Visual Abstract

As the COVID-19 pandemic continues to unfold, payers across the USA have stepped up to alleviate patients' financial burden by waiving cost-sharing for COVID-19 testing and treatment. However, there has been no substantive discussion of potential long-term effects of COVID-19 on patient health or their financial and policy implications. After recovery, patients remain at risk for lung disease, heart disease, frailty, and mental health disorders. There may also be long-term sequelae of adverse events that develop in the course of COVID-19 and its treatment. These complications are likely to place additional medical, psychological, and economic burdens on all patients, with lower-income individuals, the uninsured and underinsured, and individuals experiencing homelessness being most vulnerable. Thus, there needs to be a comprehensive plan for preventing and managing post-COVID-19 complications to quell their clinical, economic, and public health consequences and to support patients experiencing delayed morbidity and disability as a result.

Importance:  This three-part study characterizes the widespread implementation of telehealth during the first year of the COVID-19 pandemic, giving us insight into the role of telehealth as we enter a stage of “new normal” healthcare delivery in the U.S. Objective: The COVID-19 Telehealth Impact Study was designed to describe the natural experiment of telehealth adoption during the pandemic.  Using a large claims data stream and surveys of providers and patients, we studied telehealth in all 50 states to inform healthcare leaders.  Design, Setting, Participants: In March 2020, the MITRE Corporation and Mayo Clinic founded the COVID-19 Healthcare Coalition (C19HCC), to respond to the pandemic. We report trends using a dataset of over 2 billion healthcare claims covering over 50% of private insurance activity in the U.S. (January 2019-December 2020), along with key elements from our provider survey (July-August 2020) and patient survey (November 2020 - February 2021). Main Outcomes and Measures: There was rapid and widespread adoption of telehealth in Spring 2020 with over 12 million telehealth claims in April 2020, accounting for 49.4% of total health care claims. Providers and patients expressed high levels of satisfaction with telehealth. 75% of providers indicated that telehealth enabled them to provide quality care.  84% of patients agreed that quality of their telehealth visit was good. Results: Peak levels of telehealth use varied widely among states ranging from 74.9% in Massachusetts to 25.4% in Mississippi.  Every clinical discipline saw a steep rise with the largest claims volume in behavioral health. Provision of care by out-of-state provider was common at 6.5% (October-December 2020). Providers reported multiple modalities of telehealth care delivery.  74% of patients indicated they will use telehealth services in the future. Conclusions and Relevance: Innovation shown by providers and patients during this period of rapid telehealth expansion constitutes a great natural experiment in care delivery with evidence supporting widespread clinical adoption and satisfaction on the part of both patients and clinicians. The authors encourage continued broad access to telehealth over the next 12 months to allow telehealth best practices to emerge, creating a more effective and resilient system of care delivery.

Purpose: Out-of-pocket costs represent an important component of financial toxicity and may impact patients' receipt of care. Herein, we evaluated patientlevel factors associated with out-of-pocket costs for contemporary advanced prostate cancer treatment options. Materials and Methods: We identified all commercially insured men receiving treatment for advanced prostate cancer between 2007 and 2019 within the Optum-Labs Data WarehouseÒ. Patients were categorized into 3 treatment groups: androgen deprivation monotherapy, novel hormonal therapy, and nonandrogen systemic therapy. The primary outcome was out-of-pocket costs in the first year of treatment. The associations of treatment and patient variables with out-of-pocket costs were assessed using multivariable regression models. All costs were adjusted to reflect 2019 U.S. dollars using the Consumer Price Index. Results: In a cohort of 13,409 men 81% (n [ 10,926) received androgen deprivation monotherapy, 6% (n [ 832) novel hormonal therapy, and 12% (n [ 1,651) nonandrogen systemic therapy. Mean treatment-related

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ObjectiveWe examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States.DesignWe used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports.InterventionsIssuance of stay-at-home orders.Primary and secondary outcome measuresCo-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility.ResultsStay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%–6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties.ConclusionStay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

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ImportanceOptimal diabetes care requires regular monitoring and care to maintain glycemic control. How high-deductible health plans (HDHPs), which reduce overall spending but may impede care by increasing out-of-pocket expenses, are associated with risks of severe hypoglycemia and hyperglycemia is unknown.ObjectiveTo examine the association between an employer-forced switch to HDHP and severe hypoglycemia and hyperglycemia.Design, Setting, and ParticipantsThis retrospective cohort study used deidentified administrative claims data for privately insured adults with diabetes from a single insurance carrier with multiple plans across the US between January 1, 2010, and December 31, 2018. Analyses were conducted between May 15, 2020, and November 3, 2022.ExposuresPatients with 1 baseline year of enrollment in a non-HDHP whose employers subsequently forced a switch to an HDHP were compared with patients who did not switch.Main Outcomes and MeasuresMixed-effects logistic regression models were used to examine the association between switching to an HDHP and the odds of severe hypoglycemia and hyperglycemia (ascertained using diagnosis codes in emergency department [ED] visits and hospitalizations), adjusting for patient age, sex, race and ethnicity, region, income, comorbidities, glucose-lowering medications, baseline ED and hospital visits for hypoglycemia and hyperglycemia, and baseline deductible amount, and applying inverse propensity score weighting to account for potential treatment selection bias.ResultsThe study population was composed of 42 326 patients who switched to an HDHP (mean [SD] age: 52 [10] years, 19 752 [46.7%] women, 7375 [17.4%] Black, 5740 [13.6%] Hispanic, 26 572 [62.8%] non-Hispanic White) and 202 729 patients who did not switch (mean [SD] age, 53 [10] years, 89 828 [44.3%] women, 29 551 [14.6%] Black, 26 689 [13.2%] Hispanic, 130 843 [64.5%] non-Hispanic White). When comparing all study years, switching to an HDHP was not associated with increased odds of experiencing at least 1 hypoglycemia-related ED visit or hospitalization (OR, 1.01 [95% CI, 0.95-1.06]; P = .85), but each year of HDHP enrollment did increase these odds by 2% (OR, 1.02 [95% CI, 1.00-1.04]; P = .04). In contrast, switching to an HDHP did significantly increase the odds of experiencing at least 1 hyperglycemia-related ED visit or hospitalization (OR, 1.25 [95% CI, 1.11-1.42]; P < .001), with each year of HDHP enrollment increasing the odds by 5% (OR, 1.05 [95% CI, 1.01-1.09]; P = .02).Conclusions and RelevanceIn this cohort study, employer-forced switching to an HDHP was associated with increased odds of potentially preventable acute diabetes complications, potentially because of delayed or deferred care. These findings suggest that employers should be more judicious in their health plan offerings, and health plans and policy makers should consider allowing preventive and high-value services to be exempt from deductible requirements.