
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire

39180646
1020
10.1007/s40121-024-01020-2
Letter
A Response to: A Letter to the Editor Regarding ‘Comparative Effectiveness of mRNA-1273 and BNT162b2 COVID-19 Vaccines Among Older Adults: Systematic Literature Review and Meta-Analysis Using the GRADE Framework’
Beck Ekkehard Ekkehard.Beck@modernatx.com

1
Bausch-Jurken Mary T. 1
Van de Velde Nicolas 1
Wang Xuan 2
Malmenäs Mia 2
1 grid.479574.c 0000 0004 1791 3172 Moderna, Inc., 200 Technology Square, Cambridge, MA 02139 USA
2 grid.519503.b ICON plc, Stockholm, Sweden
24 8 2024
24 8 2024
10 2024
13 10 21952202
4 6 2024
2 7 2024
© The Author(s) 2024
2024
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Keywords

BNT162b2
COVID-19
Effectiveness
mRNA-1273
mRNA vaccine
Older adults
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2024
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pmcIn their letter to the editor, Hannah Volkman and colleagues raise questions about the methodology used in our recent systematic literature review and meta-analysis of the comparative effectiveness of mRNA-1273 and BNT162b2 COVID-19 vaccines among older adults [1]. We welcome the opportunity to respond to their comments and to affirm the robustness of our methodology and conclusions. In brief, we disagree with Volkman and colleagues’ claim that our meta-analysis comparing the vaccine effectiveness (VE) of mRNA-1273 versus BNT162b2 “is based on both inaccurate and out-of-date data.” Our methodology is consistent with historical precedent and relevant guidance for meta-analysis, whereby the use of unadjusted binary data allowed us to calculate risk ratios (RRs) for mRNA-1273 versus BNT162b2. We have examined each of the claims made by Volkman et al. and have exposed flaws in the examples they use to support their assertions regarding the inaccuracy of our data and exclusion of relevant studies. Further, their claim that our methodology led to conclusions “that contradicted the findings and interpretation reported by the original study authors” is unfounded because our specific research question differed from the various objectives of the individual studies included in our analysis. We further believe that the opinion of Volkman et al. regarding the difference in VE of the mRNA-1273 and BNT162b2 vaccines during periods of Omicron dominance in the vulnerable population of older adults aged ≥ 50 years is not supported by the literature referenced.

Volkman and colleagues first raise concern regarding the use of crude data in our meta-analysis, claiming that its use “renders the entire review uninterpretable, false, and misleading.” We do not share this opinion. Because adjustments to the data were not made consistently across the individual studies included in our meta-analysis, study-level-adjusted estimates were very difficult to compare; therefore, consistent with other recent examples from the COVID-19 vaccine literature, we used crude data. In a 2023 systematic review and meta-analysis of 10 longitudinal studies (9 of which were observational), Xu et al. used crude binary data for their pairwise meta-analysis and reported an overall RR to compare the effectiveness of a first-dose COVID-19 vaccine booster versus full vaccination [2]. Similarly, in a systematic review and meta-analysis of 54 observational studies, Rahmani et al. also used crude data and reported odds ratios to compare the effectiveness of COVID-19 vaccines against SARS-CoV-2 infection, hospitalization, and mortality [3]. This common approach of standardizing the data treatment across diverse studies enhances the reliability of meta-analyses in generating a generalized conclusion across populations and study designs. Importantly, because adjustments were made differently across the studies included in our analysis, our use of unadjusted data may provide greater transparency. These were the key reasons for using binary data in our meta-analysis, as supported by guidance from The National Institute for Health and Clinical Excellence Decision Support Unit [4].

