Racial and ethnic differences in COVID-19 hospitalizations by metropolitan status among Medicare beneficiaries, 1 January–31 December 2020

Abstract

Background Risk for COVID-19 hospitalizations increases with increasing age and presence of underlying medical conditions. However, the burden has not been well-assessed in metropolitan and nonmetropolitan areas by race/ethnicity among Medicare population with chronic conditions. Methods We used the 2020 Medicare data to estimate COVID-19 hospitalization rates by race/ethnicity among Medicare beneficiaries for COVID-19 by metropolitan status and to assess the association of hospitalizations from COVID-19 with each of selected 29 chronic conditions for patients by metropolitan status and by race/ethnicity. Results The COVID-19 hospitalization rate was higher among beneficiaries residing in nonmetropolitan counties than those residing in metropolitan counties in 2020. Approximately 1 in 2 AI/AN, 1 in 3 NHB, Hispanic and A/PI, and 1 in 4 NHW beneficiaries with COVID-19 residing in nonmetropolitan counties were hospitalized. Beneficiaries with COVID-19 and chronic conditions were more likely to be hospitalized compared with those without chronic conditions. Conclusions Hospitalization rates among beneficiaries with COVID-19 and chronic conditions were not distributed equally by race/ethnicity and by metropolitan status. Researchers, policymakers and practitioners can use these findings to explore more effective ways of reducing racial/ethnic and geographic disparities among minorities disproportionately affected by COVID-19 and are at highest risk of hospitalization.

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination

“…However, contrary to prior descriptive analyses performed on smaller population sizes of hospitalized patients only [35][36][37], or to a recently published report identifying risk of hospitalization associated with chronic conditions not adjusted for age, ethnicity or prior hospitalization in the fee-for-service Medicare population [15] our study reveals the lack of, or modest effect of, hypertension, diabetes, COPD, and asthma in the hospitalization and death logistic regression models. Our logistic regression results confirms the finding in this report that ESRD is the strongest COVID-19 hospitalization risk factor, and affirms North American Natives have the highest risk of hospitalization among ethnic minorities.…”

“…We developed a model to predict COVID-19 hospitalization and death for Medicare beneficiaries using de-identified Medicare claims which are an important national data resource for studies of the COVID-19 pandemic [15][16][17][18] and CDC Social Vulnerability Index (SVI) data [5]. While the initial impetus for the work, developed for the Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC) [19], was to provide logistics support to hospitals overwhelmed by the pandemic, this model can also support operationalization of the National Academy of Medicine (NAM) and CDC recommendations for a COVID-19 vaccination campaign by stratifying the population by risk, and by mapping locations of beneficiaries in different risk strata.…”

Ethnic inequalities in COVID-19 infection, hospitalisation, intensive care admission, and death: a global systematic review and meta-analysis of over 200 million study participants

“…After excluding studies which contained duplicate patient data, 77 studies comprising over 200,000,000 confirmed COVID-19 cases were included. 4 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , ...…”

Changes in prenatal testing during the COVID-19 pandemic

“…Given the geographic, socioeconomic, and racial and ethnic differences in SARS-CoV-2 infection, hospitalizations, and deaths, analyses utilized data from three different and complementary sources to increase study generalizability (10)(11)(12)(13). Insurer data came from Highmark, an independent licensee of the Blue Cross Blue Shield Association, that provided insurance coverage to people living in all of Delaware, southwestern Ohio (one county), across Pennsylvania (63 of 67 counties), and all of West Virginia during the study period.…”