COVID-19 Vaccination Still Makes Sense: Insights on Pneumonia Risk and Hospitalization from a Large-Scale Study at an Academic Tertiary Center in Italy

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please see the attachment.

Comments for author File: Comments.pdf

Author Response

Major Issues:
1. Selection Bias and Missing Data
Only 5,743 of 16,034 admissions had complete vaccination data. The authors must clarify the reason for this large proportion of missing data. Was vaccination status more likely to be recorded for certain departments or clinical scenarios? This raises concerns about potential
selection bias. A discussion of this limitation is essential for readers to gauge the representativeness of the analyzed cohort.

We thank the reviewer for this comment, that helps us clarify this issue. The full cohort includes all hospital admission during the study period, regardless of clinical presentation. Vaccination status was not uniformly recorded across all patients, particularly in cases where COVID-19 was not suspected or relevant to the admission. As a result, vaccination data were more likely to be available for urgent or medical patients compared to elective cases. Nonetheless, these real world data represent the real clinical practice across several centers. We have now better explained this in the text.

2. Lack of Multivariate Analysis
   The study presents only unadjusted comparisons using chi-square and rank-sum tests. Given that vaccination status is likely confounded by age, comorbidities, and access to care (e.g., CCI scores and age are higher in urgent admissions), a multivariable logistic regression
   should be conducted to isolate the independent effect of vaccination.
   If such analysis is not feasible due to sample size or data limitations, this should be clearly acknowledged in the discussion.

We thought about the possibility of using multivariable analysis, however due to the type of outcome and data, and the unbalance between groups (unvaccinated, vs vaccinated), with a high prevalence of patients with 2 vaccination.

We thank the reviewer for this comment. We carefully considered the use of multivariable logistic regression to adjust for potential confounders such as age, comorbidity burden (e.g., CCI score), and time since vaccination. However, we believe the structure of data and population hinders the possibility of employing effectively a multivariable regression model. First, the distribution of patients across vaccination categories was highly unbalanced, with a very small number of completely unvaccinated individuals (n = 75, 1.3%) and a large majority concentrated in the two- and three-dose groups.  This imbalance, combined with low event rates for some outcomes, posed challenges for model convergence and stability, particularly for logistic models that require adequate case numbers in each group to generate reliable estimates. We now explicitly acknowledge this in the limitation section.

Second, while some covariates such as age and comorbidities were available, other relevant factors—such as frailty, prior infections, or differential access to care—were not captured in a structured format in our dataset. As a result, even an adjusted model would likely be at high risk of residual confounding and biased inference.

Given these limitations, we chose to present unadjusted comparisons, which we believe still offer meaningful descriptive insight into the distribution of outcomes across vaccination strata in a real-world setting.

3. 
   Very Small Unvaccinated Group
   Only 75 patients were unvaccinated (1.3%), which limits the statistical power and reliability of group comparisons. The authors should present 95% CIs or p-values for these comparisons and highlight the statistical uncertainty in the discussion.

We agree that the low number of unvaccinated patients limits the statistical power of comparisons between groups. Nonetheless, this is the current prevalence of unvaccinated patients from this real world study and we believe these data still offer some potentially meaningful insights that may help generate hypotheses for future studies. We now further explain these limitations in the limitation section,  where we now write

“Despite the overall sample size was large, the distribution across categories was unbalanced, reflecting real-world prevalence. Consequently, patients with zero or more than four vaccine doses were underrepresented, which may have influenced statistical power for comparison involving these groups”

 

4. 
   Unexpected Trends in High-Dose Groups
   The highest-dose groups (e.g., 6 doses) had unusually high COVID-19 positivity rates (up to 62.5%). This likely reflects the high-risk nature of these patients (e.g., immunocompromised individuals) rather than reduced vaccine effectiveness. However, the manuscript does not
   offer a clear explanation for this reversal of trends. The authors should address this explicitly in both the results and discussion to prevent misinterpretation.

We agree with the review. We now added a more detailed explanation of higher positive rates in people who received higher doses of vaccine in both results and discussion sections.

 

5. 
   No Analysis by Vaccine Type
   Although the introduction mentions different vaccine types (mRNA vs. vector-based), the analysis does not distinguish outcomes by vaccine type. If data are available, the authors should provide stratified results. If not, the rationale for pooling all vaccine types
   should be stated, and any speculation regarding type-specific efficacy should be minimized in the discussion.

