Telemedicine catches on: changes in the utilization of telemedicine services during the COVID-19 pandemic.

OBJECTIVES: To determine the degree of telemedicine expansion overall and across patient subpopulations and diagnoses. We hypothesized that telemedicine visits would increase substantially due to the need for continuity of care despite the disruptive effects of COVID-19. STUDY DESIGN: A retrospective study of health insurance claims for telemedicine visits from January 1, 2018, through March 10, 2020 (prepandemic period), and March 11, 2020, through October 31, 2020 (pandemic period). METHODS: We analyzed claims from 1,589,777 telemedicine visits that were submitted to Independence Blue Cross (Independence) from telemedicine-only providers and providers who traditionally deliver care in person. The primary exposure was the combination of individual behavior changes, state stay-at-home orders, and the Independence expansion of billing policies for telemedicine. The comparison population consisted of telemedicine visits in the prepandemic period. RESULTS: Telemedicine increased rapidly from a mean (SD) of 773 (155) weekly visits in prepandemic 2020 to 45,632 (19,937) weekly visits in the pandemic period. During the pandemic period, a greater proportion of telemedicine users were older, had Medicare Advantage insurance plans, had existing chronic conditions, or resided in predominantly non-Hispanic Black or African American Census tracts compared with during the prepandemic period. A significant increase in telemedicine claims containing a mental health-related diagnosis was observed. CONCLUSIONS: Telemedicine expanded rapidly during the COVID-19 pandemic across a broad range of clinical conditions and demographics. Although levels declined later in 2020, telemedicine utilization remained markedly higher than 2019 and 2018 levels. Trends suggest that telemedicine will likely play a key role in postpandemic care delivery.

The Impact of the Non-essential Business Closure Policy on Covid-19 Infection Rates

In response to the Covid-19 pandemic, many localities instituted non-essential business closure orders, keeping individuals categorized as essential workers at the frontlines while sending their non-essential counterparts home. We examine the extent to which being designated as an essential or non-essential worker impacts one’s risk of being Covid-positive following the non-essential business closure order in Pennsylvania. We also assess the intrahousehold transmission risk experienced by their cohabiting family members and roommates. Using a difference-in-differences framework, we estimate that workers designated as essential have a 55% higher likelihood of being positive for Covid-19 than those classified as non-essential;in other words, non-essential workers experience a protective effect. While members of the health care and social assistance subsector contribute significantly to this overall effect, it is not completely driven by them. We also find evidence of intrahousehold transmission that differs in intensity by essential status. Dependents cohabiting with an essential worker have a 17% higher likelihood of being Covid-positive compared to those cohabiting with a non-essential worker. Roommates cohabiting with an essential worker experience a 38% increase in likelihood of being Covid-positive. Analysis of households with a Covid-positive member suggests that intrahousehold transmission is an important mechanism

The impact of the non-essential business closure policy on Covid-19 infection rates.

In response to the Covid-19 pandemic, many localities instituted non-essential business closure orders, keeping individuals categorized as essential workers at the frontlines while sending their non-essential counterparts home. We examine the extent to which being designated as an essential or non-essential worker impacts one's risk of being Covid-positive following the non-essential business closure order in Pennsylvania. We also assess the intrahousehold transmission risk experienced by their cohabiting family members and roommates. Using a difference-in-differences framework, we estimate that workers designated as essential have a 55% higher likelihood of being positive for Covid-19 than those classified as non-essential; in other words, non-essential workers experience a protective effect. While members of the health care and social assistance subsector contribute significantly to this overall effect, it is not completely driven by them. We also find evidence of intrahousehold transmission that differs in intensity by essential status. Dependents cohabiting with an essential worker have a 17% higher likelihood of being Covid-positive compared to those cohabiting with a non-essential worker. Roommates cohabiting with an essential worker experience a 38% increase in likelihood of being Covid-positive. Analysis of households with a Covid-positive member suggests that intrahousehold transmission is an important mechanism driving these effects.

Characteristics of Nursing Homes by COVID-19 Cases Among Staff: March to August 2020.

