Background Research exploring telehealth expansion during the COVID-19 pandemic has demonstrated that groups disproportionately impacted by COVID-19 also experience worse access to telehealth. However, this research has been cross-sectional or short in duration; geographically limited; has not accounted for pre-existing access disparities; and has not examined COVID-19 patients. We examined virtual primary care use by race/ethnicity and community social vulnerability among adults diagnosed with COVID-19 in a large, multi-state health system. We also assessed use of in-person primary care to understand whether disparities in virtual access may have been offset by improved in-person access. Methods Using a cohort design, electronic health records, and Centers for Disease Control and Prevention Social Vulnerability Index, we compared changes in virtual and in-person primary care use by race/ethnicity and community social vulnerability in the year before and after COVID-19 diagnosis. Our study population included 11,326 adult patients diagnosed with COVID-19 between March and July 2020. We estimated logistic regression models to examine likelihood of primary care use. In all regression models we computed robust standard errors; in adjusted models we controlled for demographic and health characteristics of patients. Results In a patient population of primarily Hispanic/Latino and non-Hispanic White individuals, and in which over half lived in socially vulnerable areas, likelihood of virtual primary care use increased from the year before to the year after COVID-19 diagnosis (3.6 to 10.3%); while in-person use remained stable (21.0 to 20.7%). In unadjusted and adjusted regression models, compared with White patients, Hispanic/Latino and other race/ethnicity patients were significantly less likely to use virtual care before and after COVID-19 diagnosis; Hispanic/Latino, Native Hawaiian/Pacific Islander, and other race/ethnicity patients, and patients living in socially vulnerable areas were also significantly less likely to use in-person care during these time periods. Conclusions Newly expanded virtual primary care has not equitably benefited individuals from racialized groups diagnosed with COVID-19, and virtual access disparities have not been offset by improved in-person access. Health systems should employ evidence-based strategies to equitably provide care, including representative provider networks; targeted, empowering outreach; co-developed culturally and linguistically appropriate tools and technologies; and provision of enabling resources and services.

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Introduction Some patients experience ongoing sequelae after discharge, including rehospitalization; therefore, outcomes following COVID-19 hospitalization are of continued interest. We examined readmissions within 90 days of hospital discharge for veterans hospitalized with COVID-19 during the first 10 months of the pandemic in the US. Methods Veterans hospitalized with COVID-19 at a Veterans Health Administration (VA) hospital from March 1, 2020, through December 31, 2020 were followed for 90 days after discharge to determine readmission rates. Results Of 20,414 veterans hospitalized with COVID-19 during this time period, 13% (n = 2,643) died in the hospital. Among survivors (n = 17,771), 16% (n = 2,764) were readmitted within 90 days of discharge, with a mean time to readmission of 21.6 days (SD = 21.1). Characteristics of the initial COVID-19 hospitalization associated with readmission included length of stay, mechanical ventilator use, higher comorbidity index score, current smoking, urban residence, discharged against medical advice, and hospitalized from September through December 2020 versus March through August 2020 (all P values <.02). Veterans readmitted from September through December 2020 were more often White, lived in a rural or highly rural area, and had shorter initial hospitalizations than veterans hospitalized earlier in the year. Conclusion Approximately 1 of 6 veterans discharged alive following a COVID-19 hospitalization from March 1 through December 31, 2020, were readmitted within 90 days. The longer the hospital stay, the greater the likelihood of readmission. Readmissions also were more likely when the initial admission required mechanical ventilation, or when the veteran had multiple comorbidities, smoked, or lived in an urban area. COVID-19 hospitalizations were shorter from September through December 2020, suggesting that hospital over-capacity may have resulted in earlier discharges and increased readmissions. Efforts to monitor and provide support for patients discharged in high bed–capacity situations may help avoid readmissions.

Ensuring access to high-quality outpatient care is an important strategy to improve COVID-19 outcomes, reduce social inequities, and prevent potentially expensive complications of disease. This study assesses the equity of health care response to COVID-19 by examining outpatient care utilization by factors at the individual and community levels in the 12 months prior to and following COVID-19 diagnosis. Employing a retrospective, observational cohort design, we analyzed electronic health record data from a sample of 11,326 adults diagnosed with COVID-19 between March and July 2020. We used two-part models to estimate changes in use of primary and specialty care by race/ethnicity and community social vulnerability in the year before and after COVID-19 diagnosis. Our findings showed that while overall probability and counts of primary and specialty care visits increased following a positive COVID-19 diagnosis, disparities in care utilization by race/ethnicity and living in a socially vulnerable community persisted in the year that followed. These findings reiterate the need for strategic approaches to improve access to and utilization of care among those diagnosed with COVID-19, especially for individuals who are traditionally undeserved by the health system. Our findings also highlight the importance of systematic approaches for addressing social inequity in health care.

