SARS-CoV-2 vaccine protection and deaths among US veterans during 2021
Veterans’ virus affairs
The US Department of Veterans Affairs (VA) provides life-long healthcare to military personnel, and VA data offer a unique resource for public health surveillance in the United States. Cohn et al. examined data from over 780,000 individuals to track the effects of vaccination rollout as the Delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the United States between February and September of 2021. Although messenger RNA (mRNA) and viral vector vaccines have effectively prevented clinically significant disease, SARS-CoV-2 Delta transmission has surged. Vaccine breakthrough infections have predominated in those given the viral vector vaccine but also occur among those given mRNA vaccines. By September 2021, the protection offered by the viral vector vaccine during the study declined to about 13% against infection and to about 50% against death. Unvaccinated individuals remained at the highest risk of infection, severe disease, and death. —CA
Abstract
We report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine effectiveness against infection (VE-I) and death (VE-D) by vaccine type in 780,225 veterans in the Veterans Health Administration, covering 2.7% of the US population. From February to October 2021, VE-I declined for all vaccine types, and the decline was greatest for the Janssen vaccine, resulting in a VE-I of 13.1%. Although breakthrough infection increased risk of death, vaccination remained protective against death in persons who became infected during the Delta variant surge. From July to October 2021, VE-D for age <65 years was 73.0% for Janssen, 81.5% for Moderna, and 84.3% for Pfizer-BioNTech; VE-D for age ≥65 years was 52.2% for Janssen, 75.5% for Moderna, and 70.1% for Pfizer-BioNTech. Findings support continued efforts to increase vaccination, booster campaigns, and multiple additional layers of protection against infection.
The mRNA vaccines BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) and the viral vector vaccine JNJ-78436735 (Janssen) have effectively prevented clinically recognized disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since their rollout in the United States in late 2020 (1, 2). Vaccines have also reduced the incidence of asymptomatic infection and associated infectivity (3). However, by July 2021, the United States experienced a surge in cases of COVID-19, dominated by the B.1.617.2 (Delta) variant (4, 5). Initial reports, including follow-up of the Pfizer-BioNTech and Moderna trials (6–8), suggested sustained vaccine protection (9), but three reports by the US Centers for Disease Control and Prevention (CDC) in August 2021 (10–12) demonstrated that protection against infection had declined in mid-summer as the Delta variant rose to dominance; protection against hospitalization and death remained high (13–15). Breakthrough infections, illness, hospitalizations, and deaths have since continued to emerge in vaccine recipients.
This phenomenon has been most comprehensively monitored in Israel, where high levels of transmission of the Delta variant led to a resurgent outbreak in mid-June 2021 (16) despite a successful nationwide campaign to vaccinate the population (17). Israel authorized boosters of the Pfizer-BioNTech vaccine for adults age ≥60 years in July 2021 and extended this authorization to adults age ≥50 years in August 2021 (18). Rates of infection and severe illness subsequently declined in those who received a booster (19). Largely on the basis of these data, as well as data from the UK (20, 21), the US Food and Drug Administration (FDA) authorized boosters of the Pfizer-BioNTech vaccine for older (age ≥65 years) and higher-risk adults in September 2021 (22); the FDA similarly authorized boosters of the Moderna vaccine in October 2021, as well as boosters for all recipients of the Janssen vaccine (23).
The debate over boosters in the United States (24) has laid bare the limitations of its public health infrastructure: National data on vaccine breakthrough are inadequate. The CDC transitioned in May 2021 from monitoring all breakthrough infections to focus on identifying and investigating only hospitalized or fatal cases attributable to any cause, including causes not related to COVID-19 (25). Some data on vaccinations, infections, and deaths are collected through a patchwork of local health departments (10), but these data are frequently out of date and difficult to aggregate at the national level. We addressed this gap and examined SARS-CoV-2 infection and deaths by vaccination status in 780,225 veterans during the period 1 February 2021 to 1 October 2021, encompassing the emergence and dominance of the Delta variant in the United States.
The distribution of SARS-CoV-2 infection by demographics, comorbidity, and vaccination status is shown in table S1 for 1 February 2021 to 1 October 2021 (n = 780,225 subjects). The percentage of polymerase chain reaction (PCR) test positivity is higher in veterans who were unvaccinated (25.8%), female (15.8%), Hispanic (13.9%), American Indian/Alaska Native (14.7%) or Native Hawaiian/Pacific Islander (14.2%), age <50 years at time of reverse transcription PCR (RT-PCR) assay (19.1%), and had a lower comorbidity score (16.2% for Charlson Comorbidity Index = 0) (26); 33,514 positive PCR tests occurred in 498,148 fully vaccinated veterans. The distribution of vaccine type by demographic is shown in table S2. Vaccine type differed by age: Younger (age <50 years) veterans were more likely to have received the Janssen vaccine than either Moderna or Pfizer-BioNTech.
