Predicting the Public Health Impact of Bivalent Vaccines and Nirmatrelvir-Ritonavir Against Coronavirus Disease 2019

Background. Uptake of coronavirus disease 2019 (COVID-19) bivalent vaccines and the oral medication nirmatrelvir-ritonavir (Paxlovid) has remained low across the United States. Assessing the public health impact of increasing uptake of these interventions in key risk groups can guide further public health resources and policy and determine what proportion of severe COVID-19 is avertable with these interventions. Methods. This modeling study used person-level data from the California Department of Public Health on COVID-19 cases, hospitalizations, deaths, and vaccine administration from 23 July 2022 to 23 January 2023. We used a quasi-Poisson regression model calibrated to recent historical data to predict future COVID-19 outcomes and modeled the impact of increasing uptake (up to 70% coverage) of bivalent COVID-19 vaccines and nirmatrelvir-ritonavir during acute illness in different risk groups. Risk groups were defined by age (>= 50, >= 65, >= 75 years) and vaccination status (everyone, primary series only, previously vaccinated). We predicted the number of averted COVID-19 cases, hospitalizations, and deaths and number needed to treat (NNT). Results. The model predicted that increased uptake of bivalent COVID-19 boosters and nirmatrelvir-ritonavir (up to 70% coverage) in all eligible persons could avert an estimated 15.7% (95% uncertainty interval [UI], 11.2%-20.7%; NNT: 17 310) and 23.5% (95% UI, 13.1%-30.0%; NNT: 67) of total COVID-19-related deaths, respectively. In the high-risk group of persons >= 65 years old alone, increased uptake of bivalent boosters and nirmatrelvir-ritonavir could avert an estimated 11.9% (95% UI, 8.4%-15.1%; NNT: 2757) and 22.8% (95% UI, 12.7%-29.2%; NNT: 50) of total COVID-19-related deaths, respectively. Conclusions. These findings suggest that prioritizing uptake of bivalent boosters and nirmatrelvir-ritonavir among older age groups (>= 65 years) would be most effective (based on NNT) but would not address the entire burden of severe COVID-19.