Opportunities and Challenges to Advance Health Equity Using Digital Health Tools in Underserved Communities in Southeast US: A Mixed Methods Study.

INTRODUCTION: Over the last 30 years, the adoption of health information technology and digital health tools (DHTs) into the US health system has been instrumental to improving access to care, especially for people living in rural, underserved, and underrepresented communities. Despite widespread adoption of DHTs by primary care clinicians, documented challenges have contributed to inequitable use and benefit. The COVID-19 pandemic necessitated rapid adoption of DHTs, accelerated by state and federal policy changes, in order to meet patient needs and ensure access to care. METHODS: The Digital Health Tools Study employed a mixed methods approach to assess adoption and use of DHTs by primary care clinicians in southeastern states and identify individual- and practice-level barriers and facilitators to DHT implementation. A survey was conducted using a multi-modal recruitment strategy: newsletters, meeting/conference presentations, social media, and emails/calls. Focus groups were conducted to assess priorities, barriers, and facilitators and were recorded/transcribed verbatim. Descriptive statistics were calculated for survey results, produced for the whole sample, and stratified by state. Thematic analysis was conducted of focus group transcripts. RESULTS: There were 1215 survey respondents. About 55 participants who had missing demographic information were excluded from the analysis. About 99% of clinicians used DHTs in the last 5 years, modalities included: telehealth (66%), electronic health records (EHRs; 66%), patient portals (49%), health information exchange (HIE; 41%), prescription drug monitoring programs (39%), remote/home monitoring (27%), and wearable devices (22%). Time (53%) and cost (51%) were identified as barriers. About 61% and 75% of clinicians reported being "satisfied" to "very satisfied" with telemedicine and EHRs, respectively. Seven focus groups with 25 clinicians were conducted and indicated COVID-19 and the use of supplemental tools/apps to connect patients to resources as major motivators for adopting DHTs. Challenges included incomplete and difficult-to-utilize HIE interfaces for providers and internet/broadband access and poor connectivity for patients. CONCLUSIONS: This study describes the impact adopting DHTs by primary care clinicians has on expanded access to healthcare and reducing health disparities in regions with longstanding health and social inequities. The findings identify opportunities to leverage DHTs to advance health equity and highlight opportunities for policy improvement.

Population-Level Disparities in Exposure to COVID-19 Mitigation Policies, April 2020-April 2021.

CONTEXT: Studies have found that COVID-19 stay-at-home orders (SHOs) and face mask policies (FMPs) were associated with reduced COVID-19 transmission and deaths. But it is unknown whether exposure to these policies varied by sociodemographic characteristics across the US population. OBJECTIVE: The goal of this study was to quantify and characterize the sociodemographic characteristics and geographic distribution of populations exposed to evidence-based COVID-19 mitigation policies. DESIGN: We obtained statewide SHOs and FMPs for all US counties from April 10, 2020, to April 10, 2021, calculated median policy lengths, and categorized counties into 4 groups based on length of policy exposure: low SHO-low FMP, high SHO-low FMP, low SHO-high FMP, and high SHO-high FMP. We described exposure groups by COVID-19 cumulative case/death and vaccination rates and county sociodemographic characteristics. SETTING: In total, 3142 counties from all 50 states and Washington, District of Columbia, were included in the analysis. MAIN OUTCOME MEASURES: County-level sociodemographic factors and county cumulative rates for COVID-19 cases, deaths, and vaccinations. RESULTS: The largest percentage of the US population lived in counties with high exposure to SHOs and FMPs. However, populations living in high SHO-high FMP counties had the lowest percent non-Hispanic Black (NHB) and highest percent non-Hispanic White (NHW) populations. Populations living in high SHO-low FMP counties had the highest percent NHB and Hispanic populations and the lowest percent NHW population. CONCLUSION: This study identified county-level racial, ethnic, and sociodemographic disparities in exposure to evidence-based statewide COVID-19 mitigation policies. POLICY IMPLICATIONS: Exposure to evidence-based policies is an important consideration for studies evaluating the root causes of health inequities.

Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Association Between Racial Segregation and COVID-19 Vaccination Rates.