For the period 1 February 2021 to 1 October 2021, vaccine effectiveness against infection (VE-I) declined over time (P < 0.01 for time dependence) (Table 1), even after adjusting for age, sex, and comorbidity. VE-I declined for all vaccine types (Fig. 1), with the largest declines for Janssen followed by Pfizer-BioNTech and Moderna. Specifically, in March, VE-I was 86.4% [95% confidence interval (CI), 85.2 to 87.6%) for Janssen, 89.2% (95% CI, 88.8 to 89.6%) for Moderna, and 86.9% (95% CI, 86.5 to 87.3%) for Pfizer-BioNTech. By September, VE-I had declined to 13.1% (95% CI, 9.2 to 16.8%) for Janssen, 58.0% (95% CI, 56.9 to 59.1%) for Moderna, and 43.3% (95% CI, 41.9 to 44.6%) for Pfizer-BioNTech.
| Adjusted hazard ratio | 95% confidence interval | P value | ||
|---|---|---|---|---|
| Janssen versus unvaccinated* | ||||
| March | 0.14 | 0.12 | 0.15 | <0.01 |
| April | 0.19 | 0.17 | 0.20 | <0.01 |
| May | 0.25 | 0.24 | 0.27 | <0.01 |
| June | 0.34 | 0.33 | 0.36 | <0.01 |
| July | 0.47 | 0.45 | 0.49 | <0.01 |
| August | 0.64 | 0.62 | 0.66 | <0.01 |
| September | 0.87 | 0.83 | 0.91 | <0.01 |
| Moderna versus unvaccinated* | ||||
| March | 0.11 | 0.10 | 0.11 | <0.01 |
| April | 0.14 | 0.13 | 0.14 | <0.01 |
| May | 0.17 | 0.17 | 0.17 | <0.01 |
| June | 0.21 | 0.21 | 0.22 | <0.01 |
| July | 0.27 | 0.26 | 0.27 | <0.01 |
| August | 0.33 | 0.33 | 0.34 | <0.01 |
| September | 0.42 | 0.41 | 0.43 | <0.01 |
| Pfizer-BioNTech versus unvaccinated* | ||||
| March | 0.13 | 0.13 | 0.14 | <0.01 |
| April | 0.17 | 0.16 | 0.17 | <0.01 |
| May | 0.21 | 0.21 | 0.22 | <0.01 |
| June | 0.27 | 0.27 | 0.28 | <0.01 |
| July | 0.35 | 0.34 | 0.35 | <0.01 |
| August | 0.44 | 0.44 | 0.45 | <0.01 |
| September | 0.57 | 0.55 | 0.58 | <0.01 |
Table 1. Vaccine effectiveness against SARS-CoV-2 infection by month after vaccination; estimated from Cox proportional hazards models; and adjusted for age, race, ethnicity, sex, and comorbidity score.
Adjusted hazard ratio <1.0 indicates lower risk of infection for vaccine, shown compared with unvaccinated. For vaccinated veterans, infection was assessed 15 days after the last vaccine that established full vaccination status. For unvaccinated veterans, infection was assessed beginning in 1 February 2021, coincident with broadscale vaccine eligibility in the VA. Time dependence was tested in Cox proportional hazards models by including product terms for vaccination status (Janssen, Moderna, Pfizer-BioNTech, or unvaccinated) by log(time)—Janssen*log(time), Moderna*log(time), Pfizer-BioNTech*log(time)—and adjusted for age, sex, race, ethnicity, and comorbidity (Charlson Comorbidity score, overweight, type II diabetes, chronic obstructive pulmonary disease, bronchitis, acute respiratory failure, and chronic lung disease). Significance levels for all product terms were P < 0.0001. Vaccination status is modeled as time-varying, assigning follow-up time for veterans before the date of full vaccination as “unvaccinated time” and time after the date of full vaccination as “vaccinated time”; vaccination is defined as (i) a single Janssen vaccine, (ii) two Moderna vaccines, or (iii) two Pfizer-BioNTech vaccines.
*Associations at each month were estimated from contrasts by using product terms for vaccination status by time in Cox proportional hazards models, including indicator terms for vaccination status (Janssen, Moderna, or Pfizer-BioNTech) product terms and age, sex, race, ethnicity, and comorbidity (Charlson Comorbidity score, overweight, type II diabetes, chronic obstructive pulmonary disease, bronchitis, acute respiratory failure, and chronic lung disease).
Fig. 1. Time-dependent vaccine effectiveness against SARS-CoV-2 infection as estimated from Cox proportional hazards models, adjusted for age, race, ethnicity, sex, and comorbidity score.
