Title: Age-period-cohort analysis of global, regional, and national cervical cancer prevalence
trends in women of childbearing age (15–49 years), 1990–2021
Version: 0 Date: 26 May 2025
Reviewer's report:
The paragraph presents the GBD data as comprehensive and high-quality, but does not mention
any limitations, assumptions, or potential biases in the dataset or modeling methods.
Terms "disability-adjusted life years" are used without brief definitions.
The use of the GBD 2021 database is a major strength due to its global scope, standardized
metrics, and comprehensive coverage. However, it also introduces methodological concerns:
• Modeled estimates may mask local heterogeneity, especially in countries with limited or poor-
quality primary data.
• GBD often relies on statistical imputation and smoothing techniques in data-sparse regions,
which can affect precision and external validity.
Include a statement discussing the potential uncertainty and bias introduced by modeled data,
particularly in low-SDI regions where surveillance systems are weaker.
Clarification about the study uses aggregate-level data across countries and regions, which is
inherently ecological. This limits the ability to draw individual-level inferences (ecological
fallacy).
The focus on women aged 15–49 is based on WHO guidelines for reproductive age. However,
cervical cancer incidence increases notably after age 40–45 and excluding women #50 may
underrepresent a significant burden. Provide justification for excluding older age groups and
discuss how this might affect generalizability or bias burden estimates.
The mention that the choice of reference cohort/period is arbitrary and doesn't affect
interpretation is partly true. However, the interpretation of Relative Risks (RRs) is always
relative to the baseline group.
State which specific cohort and period were used as reference groups and clarify that while the
choice does not affect overall model trends, the numerical RR values are interpreted relative to
these references.
Wald tests are widely used for global significance of effects but are sensitive to sample size
and model misspecification. You don’t indicate whether model diagnostics or goodness-of-fit
assessments were conducted. Consider mentioning whether model fit was evaluated, such as
residual plots, dispersion checks, or deviance statistics.
Results
Countries with weaker health systems may suffer from underreporting or misclassification of
cancer cases and deaths. Although the study uses GBD modeling to adjust for such gaps, the
reliance on modeled data may introduce biases, especially in low-income regions.
Extremely high ASDR in Kiribati and ASIR in Venezuela stand out but are not explained. It is
unclear whether these are due to data anomalies, genuine epidemiological spikes, or other socio-
political health system factors.
While the statistical trends are reported accurately, the analysis lacks explanation of underlying
causes. For example:
What accounts for the recent drop in ASPR in the high-middle SDI region post-2018?
Why does the middle SDI region still show increasing ASPR in older age groups?
The wide 95% uncertainty intervals—particularly in low SDI regions—are not discussed. This
casts some doubt on the precision of the trends and should be acknowledged as a methodological
limitation.
There is no mention of how these findings might inform policy. The persistent high burden in
women aged 35–49 years globally should prompt calls for targeted screening strategies and age-
tailored interventions, yet this implication is missing.
The most plausible explanatory variables for declining trends in high SDI regions (HPV
vaccination, early detection programs, and treatment) are not discussed, weakening the
translational value of the analysis.
While regional trends are described, there is limited synthesis across regions. For instance, how
do patterns in high-middle SDI regions compare to middle SDI regions in terms of pace and
magnitude of change?