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We read with interest the Correspondence by Kim et al. 1 reporting associations between COVID-19 vaccination and 1-year cancer risk in BMC Biomarker Research. By contributing to a critical appraisal, our aim is to maintain public and professional' confidence, in view of current scientific evidence, that COVID-19 vaccination remains a preventive measure for worldwide public health. In the Correspondence, while the large sample and medico-administrative database are strengths, several major methodological issues substantially limit the causal and associational interpretation of findings.The vaccinated and unvaccinated groups were assigned different index dates (vaccination date vs. January 1, 2022). This asymmetry introduces calendar-time differences in background cancer risk, independent of vaccination. Without time-dependent or delayed-entry survival modeling 2 , these temporal biases may have inflated post-vaccination cancer incidence estimates.The 1:4 propensity score matching applied by authors seems to implicitly target the Average Treatment effect on the Treated (ATT) estimand (i.e., estimating the effect among those who actually vaccinated) rather than the population-level Average Treatment Effect (ATE) estimand (i.e., estimating what the outcome would be if entire population was exposed vs. unexposed) 3 . Indeed, to better aligns with worldwide public health policy-level decisions to fight COVID-19 pandemic, we must consider both the treated and untreated populations, rather than effects limited to those who self-selected for vaccination (as captured by ATT). The reported hazard ratios thus uniquely reflect associations specific to vaccinated, limiting generalizability to general populations and further raising concerns about differential surveillance bias. Weighted or stratified models would yield more robust, population-relevant estimates.Despite propensity score matching, a slight imbalance persisted in income (standardized mean difference [SMD] = 0.09), a proxy for socioeconomic status that may affect both vaccination and use of cancer screening. Moreover, 77.22 % had a prior SARS-CoV-2 infection in both groups, a potential oncogenic exposure 4 . Sensitivity analyses excluding infected individuals were not conducted but would have allowed assessment of findings consistency without potential confounding carcinogenic effects of SARS-CoV-2 infection. Additionally, substantial unmeasured residual confounding remains (e.g., smoking, alcohol, comorbidities, family history, genetic susceptibility, screening frequency).The observed "increased" cancer risk may reflect opportunistic detection of pre-existing subclinical malignancies among vaccinated, who are likely to have higher healthcare use and surveillance. South Korea extensive national screening programs for thyroid, breast, gastric, and colorectal cancers could amplify this effect 5-8 .Thirty site-specific and subgroup analyses were performed without correction for multiple comparisons, increasing the risk of type I error and subsequent probable false associations due to chance alone.One-year solid tumor carcinogenesis does not seem biologically plausible. The reported associations are thus more consistent with study design biases and residual confounding than with causal vaccine effects. The apparent "protective" association for leukemia in the booster subgroup (i.e., probable type I error) further supports a very cautious interpretation. A lag period excluding cancers diagnosed within 6-12 months post-vaccination, at least, would help reduce such bias and better control for cancer latency. Furthermore, longer-term studies (at least 10 years) are needed to explore authors' hypothesis.According to GRADE criteria 9 , this study provides low-quality evidence, as the observational design, high risk of bias, and important residual confounding substantially reduce confidence in the estimated association between COVID-19 vaccination and cancer risk.We therefore recommend a re-analysis emulating a target trial framework 10 , incorporating timevarying exposure, appropriate comparison groups, longer follow-up, exclusion of early diagnoses, healthcare utilization matching, statistical multiple testing corrections, and rigorous sensitivity analyses.In conclusion, the findings of Kim et al. should be interpreted with caution, as causation cannot be proven using such study design. Furthermore, the previously highlighted potential source of multiple biases also calls for reconsideration of the reported associations. We thank the authors for hypotheses generation and transparency, while encouraging further analyses following robust epidemiological standards before communicating such sensitive scientific findings. Indeed, high responsibility is needed to clearly distinguish between association and causation and to interpret findings within their appropriate biological and methodological context. Overinterpretation or misrepresentation of such study could unjustifiably contribute to vaccine hesitancy, that could lead to preventable, sometimes life-threatening, adverse health outcomes 11 . Scientific responsibility is required to maintain such caution and nuance, crucial to avoid misinterpretation or inappropriate use of scientific evidence, in the current context of declining confidence and hesitancy in vaccination, however largely recognized as a major preventive measure to maintain worldwide public health 12,13 . Subsequently, findings reported by Kim et al. should not be used to inform or guide clinical practice or public health policy in the absence of robust associational or causal evidence, given the substantial methodological limitations identified.