OBJECTIVE: To examine the association between county-level Black-White residential segregation and COVID-19 vaccination rates. DESIGN: Observational cross-sectional study using multivariable generalized linear models with state fixed effects to estimate the average marginal effects of segregation on vaccination rates. SETTING: National analysis of county-level vaccination rates. MAIN OUTCOME MEASURE: County-level vaccination rates across the United States. RESULTS: We found an overall positive association between county-level segregation and the proportion population fully vaccinated, with a 6.8, 11.3, and 12.8 percentage point increase in the proportion fully vaccinated by May 3, September 27, and December 6, 2021, respectively. Effects were muted after adjustment for sociodemographic variables. Furthermore, in analyses including an interaction term between the county proportion of Black residents and the county dissimilarity index, the association between segregation and vaccination is positive in counties with a lower proportion of Black residents (ie, 5%) but negative in counties with the highest proportions of Black residents (ie, 70%). CONCLUSIONS: Findings highlight the importance of methodological decisions when modeling disparities in COVID-19 vaccinations. Researchers should consider mediating and moderating factors and examine interaction effects and stratified analyses taking racial group distributions into account. Results can inform policies around the prioritization of vaccine distribution and outreach.

Racial and ethnic disparities in H1N1 vaccine uptake in Medicaid: Lessons learned for a COVID-19 vaccine rollout.

BACKGROUND: Racial and ethnic minorities in the US have been disproportionately affected by the COVID-19 pandemic and are at risk for disparities in COVID-19 vaccinations. The H1N1 flu vaccine experience provides lessons learned to address and prevent racial and ethnic disparities in COVID-19 vaccinations. We aim to identify racial/ethnic and geographic disparities in H1N1 vaccinations among Medicaid enrollees to inform equitable COVID-19 vaccination policies and strategies. METHODS: The study population included people under 65 who were continuously enrolled in Medicaid in 2009 and 2010 from 28 states and the District of Columbia. H1N1 vaccinations were identified from Medicaid outpatient claims. Vaccination rates were calculated for the overall sample and subpopulations by race/ethnicity and state. RESULTS: 3,708,894 (12.3%) Medicaid enrollees in the sample were vaccinated for H1N1 in 2009-2010. Race-specific vaccination rates ranged from 8.1% in American Indian/Alaska Native (AI/AN) to 19.8% in Asian/Pacific Islander Medicaid enrollees. NHB enrollees had lower vaccination rates than non-Hispanic White (NHW) enrollees in all states, with the exceptions of Maryland, Missouri, Ohio, and Washington. The largest disparity between NHB and NHW was in Pennsylvania (1.0% vs. 7.0%), while the largest absolute difference between NHB and NHW enrollees was in Georgia (17.4% vs. 30.7%). CONCLUSIONS: Our study found huge variation in H1N1 vaccinations across states and racial/ethnic disparities in H1N1 vaccinations within states. In most states, NHB and AI/AN Medicaid enrollees had lower vaccination rates than Whites. Hispanic and Asian/Pacific Islander Medicaid enrollees in most states had higher vaccination rates than Whites.

Variation in Reporting of the Race and Ethnicity of COVID-19 Cases and Deaths Across US States: April 12, 2020, and November 9, 2020.

Despite growing evidence that COVID-19 is disproportionately affecting communities of color, state-reported racial/ethnic data are insufficient to measure the true impact.We found that between April 12, 2020, and November 9, 2020, the number of US states reporting COVID-19 confirmed cases by race and ethnicity increased from 25 to 50 and 15 to 46, respectively. However, the percentage of confirmed cases reported with missing race remained high at both time points (29% on April 12; 23% on November 9). Our analysis demonstrates improvements in reporting race/ethnicity related to COVID-19 cases and deaths and highlights significant problems with the quality and contextualization of the data being reported.We discuss challenges for improving race/ethnicity data collection and reporting, along with opportunities to advance health equity through more robust data collection and contextualization. To mitigate the impact of COVID-19 on racial/ethnic minorities, accurate and high-quality demographic data are needed and should be analyzed in the context of the social and political determinants of health.

Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties.

CONTEXT: There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. OBJECTIVE: Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. DESIGN AND SETTING: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. MAIN OUTCOME MEASURES: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. RESULTS: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp ß = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp ß = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). CONCLUSIONS: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.

Percentage of Black Population and Primary Care Shortage Areas Associated with Higher COVID-19 Case and Death Rates in Georgia Counties.