Vaccine effectiveness presented as (1 – hazard ratio × 100) and 95% CIs. Effectiveness for each month was estimated from contrasts by using product terms for vaccination status by time to most recent RT-PCR assay.
As shown in Fig. 2, risk of infection accelerated in both unvaccinated and fully vaccinated veterans beginning in July 2021 and through September 2021, which is consistent with the time dependence observed in the Cox proportional hazards models. This pattern was similar across age groups, and risk of infection was highest for unvaccinated veterans. Veterans who were fully vaccinated with the Moderna vaccine had the lowest risk of infection, followed closely by those who received the Pfizer-BioNTech vaccine, then those who received the Janssen vaccine.
Fig. 2. Kaplan-Meier curves illustrating cumulative risk of SARS-CoV-2 infection, by vaccination status and age.
(A) All ages. (B) Age <50 years. (C) Age 50 to 64 years. (D) Age ≥65 years. The survival function estimates time to infection detected by most recent RT-PCR assay.
Risk of death after SARS-CoV-2 infection was highest in unvaccinated veterans regardless of age and comorbidity (Fig. 3). However, breakthrough infections were not benign, as shown by the higher risk of death in fully vaccinated veterans who became infected compared with vaccinated veterans who remained infection-free.
Fig. 3. Kaplan-Meier curves illustrating cumulative risk of death due to any cause, by vaccination status and RT-PCR assay.
(A) Age <65 years. (B) Age ≥65 years. (C) Charlson Comorbidity Index score <3. (D) Charlson Comorbidity Index score ≥3.
We observed similar results when examining the time period corresponding to the dominance of the Delta variant (fig. S1). Specifically, among those with a positive PCR test on or after 1 July 2021, vaccination was protective against death, although with some differences by age and vaccine type. For age <65 years, vaccine effectiveness against death (VE-D) was 81.7% (95% CI, 75.7 to 86.2%) for any vaccine, 73.0% (95% CI, 52.0 to 84.8%) for Janssen, 81.5% (95% CI, 70.7 to 88.4%) for Moderna, and 84.3% (95% CI, 76.3 to 89.7%) for Pfizer-BioNTech. For age ≥65 years, VE-D was 71.6% (95% CI, 68.6 to 74.2%) for any vaccine, 52.2% (95% CI, 37.2 to 63.6%) for Janssen, 75.5% (95% CI, 71.8 to 78.7%) for Moderna, and 70.1% (95% CI, 66.1 to 73.6%) for Pfizer-BioNTech.
Benefits of vaccination in reducing risk of SARS-CoV-2 infection and death are clearly supported by this study of more than 780,225 US veterans. However, VE-I declined as risk increased in both unvaccinated and vaccinated veterans, coincident with the emergence and dominance of the Delta variant in the United States. Our analysis by vaccine type—including the Pfizer-BioNTech, Moderna, and Janssen vaccines—suggests a declining VE-I over time, particularly for the Janssen vaccine. Yet, despite increasing risk of infection because of the Delta variant, VE-D remained high, and compared with unvaccinated veterans, those fully vaccinated had a much lower risk of death after infection. These results demonstrate an urgent need to reinstate multiple layers of protection, such as masking and physical distancing—even among vaccinated persons—while also bolstering current efforts to increase vaccination.
Patterns of breakthrough SARS-CoV-2 infection among vaccinated veterans show a worrisome temporal trend, overlapping with the emergence of Delta as the dominant variant in the United States in July 2021. Although others have demonstrated high VE-I and VE-D in veterans during vaccine rollout through mid-March 2021 (27), our results suggest that vaccines are less effective in preventing infection associated with the Delta variant. The Delta variant is more infectious than other variants, likely because of increased viral load and transmission before symptoms (28). Other US studies (29–31), many conducted in large health care systems, similarly show declining VE-I as the Delta variant rose to dominance, with notable declines in older adults. For example, two studies conducted at Kaiser Permanente Southern California show that VE-I decreased from 95% at 14 to 60 days to 79% at 151 to 180 days after vaccination for ages 18 to 64 years (29), and from 80% at 1 month to 43% at 5 months after vaccination for ages ≥65 years (31). Declines in protection against infection with Delta have been observed in Israel (16), the UK (20, 21), and Qatar (32, 33).
