Methodological Considerations on the External Validity of the Kim HJ et al. COVID-19 Vaccination Study (Biomark Res 13:114, 2025): A Quantitative Analysis

Methodological Considerations on the External Validity of the Kim HJ et al. COVID-19 Vaccination Study (Biomark Res 13:114, 2025): A Quantitative Analysis Marco Roccetti [email protected] University of Bologna, Department of Computer Science and Engineering Bologna, Italy Corresponding Author: Marco Roccetti Affiliation: University of Bologna, Department of Computer Science and Engineering, Bologna, Italy Email Address: [email protected] ORCID: 0000-0003-1264-8595 Word Count: 4402 Abstract Despite the fundamental importance of retrospective studies in assessing the real-world impact of COVID-19 vaccination, many of these works employ cohort construction methodologies that do not adhere to the most basic rules of biostatistics, thus compromising the validity of their results. The objective of this study is to formally evaluate the methodology and test the external validity of a recent large-scale cohort study that reported a surprising and significantly higher 1-year cancer incidence risk in the COVID-19 vaccinated group. Aggregated raw data (n=2,975,035 individuals) from the cohort were used to calculate the overall Crude Incidence Rate CR of cancer. The resulting cohort CR was then compared against the established official national average CR for the reference period (2020–2022) to assess external validity. A secondary analysis employed the Chi-Squared Goodness-of-Fit Test to quantify the impact of the 1:4 Propensity Score Matching (PSM) on the age structure of the final cohort against the national demographic benchmark. The cohort's overall CR was 40.78 per 10,000, a substantial 22.26% downward deviation from the national average 52.46 per 10,000 (SD 2.97). This discrepancy establishes a pronounced epidemiological paradox, strongly indicating a lack of external validity. Furthermore, the Chi-Squared test revealed a profound structural alteration, with a value of 69,370 (p < 0.00001), confirming that the PSM procedure underrepresented the high-risk demographic group >= 65 years) compared to the national average (12.15% observed vs 18.00% expected). In conclusion, the reliability of the statistical associations reported by the scrutinized study are significantly challenged by the lack of external validity and the methodological ambiguities concerning the composition of the cohort. Independent validation is mandatory, necessitating the immediate public access to the underlying administrative health data sources. Keywords: COVID-19 Vaccination, Crude Incidence Rate, Cancer Incidence, Epidemiological Paradox, External validity, Cohort Representativeness. Introduction The value of retrospective studies in analyzing the real-world effectiveness of COVID-19 vaccination is undeniable. However, it is concerning that many studies do not adhere to the basic biostatistical requirements for cohort construction, effectively rendering their results less valid or unreliable. Importantly, the accurate assessment of post-marketing adverse events, particularly those associated with widespread public health interventions like COVID-19 vaccination, is critical for public trust and effective health policy. Observational studies drawing from national health databases are essential tools in this process, offering large sample sizes and real-world data. However, the reliability of such studies is fundamentally dependent on the methodological rigor applied to cohort selection and statistical adjustment [1]. A core standard of rigor in epidemiology is External Validity [2]. This concept refers to the extent to which the findings of a study can be generalized to other populations, settings, and circumstances outside the study's specific cohort. For a cohort derived from a national registry, high external validity requires that the study’s overall burden of disease (measured by the Crude Incidence Rate, or CR) is statistically consistent with the known national burden of disease for the same period. Failure to meet this standard, often due to sampling or selection issues, means the cohort is not representative of the broader population, rendering its conclusions questionable in a real-world context. Study [3] provides a recent, retrospective, population-based cohort analysis utilizing data from some South Korean administrative database to investigate the 1-year risks of cancers associated with COVID-19 vaccination in South Korea [4]. The study’s finding, suggesting a higher rate of new cancer cases among the vaccinated population compared to the unvaccinated, is a surprising and scientifically challenging result that has yet to be fully addressed and scientifically analyzed with the required depth and urgency, especially considering the global scope of the vaccination programs [5]. The present work serves as a comprehensive scrutiny that integrates two sequential computational analyses. Initially, we identified a pronounced epidemiological paradox based on raw incidence calculations derived from the study's supplementary data. This paradox established a significant external inconsistency between the study cohort’s aggregate cancer incidence and official