Author: Goldstein, J. R.; Mahmud, A.; Cassidy, T.
Title: Lives Saved from Age-Prioritised COVID-19 Vaccination Cord-id: am1j023w Document date: 2021_3_22
ID: am1j023w
Snippet: BACKGROUND The criteria used to allocate scarce COVID-19 vaccines are hotly contested. While some are pushing just to get vaccines into arms as quickly as possible, others advocate prioritization in terms of risk. OBJECTIVE Our aim is to use demographic models to show the enormous potential of vaccine risk-prioritization in saving lives. METHODS We develop a simple mathematical model that accounts for the age distribution of the population and of COVID-19 mortality. This model considers only the
Document: BACKGROUND The criteria used to allocate scarce COVID-19 vaccines are hotly contested. While some are pushing just to get vaccines into arms as quickly as possible, others advocate prioritization in terms of risk. OBJECTIVE Our aim is to use demographic models to show the enormous potential of vaccine risk-prioritization in saving lives. METHODS We develop a simple mathematical model that accounts for the age distribution of the population and of COVID-19 mortality. This model considers only the direct live-savings for those who receive the vaccine, and does not account for possible indirect effects of vaccination. We apply this model to the United States, Japan, and Bangladesh. RESULTS In the United States, we find age-prioritization would reduce deaths during a vaccine campaign by about 93 percent relative to no vaccine and 85 percent relative to age-neutral vaccine distribution. In countries with younger age structures, such as Bangladesh, the benefits of age-prioritization are even greater. CONTRIBUTION For policy makers, our findings give additional support to risk-prioritized allocation of COVID-19 vaccines. For demographers, our results show how the age-structures of the population and of disease mortality combine into an expression of risk concentration that shows the benefits of prioritized allocation. This measure can also be used to study the effects of prioritizing other dimensions of risk such as underlying health conditions.
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