OBJECTIVE: To measure the association between nursing home (NH) characteristics and Coronavirus Disease 2019 (COVID-19) prevalence among NH staff. DESIGN: Retrospective cross-sectional study. SETTING AND PARTICIPANTS: Centers for Disease Control and Prevention COVID-19 database for US NHs between March and August 2020, linked to NH facility characteristics (LTCFocus database) and local COVID-19 prevalence (USA Facts). METHODS: We estimated the associations between NH characteristics, local infection rates, and other regional characteristics and COVID-19 cases among NH staff (nursing staff, clinical staff, aides, and other facility personnel) measured per 100 beds, controlling for the hospital referral regions in which NHs were located to account for local infection control practices and other unobserved characteristics. RESULTS: Of the 11,858 NHs in our sample, 78.6% reported at least 1 staff case of COVID-19. After accounting for local COVID-19 prevalence, NHs in the highest quartile of confirmed resident cases (413.5 to 920.0 cases per 1000 residents) reported 18.9 more staff cases per 100 beds compared with NHs that had no resident cases. Large NHs (150 or more beds) reported 2.6 fewer staff cases per 100 beds compared with small NHs (<50 beds) and for-profit NHs reported 0.8 fewer staff cases per 100 beds compared with nonprofit NHs. Higher occupancy and more direct-care hours per day were associated with more staff cases (0.4 more cases per 100 beds for a 10% increase in occupancy, and 0.7 more cases per 100 beds for an increase in direct-care staffing of 1 hour per resident day, respectively). Estimates associated with resident demographics, payer mix, or regional socioeconomic characteristics were not statistically significant. CONCLUSIONS AND IMPLICATIONS: These findings highlight the urgent need to support facilities with emergency resources such as back-up staff and protocols to reduce resident density within the facility, which may help stem outbreaks.

Disruptions in preventive care: Mammograms during the COVID-19 pandemic.

OBJECTIVE: To measure the extent to which the provision of mammograms was impacted by the COVID-19 pandemic and surrounding guidelines. DATA SOURCES: De-identified summary data derived from medical claims and eligibility files were provided by Independence Blue Cross for women receiving mammograms. STUDY DESIGN: We used a difference-in-differences approach to characterize the change in mammograms performed over time and a queueing formula to estimate the time to clear the queue of missed mammograms. DATA COLLECTION: We used data from the first 30 weeks of each year from 2018 to 2020. PRINCIPAL FINDINGS: Over the 20 weeks following March 11, 2020, the volume of screening mammograms and diagnostic mammograms fell by 58% and 38% of expected levels, on average. Lowest volumes were observed in week 15 (April 8 to 14), when screening and diagnostic mammograms fell by 99% and 74%, respectively. Volumes began to rebound in week 19 (May), with diagnostic mammograms reaching levels to similar to previous years' and screening mammograms remaining 14% below expectations. We estimate it will take a minimum of 22 weeks to clear the queue of missed mammograms in our study sample. CONCLUSIONS: The provision of mammograms has been significantly disrupted due to the COVID-19 pandemic.

Long-Term Care, Residential Facilities, and COVID-19: An Overview of Federal and State Policy Responses.

The COVID-19 pandemic has disproportionately affected residents and staff at long-term care (LTC) and other residential facilities in the United States. The high morbidity and mortality at these facilities has been attributed to a combination of a particularly vulnerable population and a lack of resources to mitigate the risk. During the first wave of the pandemic, the federal and state governments received urgent calls for help from LTC and residential care facilities; between March and early June of 2020, policymakers responded with dozens of regulatory and policy changes. In this article, we provide an overview of these responses by first summarizing federal regulatory changes and then reviewing state-level executive orders. The policy and regulatory changes implemented at the federal and state levels can be categorized into the following 4 classes: (1) preventing virus transmission, which includes policies relating to visitation restrictions, personal protective equipment guidance, and testing requirements; (2) expanding facilities' capacities, which includes both the expansion of physical space for isolation purposes and the expansion of workforce to combat COVID-19; (3) relaxing administrative requirements, which includes measures enacted to shift the attention of caretakers and administrators from administrative requirements to residents' care; and (4) reporting COVID-19 data, which includes the reporting of cases and deaths to residents, families, and administrative bodies (such as state health departments). These policies represent a snapshot of the initial efforts to mitigate damage inflicted by the pandemic. Looking ahead, empirical evaluation of the consequences of these policies-including potential unintended effects-is urgently needed. The recent availability of publicly reported COVID-19 LTC data can be used to inform the development of evidence-based regulations, though there are concerns of reporting inaccuracies. Importantly, these data should also be used to systematically identify hot spots and help direct resources to struggling facilities.