national statistics [6]. The follow-up analysis we developed is as an integral part of the overall argument and posits a specific methodological explanation for this paradox: the likely misapplication or inversion of the 1:4 Propensity Score Matching (PSM) procedure [7] used in [3]. In closing, the primary objective of this paper is to quantify and demonstrate the severity of the external inconsistency observed in the scrutinized cohort, thereby challenging its external validity. We then propose a plausible methodological hypothesis, specifically the inverted Propensity Score Matching (PSM), that would reconcile this numerical discrepancy and ultimately calls into question the reliability of the study’s final association results. It is essential to undertake this critical examination using the known data, as the magnitude of the finding demands the highest level of scientific scrutiny. Methods In this Section, we provide all the necessary details on the data and methods used in our study, allowing readers to easily replicate our findings. Sources of data This analysis is based entirely on publicly available, aggregated data extracted from [3] and official South Korean national health statistics as reported in [8-11]. In particular, the raw cohort figures necessary for calculation were obtained from Table S4 ("Cumulative incidences of overall cancers in the matched cohort between vaccinated and unvaccinated individuals") from the Supplementary Material of [3]. These figures are as summarized in the following Table 1. Table 1: Raw Cohort Data showing the initial and final matched cohort counts, case numbers, and the Propensity Score Matching (PSM) details used in [3]. The Official National Cancer Statistical data, including the Official Crude Incidence Rate (CR), for all cancers in South Korea were instead sourced from the Korean Central Cancer Registry for the years immediately preceding and during the study period (2020–2022) as reported in [8-11]. This data provides the robust national baseline against which the study cohort's representativeness has been tested. Furthermore, the official South Korean demographic structure, which specifies that the population aged >= 65 years constitutes 18.00% of the total, was used as the expected national baseline for assessing the cohort's age representativeness, as reported in [12]. Definition and calculation of Crude Incidence Rate (CR) The Crude Incidence Rate (CR) per 10,000 population is a fundamental epidemiological measure used here specifically to evaluate the external validity of the cohort. Unlike Age-Standardized Rates (ASRs) which adjust for age distribution to allow comparison between populations, the CR reflects the raw burden of disease in a defined population over time [13]. Most importantly, any cohort derived from a national database should possess an aggregate CR that is statistically Metric Value Initial Cohort Size 8,407,849 individuals Final Matched Cohort Size 2,975,035 individuals Total Cancer Cases in Matched Cohort 12,133 cancer cases Unvaccinated Group (N) 595,007 individuals Unvaccinated Group (Cases) 1,989 cancer cases Vaccinated Group (N) 2,380,028 individuals Vaccinated Group (Cases) 10,144 cancer cases Propensity Score Matching (PSM) 1:4 Ratio consistent with the national average CR for the same time period. A significant deviation signals a foundational problem in the initial sampling or selection process that any given procedure used to construct the cohort would fail to correct. The CR is calculated using the established epidemiological formula: CR per 10,000 = (Number of new cases during a given period) / (Average population at risk during the same period) times 10,000. 1) This is followed by a straightforward calculation of official South Korean CR baseline [8-11], whose values for both sexes per 100,000 population were converted to a per 10,000 basis to establish the national benchmark as recorded in Table 2. Table 2: Official National Crude Incidence Rates (CR) for All Cancers in South Korea per 100,000 and the derived CR per 10,000, used to establish the national average baseline for the reference period (2020–2022). Consequently, the official average CR baseline for all cancers for the reference period (2020–2022) can be established as the mean of these values: CR (Official Average) = 52.46 per 10,000 (Standard Deviation, SD = 2.97, beingt assumed that the three annual CR values constitute the population of the reference period, thus the SD is calculated using N as the denominator). Finally, using the raw figures from Table S4 in the Supplementary material provided in [3], the following CRs of Table 3 are calculated using the CR equation for the cohort of interest. Table 3: Calculated Crude Incidence Rates (CR) for the matched cohort of [3], showing the overall rate for the entire cohort and the rates for the segregated vaccinated and unvaccinated groups. Hypothesis formulation on Propensity Score Matching (PSM) inversion A Propensity Score Matching (1:4 PSM) procedure aims to match each individual in the Treatment Group with four comparable individuals from the Control Group [7]. In general, The Propensity Score Matching (PSM) is a quasiexperimental statistical method used to reduce the confounding bias that