As a specific example to support their argument against our use of unadjusted data, Volkman and colleagues erroneously use a study by Butt et al. [5] to make the claim that “instead of using adjusted vaccine effectiveness (VE) estimates as reported by the authors of the original studies, Kavikondala et al. calculated crude risk ratios from descriptive counts for the majority of study estimates, which removed all adjustments for bias and confounding made in the original studies.” However, the original conclusion that they cite from this study relates to a completely different research question than the one we evaluated. Butt et al. compared the VE of 3 versus 2 doses of each of the COVID-19 mRNA vaccines (mRNA-1273 and BNT162b2) and concluded from their findings that there was no difference between these vaccines when comparing the VE of these 2 dosing strategies. In contrast, our objective was to compare vaccination with mRNA-1273 or BNT162b2 versus no vaccination. Specifically, the adjustments for bias and confounding described in Butt et al. were done for the comparison of 3 versus 2 doses within each vaccine, and there were no adjustments for bias or confounding done for the comparison between mRNA-1273 and BNT162b2. Thus, to address the comparative effectiveness of mRNA-1273 versus BNT162b2 for the purpose of our study, we extracted the number of events among participants receiving 3 doses of mRNA-1273 versus 3 doses of BNT162b2, along with the respective group sizes. Volkman and colleagues conclude their argument by stating that “it is critical to note that using crude data, when adjusted estimates were available, invalidated multiple components of the authors’ GRADE assessment”; however, we maintain the validity of our methods and affirm that they followed the GRADE analysis for specific components such as inconsistency, indirectness, and imprecision.

Volkman and colleagues also argue that our study is biased due to exclusion of eligible studies and “extensive errors in data extraction,” but these claims are erroneous and misleading. First, they claim to have identified 14 additional studies meeting our inclusion criteria that were missing from our systematic review. However, 3 of these 14 studies were in fact retrieved by our search and were excluded per our screening criteria, including one study that evaluated nonhomologous vaccination [6]; one in which the majority of the study population had comorbidities, including clinically extremely vulnerable (CEV) 1–2 groups and an unspecified number of individuals with autoimmune conditions [7]; and another study that included VE data for only one of the vaccines in the subgroup of patients aged ≥ 50 years [8]. Of the remaining 11 studies, 6 were published or indexed in the WHO COVID-19 Research Database outside the date range of our search [9–14] and 5 were not indexed in the WHO COVID-19 Research Database used for our literature search [15–19]. Notably, these articles were also not identified in our cross-check of previously published systematic literature reviews [3, 20–34]. Volkman and colleagues also suggest that one study included in our analysis should have been excluded because it does not report VE data [35]. However, the study reported event data for symptomatic laboratory-confirmed SARS-CoV-2 infections and COVID-19-related hospitalizations and deaths; therefore, based on the methodology used in our study, as described in the Methods section of the article, we extracted numbers of events and sample sizes from this article instead of VE estimates, thus allowing a straightforward calculation of the RR for mRNA-1273 versus BNT162b2.

Additionally, Volkman and colleagues claim that Fig. 3 in our article contains numerous errors that affect the meta-analysis results. Although we acknowledge 2 minor errors that were not previously identified, these did not affect the results of our meta-analysis. Specifically, for the study by Rosenberg et al. [36], we used the correct denominator of 1,793,698 in the BNT162b2 group for both the severe infection and COVID-19 hospitalizations outcomes, as reflected in Fig. 3C and 3D, but inaccurately used the denominator 1,793,696 (i.e., 2 fewer individuals) for the infection outcome in Fig. 3A. Similarly, for the study by Voko et al. [37], we used the correct denominator of 116,251 in the mRNA-1273 group for severe infection, as shown in Fig. 3C, but inaccurately used the denominator 116,247 (i.e., 4 fewer individuals) for the infection and death outcomes in Fig. 3A and E. Because of the small difference between the numbers in both examples, results from the meta-analysis do not change after correcting the denominators. Volkman and colleagues also note that, “In Fig. 3A, numerators for Chemaitelly et al. were extracted from the matched cohort, while denominators were extracted from the unmatched cohort, thereby creating an uninterpretable measure.” However, Chemaitelly et al. do not specify that the data for their sensitivity analysis in participants aged ≥ 50 years (i.e., from supplemental Table S2 of the article, from which we extracted data for the numerator) are from the matched cohort [38]. Notably, our calculations using the matched cohort for the denominator indicate that the relative risk of SARS-CoV-2 infection with mRNA-1273 versus BNT162b2 would be reduced by 29%, further favoring mRNA-1273 (i.e., RR of 1.14 vs. 1.43 using the unmatched cohort for the denominator, as originally reported).

