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Replication Data for: Covid-19 and Excess Mortality in Medicare Beneficiaries

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Covid-19 and Excess Mortality in Medicare Beneficiaries
 
Identifier https://doi.org/10.7910/DVN/GFBLK5
 
Creator Greenwald, Scott
Chamoun, Nassib
Manberg, Paul
Gray, Josh
Clain, David
Maheshwari, Kamal
Sesslier, Daniel
 
Publisher Harvard Dataverse
 
Description ABSTRACT: We estimated excess mortality in Medicare recipients with probable and confirmed Covid-19 infections in the general community and amongst residents of long-term care (LTC) facilities. We considered 28,389,098 Medicare and dual-eligible recipients from one year before February 29, 2020 through September 30, 2020, with mortality followed through November 30th, 2020. Probable and confirmed Covid-19 diagnoses, presumably mostly symptomatic, were determined from ICD-10 codes. We developed a Risk Stratification Index (RSI) mortality model which was applied prospectively to establish baseline mortality risk. Excess deaths attributable to Covid-19 were estimated by comparing actual-to-expected deaths based on historical (2017-2019) comparisons and in closely matched concurrent (2020) cohorts with and without Covid-19. 677,100 (2.4%) beneficiaries had confirmed Covid-19 and 2,917,604 (10.3%) had probable Covid-19. 472,329 confirmed cases were community living and 204,771 were in LTC. Mortality following a probable or confirmed diagnosis in the community increased from an expected incidence of about 4.0% to actual incidence of 7.5%. In long-term care facilities, the corresponding increase was from 20.3% to 24.6%. The absolute increase was therefore similar at 3-4% in the community and in LTC residents. The percentage increase was far greater in the community (89.5%) than among patients in chronic care facilities (21.1%) who had higher baseline risk. The LTC population without probable or confirmed Covid-19 diagnoses experienced 38,932 excess deaths (34.8%) compared to historical estimates. Limitations in access to Covid-19 testing and disease under-reporting in LTC patients probably were important factors, although social isolation and disruption in usual care presumably also contributed. Remarkably, there were 31,360 (5.4%) fewer deaths than expected in community dwellers without probable or confirmed Covid-19 diagnoses. Disruptions to the healthcare system and avoided medical care were thus apparently offset by other factors, representing overall benefit. The Covid-19 pandemic had marked effects on mortality, but the effects were highly context-dependent.
 
Subject Medicine, Health and Life Sciences
COVID-19; SARS-CoV-2; Coronavirus; Machine Learning; Predictive Analytics; Risk Assessment; Risk Stratification; Patient Outcomes; Medicare; Medicaid
 
Contributor Greenwald, Scott