Population-Level Strategies for Nirmatrelvir/Ritonavir Prescribing—A Cost-effectiveness Analysis
Year: 2022
Type: article
Abstract: Background: New coronavirus disease 2019 (COVID-19) medications force decision-makers to weigh limited evidence of efficacy and cost in determining which patient populations to target for treatment. A case in point is nirmatrelvir/ritonavir, a drug that has been recommended for elderly, high-risk individuals, regardless of vaccination status, even though clinical trials have only evaluated it in unvaccinated patients. A simple optimization framework might inform a more reasoned approach to the trade-offs implicit in the treatment allocation decision. Methods: We conducted a cost-effectiveness analysis using a decision-analytic model comparing 5 nirmatrelvir/ritonavir prescription policy strategies, stratified by vaccination status and risk for severe disease. We considered treatment effectiveness at preventing hospitalization ranging from 21% to 89%. Sensitivity analyses were performed on major parameters of interest. A web-based tool was developed to permit decision-makers to tailor the analysis to their settings and priorities. Results: Providing nirmatrelvir/ritonavir to unvaccinated patients at high risk for severe disease was cost-saving when effectiveness against hospitalization exceeded 33% and cost-effective under all other data scenarios we considered. The cost-effectiveness of other allocation strategies, including those for vaccinated adults and those at lower risk for severe disease, depended on willingness-to-pay thresholds, treatment cost and effectiveness, and the likelihood of severe disease. Conclusions: Priority for nirmatrelvir/ritonavir treatment should be given to unvaccinated persons at high risk of severe disease from COVID-19. Further priority may be assigned by weighing treatment effectiveness, disease severity, drug cost, and willingness to pay for deaths averted.
Source: Open Forum Infectious Diseases
DOI: 10.1093/ofid/ofac637
Institutions: Yale University, Yale New Haven Hospital
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,676
Awards: G1759066555, G316364202, Making Better Decisions: Policy Modeling for AIDS and Drug Abuse, G7168163869, G747006993, UNDERSTANDING HOSPITAL READMISSION RATES: PATIENT, HOSPITAL AND COMMUNITY EFFECTS, G8313649646, G8475715394, Novel, Adaptive Approaches to Predicting and Responding to Outbreaks of Overdose, HIV and HCV Among People Who Use Drugs, Yale Clinical and Translational Science Award, Making Better Decisions: Policy Modeling for AIDS and Drug Abuse
OpenAlex ID: W4310055254