Endurance of VE-I in the face of the Delta variant in this large, population-based sample was dependent on vaccine type, and this was consistent across all age groups and time since vaccination. Most studies of VE-I have examined Moderna or Pfizer-BioNTech vaccines (16, 20, 21, 29–33), and our study adds to this literature by showing dramatic declines in VE-I for the Janssen vaccine. Similarly, we found that VE-D for the Janssen vaccine was much lower—about 50%—compared with that of the randomized trial. These findings are consistent with the better neutralizing antibody response observed after vaccination with Moderna or Pfizer-BioNtech compared with Janssen vaccines, and in response to the Delta variant (34). In addition, differences in immune response to mRNA vaccines by type of immunity support the more enduring protection against death (through cellular immunity) compared with protection against infection (which is more dependent on antibodies) (35).
Our findings on increased risk of death after breakthrough infection provide further support for continuing efforts to discover and implement effective interventions to prevent infection in all persons, including those who have been fully vaccinated. Fully vaccinated veterans were more likely to survive when infected with SARS-CoV-2 (breakthrough infections) compared with unvaccinated veterans who were also infected; this was true even for older age groups, those with more chronic conditions, and during and after the Delta surge in July 2021. However, breakthrough infections still carried some risk, as evidenced by the higher risk of death in vaccinated veterans who were subsequently infected compared with those who were vaccinated but remained infection-free. Breakthrough infections are also a concern for transmission, and the Delta variant in particular results in high viral loads in the nose similar to that of infections in unvaccinated persons (36). Because viral load is a key determinant of transmissibility (37), the benefit of vaccination is less for the Delta variant compared with the earlier Alpha variant (38), suggesting that additional, alternative prevention practices will be essential to reduce infection. Higher risk of death after breakthrough infection implies higher rates of hospitalizations, and these prevention practices will likely also conserve medical resources.
Infection prevention in all persons will have the added, worldwide benefit of reducing the potential for deleterious evolution of the viral genome as the infection is transmitted from person to person (37, 39). However, rates of vaccination—among other viral, social, political, and behavioral parameters—will determine the future evolution of the virus (37). Viral evolution may result in more lethal or infectious variants, or variants that escape protection the vaccine, and should be constricted by reducing infection rates.
It is not yet known whether breakthrough infections increase risk of “long COVID” [otherwise known as post-acute sequelae of COVID-19 (PASC)], a constellation of debilitating and lingering symptoms after infection. These symptoms can lead to physiologic disruption of multiple organ systems; substantial disruption of daily life, employment, and mental health; and a higher burden on the health care system (40, 41). Long COVID has been observed as a consequence of both mild and severe infection (42), raising the possibility that survivors of breakthrough infections may also be at risk for long COVID. Therefore, prevention of breakthrough infections may avoid the overwhelming, long-term consequences of long COVID due to widespread infection.
As of this report, the scientific community continues to debate booster vaccines in the United States. The FDA authorized Pfizer-BioNTech boosters in September 2021 and Moderna and Janssen boosters in October 2021, and the CDC has made similar recommendations. Although our study does not directly address the benefits and risks of booster vaccines, findings may be interpreted in the context of this ongoing debate. First, VE-I declined most precipitously for the Janssen vaccine, and a booster with one of the mRNA vaccines may result in more durable protection for those initially vaccinated with Janssen. This is further supported by the available, albeit limited, evidence that suggests a stronger antibody response when Janssen vaccination is followed by an mRNA booster (43). Second, although their risk of death is much lower, younger veterans (age <65 years) experienced the greatest relative reduction in risk of death associated with vaccination, which suggests that this age group in addition to older adults may benefit from a booster. Early results of the first randomized trial on boosters demonstrates that a booster of Pfizer-BioNTech is 95.6% effective against infection compared with two shots and a placebo (43). Some unknowns remain—namely, how effective booster vaccines are against Delta and other emerging variants and how long immunity from a booster may last.
A strength of our study is the use of large-scale, national US Department of Veterans Affairs (VA) data, covering 2.7% of the US population and collected in real time. After transitioning to focus on breakthrough hospitalizations and deaths, the CDC now reports COVID-19 cases, associated hospitalizations, and deaths by vaccination status and age group (available at https://covid.cdc.gov/covid-data-tracker) as weekly rates per 100,000 persons; these data are derived from a network of acute-care hospitals in 14 states and 16 health departments that links case surveillance to immunization systems. Although informative, data lag behind by about 2 months and do not illustrate risk of hospitalization or death after a breakthrough infection. The VA Corporate Data Warehouse was essential to our timely analysis of breakthrough infections and deaths up until 1 October 2021, and moving forward, these data may be used as a tool to comprehensively monitor vaccine effectiveness because other variants are likely to emerge.
