Risk factor targeting for vaccine prioritization during the COVID-19 pandemic

  1. Lloyd A. C. Chapman
  2. Poojan Shukla
  3. Isabel Rodríguez-Barraquer
  4. Priya B. Shete
  5. Tomás M. León
  6. Kirsten Bibbins-Domingo
  7. George W. Rutherford
  8. Robert Schechter
  9. Nathan C. Lo

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Abstract

A key public health question during any disease outbreak when limited vaccine is available is who should be prioritized for early vaccination. Most vaccine prioritization analyses only consider variation in risk of infection and death by a single risk factor, such as age. We provide a more granular approach with stratification by demographics, risk factors, and location. We use this approach to compare the impact of different COVID-19 vaccine prioritization strategies on COVID-19 cases, deaths and disability-adjusted life years (DALYs) over the first 6 months of vaccine rollout, using California as a case example. We estimate the proportion of cases, deaths and DALYs averted relative to no vaccination for strategies prioritizing vaccination by a single risk factor and by multiple risk factors (e.g. age, location). When targeting by a single risk factor, we find that age-based targeting averts the most deaths (62% for 5 million individuals vaccinated) and DALYs (38%) and targeting essential workers averts the least deaths (31%) and DALYs (24%) over the first 6 months of rollout. However, targeting by two or more risk factors simultaneously averts up to 40% more DALYs. Our findings highlight the potential value of multiple-risk-factor targeting of vaccination against COVID-19 and other infectious diseases, but must be balanced with feasibility for policy.

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  1. Version published to 10.1038/s41598-022-06971-5
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    SciScore for 10.1101/2021.03.04.21251264: (What is this?)

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    This analysis has limitations that should be considered. We used a static model to assess vaccination impact, which only accounts for direct effects of vaccination (protection from infection and severe disease), and not potential indirect effects through reduction in transmission. We took this conservative approach to estimating vaccination impact since there is currently very limited data from vaccine trials on prevention of transmission. Furthermore, given that our analysis focused on allocation of the first available vaccinations (with relatively low coverage in the population), reduction in transmission is likely to be limited for the scenarios in this study. If the vaccines prove to be effective at reducing transmission and vaccine supply increases, then consideration will need to be given to switching to vaccinate high-transmision groups, i.e., younger individuals and essential workers (16, 17). We used the average baseline hazard rate for COVID-19 death from data up to the end of 2020 to predict numbers of deaths, cases and infections over the first 6 months of 2021, which may underestimate incidence given significant recent increases in cases and deaths in California. However, the focus of this study was not prediction of future COVID-19 cases and deaths, but comparison of the relative impact of different vaccination strategies, which is not affected by the baseline death rate in our model. Assessment of predictive performance of different forecasting models suggests ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  3. Version published to 10.1101/2021.03.04.21251264 on medRxiv