occurs when estimating the effect of a treatment or intervention (like vaccination, in our case) in observational studies. The Propensity Score is defined as the conditional probability of an individual receiving the treatment given a set of observed covariates (e.g., age, sex, comorbidities). Hence, the propensity score e(X) is given by e(X)) = Prob (Z = 1 | X), where Z is the treatment assignment and X is the vector of baseline covariates. The PSM calculation procedure involves a multi-step process. First, a logistic regression model is constructed to estimate the propensity score for every individual, based on the set of observed confounders. Once the propensity scores are calculated, the matching phase begins. Different matching algorithms exist (e.g., nearest neighbor, caliper, or kernel matching). In the reported 1:4 PSM, each treated individual (or the base group) should be paired with four comparable control individuals whose propensity scores are nearly identical. This process would effectively create a synthetic, balanced cohort where the two groups are comparable on all measured confounders, thereby minimizing selection bias. The primary rationale for using PSM is to mimic the randomization process of a randomized controlled trial in nonrandomized observational data. By balancing the distribution of baseline covariates between the treated and control groups, PSM aims to isolate the true effect of the treatment from the effects of confounding factors that influenced the decision to vaccinate. If the PSM is successfully implemented, any residual difference in outcome between the matched Year CR per 100,000 CR per 10,000 2020 482.9 48.29 2021 540.6 54.06 2022 550.2 55.02 Group Calculation (New Cancer Cases / Population) x 10,000 Crude Incidence Rate (CR) CR (Cohort Overall) (12,133 / 2,975,035) x 10,000 40.78 per 10,000 CR (Vaccinated) (10,144 / 2,380,028) x 10,000 42.63 per 10,000 CR (Unvaccinated) (1,989 / 595,007) x 10,000 33.43 per 10,000 groups can be more confidently attributed to the treatment itself. A failure in the PSM process, or a misapplication like the hypothesized inversion, fundamentally undermines this rationale and reintroduces significant bias into the analysis. Age-Stratified Data and Chi-Squared Goodness-of-Fit Test To formally assess the structural integrity of the final matched cohort, we utilize the age-stratified data provided in the supplementary materials (Table S4) of [3]. The full breakdown, presented in Table 4, is essential for validating the cohort's external validity against the national age structure Table 4: Age-stratified composition of the matched cohort, based on publicly available supplementary data of [3]. The Chi-Squared Goodness-of-Fit Test is used to formally test the null hypothesis that the age distribution of the final PSM-matched cohort is consistent with the known age distribution of the general South Korean population (82% under 65 years, 18% 65 years and older). This statistical test quantifies the magnitude of the difference between the observed frequencies in the study cohort and the expected frequencies based on the national demographic benchmark. A statistically significant result from this test indicates that the sampling or matching procedure introduced a profound structural bias, challenging the cohort's representativeness and, consequently, its external validity. The calculation uses a degree of freedom =1 (two age categories minus one degree of freedom), based on the formula: 𝐶ℎ𝑖 − 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 = - ∑("#$)! $ 2) where O is the observed frequency in the cohort and E is the expected frequency based on the national demographic proportion. To conclude, it is to reiterate that all data used for calculations are publicly available and explicitly cited from hte supplementary materials [3] and official Korean cancer registries as reorted in [8-11]. All calculations of this study are fully reproducible by using the method described in this Section, plus the data referenced above. Patient and Public Involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Results This Results Section presents three types of results: first, the quantification of the Epidemiological Paradox through the comparison of the calculated Crude Incidence Rate (CR) against the national baseline; second, the numerical evidence supporting the hypothesis of Propensity Score Matching (PSM) inversion; and third, the statistical quantification of the PSM's impact on the age structure of the cohort. Quantification of the epidemiological paradox The comparison between the study cohort's aggregated CR and the national average CR revealed a substantial and significant downward deviation, confirming the epidemiological paradox as summarized in the following Table 5. Metric Rate per 10,000 Reference/Calculation Official National Average CR (2020–2022) 52.46 Table 2 Cohort Overall CR 40.78 Table 3 Absolute Deviation - 11.68 (40.78 - 52.46) Percentage Deviation - 22.26% (- 11.68) / 52.46 x 100 Group Total Partecipants Total Cancer Cases % Partecipants < 65 % Cases < 65 % Partecipants >= 65 % Cases >= 65 Unvaccinated 595,507 1,989 87.85 69.03 12.15 30.97 Vaccinated 2,380,028 10,144 87.85 67.64 12.15 32.36 Total 2,975,035 12,133 