Our results should be interpreted in the context of limitations. There are many approaches to evaluating vaccine effectiveness (such as test-negative, case-control, and cohort registry). We required a recent RT-PCR assay to be included in the analysis, a feature of test-negative designs that may minimize confounding owing to health-seeking behavior. However, there may still be differences in testing intervals and frequency by vaccination status. The specific setting or reason for testing is not known, and it is also possible that persons with asymptomatic infections may not have been tested and therefore not included in the analysis. Our sample has proportionately fewer women, although a large number are still included. We did not have information on genotyping of infections to determine the proportion caused by the Delta variant. Patterns of survival for those with a negative PCR test by vaccination status suggests that there are underlying differences in unvaccinated compared with vaccinated persons, and that we did not measure or account for these in our analysis; these differences may contribute to the different risks of death we observed. For example, recent polls suggest that unvaccinated Americans are less willing to adopt COVID-19 precautions, such as mask-wearing and social distancing (44). Last, we did not examine VE against hospitalization but used death as a surrogate for clinically severe infection. Our finding that VE-D remained high during the Delta surge is consistent with US studies that show sustained protection against hospitalization (15, 30, 45).
Although vaccination remains protective against SARS-CoV-2 infection, protection waned as the Delta variant emerged in the United States, and this decline did not differ by age. The Janssen vaccine showed the greatest decline in VE-I. Breakthrough infections were not benign; vaccinated persons who were subsequently infected had a higher risk of death compared with that of vaccinated persons who remained infection-free. Vaccination still provided protection against death in infected persons, and this benefit was observed for the Moderna, Pfizer-BioNTech, and Janssen vaccines during the Delta surge, although the benefit was greater for Moderna and Pfizer-BioNTech compared with Janssen vaccines. Our findings support the conclusion that COVID-19 vaccines remain the most important tool to prevent infection and death. Vaccines should be accompanied by additional measures for both vaccinated and unvaccinated persons, including masking, hand washing, and physical distancing. It is imperative to implement public health interventions, such as strategic testing for control of outbreaks, vaccine passports, employment-based vaccine mandates, vaccination campaigns for eligible children as well as adults, and consistent messaging from public health leadership in the face of increased risk of infection from the Delta and other emerging variants.
Acknowledgments
We acknowledge the invaluable efforts of the Veterans Affairs data architects, managers, and clinicians who assembled the Centralized Interactive Phenomics Resource (CIPHER), rapidly compiling a library of numerous COVID-19–related phenotypes that are the basis for this research. Our work was supported by using resources and facilities of the Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI), VA HSR RES 14-457. We deeply appreciate the steady service and support of the VA Informatics and Computing Infrastructure (VINCI) staff. Without the efforts of these teams, this study would not have been possible. We are grateful for the veterans who have so selflessly served their country. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government. Insititutional Review Board (IRB): This research is covered under the University of California, San Francisco IRB 10-03609 Reference 320151
Funding: This work was supported by the Mercatus Center at George Mason University (Fast Grants 2207) and the University of California Office of the President (Emergency COVID-19 Research Seed Funding R00RG3118).
Author contributions: Conceptualization: B.A.C. and P.M.C. Methodology: P.M.C., C.C.M., and B.A.C. Statistical analysis: P.M.C. Funding acquisition: A.W.W., P.M.C., and N.Y.K. Data interpretation: all authors. Writing, original draft: C.C.M., P.M.C., and B.A.C. Writing, review and editing: all authors.
Competing interests: C.C.M. reports consulting for Freenome. A.W.W. reports consulting for ECOM Medical, Obelab, Sensifree, and Shifamed. B.A.C., P.M.C., and N.Y.K. declare that they have no competing interests.
Data and materials availability: Data and materials availability: The data that support the findings of this study are available from the Department of Veterans Affairs (VA). Code is available at (46). Data are made freely available to researchers behind the VA firewall with an approved study protocol. Summary data can be accessed from a commercial source Data Lake Analysis for Real-World Evidence Solutions–STATinMED: https://statinmed.com/data. More information is available at https://www.virec.research.va.gov or by contacting the VA Information Resource Center at [email protected] This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
Supplementary Materials
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Material and Methods
Fig. S1
Tables S1 to S3
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References and Notes
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Volume 375 | Issue 6578
21 January 2022
21 January 2022
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Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
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Received: 24 August 2021
Accepted: 2 November 2021
Published in print: 21 January 2022
Acknowledgments
We acknowledge the invaluable efforts of the Veterans Affairs data architects, managers, and clinicians who assembled the Centralized Interactive Phenomics Resource (CIPHER), rapidly compiling a library of numerous COVID-19–related phenotypes that are the basis for this research. Our work was supported by using resources and facilities of the Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI), VA HSR RES 14-457. We deeply appreciate the steady service and support of the VA Informatics and Computing Infrastructure (VINCI) staff. Without the efforts of these teams, this study would not have been possible. We are grateful for the veterans who have so selflessly served their country. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government. Insititutional Review Board (IRB): This research is covered under the University of California, San Francisco IRB 10-03609 Reference 320151
Funding: This work was supported by the Mercatus Center at George Mason University (Fast Grants 2207) and the University of California Office of the President (Emergency COVID-19 Research Seed Funding R00RG3118).
