Estimating Vaccine-Preventable COVID-19 Deaths Among Adults Under Counterfactual Vaccination Scenarios in The United States: A Modeling Study Using Observational Data

Article Information

Ming Zhong1, Tamara Glazer1, Meghana Kshirsagar1, Richard Johnston1, Rahul Dodhia1, Allen Kim1, Divya Michael1, Santiago Salcido1, Sameer Nair-Desai2, Thomas C. Tsai3, 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.

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Abstract

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, daily vaccination rates peaked, and COVID-19 vaccines were found highly effective in adult populations. Accurate estimates of the number of vaccine-preventable deaths had higher vaccination rates been attained could have helped local policymakers and possibly persuaded more to get vaccinated.

Methods: Because existing estimation methodologies are limited, for the period 1/1/21 – 4/30/22, 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.

Results: Nationally, we found that had 100% of the population became fully vaccinated during the period examined, 318,979 deaths, or approximately 50% of reported COVID-19 deaths, might have been prevented; had 85% been so, 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.

Conclusion: Our findings are sobering when considering the number of deaths and diversion of scarce and expensive healthcare resources that might have been averted had peak vaccination administration efforts been maintained.

Keywords

COVID-19; vaccine-preventable deaths; modeling

COVID-19 articles; vaccine-preventable deaths articles; modeling articles

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.

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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.

State or district

Population aged 18 and older

Reported deaths

Avoidable number and percentage of reported deaths had this proportion of the adult population been vaccinated:

100%

90%

85%

N

%

N

%

N

%

Alabama

38,11,122

14,695

6,527

44%

5,358

36%

4,671

32%

Alaska

5,51,685

1,005

748

74%

550

55%

451

45%

Arizona

56,36,931

20,890

9,427

45%

6,883

33%

5,579

27%

Arkansas

23,20,067

7,660

4,497

59%

3,562

47%

3,066

40%

California

3,06,18,852

63,877

21,730

34%

13,944

22%

10,034

16%

Colorado

44,97,682

7,390

4,575

62%

2,994

41%

2,199

30%

Connecticut

28,39,879

4,751

1,962

41%

971

20%

476

10%

Delaware

7,70,098

1,977

1,012

51%

686

35%

523

26%

District of Columbia

5,77,314

548

164

30%

107

20%

78

14%

Florida

1,72,34,469

52,059

29,200

56%

20,405

39%

15,918

31%

Georgia

81,07,219

25,737

13,598

53%

10,549

41%

9,021

35%

Hawaii

11,15,979

1,128

734

65%

453

40%

312

28%

Idaho

13,39,965

3,479

2,318

67%

1,793

52%

1,523

44%

Illinois

98,59,679

19,681

10,173

52%

6,973

35%

5,374

27%

Indiana

51,64,353

15,177

7,467

49%

5,792

38%

4,842

32%

Iowa

24,28,640

5,583

2,879

52%

2,050

37%

1,635

29%

Kansas

22,13,106

5,756

2,977

52%

2,187

38%

1,792

31%

Kentucky

34,64,554

12,419

7,154

58%

5,373

43%

4,480

36%

Louisiana

35,60,226

9,760

5,182

53%

4,208

43%

3,641

37%

Maine

10,95,969

1,928

1,092

57%

529

27%

247

13%

Maryland

47,13,082

8,489

3,876

46%

2,331

27%

1,560

18%

Massachusetts

55,39,447

7,761

1,957

25%

1,180

15%

793

10%

Michigan

78,46,375

22,706

12,950

57%

9,566

42%

7,873

35%

Minnesota

43,36,514

7,342

4,258

58%

2,778

38%

2,034

28%

Mississippi

22,78,354

7,604

3,302

43%

2,795

37%

2,509

33%

Missouri

47,66,387

14,289

8,585

60%

6,595

46%

5,571

39%

Montana

8,40,029

2,392

1,464

61%

1,111

46%

934

39%

Nebraska

14,59,044

2,489

1,456

58%

1,030

41%

817

33%

Nevada

23,85,240

7,617

4,223

55%

3,120

41%

2,556

34%

New Hampshire

11,04,426

1,711

926

54%

607

35%

447

26%

New Jersey

69,43,663

14,246

5,540

39%

3,139

22%

1,938

14%

New Mexico

16,19,953

4,965

2,467

50%

1,493

30%

1,005

20%

New York

1,54,17,677

29,830

11,195

38%

5,983

20%

3,392

11%

North Carolina

81,95,134

16,482

8,604

52%

6,462

39%

5,389

33%

North Dakota

5,81,523

998

650

65%

496

50%

418

42%

Ohio

91,09,592

29,411

15,875

54%

11,956

41%

9,996

34%

Oklahoma

30,06,685

11,744

5,833

50%

4,384

37%

3,654

31%

Oregon

33,52,774

5,999

3,798

63%

2,449

41%

1,773

30%

Pennsylvania

1,01,66,029

28,407

14,146

50%

9,561

34%

7,276

26%

Puerto Rico

26,18,532

2,681

1,232

46%

650

24%

359

13%

Rhode Island

8,54,658

1,763

611

35%

279

16%

113

6%

South Carolina

40,33,799

12,382

6,784

55%

5,187

42%

4,388

35%

South Dakota

6,67,244

1,411

754

53%

543

38%

438

31%

Tennessee

53,19,360

18,987

11,047

58%

8,507

45%

7,192

38%

Texas

2,16,13,563

59,795

29,773

50%

21,741

36%

17,692

30%

Utah

22,75,225

3,453

1,815

53%

1,233

36%

939

27%

Vermont

5,09,761

498

287

58%

137

28%

63

13%

Virginia

66,79,468

15,119

7,123

47%

4,418

29%

3,062

20%

Washington

59,52,622

9,223

5,299

57%

3,207

35%

2,156

23%

West Virginia

14,33,072

5,483

3,350

61%

2,566

47%

2,166

40%

Wisconsin

45,57,330

9,154

5,445

59%

3,809

42%

2,986

33%

Wyoming

4,44,716

1,374

938

68%

744

54%

647

47%

Total

25,78,29,067

6,41,305

3,18,979

50%

2,25,424

35%

1,77,998

28%

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

  1. New York Times. An ongoing repository of data on Coronavirus cases and deaths in the US.
  2. Centers for Disease Control and Prevention. Rates of Covid-19 cases or deaths by age group and vaccination status.
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  4. Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status - 13 U S Jurisdictions, April 4-July 17, 2021. MMWR Morb Mortal Wkly Rep 70 (2021): 1284-90.
  5. Pierson E, Gerardin J, Lash N. The lives lost to undervaccination, in charts. The New York Times 2021 September 14 (2021).
  6. Steele MK, Couture A, Reed C, et al. Estimated Number of COVID-19 Infections, Hospitalizations, and Deaths Prevented Among Vaccinated Persons in the US, December 2020 to September 2021. JAMA Netw Open 5 (2022): e2220385.

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