Article Information

Ming Zhong¹, Tamara Glazer¹, Meghana Kshirsagar¹, Richard Johnston¹, Rahul Dodhia¹, Allen Kim¹, Divya Michael¹, Santiago Salcido¹, Sameer Nair-Desai², Thomas C. Tsai^{3, 4}, Stefanie Friedhoff², William B Weeks^{*, 1}, Juan M. Lavista¹

¹Microsoft AI for Good Lab, Redmond, WA

²School of Public Health, Brown University, Providence, RI

³Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA

⁴Department of Surgery, Brigham and Women’s Hospital, Boston, MA

*Corresponding author: William B Weeks. Microsoft AI for Good Lab, Redmond, WA, USA

Received: 30 August 2023; Accepted: 06 September 2023; Published: 13 September 2023

Citation: Ming Zhong, Tamara Glazer, Meghana Kshirsagar, Richard Johnston, Rahul Dodhia, Allen Kim, Divya Michael, Santiago Salcido, Sameer Nair-Desai, Thomas C. Tsai, Stefanie Friedhoff, William B Weeks, Juan M. Lavista. Estimating Vaccine-Preventable COVID-19 Deaths Among Adults Under Counterfactual Vaccination Scenarios in The United States: A Modeling Study Using Observational Data. Journal of Pharmacy and Pharmacology Research. 7 (2023): 163-167.

Abstract

Keywords

COVID-19; vaccine-preventable deaths; modeling

Article Details

1. Introduction

In early 2021, effective SARS-CoV-2 (COVID-19) vaccines became available in the United States; by mid-April 2021, vaccine availability outstripped demand and daily vaccination rates peaked [1,2]. Early on, COVID-19 vaccines were found highly effective in adult populations: analysis of 50 real-world studies showed full vaccination was associated with ≥82%, ≥81%, and ≥94% reductions in hospitalization, severe disease or ICU admission, and death, respectively [3]. Availability of accurate estimates of the number of vaccine-preventable deaths if higher vaccination rates been attained might have helped local policymakers persuade more to get vaccinated.  However, existing estimation methodologies rely on data from a limited number of states [4] or use a single vaccine maximization rate scenario [5]. To address these limitations, for the adult US population, we simulated the number of vaccine-preventable deaths associated with two-dose COVID-19 vaccination that incorporated state-level population, death, and vaccination numbers and three scenarios of vaccination rate achievement. We anticipated that models of increasingly higher vaccination uptake would generate estimates of increasing potentially avoidable deaths from COVID-19.

2. Methods

2.1 Data sources

We conducted a modeling study that relied on United States state-level observational data on adult COVID-19 deaths and vaccination rates that were publicly reported between 1/1/21-4/30/22.  From the New York Times, [1] we obtained numbers of adult (aged 18+) COVID-19 deaths; from the Centers for Disease Control (CDC), [2] we obtained numbers of adults (aged 18+) who had received at least two COVID-19 vaccination injections.

2.2 Statistical methods

We developed a COVID-19 vaccine-preventable death estimation model based on the assumptions that a) once a state reached its peak vaccination administration capacity, it could maintain that level indefinitely, and b) the effective reproductive number of COVID-19 cases did not change.  These are both conservative assumptions: as there were no local supply constraints, weekly vaccination administration might have increased; as more of the population becomes vaccinated, the effective reproductive number decreases. These assumptions allowed us to create a model that generates a lower-bound estimate of COVID-19 deaths had different proportions of the adult population been vaccinated following state-specific peak vaccination administration rates, as follows.

For a given state, the week when vaccine administration reached its peak marks the starting point of the counterfactual simulation. Until then, the estimated number equals the reported number of weekly deaths.  After that, we assumed the number of vaccines administered remained at the state- and week-specific peak level until 85%, 90% or 100% of the state’s adult population was vaccinated. For every week after peak vaccine administration, we applied state-specific death rates of vaccinated and unvaccinated populations to the modeled size of those populations to estimate a counterfactual COVID-19 death count. Vaccine-preventable COVID-19 deaths were obtained by subtracting estimated counterfactual COVID-19 death count estimates from reported ones (Figure). We repeated this computation for each state and totaled the results to obtain state and national estimates of avoidable COVID-19 deaths between 1/1/21-4/30/22. We followed STROBE guidelines.  No administrative permissions are required to access the data used in this study.  Because we used aggregated, publicly available data, the study did not require human subjects approval.  The authors did not have access to information that could identify individual participants during or after the study.

Figure 2:  Demonstration of the model, showing the process for estimating vaccine-preventable deaths had 85% (left, orange) or 100% (right, blue) of the adult population been vaccinated.  The figure is a representation and does not reflect data for a particular state.

3. Results

Nationally, assuming 100% of the population became fully vaccinated during the period examined, we estimated that 318,979 deaths, or approximately 50% reported COVID-19 deaths, might have been prevented (Table); had 85% been so, we estimated that 28% might have been prevented. Across states, we found substantial variation in the proportion of avoidable COVID-19 deaths that might have been avoided at the state level, from 25% in Massachusetts to 74% in Alaska.

Table 1: State-level estimates of the avoidable number and percentage of COVID-19 deaths had 100%, 90%, or 85% of the adult population been vaccinated, at peak state-level vaccination administration rates, between 1/1/21-4/30/22.

4. Discussion

We found that sustaining peak vaccination administration rates might have prevented half of COVID-19 deaths during the period examined. Complemented by recent estimates that 235,000 deaths were avoided because of successful vaccination between 12/1/20-9/31/21, [6] our work helps articulate the public health costs associated with suboptimal vaccination coverage. While our findings likely reflect local decision-making regarding the balance of social, public health, and economic priorities, such decisions might have differed had studies like ours informed them. Our study was limited because we did not have access to age- or race-specific data, causing us to assume that vaccine effectiveness was the same for all, regardless of age or race.  While vaccine effectiveness varies according to age and race, the state-specific unvaccinated- and vaccinated-specific death rates that we used were the reported rates at the time reflecting the experienced death rates of the vaccinated and unvaccinated populations. In addition, we did not have information on variants, waning immunity, or single dose vaccines.  Finally, we only had access to reported deaths – it is possible that COVID-19 deaths were underreported during the period examined, possibly making the proportion of deaths potentially saved even higher than what we estimated. Despite these limitations, our findings are sobering when considering the deaths and diversion of scarce and expensive healthcare resources that might have been averted had vaccination administration efforts been maintained at peak levels.

Acknowledgements: All authors have contributed to writing, designing, compiling and editing the final manuscript

Data availability: The datasets analyzed during the current study are publicly available at https://github.com/nytimes/covid-19-data and https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Casesor-Deaths-by-Age-Group-and/3rge-nu2a

Conflict of Interest: The authors have no conflicts of interest to report. As the study used publicly available data, IRB review was not required.

Disclosures: Views expressed in this manuscript are those of the authors and do not reflect the official views of the US government; the initial draft of this manuscript was prepared prior to the government service of Dr. Tsai and Ms. Friedhoff.

Competing Interests Statement: The authors have no conflicts of interest to report.

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References

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2. Centers for Disease Control and Prevention. Rates of Covid-19 cases or deaths by age group and vaccination status.
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