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Importance: Since the start of the COVID-19 pandemic, few studies have assessed the association of telehealth with outcomes of care, including patterns of health care use after the initial encounter., Objective: To assess the association of telehealth and in-person visits with outcomes of care during the COVID-19 pandemic., Design, Setting, and Participants: This cohort study assessed continuously enrolled members in private health plans of the Blue Cross and Blue Shield Association from July 1, 2019, to December 31, 2020., Main Outcomes and Measures: Main outcomes were ambulatory encounters per enrollee stratified by characteristics derived from enrollment files, practitioner claims, and community characteristics linked to the enrollee's zip code. Outcomes of care were assessed 14 days after the initial encounters and included follow-up encounters of any kind, emergency department encounters, and hospitalizations after initial telehealth or in-person encounters., Results: In this cohort study of 40 739 915 individuals (mean [SD] age, 35.37 [18.77] years; 20 480 768 [50.3%] female), ambulatory encounters decreased by 1.0% and the number of in-person encounters per enrollee decreased by 17.0% from 2019 to 2020; however, as a proportion of all ambulatory encounters, telehealth encounters increased substantially from 0.6% (n = 236 220) to 14.1% (n = 5 743 718). For members with an initial telehealth encounter for a new acute condition, the adjusted odds ratio was 1.44 (95% CI, 1.42-1.46) for all follow-ups combined and 1.11 (95% CI, 1.06-1.16) for an emergency department encounter. For members with an initial telehealth encounter for a new chronic condition, the adjusted odds ratios were 0.94 (95% CI, 0.92-0.95) for all follow-ups combined and 0.94 (95% CI, 0.90-0.99) for in-patient admissions., Conclusions and Relevance: In this cohort study of 40.7 million commercially insured adults, telehealth accounted for a large share of ambulatory encounters at the peak of the pandemic and remained prevalent after infection rates subsided. Telehealth encounters for chronic conditions had similar rates of follow-up to in-person encounters for these conditions, whereas telehealth encounters for acute conditions seemed to be more likely than in-person encounters to require follow-up. These findings suggest a direction for future work and are relevant to policy makers, payers, and practitioners as they manage the use of telehealth during the COVID-19 pandemic and afterward.

Background: The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents' mobility across neighborhoods of different levels of socioeconomic disadvantage., Methods: This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately., Results: On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation., Conclusions: Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy. The groups included: (1) High Change (19 states; 7-day prevalence change ≥50th percentile), (2) Low Change (19 states; 7-day prevalence change <50th percentile), (3) No SAH (11 states: did not adopt SAH order), and (4) No SAH End (2 states: did not relax SAH order). We performed regression modeling assessing the association between change in prevalence at the time of SAH order relaxation and COVID-19 prevalence days after the relaxation of SAH order for four selected groups. After adjusting for other factors, compared to the High Change group, counties in the Low Change group had 33.8 (per 100,000 population) fewer cases (standard error (SE): 19.8, p < 0.001) 7 days after the relaxation of SAH order and the difference was larger by time passing. On August 21, 2020, the No SAH End group had 383.1 fewer cases (per 100,000 population) than the High Change group (SE: 143.6, p < 0.01). A measured, evidence-based approach is required to safely relax the community mitigation strategies and practice phased-reopening of the country., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Importance: This study assesses the role of telehealth in the delivery of care at the start of the COVID-19 pandemic., Objectives: To document patterns and costs of ambulatory care in the US before and during the initial stage of the pandemic and to assess how patient, practitioner, community, and COVID-19-related factors are associated with telehealth adoption., Design, Setting, and Participants: This is a cohort study of working-age persons continuously enrolled in private health plans from March 2019 through June 2020. The comparison periods were March to June in 2019 and 2020. Claims data files were provided by Blue Health Intelligence, an independent licensee of the Blue Cross and Blue Shield Association. Data analysis was performed from June to October 2020., Main Outcomes and Measures: Ambulatory encounters (in-person and telehealth) and allowed charges, stratified by characteristics derived from enrollment files, practitioner claims, and community characteristics linked to the enrollee's zip code., Results: A total of 36 568 010 individuals (mean [SD] age, 35.71 [18.77] years; 18 466 557 female individuals [50.5%]) were included in the analysis. In-person contacts decreased by 37% (from 1.63 to 1.02 contacts per enrollee) from 2019 to 2020. During 2020, telehealth visits (0.32 visit per person) accounted for 23.6% of all interactions compared with 0.3% of contacts in 2019. When these virtual contacts were added, the overall COVID-19 era patient and practitioner visit rate was 18% lower than that in 2019 (1.34 vs 1.64 visits per person). Behavioral health encounters were far more likely than medical contacts to take place virtually (46.1% vs 22.1%). COVID-19 prevalence in an area was associated with higher use of telehealth; patients from areas within the top quintile of COVID-19 prevalence during the week of their encounter were 1.34 times more likely to have a telehealth visit compared with those in the lowest quintile (the reference category). Persons living in areas with limited social resources were less likely to use telehealth (most vs least socially advantaged neighborhoods, 27.4% vs 19.9% usage rates). Per enrollee medical care costs decreased by 15% between 2019 and 2020 (from $358.32 to $306.04 per person per month). During 2020, those with 1 or more COVID-19-related service (1 470 721 members) had more than 3 times the medical costs ($1701 vs $544 per member per month) than those without COVID-19-related services. Persons with 1 or more telehealth visits in 2020 had considerably higher costs than persons having only in-person ambulatory contacts ($2214.10 vs $1337.78 for the COVID-19-related subgroup and $735.87 vs $456.41 for the non-COVID-19 subgroup)., Conclusions and Relevance: This study of a large cohort of patients enrolled in US health plans documented patterns of care at the onset of COVID-19. The findings are relevant to policy makers, payers, and practitioners as they manage the use of telehealth during the pandemic and afterward.

Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia). Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods. Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020. Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease., (Copyright © 2020 Hatef, Chang, Kitchen, Weiner and Kharrazi.)

Background: The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S. Objectives: To understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions. Method: Kendall's Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and stratified Poisson modeling was used to explore the association between ADI and COVID-19 prevalence in the context of county designation. Results: Results show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones. Family income, property value and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type. Conclusions: Though most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models predicting COVID-19 prevalence by ADI and county type reinforced this observation and may inform health policy decisions. [ABSTRACT FROM AUTHOR]