Rubens C. Costa-Filho1,2*‡
José Paulo G. Leite3Marco Aurélio Horta4†
Felipe Gomes Naveca5,6Felipe Saddy7
Hugo C. Castro de Faria Neto1,8- 1Laboratory of Immunopharmacology, IOC/FIOCRUZ, Rio de Janeiro, RJ, Brazil
- 2Estácio Medical School - IDOMED, Rio de Janeiro, Brazil
- 3Laboratory of Comparative and Environmental Virology, IOC/FIOCRUZ, Rio de Janeiro, RJ, Brazil
- 4Biosafety Level 3 Platform (NB3), Rio de Janeiro, RJ, Brazil
- 5Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil
- 6Leônidas and Maria Deane Institute, Rio de Janeiro, Amazonas, Brazil
- 7Intensive Care Unit, Hospital Pró-Cardíaco, Rio de Janeiro, RJ, Brazil
- 8Faculdade de Medicina, Universidade Estácio de Sá, IDOMED, Rio de Janeiro, RJ, Brazil
by Rodrigues, N. C. P., and Andrade, M. K. d. N. (2024). Front. Med. 11:1495428. doi: 10.3389/fmed.2024.1495428
Introduction
The study by Rodrigues and Andrade explores medium- and long-term mortality risks among individuals with severe acute respiratory syndrome (SARS) due to COVID-19 in Brazil, using data from the SIVEP-Gripe database. Their analysis, based on Cox proportional hazards models, suggests an increased mortality risk over time among vaccinated individuals. However, while addressing a critical public health issue, several methodological flaws undermine the reliability of these conclusions.
Key concerns include inappropriate use of the SIVEP-Gripe database, which was not designed for long-term mortality analysis, the exclusion of early deaths, which introduces survival bias, and insufficient statistical adjustments, particularly regarding proportional hazards assumptions. These issues limit the scientific rigor of the study and raise broader concerns about its potential to misinform public health discussions on vaccine safety and effectiveness.
A recent report by the Brazilian Ministry of Health (1) has explicitly criticized the misuse of the SIVEP-Gripe database for long-term mortality studies, warning that such methodological oversights may lead to misleading conclusions about COVID-19 and vaccination outcomes. If left unaddressed, these issues risk fueling misinformation and may inadvertently provide misleading narratives that undermine public confidence in vaccines. This commentary critically examines these limitations, presenting a structured analysis of the study's methodological concerns and their potential impact. We aim to promote methodological integrity and the responsible interpretation of epidemiological data in public health research, ensuring that findings contribute constructively to the scientific and policy landscape. The main methodological concerns are summarized in Table 1.
Table 1. Key points, authors' assertions, and proposed refinements.
Discussion
The methodological approach adopted by Rodrigues and Andrade (2) presents critical limitations, particularly the exclusion of early deaths, which introduces substantial survival bias and affects the statistical modeling of post-COVID-19 mortality. By omitting patients who succumbed early, the study inherently underestimates overall mortality, especially in resource-limited regions, skewing its long-term conclusions.
Another fundamental concern is the lack of verification of the proportional hazards assumption in their Cox regression model. This assumption is central to the validity of the hazard ratios (HRs) reported, as it dictates that the relationship between each covariate (e.g., vaccination status, age, and comorbidities) and mortality remains constant over time. Failure to assess this assumption, particularly through Schoenfeld residual analysis (3), raises serious concerns about the reliability of their findings.
Additionally, the reliance on the SIVEP-Gripe database represents a critical methodological constraint, as recently highlighted by the Brazilian Ministry of Health (1). This database was developed specifically for acute surveillance of severe respiratory cases and was not designed or validated for tracking long-term mortality post-hospitalization. Integrating SIVEP-Gripe data with more comprehensive databases, such as the Mortality Information System (SIM), would significantly improve mortality assessments and reduce the risks of misclassification and underreporting.
The authors report an increased long-term mortality risk among vaccinated individuals, a finding that contradicts extensive population-based studies demonstrating vaccine effectiveness in reducing mortality (4, 5). A plausible explanation for this discrepancy lies in the potential violation of the proportional hazards assumption. The protective effect of vaccination is strongest in the early months following infection but may appear to diminish over time due to the selection bias inherent in vaccination prioritization—i.e., more vulnerable individuals, who already have a higher long-term mortality risk, were vaccinated earlier (2, 6). If this time-dependent effect was not accounted for, the study may be misinterpreting an expected epidemiological trend as a causal link between vaccination and increased mortality.
A standard statistical approach to verify this issue would be to calculate Schoenfeld residuals for key covariates, particularly vaccination status, and examine whether the assumption of proportionality holds (3). If these residuals exhibited a systematic trend over time rather than random dispersion, it would indicate a violation of the proportional hazards assumption, necessitating a more flexible modeling strategy, such as time-dependent Cox models or stratified analyses (6). The omission of this verification is a serious methodological flaw, as it leaves open the possibility that the reported increase in long-term mortality among vaccinated individuals is a statistical artifact rather than a genuine epidemiological effect.