We agree with the reviewer that this is an important issue. However, our dataset does not include information on the type of vaccine administered. In addition, in Italy, after the initial phase of the vaccination campaign, there was a progressive shift toward the exclusive use of mRNA vaccines. We have now added this point to the limitations section of the manuscript.

“Additionally, information on the type of vaccine administered was not available in our data, preventing vaccine specific analysis”

6. 
   Interpretation of “Pneumonia” Outcome
   The study lumps all-cause pneumonia (COVID and non-COVID) into a single outcome in several places. This may mislead readers into thinking COVID-19 vaccines reduce all pneumonia types. The manuscript should clarify that the protective effect is likely driven by COVID-19 pneumonia reduction, and provide, where possible, data on non-COVID pneumonia separately.

We thank the reviewer for this comment. We now further distinguish noncovid pneumonia from covid pneumonia in the manuscript and we now further clarified this in the discussion section. We now also add a flowchart in supplemental material to further clarify the difference.

7. 
   Incomplete Information on COVID-19 Testing Strategy
   Only 2,115 of 5,743 patients with vaccine data underwent COVID-19 testing (36.9%). This suggests testing was likely limited to symptomatic or urgent cases. The manuscript should clarify testing criteria (e.g., universal vs. symptom-based screening) and acknowledge
   how selective testing may affect the interpretation of positivity rates across vaccination groups. Also, the meaning and clinical relevance of "weakly positive" results should be briefly explained.

We thank the reviewer for highlighting this point. We have now clarified the definition and relevance of weakly positive results in the method section. Moreover, we are now providing better explanation of the testing strategy: during the study period, universal COVID-19 screening was no longer in place. Testing was primarily conducted for selected patient groups, including those with respiratory symptoms and individuals at higher clinical risk. We acknowledge that this selective testing approach may influence the interpretation of positivity rates across vaccination groups. However, it accurately reflects the testing policies implemented by the regional health authority and thus represents the real-world clinical context of Lombardy hospitals during the study period.

 

Minor issues

1. Clarity of Data Presentation
   The upward trend in positivity among high-dose groups in Figure 2 could be misleading. Sample sizes for these groups should be clearly stated, and confidence intervals should be included in the plots or figure captions.
   We now have improved Figure 2 according to the suggestion of the reviewer
2. Terminology Consistency
   The terms “urgent admission” and “emergency admission” are used interchangeably. Standardize terminology throughout.

We thank the reviewer for pointing this out, we have now corrected this misalignment using only “emergency admission”

3 Relevance of Figure 4
While the WHO's “10 Threats to Global Health” figure provides context on vaccine hesitancy, it feels only tangentially related to the core findings. The authors may consider removing it or more clearly tying it to the implications of their data.
Thank you for pointing this out. We completely agree with this suggestion. We removed the image and better explained vaccine hesitancy among the population.

4 Literature Citations
While several important references are included, the manuscript would benefit from citing more recent studies (2023– 2024) on vaccine effectiveness and waning immunity among the elderly.

We now add more recent articles to our reference.

Reviewer 2 Report

Comments and Suggestions for Authors

Individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) frequently manifest acute pneumonia. The incidence of acute respiratory distress syndrome (ARDS), which includes pneumonia, has been reported to range from 15 to 30% among hospitalized patients with coronavirus disease 2019 (COVID-19). The available literature offers scant insight into the impact of the third dose of vaccine, commonly referred to as a "booster shot," or the specific type of vaccine administered (mRNA-based or virus vector vaccine) on the occurrence of pneumonia. In this study, the authors sought to ascertain whether the type and dosage of the vaccine influence the risk of developing pneumonia and requiring hospitalization in a large population of 54 patients admitted to an academic, tertiary care center in Milan, Italy. The study is primarily focused on addressing the identified problem through the implementation of appropriate statistical methodologies for processing the derived results.

Major concern.

The article's primary weakness is its failure to provide adequate information regarding the nature of the utilized vaccines. This is notable given that the authors assert it to be the investigative objective. The type of vaccine utilized may have a substantial impact on the parameters discussed in the paper.