Volkman and colleagues also argue that the study by Thompson et al. [39] is incorrectly included in the analysis of infection instead of hospitalization. However, while the Thompson et al. study does include hospitalized patients, not all hospitalizations were COVID-19–related; 10% of the cases involving hospitalization were COVID-19–positive and 90% were COVID-19–negative. For the purpose of our meta-analysis, given the small number of COVID-19–positive cases (n = 95 for mRNA-1273; n = 163 for BNT162b2) out of the total number of hospitalized patients (denominator) for each vaccine (n = 6347 for mRNA-1273; n = 8500 for BNT162b2), these data were categorized as SARS-CoV-2 infection (some of which were in the hospital setting). We also wish to rectify the assertions that the following endpoints were missed from our analysis: severe infection from Chemaitelly et al. [38], symptomatic infection and hospitalization from Bello-Chavolla et al. [40], and severe infection from Nguyen et al. [41]. Chemaitelly et al. reported VE data as hazard ratios for both infection and severe infection for the overall population aged ≥ 50 years [38]. However, incidence data for the age group of interest for our meta-analysis (adults aged ≥ 50 years) by vaccine type were reported only for the endpoint of infection, thus there were no event-level data for severe infection that could be extracted for our meta-analysis. Bello-Chavolla et al. reported data on confirmed SARS-CoV-2 infections, including both symptomatic and asymptomatic individuals [40], thus these were categorized as SARS-CoV-2 infection according to the definition specified in our analysis. Further, while Bello-Chavolla et al. report VE data by vaccine type for other endpoints, including hospitalization, for the overall population aged ≥ 18 years, data for the endpoints we assessed were not reported for the older age subgroup of interest for our meta-analysis. The Nguyen et al. study reported data for the endpoint of medically attended infections, which included any outpatient or hospitalized medical encounter with a COVID-19 diagnosis [41]. Because a mixture of mild and severe cases was included for this endpoint, this did not align with the definition for severe infection specified in our analysis and, thus, was not included for that endpoint. Finally, Volkman and colleagues claim that our article cites the study by Braeye et al. [42] “as an Omicron era study, but the numbers extracted from the study were from the Delta period.” However, in the second paragraph of the Results section of our article, we clearly cite the Braeye et al. study for all COVID-19 variants listed (Delta, Alpha, and Omicron), as this study included data for each variant. For our study analyses, we extracted and used data specific to the Delta variant because this represented the largest sample size over the period of our study, as our systematic search was conducted through June 2023 (i.e., most articles were published before the Omicron period).

Volkman and colleagues argue that, because our meta-analysis was intended to compare mRNA-1273 and BNT162b2, it should have included only head-to-head VE estimates. However, most studies identified in our search and included in our meta-analysis reported VE estimates comparing each of the two mRNA vaccines versus no vaccination or placebo; only 2 studies reported head-to-head comparisons between the two vaccines. Therefore, to allow calculation of the RR for mRNA-1273 versus BNT162b2, we used count data (i.e., number of events and sample size for each vaccine) instead of VE data, including for studies in which the authors evaluated VE. Count data were available from approximately two-thirds of the studies included in our analysis; 8 studies did not report binary data, and in these cases, we calculated RRs by indirect comparison. Of note, we also performed a sensitivity analysis excluding the studies that reported only VE data (i.e., Figure S8 in our article), and findings were consistent with the base case analysis. Additionally, our inclusion of studies that reported absolute VE (aVE; usually determined by comparing disease incidence with a vaccine vs. no vaccine) for both vaccines, in addition to those that reported relative VE of two different vaccines (determined by comparing disease incidence between the vaccines), is supported by an analysis conducted by the US Centers for Disease Control and Prevention (CDC), Influenza Division, where they note that for “practical reasons…most estimates of rVE for influenza both (1) use data from observational studies and (2) are derived from comparing the aVE of 2 different vaccines” [43]. This further supports the validity of our methodology, particularly considering the limited number of studies that included head-to-head VE estimates.

Finally, Volkman and colleagues claim that the studies included in our analysis were predominantly conducted during the pre-Omicron era and share their opinion that recent head-to-head studies show no evidence of a difference in effectiveness between the two mRNA COVID-19 vaccines. As clarified in our article, most studies that were published as of the cutoff date for our systematic literature review (i.e., June 2023), or that were included in the 16 systematic literature reviews that we cross-checked, reported data for the Delta variant, which was dominant during that period. We note in the second paragraph of the Results section of our article that 4 of the studies included in our meta-analysis reported data for the Omicron variant. However, only 2 of these studies reported data specific to the Omicron variant, while the other 2 studies also included data for other variants, and for studies that reported data on several variants, we used data for the variant with the largest sample size by vaccine arm in our analysis.