Furthermore, the study fails to adequately consider vaccination's impact beyond mortality, including hospitalization duration, reinfection rates, and morbidity outcomes. A more comprehensive evaluation of vaccine efficacy requires assessing these broader outcomes, particularly in populations with high frailty and preexisting conditions, where confounding factors must be carefully addressed to avoid misinterpretation (4, 5).
The methodological concerns raised by this study have also been acknowledged at an institutional level. The Brazilian Ministry of Health (1) issued a formal statement in the official website emphasizing that the SIVEP-Gripe database was not designed for long-term mortality analysis and that misinterpretation of its data could lead to misleading conclusions about vaccine effectiveness. The Ministry reaffirmed that vaccines remain a crucial tool in reducing COVID-19 mortality and preventing severe disease, stressing that the study's findings should not be used to undermine vaccination campaigns.
In conclusion, failure to incorporate these necessary methodological safeguards compromises the validity of the study's conclusions. Without proper adjustments for time-dependent effects and verification of statistical assumptions, the reported associations risk being misinterpreted, potentially disseminating misinformation, misguiding public health policies, and reinforcing misleading narratives regarding vaccine efficacy and safety.
Author's note
Dr. Rubens C. Costa-Filho is a PhD affiliated with the Laboratory of Immunopharmacology, IOC/FIOCRUZ, and the Estácio Medical School-IDOMED, where Dr. Rubens C. Costa-Filho currently serves as a Professor of Pharmacology. Dr. José Paulo G. Leite is a PhD and Senior Researcher at the Laboratory of Comparative and Environmental Virology, IOC/FIOCRUZ, Rio de Janeiro. Dr. Marco Aurélio Horta holds a PhD and is affiliated with the Laboratory of Hantavirus and Rickettsial Disease, IOC/FIOCRUZ, Rio de Janeiro. Dr. Felipe Gomes Naveca is a PhD affiliated with the Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, and the Leônidas and Maria Deane Institute, Fiocruz, Amazonas. Dr. Felipe Saddy holds a PhD and serves as the Coordinator of the Intensive Care Unit (ICU) at Hospital Pró-Cardíaco in Rio de Janeiro, Brazil. Dr. Hugo Caire Castro-Faria-Neto holds a PhD and is a Senior Researcher at the Laboratory of Immunopharmacology, IOC/FIOCRUZ, and also a Professor of Pharmacology at Estácio Medical School – IDOMED in Rio de Janeiro, Brazil.
Author contributions
RC-F: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. JL: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. MH: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing. FN: Supervision, Writing – original draft, Writing – review & editing. FS: Supervision, Writing – review & editing. HC: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Council for Scientific and Technological Development (CNPq) [grant number 401700/2020-8] and by the Carlos Chagas Filho Foundation for Research Support in the State of Rio de Janeiro (FAPERJ) [grant numbers E-26/211331/21 and E-26/201029/2021].
Acknowledgments
We extend our gratitude to the COVID-19 Study Group, led by one of our authors, JPGL, whose members, including esteemed scientists from diverse fields—primarily from Fiocruz—provided valuable insights and stimulating discussions that shaped the development of these comments and critiques. Their contributions and encouragement have been instrumental in preparing this General Commentary.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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The author(s) declare that no Gen AI was used in the creation of this manuscript.
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Keywords: COVID-19 mortality analysis, observational study limitations, vaccine safety misinterpretation, cox regression model assumptions, epidemiological data quality
Citation: Costa-Filho RC, Leite JPG, Horta MA, Naveca FG, Saddy F and Castro de Faria Neto HC (2025) Commentary: Evaluation of post-COVID mortality risk in cases classified as severe acute respiratory syndrome in Brazil: a longitudinal study for medium and long term. Front. Med. 12:1558264. doi: 10.3389/fmed.2025.1558264
Received: 09 January 2025; Accepted: 30 April 2025;
Published: 16 May 2025.
Edited by:
Shisan Bao, The University of Sydney, AustraliaReviewed by:
Bruna Spolador De Alencar Silva, São Paulo State University, BrazilCopyright © 2025 Costa-Filho, Leite, Horta, Naveca, Saddy and Castro de Faria Neto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rubens C. Costa-Filho, cnViZW5zMTk1NkBnbWFpbC5jb20=
†Present address: Marco Aurélio Horta, Laboratory of Hantavirus and Rickettsial Disease, IOC/FIOCRUZ, Rio de Janeiro, Brazil
‡ORCID: Rubens C. Costa-Filho orcid.org/0000-0002-9725-3762