Author contributions: Conceptualization: B.A.C. and P.M.C. Methodology: P.M.C., C.C.M., and B.A.C. Statistical analysis: P.M.C. Funding acquisition: A.W.W., P.M.C., and N.Y.K. Data interpretation: all authors. Writing, original draft: C.C.M., P.M.C., and B.A.C. Writing, review and editing: all authors.
Competing interests: C.C.M. reports consulting for Freenome. A.W.W. reports consulting for ECOM Medical, Obelab, Sensifree, and Shifamed. B.A.C., P.M.C., and N.Y.K. declare that they have no competing interests.
Data and materials availability: Data and materials availability: The data that support the findings of this study are available from the Department of Veterans Affairs (VA). Code is available at (46). Data are made freely available to researchers behind the VA firewall with an approved study protocol. Summary data can be accessed from a commercial source Data Lake Analysis for Real-World Evidence Solutions–STATinMED: https://statinmed.com/data. More information is available at https://www.virec.research.va.gov or by contacting the VA Information Resource Center at [email protected] This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
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George Mason University: 2207; 2005
University of California Office of the President: R00RG3118
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These models differ from multiple other studies
There are two important things to note about this paper by Cohn et al. (1) Results differ substantially multiple other studies, especially for the single-dose Janssen vaccine for which Cohn et al. claim a vaccine effectiveness (VE) against infection falling faster than for the Pfizer and Modern vaccines. Other studies show the Janssen vaccine to have the most durable protection. (2) The vaccine effectiveness curves and points in Figure 1 have been calculated by a single equation for each vaccine (i.e., a simple model) and are not individual VE determinations at each time point.
VE can change with time for two reasons: (a) if vaccination efficacy changes with time since vaccination, and (b) because new variants appear. Interpretation of the Cohn et al. results is confusing because the time axis of Figure 1 is labelled in both calendar months and as “months after full vaccination”. There is a similar confusion in Table 1. Either interpretation has problems because the single-dose Janssen vaccine was not authorized for use until March 2021. The authors’ press release speaks of “a dramatic decline in effectiveness for the Janssen vaccine, from 86.4% in March to 13.1% in September” [1]. The press release includes Figure 1, which shows the effectiveness of the Janssen vaccine heading to zero by October.
In contrast to this, multiple studies [2-7] covering all or much of the same time period have found the real-world effectiveness against infection of the single-dose Janssen vaccine to be stable around 70%, consistent with the efficacy found in the Phase 3 clinical trials [8]. Notably, the study of Zheutlin et al. [7], covering the same time period as Cohn et al. but with a much larger sample size, shows no decline in VE with time since vaccination. This is consistent with the increase in neutralizing antibody responses over an 8-month period after vaccination [9].
Unlike the results of these studies [2-9], the claimed VE values in Figure 1 of Cohn et al. come directly from an equation VE(t) = 1 – A exp (Bt), where t is time and A and B are constants. This equation is not stated in the paper. For the single-dose Janssen vaccine, one can determine that the equation is VE(t) = 1 – 0.102 exp (0.305t), when t is in months with t = 1 for March. The quoted upper and lower confidence intervals are given by a similar equation with slightly different constants. The equation VE(t) = 1 – A exp (Bt) is radically different from the exponential decay one expects at large times in a simple pharmacokinetic model. Such a model gives VE = VE(max) exp (-0.69t/h), where VE(max) is the maximum efficiency, t is again the time, and h is the half-life. The declines in VE(t) derived by Zheutlin et al. [7] are consistent with exponential declines for the Moderna and Pfizer vaccines of 2% and 4% per month respectively, corresponding to half-lives of about 1.5 and 3 years. The very different, non-standard functional form used by Cohn et al. corresponds not to an exponential decline in VE, but to an exponential runaway in the hazard ratio for infection, (1-VE). It has the property that if A and B are positive, VE will rapidly go to zero. This can be seen to be the case with a slight extrapolation of the curves in Figure 1. Such a model has no pharmacokinetic basis and, as Prof. Plaxco has already noted in these eLetters, Cohn et al. have provided “zero indication of how well their model actually fits the data.” Inspection of the related preprint by Cohn et al. (10) shows that the p values given in Table 1 are the probabilities that VE is not zero at a given time point and are not an indication of how well the model fits the data.
References
[1] Public Health Institute, press release (2021). https://www.phi.org/press/breakthrough-infection-study-compares-decline-in-vaccine-effectiveness-and-consequences-for-mortality/ .