87.85 67.87 12.15 32.13 Table 5: Quantification of the Epidemiological Paradox: Comparison of the scrutinized Cohort's overall Crude Incidence Rate (CR) against the Official National Average CR, highlighting the severe downward deviation. The paradox is summarized as follows: the study’s analysis suggests an elevated cancer risk within the majority group (vaccinated CR is 27.7% higher than unvaccinated CR), which should intuitively push the overall cohort CR higher, yet the overall CR is 22.26% lower than the national baseline. This profound inconsistency represents a strong presumption of the cohort's lack of representativeness, which awaits formal refutation, though such a refutation appears mathematically challenging. To comprehend the full impact of this deviation, one must translate these statistical discrepancies into absolute numbers, which reveal the magnitude of the effect. Based on the cohort's overall rate of 40.78 per 10,000 and applying this to South Korea’s population (approx. 51.77 million inhabitants [12]), the cohort rate would translate to approximately 210,873 new annual cancer cases. This is over 61,000 fewer new cases than the 271,957 derived from the official national average rate of 52.46 per 10,000 for the same population. This massive deficit in expected cases underscores the profound lack of representativeness. Evidence supporting the PSM inversion hypothesis The hypothesis that the 1:4 PSM was inverted is strongly supported by the final cohort sizes reported in [3]. In fact, given the study’s focus on the COVID-19 vaccine, the standard and appropriate group assignment should have been: Treatment = Vaccinated; Control = Unvaccinated. The hypothesis of Inverted PSM is here formulated by analyzing the final reported cohort sizes: 595,007 Unvaccinated and 2,380,028 Vaccinated. This distribution is mathematically consistent with taking the smaller group (approx. 600,000) as the base "1" and matching it to the larger group (approx. 2.4 million) as the "4" component, suggesting the reverse assignment: Base Group = Unvaccinated; Matched Group = Vaccinated. All this is further evidenced by the "numerical signature" where the total matched cohort (2,975,035) is exactly five times the size of the smaller unvaccinated group (595,007), representing the sum of the 1:4 ratio. This result confirms that the smaller unvaccinated group was used as the base '1' for the matching, thus inverting the standard procedure and yielding a final 4:1 ratio which is (erroneously) as follows: Ratio (Vaccinated / Unvaccinated) = 2,380,028 / 595,007 = 4, approximately. This calculated ratio confirms the numerical correspondence: the vaccinated group (2,380,028) is precisely four times the size of the unvaccinated group (595,007). This exact numerical construction solidifies the argument for an inversion, where the small, unvaccinated pool defined the base cohort size for the 1:4 matching. PSM Impact on Cohort Age Structure: Chi-squared Test Quantification The analysis of the age-stratified data from Table 4 reveals a severe structural alteration in the cohort resulting from the PSM procedure. The cohort's total population aged >= 65 years (from Table 4), constitutes only 12.15% of the total cohort (361,425 / 2,975,035 x 100). This represents a substantial 5.6 percentage point downward deviation from the expected national demographic average of 18.00% for the >= 65 age bracket. To formally assess the statistical significance of this deviation, a Chi-Squared Goodness-of-Fit Test was performed, comparing the observed age structure of the cohort against the national benchmark (82% vs 18%). The test yielded an exceptionally high value of Chi-squared = 69,370 (with degree of freedom =1). This result translates into a p value significantly lower than 0.00001. This overwhelming statistical evidence confirms that the final matched cohort is not a random representation of the South Korean population and possesses a structural age distribution that is profoundly biased towards the younger, lower-risk demographic. This quantified structural defect provides the direct methodological explanation for the suppressed Crude Incidence Rate (CR) identified in the previous Section. Discussion Principal Findings The first part of this Discussion synthesizes the main quantitative findings, focusing on the core issues raised. First, it is worth while reminding that this research has integrated computational epidemiological analyses to critically evaluate the methodology and findings of a given scrutinized cohort from a given study [3] with a surprising finding of a higher cancer incidence in the vaccinated group. Our initial critique established a pronounced epidemiological paradox: while the cohort suggested a higher crude cancer incidence rate (CR) among the vaccinated group, the overall cohort CR was found to deviate downwards by over 22.26% from the official national average CR (2020–2022). This fundamental discrepancy suggested a lack of external validity. We then found and statistically validated a plausible methodological explanation for this paradox: the reported 1:4 Propensity Score Matching (PSM) procedure resulted in a quantifiable structural bias in the cohort's age composition. This bias essentially