The purpose of this study is to examine the trends in bias-based bullying between 2013 and 2019 among California youth overall and by type of bias-based bullying and explore the extent to which Trump’s announcement of his candidacy for U.S. President in June 2015 impacted these bullying outcomes. We pooled the student-level survey data from multiple waves of the California Healthy Kids Survey. The final study sample included 2,817,487 middle- and high-school students (48.3% female, 47.9% male, and 3.7% not reported). We examined five specific types of bias-based bullying and any bias-based bullying overall. We employed logistic regression and calculated odds ratios to compare differences in the odds of bias-based bullying before and after Trump announced his candidacy for U.S. President. Between 2013 and 2019, approximately one in four students reported experiencing at least one type of bias-based bullying, based on race, ethnicity, or national origin being the most commonly reported. Trump’s announcement for candidacy was inconsistently associated with differences in the odds of bias-based bullying. Counties in which a higher proportion of the electorate voted for Trump had slightly higher odds of bullying for any bias-based bullying and for all specific types of bias-based bullying. Findings highlight the need for a commitment to protecting students from bullying regardless of their identity. Public health and education researchers and practitioners should draw on our growing understanding of the different dimensions of bullying in designing, implementing, and evaluating intervention approaches that address bias-based bullying, a particularly important cause given the growing polarization in the United States and the increasing salience of identity in the lead-up to and since the 2016 and 2020 elections.

Background and Objectives The Diabetes Self-Management Program (DSMP) and Programa de Manejo Personal de la Diabetes (PMPD) have been shown to reduce complications from poorly controlled diabetes. Only a few research studies have examined Latino individuals’ participation in them. This study examines workshop completion among DSMP and PMPD participants and the effects of race/ethnicity, workshop language, workshop type, and workshop site on program completion rates by participants. Research Design and Methods We used data from the National Council on Aging’s data repository of individuals who participated in DSMP or PMPD between January 2010 and March 2019. Using a pooled cross-sectional study design, we examined workshop completion among 8,321 Latino and 23,537 non-Latino white (NLW) participants. We utilized linear probability models to estimate the effects of race/ethnicity and workshop language/type among the full sample; a stratified model estimated the separate effects of workshop language, type, and delivery site among Latinos. Participant characteristics included age, sex, education, number of chronic health conditions, living arrangement, health insurance status, and geographic location of workshop. Results Compared to NLW participants in DSMP English workshops, Latinos enrolled in any workshop had a higher probability of completing at least four workshop sessions, and Latinos enrolled in PMPD Spanish workshops had a higher probability of completing six of six sessions. Among the Latino subsample, participation in PMPD Spanish or English workshops was associated with completing at least four sessions or all six sessions compared with participation in DSMP Spanish or English workshops. Among Latino participants, the effects of workshop site on completion rates were mixed. Discussion and Implications Diabetes self-management education programs tailored for Latino participants had higher completion rates. Further research is warranted to better understand the effect of workshop site and participant characteristics on completion of DSMP and PMPD programs.

Background Through Community Care Networks (CCNs) implemented with the VA MISSION Act, VA expanded provider contracting and instituted network adequacy standards for Veterans’ community care. Objective To determine whether early CCN implementation impacted community primary care (PC) appointment wait times overall, and by rural/urban and PC shortage area (HPSA) status. Design Using VA administrative data from February 2019 through February 2020 and a difference-in-differences approach, we compared wait times before and after CCN implementation for appointments scheduled by VA facilities that did (CCN appointments) and did not (comparison appointments) implement CCNs. We ran regression models with all appointments, and stratified by rural/urban and PC HPSA status. All models adjusted for Veteran characteristics and VA facility–level clustering. Appointments 13,720 CCN and 40,638 comparison appointments. Main Measures Wait time, measured as number of days from authorization to use community PC to a Veteran’s first corresponding appointment. Key Results Overall, unadjusted wait times increased by 35.7 days ([34.4, 37.1] 95% CI) after CCN implementation. In adjusted analysis, comparison wait times increased on average 33.7 days ([26.3, 41.2] 95% CI, p < 0.001) after CCN implementation; there was no significant difference for CCN wait times (across-group mean difference: 5.4 days, [−3.8, 14.6] 95% CI, p = 0.25). In stratified analyses, comparison wait time increases ranged from 29.6 days ([20.8, 38.4] 95% CI, p < 0.001) to 42.1 days ([32.9, 51.3] 95% CI, p > 0.001) after CCN implementation, while additional differences for CCN appointments ranged from 13.4 days ([3.5, 23.4] 95% CI, p = 0.008) to −15.1 days ([−30.1, −0.1] 95% CI, p = 0.05) for urban and PC HPSA appointments, respectively. Conclusions After early CCN implementation, community PC wait times increased sharply at VA facilities that did and did not implement CCNs, regardless of rural/urban or PC HPSA status, suggesting community care demand likely overwhelmed VA resources such that CCNs had limited impact.