Author Response

Individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) frequently manifest acute pneumonia. The incidence of acute respiratory distress syndrome (ARDS), which includes pneumonia, has been reported to range from 15 to 30% among hospitalized patients with coronavirus disease 2019 (COVID-19). The available literature offers scant insight into the impact of the third dose of vaccine, commonly referred to as a "booster shot," or the specific type of vaccine administered (mRNA-based or virus vector vaccine) on the occurrence of pneumonia. In this study, the authors sought to ascertain whether the type and dosage of the vaccine influence the risk of developing pneumonia and requiring hospitalization in a large population of 54 patients admitted to an academic, tertiary care center in Milan, Italy. The study is primarily focused on addressing the identified problem through the implementation of appropriate statistical methodologies for processing the derived results.

We thank the reviewer for recognizing our manuscript employs the appropriate statistical methodologies for processing of data

Major concern.

The article's primary weakness is its failure to provide adequate information regarding the nature of the utilized vaccines. This is notable given that the authors assert it to be the investigative objective. The type of vaccine utilized may have a substantial impact on the parameters discussed in the paper.

We thank the reviewer for this comment, we now further clarified this issue and focused on how the number and timing of vaccination is able tom impact the type of admission and the incidence of pneumonia. Unfortunately, we don’t have data on vaccination type, and we now better explained this in introduction, methods and limitations. Nonetheless, we believe this real world data can provide insights on the timing and dose of vaccination.

We thank the reviewer for this valuable comment. We have now further clarified this issue, emphasizing how the number and timing of vaccinations may influence both the type of hospital admission and the incidence of pneumonia. Unfortunately, data on the specific type of vaccine administered were not available; we have now better clarified this in the introduction, methods, and limitations sections. Moreover, in Italy after the initial phase of vaccination the type of vaccine employed was mainly related with mRNA vaccine, as we now better explain in the introduction. In conclusion, we believe that this real-world dataset offers meaningful insights into the impact of vaccination timing and dose.

 

 

 

 

 

 

Reviewer 3 Report

Comments and Suggestions for Authors

The topic of this article is interesting and very important for the management of millions of patients. COVID-19 is far from being eradicated and the answer to the question if vaccination should be continued is very important for physicians world-wide.

I have some suggestions for the authors:

1. From the abstract, I do not understand if the patients with pneumonia had this pathology due to COVID-19 or to other viruses. The relationship between vaccination and COVID-19 pneumonia should be clearer in the abstract.
2. All abbreviation should be described in the text, when first mentioned.
3. The study would benefit if the statistical method would have its own subchapter.
4. The Material and Methods section contains a scarce description of the selection process and of the study group. The authors should detail the inclusion and exclusion criteria. A flow chart might be helpful.
5. I understand that all patients with pneumonia were initially selected. Were patients with non-COVID-19 pneumonia used as controls? In statistical analysis the authors mention subgroup analysis but these categories are not previously described.
6. The relation between COVID-19 pneumonia and vaccination status in the selected group is not clear. It should be clarified in the Materials and methods section.
7. Table 1 would have a greater impact if the authors would describe the characteristics of patients with COVID-19 pneumonia and of those with pneumonia of tother causes.
8. I consider that the authors could improve their manuscript by determining the group of patients they intend to focus on and a control group consisting of the other patients. These 2 subgroups could be compared in Table 1. One suggestion is to divide the patients according to vaccination status in vaccinated and non-vaccinated patients. These subgroups should be described in Material and Methods and the differences between them determined in Results. P-values could be determined between the 2 subgroups.
9. In Figure 1, what does p=ns stand for?
10. Figure 2 should contain more information from the text. The figures should help the reader understand a result.
11. In Discussions, the authors should point out the positive effects of vaccination on the outcome of a patients infected with COVID-19. They mention briefly the short-term outcome without any notice of the impact of COVID-19 pneumonia on the recovery period of a patient. Several studies determined that there is a relationship between the lung injury (pneumonia) and the recovery period of an individual: Tudoran, C.; Tudoran, M.; Lazureanu, V.E.; Marinescu, A.R.; Cut, T.G.; Oancea, C.; Pescariu, S.A.; Pop, G.N. Factors Influencing the Evolution of Pulmonary Hypertension in Previously Healthy Subjects Recovering from a SARS-CoV-2 Infection. J. Clin. Med. 2021, 10, 5272. https://doi.org/10.3390/jcm10225272. Vaccination could reduce the recovery period of patients with COVID-19.
12. Some information is repeated in Discussions.
13. The authors should include a strengths and limitation section at the end of discussions.