introduces unidentified confounding factors and artificially deviate the overall CR downwards. The core issue of our research is the epidemiological paradox demonstrating a profound lack of external validity for the cohort of [3]. This is evidenced by the suppressed overall CR 40.78 per 10,000 compared to the national average 52.46 per 10,000. This fundamental inconsistency suggests that the sample is not representative of the underlying population's cancer incidence. Further, the discrepancy is so large that it cannot be dismissed as a minor fluctuation, calling into question the reliability of the study's conclusions. Second, the hypothesis that the 1:4 PSM was inverted provides a concrete methodological explanation for the observed low CR. While the numerical signature (the exact 4:1 ratio) initially suggested an inversion in the base group assignment, the subsequent Chi-squared test definitively quantifies the structural consequence of this procedure. The highly significant value of Chi-squared test has proven that the PSM process, or the initial sampling it followed, resulted in a cohort that severely underrepresents the high-risk demographic group (>= 65 years), which makes up only 12.15% of the final sample instead of the expected 18.00%. This quantified alteration of the age structure, favoring a younger population with a lower baseline cancer incidence, explains the suppression of the overall CR compared to the national average. Thus, the PSM procedure, intended to balance known confounders, effectively destroyed the external validity of the cohort by altering its fundamental demographic signature. In fairness, we must also remember that, while matching from the less numerous group (unvaccinated) is standard statistical practice, in a scenario where the exposed group (vaccinated) is the overwhelming majority, in this case this choice has significantly constrained the cohort construction. Even if the PSM were executed correctly according to its internal algorithm, this choice would always drastically reduce the external validity, as the resulting cohorts would lose their ability to reflect the true epidemiological background of the original population. Strengths and Limitations Iit should be noticed that the present analysis study has several strengths, which we condense into three fundamental points: a) the study provides concrete, reproducible mathematical evidence (the 22.26% CR deviation) establishing a fundamental lack of external validity, which overrides subsequent statistical associations; b) it formulates a specific, testable hypothesis, the inverted PSM procedure, which would numerically reconcile the observed epidemiological paradox (4:1 ratio), offering a concrete explanation for the bias; c) the critique shifts the focus from clinical outcomes to core methodological integrity (CR and PSM), serving as a crucial cautionary example for future large-scale epidemiological studies utilizing administrative data. The limitations inherent to this present study, instead, stem primarily from the nature of observational analysis built upon aggregated, externally published data. Specifically, our quantitative findings regarding the suppressed Crude Incidence Rate (CR) and the hypothesis of Propensity Score Matching (PSM) inversion cannot be definitively resolved without primary data access. However, these limitations are ultimately attributable to the original study [3], which, despite its vast scale, did not provide the necessary data access for independent validation. The ethical and scientific imperative for data transparency remains the single greatest constraint, forcing our scrutiny to rely on numerical signatures and logical inference rather than direct verification. Moreover, we must clarify that our intention is not to reject the statistical associations found in [3], showing a higher incidence of cancers in the COVID-19 vaccinated population. Such associations, while potentially valid within the limits of their non-representative cohort, are not the focus of this critique. However, it is impossible to ignore the profound discrepancies in the Crude Incidence Rate and the strong evidence suggesting the inverted application of the 1:4 PSM. These possible methodological flaws would render the external validity and, consequently, the reliability of the hazard ratios derived from this specific cohort, highly questionable until proven otherwise. Conclusion The fundamental premise of any large population-based study is that the raw incidence rate of any common background event must be consistent with national epidemiological surveillance and its gold standad [13-15]. The Paradox of Crude Rates showing simultaneously increases in the Vaccinated and Overall Decrease border on nonsensical from the perspective of public health and surveillance. Specifically, the 22.26% downward deviation observed in the cohort’s CR of [3], supported by our Chi-squared test quantifying the structural age bias, constitutes a plausible evidence of the lack of external validity of this exemplar case, in which the compatibility checks of the selected cohort against the gold standards were not properly conducted. This failure is particularly alarming because it has driven to a situation where, due to