Review for 10.1186/s12876-025-03665-w (2025)

Title: Big Data Analysis of Influenza Vaccination and Liver Cancer Risk in Hypertensive
Patients: Insights from a Nationwide Population-Based Cohort Study
Version: 2 Date: 05 Sep 2024
Reviewer's report:
Thank you for addressing my comments. However, there are still some parts that have not been
sufficiently revised. Below are my remaining concerns:
1. “2. The data usage period spans from 2001 to 2012. Could more recent data not be utilized?
Given that the medical environment has advanced significantly in the past 10 to 20 years, I
believe it would be better to use newer data. Response: We thank the reviewer for the opinion.
Based on the strict regular policy of Taiwan’s National Health Insurance Research Database and
limited research funding in this study, our data currently only covers up to 2012. Whether the
result corresponds to the modern population would be one of the research limitations. It is hoped
that future studies will incorporate updated data. We revised the limitation section as follow:
“Fifth, the chosen cohort for this study spans from 2001 to 2012. It is necessary to analyze more
recent data in future studies to confirm the findings of the current research. “(Page 8, line 27-29)”
#I think there are issues of strict rules of use of the database and research funding, but even taking
these issues into account, the period of data use is very old. I think it is difficult to implement
the obtained results into the current clinical practice. I would suggest that this study should be
conducted when the most recent data is available.
2. “3. You have designated the year 2000 as a washout period; does this mean that patients
who were prescribed antihypertensive drugs were excluded? If so, please extend the data usage
period to 2000– 2012 and include this in the exclusion criteria.
Response: We thank the reviewer for the opinion. In this study, we included patients diagnosed
with hypertension since 2001. We excluded patients without at least two subsequent outpatient
department visits and one instance of hospitalization for hypertension in the following year
or without at least two records of antihypertension medication use. A washout period was
implemented to exclude patients diagnosed with cancer one year before enrolling in this study.
Since the first year of the study was 2011, the washout period for patients diagnosed with
hypertension in 2011 would be 2010. Therefore, patients who were prescribed antihypertensive
drugs in the year of 2010 might not be excluded if these patients fulfilled the inclusion criteria
(hypertension diagnosis in the year 2011 and had at least two subsequent outpatient department
visits and one instance of hospitalization for hypertension in the following year or without at
least two records of antihypertension medication use); however, patients with a diagnosis of
cancer in the year of 2010 would be excluded.”
#You mentioned that the washout period is 2010 because the first year of the study is 2011.
However, isn’t the first year of the study 2001? If patients were diagnosed with hypertension in
2001, the washout period should be 2000, and therefore, the data usage period should be from
2000 to 2012.
3. “4. To clarify the study design, please create a timeline diagram of the study.
Response: We thank the reviewer for the opinion. We add a timeline diagram of the study as
follows:”
#Thank you for including Figure 2. However, the timeline diagram provides limited information.
Please add details regarding the data extraction period, identification period, washout period,
covariate assessment period, and follow-up period to Figure 2.
4. “5. Indicate the number of patients meeting the inclusion and exclusion criteria in the Results
section.
Response: We thank the reviewer for the opinion. We revised the result section as follow: “From
2001 to 2012, there were a total of 206,077 patients diagnosed with hypertension. Some patients
were excluded due to the uncertainty of the hypertension diagnosis (n = 85,168), diagnosis
of cancer before the selection procedure (n = 4,216), being under 55 years (n = 77,142) and
receiving influenza vaccination within half a year before the enrollment date (n = 2,529). Finally,
there were 37,022 patients included in our study. “(Page 6, line 12-16)”
#The same information has been added to the Results section regarding eligible patients, but the
same information remains in the Methods section, so please remove it from the Methods section.
5. “8. The description of propensity score matching is insufficient. Please specify the caliper
setting, whether the sampling was done with or without replacement, and what percentage
balance you aimed to achieve between the two groups using the absolute standardized difference.
Response: We thank the reviewer for the opinion. Upon re-examining the description of the
method, we did not use the propensity score matching but propensity score adjusting. We
revised the article as follow: “To minimize selection bias and evaluate the effects of vaccination,
propensity score (PS) adjusting was used to account for covariates with a logistic regression
model between the vaccinated and unvaccinated groups” (Page 5, line 38-40)”
#Did you use the propensity score estimated from the logistic regression as one independent
variable in your analysis? From the results, it would seem that the multivariate regression
analysis was performed, but how was the propensity score used? Please add that explanation.
6. “9. Is the follow-up start date the day the hypertensive patient last received the influenza
vaccination?
Response: We thank the reviewer for the opinion. The follow-up start date is the day the
hypertensive patient first received the influenza vaccination. We add the detail information in
the timeline diagram of the study.”
#Please add the exact period during which hypertensive patients could have received their
first influenza vaccination to Figure 2. For example, include a timeline such as "2002/01/01–
2011/12/31" in the diagram to clearly indicate the possible time frame.
7. “11. Since this study evaluates the incidence of primary liver cancer in hypertensive patients,
death from causes other than primary liver cancer should be considered a competing risk. For
prognostic prediction considering competing risks, the Fine & Gray subdistribution hazard
model is more appropriate than the Cox proportional hazards model. Please consider changing
the main analysis.
Response: We thank the reviewer for the opinion. We agreed that death from causes other
than primary liver cancer should be considered a competing risk. We change the analysis to
Fine & Gray subdistribution hazard model and revised the article and table as follow: “Fine-
Gray subdistribution hazard model was used to evaluate the hazard ratios (HRs) and 95%
confidence intervals (CIs) of liver cancer between vaccinated and unvaccinated patients with
hypertension.” (Page 5, line 41-42)”
#Under Tables 2 and 3, it is stated that ” Main model is adjusted for Age, Sex, CCI Index,
Diabetes, Hyperlipidemia, HF, AMI, AF, Ischemic heart disease, Angina, Peripheral vascular
disease, Cerebrovascular diseases, Renal failure, Hepatitis B virus, Hepatitis C virus, Beta
blocking agents, Calcium channel blockers, Diuretics, RAASI, Other antihypertensive drugs,
Statin, Metformin, Aspirin, level of urbanization, monthly income in propensity score. ‡A
subdistribution hazard model was used as a sensitivity analysis to account for death as a
competing risk.” However, the Methods section does not describe what the main analysis and
sensitivity analysis are. If you conducted both a main analysis and a sensitivity analysis, please
include this information in the Methods section.
8. “13. In Table 1, for “Sex,” specify whether they are male or female. For “Diabetes” and
“Hyperlipidemia,” provide results only for “Yes.”
Response: We thank the reviewer for the opinion. We revised the Table as follow:”
#If one category of "Sex" (either male or female) is stated, the other can be inferred. Therefore,
please specify only one (either male or female).