[2] Corchado-Garcia et al., JAMA Netw Open (2021) https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2785664
[3] Polinski et al., preprint (2021). https://www.medrxiv.org/content/10.1101/2021.09.10.21263385v2
[4] Rosenberg et al., N Engl J Med (2021). https://www.nejm.org/doi/full/10.1056/NEJMoa2116063
[5] Lin et al., N Engl J Med (2022). https://www.nejm.org/doi/full/10.1056/NEJMoa2117128
[6] Centers for Disease Control and Prevention. Rates of COVID-19 cases or deaths by age group and vaccination status (2021). https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/3rge-nu2a
[7] Zheutlin et al., preprint (2022). https://www.medrxiv.org/content/10.1101/2022.01.05.22268648v1
[8] Sadoff et al., N Engl J Med (2021). https://www.nejm.org/doi/full/10.1056/NEJMoa2101544
[9] Barouch et al., N Engl J Med (2021). https://www.nejm.org/doi/full/10.1056/NEJMc2108829
[10] Cohn et al., preprint (2021). https://www.medrxiv.org/content/10.1101/2021.10.13.21264966v1
RE: Better to Provide Vaccine Dose to Immune Compromised Individuals than Booster Dose to Vaccinated Individuals
SARS-CoV 2 is evolving into new variants rapidly and efficacy of existing vaccines is under debate. It is important to understand that emergence of new virus variants is taking place in immune compromised individuals (1). It has been observed that the delta variant (B.1.617.2) was first identified in the state of Maharashtra (India) in 2021 (2). At that time the rate of vaccination was comparatively less. Further, the new omicron variant was reported from Botswana on Nov 2021 (3) emphasizing that the immune compromised people may aid in emergence of new variant. Further, we found the vaccination rate in Botswana is 49% (population given at least one dose of vaccine) (graphics.reuters.com/world-coronavirus-tracker-and-maps/vaccination-rollout-and-access/) which is very low in comparison to the developed nations. The low vaccination rate in under privileged nations leads to the evolution of more pathogenic variants which curbs the efforts to end this pandemic. We highlight to the policy makers and global health advisors in order to contain the evolving new strains of SARS-CoV, it is better to identify the regions where preferential vaccination drive should be started rather than providing booster dose to the individuals who are completely vaccinated.
References:
UNLEASHING THE POWER OF CLINICAL INFORMATICS FROM THE VETERAN HEALTH ADMINISTRATION
In their interesting November 4, 2021 report, Cohen BA, et. al. presented vaccine effectiveness against COVID-19 infection and death among 780,225 U.S. veterans from February to October 2021. However, it is unclear how the cohort was assembled, as a sample size of 780,225 veterans represents a fraction of the fully vaccinated veteran population. There are approximately 7 million unique veteran patients within the Veteran Health Administration (VHA) and more than half have been fully vaccinated as of July. 2021 (1). The VHA maintains two databases containing information on SARS-Cov-2 vaccinations. In addition to the Corporate Data Warehouse (CDW), a nationwide repository of electronic health records (EHR) dating back to October 1999, the VA COVID shared Data Resource (CSDR) comprises a robust and highly vetted registry of patients who have been vaccinated within and outside of the agency.
Beyond providing resources for significant, descriptive findings that may impact public health, the vast, longitudinal EHR from VHA offers phenomenal research opportunities for developing the next generation of predictive models that will help shape public health policies for COVID-19 management and other complex clinical problems in a timely manner. Conventional predictive models are derived from limited epidemiologic studies using demographic traits, co-morbidity scores and/or pre-existing conditions. Such approaches are subject to considerable biases including misclassification. Using artificial intelligence and machine learning techniques, algorithms can be developed to combine relevant, temporal and objective physiologic, laboratory and pharmacologic data with patient characteristics at the individual level to minimize selection biases and augment model accuracy (2, 3). Such an enhanced predictive model system promises the ability to geographically forecast disease prevalence, optimize allocation of scare resources, allow rational, timely prioritization of boosters or new vaccinations, prevention and mitigation efforts, thereby enable systematic delivery of Precision Medicine against COVID-19, its related sequelae and revolutionize our management paradigm.
References
Plotting models and not data?
The results in this paper seem misrepresented. Figure 1, for example, is a plot of their model and thus provides zero indication of how well their model actually fits the data. Indeed, I cannot find any meaningful information regarding that absolutely key point in this paper. Likewise, best I can tell figure 2 is implying that, by September, more than 70% of their unvaccinated cohort and >40% of even their mRNA vaccinated cohorts had caught COVID. These numbers are improbably high. The former, for example, is more than 2x national levels. Probably much more than 2x, as the 31% national prior infection rate is an estimate of total infections, not known PCR-diagnosed infections.
