Title: Current situation of menstruation and gynecological diseases prevalence among Chinese
women: A cross-sectional study
Version: 0 Date: 18 May 2022
Reviewer:  Farideh Shiraseb
Reviewer's report:
I appreciate the time authors devoted to reading this manuscript and thanks for paying attention
and addressing this issue as one of the most important diseases for women and for comprehensive
evaluation of its various types.
Introduction
1. page 6, line 93
It seems much better for some repetitive disease names and terms to use their abbreviations,
which in some Mesh terms also increase the ability to search for the paper.
For example, using PCOS instead of Polycystic ovary syndrome
Method
Study design, sampling, and setting
1. Because some of the major confounding variables need to be controlled in the design of the
study, and since body mass index (BMI) is considered as one of the factors related to body fat
mass as one of the important factors in the onset of menstruation and hormonal status in women.
Was it not a preference to control it in design as eligibility criteria?
2. For other inclusion criteria such as history of other underlying diseases especially chronic
disease, thyroid disease, and some other hormonal or even taking chemical or traditional/herbal
medicines or various supplements, there were not considered as a criterion for non-entry?
3. How is the study population selected, randomly? if yes With which method?  Can the authors
explain this part clearly, according to STROBE guidelines? Of course, it is mentioned in the
limitations, but I suggest explain at this section as well
Data collection method
1. According to previous studies, cannot "energy intake per day" be effective in investigating
the relationship between pubertal timing and gynecological disease prevalence?
If effective, therefore, a food frequency questionnaire (FFQ) or other methods should be assessed
using a questionnaire. According to its importance, it can be considered in the exclusion criteria
(such as 600-4200 kcal/d or twice less and more than the interquartile range) or at the statistical
analysis stage. From my point of view even if you didn’t consider it as a confounding variable,
you could consider it as a covariate view of this variable. I suggest that if these cases are not
investigated, they should be mentioned in the limitation
2. On the other hand, looking at diet according to food culture is also important in this study.
If I am not mistaken, the consumption of soy and its products is common in China because soy
contains a high concentration of isoflavones, a type of plant estrogen (phytoestrogen) that is
similar in function to human estrogen but with much weaker effects. Please explain why food
intake was not evaluated in this study due to some important confounders. Please add it to the
study limitations.
3. What about socioeconomic status and physical activity didn’t consider as a cofounder? It
seemed better to include the variables needed to evaluate it in the general questionnaire.
4. How to assess weight and height in the study population with which instrument, it is not
reported, and no reference is mentioned for guidelines
Statistical analysis
1. For univariate and multivariate logistic regression, it is better to mention analytical outputs
such as odds ratio (OR) and 95% confidence interval (CI) and it seems more complementary to
say the same about reference groups
2. With respect to the two logistic regressions, a measure of agreement between the model and
the observed data can be reported for sake of completeness (pseudo-R-Squared or AIC/BIC).
3.  For more clarity, that is better if you explain how to calculate sample size and power, or
maybe u have done post hoc power calculation? please clarify this concern
4. Checking the normality of quantitative variables is not a prerequisite for univariate and
multivariate logistic regression analysis, but because of the frequency report for quantitative
variables, it is better to mention this issue, because if there is no normality for quantitative
variables, it is better to use median/IQR instead of mean (SD). Please explain the normality of
the quantitative data and what kind of method was used to perform it.
Discussion
1. It seems that because some potential confounders are categorically controlled in the adjusted
model or are not included in the design, refer to the residual confounding in the limitations.
2. Another limitation that seems to be pointed out is that due to the type of cross-sectional study,
it is not possible to assess causality.
Tables
1. The table number 5, it is better that the order of the categories related to the “Age group at
menarche (Year)” variable  present as in the previous tables