 

Author Response

1. From the abstract, I do not understand if the patients with pneumonia had this pathology due to COVID-19 or to other viruses. The relationship between vaccination and COVID-19 pneumonia should be clearer in the abstract.

Thank you for pointing this out. We tried to better explain and describe the type of pneumonia.

 

1. All abbreviation should be described in the text, when first mentioned.

We thank the reviewer for pointing this out. We now describe all abbreviations in the text as requested

 

1. The study would benefit if the statistical method would have its own subchapter.

We now add a separate subchapter on statistical methods in the method section

1. The Material and Methods section contains a scarce description of the selection process and of the study group. The authors should detail the inclusion and exclusion criteria. A flow chart might be helpful.

We now add a flowchart in supplemental material

1. I understand that all patients with pneumonia were initially selected. Were patients with non-COVID-19 pneumonia used as controls? In statistical analysis the authors mention subgroup analysis but these categories are not previously described.

 

1. The relation between COVID-19 pneumonia and vaccination status in the selected group is not clear. It should be clarified in the Materials and methods section.

We have now deeply revised the text and further clarified this issue in the method section, results and discussion, following the suggestion of the expert reviewrs

 

1. Table 1 would have a greater impact if the authors would describe the characteristics of patients with COVID-19 pneumonia and of those with pneumonia of tother causes.

We have now revised and improved table 1 as suggested, and hope that now this is better clarified and useful for the reader.

1. I consider that the authors could improve their manuscript by determining the group of patients they intend to focus on and a control group consisting of the other patients. These 2 subgroups could be compared in Table 1. One suggestion is to divide the patients according to vaccination status in vaccinated and non-vaccinated patients. These subgroups should be described in Material and Methods and the differences between them determined in Results. P-values could be determined between the 2 subgroups.

We appreciate this constructive recommendation. In the revised version, we have clarified in the Methods section that our primary focus is on comparing patient characteristics and outcomes according to the number of COVID-19 vaccine doses received. The revised Table 1 now includes comparisons across vaccination strata, including a specific analysis comparing vaccinated versus unvaccinated individuals. These groups are now clearly defined in the Materials and Methods, Results (including table 1) and discussion.

 

 

1. In Figure 1, what does p=ns stand for?

Thanks for helping us further clarify this. We added a brief explanation under the figure

1. Figure 2 should contain more information from the text. The figures should help the reader understand a result.

Thanks for this comment. We have now redrawn figure 2, adding more information on the numerosity of each group and confidence interval. We believe that now the figure is clearer and more informative for the reader.

1. In Discussions, the authors should point out the positive effects of vaccination on the outcome of a patients infected with COVID-19. They mention briefly the short-term outcome without any notice of the impact of COVID-19 pneumonia on the recovery period of a patient. Several studies determined that there is a relationship between the lung injury (pneumonia) and the recovery period of an individual: Tudoran, C.; Tudoran, M.; Lazureanu, V.E.; Marinescu, A.R.; Cut, T.G.; Oancea, C.; Pescariu, S.A.; Pop, G.N. Factors Influencing the Evolution of Pulmonary Hypertension in Previously Healthy Subjects Recovering from a SARS-CoV-2 Infection. J. Clin. Med. 2021, 10, 5272. https://doi.org/10.3390/jcm10225272. Vaccination could reduce the recovery period of patients with COVID-19.

We have now further clarified this issue

1. Some information is repeated in Discussions.

We have now improved the discussion section to avoid repetition of information

1. The authors should include a strengths and limitation section at the end of discussions.

We thank the reviewer for this comment. We have now added the limitation section at the end of the discussion as suggested

 

Reviewer 4 Report

Comments and Suggestions for Authors

Dear Authors,

thank you for the interesting article. It points out importance of vaccinating and the long-term resistance to severe pneumonia due to Covid-19, which to my mind is really important aspect.