OBJECTIVES: We hypothesized that the proportion of Black individuals in a county would be associated with higher rates of coronavirus disease 2019 (COVID-19) cases and deaths, even after accounting for other high-risk socioecologic factors such as poverty, population density, and household crowding, and uninsured rates. We also expected that counties designated as primary care health professional shortage areas (PCHPSAs) would be associated with higher COVID-19 death rates, and the lack of primary care access would exacerbate racial disparities in death rates. We undertook this study to test these hypotheses and discern the independent effects of racial composition, socioecologic characteristics, and healthcare system factors on COVID-19 cases and deaths in Georgia counties. METHODS: We used county-level COVID-19 cases and deaths on April 23, 2020 from the Johns Hopkins Coronavirus Resource Center and estimates of 2019 county-level populations from the US Census Bureau to calculate the cumulative event rates for the state of Georgia. We used multiple regression models to examine crude and adjusted associations of socioecologic and health system variables with county-level COVID-19 case and mortality rates. RESULTS: After adjustment, a 1% increase in the proportion of Black people in the county resulted in a 2.3% increase in the county COVID-19 confirmed case rate and a 3.0% increase in the death rate (relative risk 1.03, 95% confidence interval 1.01-1.05, P < 0.001). Primary care shortage areas had a 74% higher death rate (relative risk 1.74, 95% confidence interval 1.00-3.00, P = 0.049). CONCLUSIONS: These results highlight the impact of racial disparities on the spatial patterns of COVID-19 disease burden in Georgia, which can guide interventions to mitigate racial disparities. The results also support the need for robust primary care infrastructure throughout the state.

Morehouse Choice Accountable Care Organization and Education System (MCACO-ES): Integrated Model Delivering Equitable Quality Care.

Accountable Care Organizations (ACOs) seek sustainable innovation through the testing of new care delivery methods that promote shared goals among value-based health care collaborators. The Morehouse Choice Accountable Care Organization and Education System (MCACO-ES), or (M-ACO) is a physician led integrated delivery model participating in the Medicare Shared Savings Program (MSSP) offered through the Centers for Medicare and Medicaid Services (CMS) Innovation Center. The MSSP establishes incentivized, performance-based payment models for qualifying health care organizations serving traditional Medicare beneficiaries that promote collaborative efficiency models designed to mitigate fragmented and insufficient access to health care, reduce unnecessary cost, and improve clinical outcomes. The M-ACO integration model is administered through participant organizations that include a multi-site community based academic practice, independent physician practices, and federally qualified health center systems (FQHCs). This manuscript aims to present a descriptive and exploratory assessment of health care programs and related innovation methods that validate M-ACO as a reliable simulator to implement, evaluate, and refine M-ACO's integration model to render value-based performance outcomes over time. A part of the research approach also includes early outcomes and lessons learned advancing the framework for ongoing testing of M-ACO's integration model across independently owned, rural, and urban health care locations that predominantly serve low-income, traditional Medicare beneficiaries, (including those who also qualify for Medicaid benefits (also referred to as "dual eligibles"). M-ACO seeks to determine how integration potentially impacts targeted performance results. As a simulator to test value-based innovation and related clinical and business practices, M-ACO uses enterprise-level data and advanced analytics to measure certain areas, including: 1) health program insight and effectiveness; 2) optimal implementation process and workflows that align primary care with specialists to expand access to care; 3) chronic care management/coordination deployment as an effective extender service to physicians and patients risk stratified based on defined clinical and social determinant criteria; 4) adoption of technology tools for patient outreach and engagement, including a mobile application for remote biometric monitoring and telemedicine; and 5) use of structured communication platforms that enable practitioner engagement and ongoing training regarding the shift from volume to value-based care delivery.

Health Policy Engagement Strategy for the Health Information Technology Policy Project of the Transdisciplinary Collaborative Center for Health Disparities Research.

Rulemaking is one of the most important ways the federal government makes public policy. It frequently has significant impact on individuals, communities, and organizations. Yet, few of those directly affected are familiar with the rulemaking process, and even fewer understand how it works. This article describes a case study of the Transdisciplinary Collaborative Center for Health Disparities Research Health Information Technology (TCC HIT) Policy Project's approach to health-policy engagement using: 1) social media; and 2) a webinar to educate stakeholders on the rulemaking process and increase their level of meaningful engagement with the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) proposed rule public comment submission. The webinar "Paying for Quality: What Is the Impact on Health Equity" was promoted through Twitter and held in June 2016. In total, we posted 19 tweets using two distinct hashtags (#MACRA4Equity, #MACRA2Equity) to raise awareness of the upcoming MACRA proposed rule and its possible effects on health equity. Overall, 252 individuals registered for the webinar, and more than half participated (n=133). Most (67%) registrants reported that health policy was not the primary focus of their current position. Based on information provided in the webinar, 95% agreed that their understanding of the topic improved. By the end of the webinar, 44% of participants indicated that they planned to submit public comments for MACRA, a 12% increase compared with those who planned to submit at the time of registration. The TCC health-policy engagement strategy demonstrates the feasibility of engaging a diverse audience around health policy issues, particularly those who are not typically engaged in policy work.

