Evaluation of Vaccination Strategies to Compare Efficient and Equitable Vaccine Allocation by Race and Ethnicity Across Time

Abstract

IMPORTANCEIdentifying the most efficient COVID-19 vaccine allocation strategy may substantially reduce hospitalizations and save lives while ensuring an equitable vaccine distribution. OBJECTIVE To simulate the association of different vaccine allocation strategies with COVID-19associated morbidity and mortality and their distribution across racial and ethnic groups. DESIGN, SETTING, AND PARTICIPANTS We developed and internally validated the risk of COVID-19 infection and risk of hospitalization models on randomly split training and validation data sets. These were used in a computer simulation study of vaccine prioritization among adult health plan members who were drawn from an integrated health care delivery system. The study was conducted from January 3, 2021, to June 1, 2021, in Oakland, California, and the data were analyzed during the same period. MAIN OUTCOMES AND MEASURES We simulated the association of different vaccine allocation strategies, including (1) random, (2) a US Centers for Disease Control and Prevention (CDC) proxy, (3) age based, and (4) combinations of models for the risk of adverse outcomes (CRS) and COVID-19 infection (PROVID), with COVID-19-related hospitalizations between May 1, 2020, and December 31, 2020, that were randomly permuted by month across 250 simulations and assessed vaccine allocation by race and ethnicity and the neighborhood deprivation index across time.

Risk of severe COVID-19 infection among adults with prior exposure to children

“…First, we are unable to completely identify and control for all confounding factors that may contribute to severe COVID-19 illness. However, we did control and propensity-match for all known and significant risk factors for severe COVID-19 illness, including age, hypertension, diabetes, BMI, and matching with a novel COVID-19 risk score as a propensity variable ( 24 ). We also could not control for differences in potential exposure to contracting COVID-19 infection, although, as noted, we did control and balance for known risk factors for severe COVID-19 illness.…”

“…The primary outcomes were 1) SARS-CoV-2 infection based upon PCR testing, 2) hospitalization for COVID-19, and 3) hospitalization for COVID-19 requiring treatment in the intensive care unit (ICU). KPNC maintains a validated database for all three COVID-19 outcomes ( 20 , 22 – 24 ).…”

“…To improve comparability between the Study and Comparator groups and balance baseline characteristics for factors related to the risk of severe COVID-19 illness, we developed 1:1 propensity-matched cohorts between the Study group and each of the three Comparison Groups. We estimated a propensity score for the risk of an individual contracting COVID-19 illness based upon previously defined, multivariable predictive equations that take into account age, sex, race/ethnicity, a longitudinal comorbidity score, an outpatient physiology-based severity-of-illness score, and neighborhood deprivation index ( 24 ). We used this propensity score to match patients from the Study group to patients within each of the three Comparator groups in a 1:1 fashion, based on a greedy matching algorithm ( 25 ) using the caliper method and a maximum absolute difference of 0.005 in the propensity score, with additional hard matching on age (±1 y), hypertension status, diabetes status, and BMI (±3), to further balance the matched groups on factors that have been demonstrated to increase risk for severe COVID-19 illness.…”

Did Risk-based or Age-based Vaccine Prioritization for Covid-19 Save More Lives?

“…1,3-5 A decision-modeling study using electronic health records for simulation predicted that risk-based prioritization could be more effective in preventing deaths and household transmission compared to an age-based approach. 6…”

“…1,[3][4][5] A decision-modeling study using electronic health records for simulation predicted that riskbased prioritization could be more effective in preventing deaths and household transmission compared to an age-based approach. 6 Here we take an empirical approach, utilizing the variation in vaccination policies and prioritization across the 50 U.S. states to evaluate which type of prioritization ultimately proved to be more effective at reducing reported Covid-19 cases and deaths.…”