methodological and numerical inconsistencies, the signaled results cannot be accepted at a larger scale. To resolve these ambiguities and reinforce scientific rigor, the following actions are essential: a) transparency from those who should disclose the detailed algorithmic methodology and group assignment used for the cohort construction, and b) full disclosure of the initial data to allow for independent verification and resolution of the raised methodological discrepancies. While public availability of the entire South Korean database is ideal, a comprehensive, extended summary of the raw data, consistent with privacy regulations, would be acceptable, provided it is detailed enough to confirm or invalidate the methodological concerns raised here. Resolving the identified methodological ambiguity is fundamental to the global trust in reported associations and broader COVID-19 vaccine safety assessments. Acknowledgments: Not applicable Funding: The author does not declare a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Data availability statement: All data used for calculations are publicly available and explicitly cited from the cited lierature and official South Korean cancer registries. All the calculations and results of this study are fully reproducible by using the method described in the article and the data mentioned above. Further reasonable requests can be addressed to the corresponding author (email: [email protected]). Authors’ Contributions: MR as a single author conceived, designed, wrote, managed, and revised this manuscript, and has read and agreed to the published version of the manuscript. Conflict of Interests: None declared. Research Ethics Approval: Not applicable, neither humans nor animals nor plants nor personal data were involved in this study. Patient consent for publication: Not required. Transparency: The sole author (MR) affirms that the manuscript is an honest, accurate, and transparent account of the computational and methodological analysis being reported; that no important aspects of the publicly available data and calculations have been omitted; and that all findings are derived exclusively from the cited public data sources Abbreviations: CR = Crude Incidence Rate; PSM = Propensity Score Matching; ASR = Age-Standardized Rate; SD = standard deviation References 1 Chirico F, Teixeira da Silva JA. (2022). COVID-19 Health Policies: The need for transparent data sharing etween Scientists, Governments, and Policymakers. Oman Med J, 37(5). doi: 10.5001/omj.2022.63 2 Moro PL, Haber P, McNeil MM. (2019). Challenges in evaluating post-licensure vaccine safety: observations from the Centers for Disease Control and Prevention. Expert Rev Vaccines, 18(10):1091–1101. doi: 10.1080/14760584.2019.1676154 3 Kim HJ, Kim M-H, Choi MG, Chun EM. (2025). 1-year risks of cancers associated with COVID-19 vaccination: a large population-based cohort study in South Korea. Biomark Res., 13(114). doi: 10.1186/s40364-025-00831w 4 The Strait Times Editor, South Korea opens Covid-19 vaccine reservations for all adults, (2023) (August). The Straits Times. https://www.straitstimes.com/asia/east-asia/south-korea-opens-covid-19-vaccine-reservationsfor-all-adults (accessed online 15 October 2025) 5 Paul E, Steptoe A, Fancourt D, (2021). Attitudes towards vaccines and intention to vaccinate against COVID19: Implications for public health communications. Lancet Reg Health Eur, 1:100012. doi: 10.1016/j.lanepe.2020.10001 6 Murad MH, Katabi A, Benkhadra R, et al. (2018). External validity, generalisability, applicability and directness: a brief primer. BMJ Evid Based Med, 23:17-19. doi: 10.1136/ebmed-2017-110800. 7 Wijn SRW, Rovers MM, Hannink G. (2022). Confounding adjustment methods in longitudinal observational data with a time-varying treatment: a mapping review. BMJ Open, 12:e058977. doi: 10.1136/bmjopen-2021058977. 8 Kang MJ, Jung K-W, Bang SH, et al. (2023). Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2020. Cancer Res Treat., 55(2):385-399. doi: 10.4143/crt.2023.447 9 Park EH, Jung K-W, Park NJ, et al. (2024). Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2021. Cancer Res Treat., 56(2):357-371. doi: 10.4143/crt.2024.253 10 Park EH, Jung K-W, Park NJ, et al. (2025). Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2022. Cancer Res Treat., 57(2):312-330. doi: 10.4143/crt.2025.264 11 WHO, (2023). Republic of Korea: Health data overview. World Health Organization, https://data.who.int/countries/410 (accessed online 15 October 2025) 12 WorldBank. Population ages 65 and above(of total population) – S Korea Rep. World Population Prospects, United Nations (UN). [Accessed 2025 Nov 2]. Available from: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?locations=KR 5 13 Last JM (2014). A Dictionary of Public Health. Oxford University Press. doi: 10.1093/acref/9780195160901.001.0001 14 Roccetti M, Cacciapuoti G, (2025). Beyond the Gold Standard: Linear Regression and Poisson GLM Yield Identical Mortality Trends and Deaths Counts for COVID-19 in Italy: 2021–2025. Computation, 13(10):233. doi: 10.3390/computation13100233 15 Eysenbach G, (1999). Research Questions for Systematic Reviews must be Unambiguous fromPprotocol Stage. The BMJ, 319:1265. doi: 10.1136/bmj.319.7219.1265a