Racial and ethnic estimates of Alzheimer's disease and related dementias in the United States (2015-2060) in adults aged ≥65 years.

INTRODUCTION: Alzheimer's disease and related dementias (ADRD) cause a high burden of morbidity and mortality in the United States. Age, race, and ethnicity are important risk factors for ADRD. METHODS: We estimated the future US burden of ADRD by age, sex, and race and ethnicity by applying subgroup-specific prevalence among Medicare Fee-for-Service beneficiaries aged ≥65 years in 2014 to subgroup-specific population estimates for 2014 and population projection data from the United States Census Bureau for 2015 to 2060. RESULTS: The burden of ADRD in 2014 was an estimated 5.0 million adults aged ≥65 years or 1.6% of the population, and there are significant disparities in ADRD prevalence among population subgroups defined by race and ethnicity. ADRD burden will double to 3.3% by 2060 when 13.9 million Americans are projected to have the disease. DISCUSSION: These estimates can be used to guide planning and interventions related to caring for the ADRD population and supporting caregivers.

Developing a framework for integrating health equity into the learning health system.

While there have been gains in the overall quality of health care, racial and ethnic disparities in health outcomes continue to persist in the United States. The Learning Health System (LHS) has the potential to significantly improve health care quality using patient-centered design, data analytics, and continuous improvement. To ensure that health disparities are also being addressed, targeted approaches must be used. This document sets forth a practical framework to incorporate health equity into a developing LHS. Using a case study approach, the framework is applied to 2 projects focused on the reduction of health disparities to highlight its application.

Assessing Telemedicine Utilization by Using Medicaid Claims Data.

OBJECTIVE: This study characterized telemedicine utilization among Medicaid enrollees by patients' demographic characteristics, geographic location, enrollment type, eligibility category, and clinical conditions. METHODS: This study used 2008-2009 Medicaid claims data from 28 states and the District of Columbia to characterize telemedicine claims (indicated by GT for professional fee claims or Q3014 for facility fees) on the basis of patients' demographic characteristics, geographic location, enrollment type, eligibility category, and clinical condition as indicated by ICD-9 codes. States lacking Medicaid telemedicine reimbursement policies were excluded. Chi-square tests were used to compare telemedicine utilization rates and one-way analysis of variance was used to estimate mean differences in number of telemedicine encounters among subgroups. RESULTS: A total of 45,233,602 Medicaid enrollees from the 22 states with telemedicine reimbursement policies were included in the study, and .1% were telemedicine users. Individuals ages 45 to 64 (16.4%), whites (11.3%), males (8.5%), rural residents (26.0%), those with managed care plans (7.9%), and those categorized as aged, blind, and disabled (28.1%) were more likely to receive telemedicine (p<.001). Nearly 95% of telemedicine claims were associated with a behavioral health diagnosis, of which over 50% were for bipolar disorder and attention-deficit disorder or attention-deficit hyperactivity disorder (29.3% and 23.4%, respectively). State-level variation was high, ranging from .0 to 59.91 claims per 10,000 enrollees (Arkansas and Arizona, respectively). CONCLUSIONS: Despite the touted potential for telemedicine to improve health care access, actual utilization of telemedicine in Medicaid programs was low. It was predominantly used to treat behavioral health diagnoses. Reimbursement alone is insufficient to support broad utilization for Medicaid enrollees.

Disparities in Primary Care EHR Adoption Rates.

This study evaluates electronic health record (EHR) adoption by primary care providers in Georgia to assess adoption disparities according to practice size and type, payer mix, and community characteristics. Frequency variances of EHR "Go Live" status were estimated. Odds ratios were calculated by univariate and multivariate logistic regression models. Large practices and community health centers (CHCs) were more likely to Go Live (>80% EHR adoption) than rural health clinics and other underserved settings (53%). A significantly lower proportion (68.9%) of Medicaid predominant providers had achieved Go Live status and had a 47% higher risk of not achieving Go Live status than private insurance predominant practices. Disparities in EHR adoption rates may exacerbate existing disparities in health outcomes of patients served by these practices. Targeted support such as that provided to CHCs would level the playing field for practices now at a disadvantage.