Here are some suggestions how to improve it:

1. Line 37 - which types?
2. Line 45 - please, add some information on how the mortality rate ranges in eldery patients - that they are most esposed ones to Covid-19 infections, as well as problems with general health and complications (eg. doi: 10.17219/dmp/177329).
3. Line 164 - discuss it with a need to re-vaccine each 3-4 months. Add to it novel variants of virus. Compare it to flue vaccination
4. In the discussion, add the aspect of importance of vaccination among the medical workers - compare short-trem results of post-vaccination seropositivity (eg. DOI:10.1186/s12879-023-08534-z) with long-term ones (find more). Disucss the importance of protection against getting sick

Thank you

Author Response

Here are some suggestions how to improve it:

1. Line 37 - which types?

Thank you for pointing this out. We added a better explanation of the types of vaccines that were banned.     

1. Line 45 - please, add some information on how the mortality rate ranges in eldery patients - that they are most esposed ones to Covid-19 infections, as well as problems with general health and complications (eg. doi: 10.17219/dmp/177329).

We thank the reviewer for this comment. We have now added more information about elderly people and consequences related with COVID-19 infection in this patients, citing the article suggested and others.

1. Line 164 - discuss it with a need to re-vaccine each 3-4 months. Add to it novel variants of virus. Compare it to flue vaccination

Thank you for pointing this out. We added more information about the need of re-vaccine and the relationship with flue. Adding also a reference underlining the importance of vaccination to prevent severe disease related to new variants

1. In the discussion, add the aspect of importance of vaccination among the medical workers - compare short-trem results of post-vaccination seropositivity (eg. DOI:10.1186/s12879-023-08534-z) with long-term ones (find more). Disucss the importance of protection against getting sick

Thank you for pointing this out. We tried to better explain the importance of vaccination among heath workers adding the article suggested and others that seemed related to that.

 

Reviewer 5 Report

Comments and Suggestions for Authors

In this study the authors aim to explore the relationship between COVID-19 vaccination and pneumonia or hospital admissions relying on data from >16,000 patients in Italy. The results showed that people who were vaccinated (in particular with many doses) were less likely to get COVID-related pneumonia or need emergency care.

Suggestions/Comments:

1. Did you compare and/or analyze in terms of the vaccine type? 
Since previous studies have shown different performances (protection) from the different vaccines?

2. There were only quite few patients that were completely unvaccinated (75 or 1.3%). So, are the conclusions reliable given that very low number? Also, the same for high vaccination numbers.

3. Did you made any adjustments for age, health status, and time since vaccination when analyzing the outcomes?

4. In what degree do you believe that natural immunity from past infections can explain the lower pneumonia rates in vaccinated people?

5. Why you did not apply any regression techniques to find relationships and other possible associations ?

6. You used a classification for Covid19 positivity based on the scale: "positive," "weakly positive," and "negative". Please clarify how these groups have been defined. 

7. You can also include in your analysis (and results) Kaplan-Meier plots in order to show pneumonia over time by vaccination status.

Author Response

In this study the authors aim to explore the relationship between COVID-19 vaccination and pneumonia or hospital admissions relying on data from >16,000 patients in Italy. The results showed that people who were vaccinated (in particular with many doses) were less likely to get COVID-related pneumonia or need emergency care.

Suggestions/Comments:

1. Did you compare and/or analyze in terms of the vaccine type? 
   Since previous studies have shown different performances (protection) from the different vaccines?

We agree with the reviewer this could be an interesting point. Unfortunately, the type of vaccine where not available in our data.  We have added this point to the limitation section, where we write: “Additionally, information on the type of vaccine administered was not available in our data, preventing vaccine specific analysis.”

 

2. There were only quite few patients that were completely unvaccinated (75 or 1.3%). So, are the conclusions reliable given that very low number? Also, the same for high vaccination numbers.

We agree that the low number of completely unvaccinated patients limits the statistical power of comparisons between groups. Nonetheless, this is the current prevalence of unvaccinated people and we believe these data still offer some potentially meaningful insights that may help generate hypotheses for future studies. We now further explain these limitations in the limitation section,  where we now write

“Despite the overall sample size was large, the distribution across categories was unbalanced, reflecting real-world prevalence. Consequently, patients with zero or more than four vaccine doses were underrepresented, which may have influenced statistical power for comparison involving these groups”

 

3. Did you made any adjustments for age, health status, and time since vaccination when analyzing the outcomes?

We thank the reviewer for this comment. We considered the use of adjustment when analyzing the outcomes, including multivariable logistic regression to adjust for potential confounders such as age, comorbidity burden, and time since vaccination. However, we believe the structure of data and population hinders the possibility of employing effectively a multivariable regression model. First, the distribution of patients across vaccination categories was highly unbalanced, with a very small number of completely unvaccinated individuals (n = 75, 1.3%) and a large majority concentrated in the two- and three-dose groups.  This imbalance, combined with low event rates for some outcomes, posed challenges for model convergence and stability, particularly for logistic models that require adequate case numbers in each group to generate reliable estimates. We now explicitly acknowledge this in the limitation section.