How am I supposed to judge the validity of this work?
Are conclusions about vaccine passports and mandates supported by the study data?
Calls for vaccine passports and mandates appear to lack evidence
In Cohn et. al., Science, 2021, the authors appear to provide no evidence that vaccine mandates or passports will achieve disease control, yet conclude that "It is essential to implement public health interventions, such as strategic testing for control of outbreaks, vaccine passports, employment-based vaccine mandates, vaccination campaigns for eligible children as well as adults, and consistent messaging from public health leadership in the face of increased risk of infection due to the Delta and other emerging variants."
This is thus a surprising conclusion. The implementation of employer mandates or passports requires additional evidence through observational data or modelling studies that demonstrates a clear public health benefit, even when weighed against the potential harms of these policies.
It is not immediately obvious whether employer mandates or vaccine passports would reduce disease risk. (A reasonable risk to consider here might be that of [severe] disease caused by the unvaccinated onto the vaccinated and that this should be an intolerable increase in absolute risk weighed against policy harms.) Any such calculation of the putative benefits of mandates or passports should at least account for: the relative proportions of vaccinated versus unvaccinated in relevant populations; the possibility that passports and mandates may increase the perception of safety, leading to a false sense of security which could be harmful to at-risk (vaccinated or exempt) workers; the level of natural immunity likely in the unvaccinated population [1]; the difference in epidemiological promiscuity between infected vaccinated and infected unvaccinated individuals (as Covid-19 vaccines reduce the severity of symptoms [2], it is not unreasonable to suppose that an infected vaccinated individual may engage in more `risky' interactions than an infected unvaccinated person, all else equal); and – of course – waning immunity (emerging evidence even suggests that the efficacy against infection of the Oxford/AstraZeneca vaccine may fall to zero after about four months [3]) .
Once this absolute risk has been estimated for mandated versus non-mandated settings, it must be balanced against the multiple potential public health harms of vaccine mandates and passports.
For example, one of these harms is that these policies are likely to lead to indirect discrimination against race. In both the UK and US, for example, Black communities are overall less willing to receive a COVID-19 vaccine [4-6]. Moreover, in a UK context, vaccine passports have been shown to lower vaccination inclination [4, 7], including the majority of people who do not state a firm intention to vaccinate (or not) [4]. Although many individuals may likely go on to vaccinate regardless of a stated survey response, it is unclear whether this process is ethical. For instance, is it ethical if individuals take the vaccine solely to resume elements of society (such as the workplace) even if they do not wish to take the vaccine? When a loss of household income via a job loss is involved, this ethical question surely becomes more important and is another risk to consider.
There are further harms to take into consideration too. If passports or mandate policies do drive lower vaccine confidence among populations who are reluctant to vaccinate, do we stand to damage trust in governments, policymakers, public health institutions and, ultimately, in vaccines? As confidence in vaccines is lowest among the young in many settings around the world, it is not unreasonable to speculate that mandates or passports may lower trust in routine immunisations, as a confidence loss in one vaccine can trigger confidence losses in others [8]. In addition, there are also clear economic costs to such a policy as businesses may lose staff and customers.
Broad public health policy encouraging isolation of the sick (with free testing and support for employers and employees), regardless of vaccination status, may yield positive results without many of the associated harms.
Vaccine mandates and passports need a rethink.
Conflicting interests: AF has received funding from Janssen Pharmaceutica for a Vaccine Confidence Project collaborative grant and from Merck to investigate Covid-19 vaccine confidence in the UK and USA.
[1] Vaccinating people who have had covid-19: why doesn’t natural immunity count in the US? BMJ 374:n2101 (2021)https://doi.org/10.1136/bmj.n2101
[2] Antonelli et. al. Lancet Infectious Diseases (2021) https://doi.org/10.1016/S1473-3099(21)00460-6
[3] Nordström et. al. pre-print (2021) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3949410
[4] de Figueiredo et. al. EClinicalMedicine (2021). https://doi.org/10.1016/j.eclinm.2021.101109
[5] Woolf et. al. Lancet Regional Health Europe 9; 100180 (2021). https://doi.org/10.1016/j.lanepe.2021.100180
[6] Latest Data on COVID-19 Vaccinations by Race/Ethnicity Kaiser Family Foundation
https://www.kff.org/coronavirus-covid-19/issue-brief/latest-data-on-covid-19-vaccinations-by-race-ethnicity/ (2021)
[7] Porat et. al. Vaccines 9; 8 (2021) https://doi.org/10.3390/vaccines9080902
[8] de Figueiredo et. al. Lancet 396;10255 (2020) https://doi.org/10.1016/S0140-6736(20)31558-0