Improving Service Coordination and Reducing Mental Health Disparities Through Adoption of Electronic Health Records.

Despite widespread support for removing barriers to the use of electronic health records (EHRs) in behavioral health care, adoption of EHRs in behavioral health settings lags behind adoption in other areas of health care. The authors discuss barriers to use of EHRs among behavioral health care practitioners, suggest solutions to overcome these barriers, and describe the potential benefits of EHRs to reduce behavioral health care disparities. Thoughtful and comprehensive strategies will be needed to design EHR systems that address concerns about policy, practice, costs, and stigma and that protect patients' privacy and confidentiality. However, these goals must not detract from continuing to challenge the notion that behavioral health and general medical health should be treated as separate and distinct. Ultimately, utilization of EHRs among behavioral health care providers will improve the coordination of services and overall patient care, which is essential to reducing mental health disparities.

Missed policy opportunities to advance health equity by recording demographic data in electronic health records.

The science of eliminating health disparities is complex and dependent on demographic data. The Health Information Technology for Economic and Clinical Health Act (HITECH) encourages the adoption of electronic health records and requires basic demographic data collection; however, current data generated are insufficient to address known health disparities in vulnerable populations, including individuals from diverse racial and ethnic backgrounds, with disabilities, and with diverse sexual identities. We conducted an administrative history of HITECH and identified gaps between the policy objective and required measure. We identified 20 opportunities for change and 5 changes, 2 of which required the collection of less data. Until health care demographic data collection requirements are consistent with public health requirements, the national goal of eliminating health disparities cannot be realized.

Using appendiceal perforation rates to measure impact of a disaster on healthcare system effectiveness.

OBJECTIVES: To understand baseline inequities in appendiceal perforation rates and the impact of hurricane destruction on the healthcare system with respect to perforation rates and racial disparities. METHODS: We used claims data extracted from Medicaid Analytic Extract files to identify appendicitis diagnoses in children and adolescents based on International Classification of Diseases-9 codes and appendectomy procedures based on Current Procedural Terminology codes in the hurricane-affected states of Mississippi and Louisiana. County-level summary data obtained from 2005 Area Resource Files were used to determine high and low hurricane-affected areas. We estimated logistic regression models, mutually adjusting for race, sex, and age, to examine disparities and mixed logistic regression models to determine whether county-level effects contributed to perforation rates. RESULTS: There were nine counties in the high-impact area and 133 counties in the low-impact area. Living in the high- or low-impact area was not associated with a statistically different rate of perforation before or after Hurricane Katrina; however, living in the high-impact area was associated with a change from a lower risk (odds ratio [OR] 0.62) of perforation prehurricane to a higher risk (OR 1.14) posthurricane compared with those living in the low-impact areas. African Americans had statistically higher perforation rates than whites in the high-impact areas both before (OR 1.46) and after (OR 1.71) Hurricane Katrina. CONCLUSIONS: Health professionals and hospital systems were able to maintain effective levels of care before and after Hurricane Katrina; however, perforation rates in African Americans suggest ongoing racial disparities during disasters.

Paths to success: optimal and equitable health outcomes for all.

Abstract:U.S. health disparities are real, pervasive, and persistent, despite dramatic improvements in civil rights and economic opportunity for racial and ethnic minority and lower socioeconomic groups in the United States. Change is possible, however. Disparities vary widely from one community to another, suggesting that they are not inevitable. Some communities even show paradoxically good outcomes and relative health equity despite significant social inequities. A few communities have even improved from high disparities to more equitable and optimal health outcomes. These positive-deviance communities show that disparities can be overcome and that health equity is achievable. Research must shift from defining the problem (including causes and risk factors) to testing effective interventions, informed by the natural experiments of what has worked in communities that are already moving toward health equity. At the local level, we need multi-dimensional interventions designed in partnership with communities and continuously improved by rapid-cycle surveillance feedback loops of community-level disparities metrics. Similarly coordinated strategies are needed at state and national levels to take success to scale. We propose ten specific steps to follow on a health equity path toward optimal and equitable health outcomes for all Americans.