With regard to the opinion of Volkman et al. that, in recent head-to-head studies [17, 44–50] during contemporary periods of Omicron predominance, “there is no evidence of a difference in effectiveness between the two mRNA COVID-19 vaccines”, first, it is unclear how and why these studies were chosen to support this opinion. For example, Kirk et al. [48] considered a study period of September 22, 2021 to August 31, 2022, which included periods when both Delta and Omicron were dominant, with more than 50% of patients included in both the mRNA1273 and BNT162b2 arms having received their booster dose by November 2021; the Omicron variant only became dominant by the end of December 2021 (i.e., determined to be in > 100 countries in December 2021, and in 149 countries across all 6 WHO regions by January 2022) [51]. Second, our study focused on the vulnerable population of older adults (aged ≥ 50 years), whereas the target populations in the studies cited by Volkman et al. include participants spanning age groups ≥ 1 year of age, and only 5 of the 8 peer-reviewed publications reported comparative effectiveness estimates for our target population of interest (adults aged ≥ 50 years) [17, 45, 48–50]. Third, among the studies cited by Volkman et al. that did include data for our population of interest for the outcome of SARS-CoV-2 infection, 4 of the 5 studies found a statistically significant reduction in risk for mRNA1273 versus BNT162b2 per the definition of Kavikondala et al. [1] [i.e., Breznik et al. [45] hazard ratio (HR): 0.53 (95% CI 0.31–0.90); Kirk et al. [48] HR: 0.93 (95% CI 0.89–0.98); Kopel et al. [49] HR: 0.89 (95% CI 0.87–0.92), based on rVE = 100 × (1–HR); Ono et al. [17] for population aged 65–84 years HR: 0.62 (95% CI 0.41–0.82) and for population aged ≥ 85 years HR: 0.64 (95% CI 0.35–0.93)]; the remaining study by Liu et al. [50] reported a point estimate in favor of mRNA1273 versus BNT162b2 for the population aged ≥ 50 years, although this was not statistically significant [primary series HR: 0.90 (95% CI 0.54–1.51), first booster HR: 0.98 (95% CI 0.95–1.02)]. Current COVID-19 vaccination policy in many countries focuses on the prevention of severe COVID-19 disease including hospitalization. There were only 2 studies referenced by Volkman et al. that reported on this outcome in adults aged ≥ 50 years, and both found a significant reduction in risk of COVID-19 hospitalization for mRNA1273 versus BNT162b2 [Kirk et al. [48] HR: 0.82 (95% CI 0.69–0.98); Kopel et al. [49] HR: 0.87 (95% CI 0.79–0.94), based on rVE = 100 × [1–HR]). Based on the above analysis, we believe that the opinion asserted by Volkman et al. regarding a lack of difference in VE between the two mRNA COVID-19 vaccines during the Omicron dominance period among older adults aged ≥ 50 years is not supported by the literature.

In conclusion, we disagree with Volkman and colleagues’ opinion and reemphasize the importance and quality of the systematic and robust literature search and meta-analysis reported by Kavikondala et al. [1], which was conducted in accordance with established guidelines and standard methods used in other meta-analyses.

Acknowledgements

Medical Writing Assistance

Writing assistance was provided by Erin Bekes, PhD, and Sheri Arndt, PharmD, of ICON (Blue Bell, PA, USA) in accordance with Good Publication Practice (GPP3) guidelines, funded by Moderna, Inc., and under the direction of the authors.

Author Contribution

Ekkehard Beck, Mary T. Bausch-Jurken, Nicolas Van de Velde, Xuan Wang, and Mia Malmenäs conceptualized the article, provided oversight and critical evaluation of the manuscript, and approved the submitted version.

Funding

The original study was funded by Moderna, Inc. Authors employed by Moderna, Inc., were involved in the study design, analysis and interpretation of data, the writing of the manuscript, and the decision to submit the manuscript for publication. Medical writing assistance for this reply letter was funded by Moderna, Inc.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Declarations

Conflict of Interest

Ekkehard Beck, Mary T. Bausch-Jurken, and Nicolas Van de Velde are employees of Moderna, Inc., and hold stock/stock options in the company. Xuan Wang and Mia Malmenäs are employees of ICON plc, a clinical research organization paid by Moderna, Inc., to conduct this study.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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