHI) Fellowship alumni and learn more about information sharing, mentoring, and project development activities among alumni. The CAHI Fellowship provides leadership and management training for multidisciplinary healthcare professionals to reduce health inequities in the region. Access to a network was previously reported as one of the top benefits of the program.

Results

Information shared from the work of 100 CAHI fellows from six countries, especially within the same country, was analyzed. Mentoring relationships clustered around professions and project types, and networks of joint projects clustered by country. Mentorship, which CAHI management promoted, and joint project networks, in which members voluntarily engaged, had similar inclusiveness ratios.

Conclusion

Social networks are strategic tools for health care leadership development programs to increase their impact by promoting interactions among participants. These programs can amplify intergenerational and intercountry ties by organizing events, provide opportunities for alumni to meet, assign mentors, and support collaborative action groups. Collaborative networks have great value to potentiate health professionals’ leadership and management capabilities in a resource-constrained setting, such as the Global South.

Do State Opioid Policies Influence Nonprofit Hospitals' Decisions to Address Substance Abuse in Their Communities?

The U.S. epidemic of opioid abuse calls for broad collaboration between a wide range of health care institutions and the various levels of government. Through the community benefit programs they provide, nonprofit hospitals are well positioned to be key partners in local efforts. Although substance abuse appears on approximately 90% of the most recent community health needs assessments completed by hospitals, many hospitals are not addressing substance abuse in their programmatic efforts. Given wide state variation in policies to combat opioid abuse, we assess whether state leadership to address the opioid crisis influences hospital decisions to invest in substance abuse programs. Our findings suggest that several key state policies are related to hospital investments in substance abuse initiatives. To capitalize on the community benefit responsibilities of local hospitals, policies that provide specific direction for and engagement with local hospitals may increase cooperation and investments to address substance abuse.