Emerging Therapies for COVID-19: the value of information from more clinical trials (preprint)

ObjectivesThe COVID-19 pandemic necessitates time-sensitive policy and implementation decisions regarding new therapies in the face of uncertainty. The aim of this study was to quantify consequences of approving therapies or pursuing further research: either immediate approval, use only in research, approval with research (e.g., Emergency Use Authorization), or reject. MethodsUsing a cohort state-transition model for hospitalized COVID-19 patients, we estimated quality-adjusted life years (QALYs) and costs associated with the following interventions: Hydroxychloroquine, Remdesivir, Casirivimab-Imdevimab, Dexamethasone, Baricitinib-Remdesivir, Tocilizumab, Lopinavir-Ritonavir, and Interferon beta-1a, and usual care. We used the model outcomes to conduct cost-effectiveness and value of information analyses from a US healthcare perspective and a lifetime horizon. ResultsAssuming a $100,000-per-QALY willingness-to-pay-threshold, only Remdesivir, Casirivimab-Imdevimab, Dexamethasone, Baricitinib-Remdesivir and Tocilizumab were (cost-) effective (incremental net health benefit 0.252, 0.164, 0.545, 0.668 and 0.524 QALYs and incremental net monetary benefit $25,249, $16,375, $54,526, $66,826 and $52,378). Our value of information analyses suggest that most value can be obtained if these 5 therapies are approved for immediate use rather than requiring additional RCTs (net value $20.6 Billion, $13.4 Billion, $7.4 Billion, $54.6 Billion and $7.1 Billion); Hydroxychloroquine (net value $198 Million) only used in further RCTs if seeking to demonstrate decremental cost-effectiveness, and otherwise rejected; and Interferon beta-1a and Lopinavir-Ritonavir are rejected (i.e., neither approved nor additional RCTs). Conclusions and RelevanceEstimating the real-time value of collecting additional evidence during the pandemic can inform policymakers and clinicians about the optimal moment to implement therapies and whether to perform further research.

Impact of the COVID-19 pandemic on treatment patterns for US patients with metastatic solid cancer (preprint)

BackgroundThe COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemics impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. MethodsWe used an electronic health record-derived longitudinal database curated via technology-enabled abstraction to identify 14,136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at [~]280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. ResultsThe adjusted probability of treatment within 30 days of diagnosis [95% confidence interval] was similar across periods: January-March 2019 41.7% [32.2%, 51.1%]; April-July 2019 42.6% [32.4%, 52.7%]; January-March 2020 44.5% [30.4%, 58.6%]; April-July 2020 46.8% [34.6%, 59.0%]; adjusted percentage-point difference-in-differences 1.4% [-2.7%, 5.5%]. Among 5,962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences 1.6% [-2.6%, 5.8%]). There was no meaningful effect modification by cancer type, race, or age. ConclusionsDespite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not impact time to treatment initiation or treatment selection for patients with metastatic solid cancers.

Racial and Ethnic Disparities in Population Level Covid-19 Mortality (preprint)

Background: Current reporting of Covid-19 mortality data by race and ethnicity across the United States could bias our understanding of population-mortality disparities. Moreover, stark differences in age distribution by race and ethnicity groups are seldom accounted for in analyses. Methods: To address these gaps, we conducted a cross-sectional study using publicly-reported Covid-19 mortality data to assess the quality of race and ethnicity data (Black, Latinx, white), and estimated age-adjusted disparities using a random effects meta-analytic approach. Results: We found only 28 states, and NYC, reported race and ethnicity-stratified Covid-19 mortality along with large variation in the percent of missing race and ethnicity data by state. Aggregated relative risk of death estimates for Black compared to the white population was 3.57 (95% CI: 2.84-4.48). Similarly, Latinx population displayed 1.88 (95% CI: 1.61-2.19) times higher risk of death than white patients. Discussion: In states providing race and ethnicity data, we identified significant population-level Covid-19 mortality disparities. We demonstrated the importance of adjusting for age differences across population groups to prevent underestimating disparities in younger population groups. The availability of high-quality and comprehensive race and ethnicity data is necessary to address factors contributing to inequity in Covid-19 mortality.