ABSTRACT
BackgroundIt is important to understand how BNT162b2, mRNA-1273, and JNJ-78436735 COVID-19 vaccines, as well as prior infection, protect against breakthrough cases and reinfections. Real world evidence on acquired immunity from vaccines, and from SARS-CoV-2 infection, can help public health decision-makers understand disease dynamics and viral escape to inform resource allocation for curbing the spread of pandemic. MethodsThis retrospective cohort study presents demographic information, survival functions, and probability distributions for 2,627,914 patients who received recommended doses of COVID-19 vaccines, and 63,691 patients who had a prior COVID-19 infection. In addition, patients receiving different vaccines were matched by age, sex, ethnic group, state of residency, and the quarter of the year in 2021 the COVID-19 vaccine was completed, to support survival analysis on pairwise matched cohorts. FindingsEach of the three vaccines and infection-induced immunity all showed a high probability of survival against breakthrough or reinfection cases (mRNA-1273: 0.997, BNT162b2: 0.997, JNJ-78436735: 0.992, previous infection: 0.965 at 180 days). The incidence rate of reinfection among those unvaccinated and previously infected was higher than that of breakthrough among the vaccinated population (reinfection: 0.9%; breakthrough:0.4%). In addition, 280 vaccinated patients died (0.01% all-cause mortality) within 21 days of the last vaccine dose, and 5898 (3.1 %) died within 21 days of a positive COVID-19 test. ConclusionsDespite a gradual decline in vaccine-induced and infection-induced immunity, both acquired immunities were highly effective in preventing breakthrough and reinfection. In addition, for unvaccinated patients with COVID-19, those who did not die within 90 days of their initial infection (9565 deaths, 5.0% all-cause mortality rate), had a comparable asymptotic pattern of breakthrough infection as those who acquired immunity from a vaccine. Overall, the risks associated with COVID-19 infection are far greater than the marginal advantages of immunity acquired by prior infection.
ABSTRACT
BackgroundRisk stratification for hospitalized adults with COVID-19 is essential to inform decisions for individual patients and allocation of potentially scarce resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Additionally, existing risk models have been limited to either small sample sizes, or modeling mortality over an entire hospital admission. Further, previous models were developed on data from early in the pandemic, before improvements in COVID-19 treatment, the SARS-CoV-2 delta variant, and vaccination. There remains a need for early, accurate identification of patients who may need invasive mechanical ventilation (IMV) or die, considering multiple time horizons. MethodsThis retrospective study analyzed data from 6,906 hospitalized adults with COVID-19 from a community health system with 51 hospitals and 1085 clinics across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data collected available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. The relative importance of predictive risk factors features for all models was determined using Shapley additive explanations. FindingsThe percentage of patients who required mechanical ventilation or died within seven days of admission to the hospital due to COVID-19 was 10.82%. For the seven-day interval, models for age [≥] 18 and < 50 years reached AUROC 0.80 (95% CI: 0.70-0.89) and models for age [≥] 50 years reached AUROC 0.83 (95% CI: 0.79-0.88). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients, including age, BMI, vital signs, and laboratory results. In addition, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results. InterpretationFor hospitalized adults, baseline data that is readily available within one hour after hospital admission or a first positive inpatient SARS-CoV-2 test can predict critical illness within one day, and up to 56 days later. Further, the relative importance of risk factors differs between older and younger patients.