Given these limitations, we chose to present unadjusted comparisons, which we believe still offer meaningful descriptive insight into the distribution of outcomes across vaccination strata in a real-world setting.

4. In what degree do you believe that natural immunity from past infections can explain the lower pneumonia rates in vaccinated people?

We thank the reviewer for this important point. Our interpretation is that individuals who were previously infected and subsequently vaccinated may benefit from hybrid immunity, which has been shown to offer enhanced protection.

 

5. Why you did not apply any regression techniques to find relationships and other possible associations ?

We thank the reviewer for point out this issue. We carefully considered the use of multivariable logistic regression to adjust for potential confounders such as age, comorbidity burden (e.g., CCI score), and time since vaccination. However, as explained above, we believe the structure of data and population hinders the possibility of employing effectively a multivariable regression model. The distribution of patients across vaccination categories was highly unbalanced, with a very small number of completely unvaccinated individuals (n = 75, 1.3%) and a large majority concentrated in the two- and three-dose groups.  This imbalance, combined with low event rates for some outcomes, posed challenges for model convergence and stability, particularly for logistic models that require adequate case numbers in each group to generate reliable estimates. We now explicitly acknowledge this in the limitation section. Second, while some covariates such as age and comorbidities were available, other relevant factors—such as frailty, prior infections, or differential access to care—were not captured in our data, so confounding adjustment was difficult to achieve.

Given these limitations, we chose to present unadjusted comparisons, which we believe still offer meaningful descriptive insight into the distribution of outcomes across vaccination strata in a real-world setting.

 

6. You used a classification for Covid19 positivity based on the scale: "positive," "weakly positive," and "negative". Please clarify how these groups have been defined. 

We thank the reviewer for this comment, and we now further clarify this in the method section. Covid19 positivity was defined according to the swab test results, using standardized terminology from our laboratory. Positive corresponded to standard RT-PCR detection of SARS-CoV-2 with high cycle threshold (Ct) values,  Weakly positive included borderline RT-PCR positivity with low Ct values, and negative corresponded to undetectable viral RNA.

7. You can also include in your analysis (and results) Kaplan-Meier plots in order to show pneumonia over time by vaccination status.

Thank you for the suggestion regarding Kaplan-Meier analysis. We attempted to generate a survival plot using the time from the last vaccination to hospital admission (as a proxy for time at risk) and the occurrence of COVID-19 pneumonia as the event.

However, this approach inherently excludes unvaccinated patients, as no time-to-event variable is defined for those without any prior vaccination. As a result, the Kaplan-Meier curves only reflect vaccinated individuals and may mislead the interpretation, as higher dose groups appear to have higher event rates, which is likely an artifact rather than true increased risk. Given this limitation, we did not included the Kaplan-Meier in the current version of the manuscript

 

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have completed the revision according to the peer review opinions, and the paper has been improved.

Author Response

Thank you very much for your constructive feedback.
We appreciate your support and are pleased that the revised manuscript meets your expectations.

Reviewer 2 Report

Comments and Suggestions for Authors

After the corrections have been made, the article may be recommended for publication in the  "Microorganisms".

Author Response

Thank you very much for your constructive feedback.
We appreciate your support and are pleased that the revised manuscript meets your expectations.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript has significantly improved.

I have 2 minor suggestions and after this are addressed, the manuscript can be published.

1. In Table 1, between which category of patients (columns) was the p-value calculated?
2. The authors should read careful the text because during revision, some minor typos have occurred.

Author Response

We appreciate your support and are pleased that the revised manuscript meets your expectations.
It is thanks to your comments, that the manuscript has significantly improved. Thanks! We have corrected the minor typos that occurred during the revision process and also updated the numbering in the bibliography. In Table 1, p-values were calculated using the Chi-square test across all vaccination categories (0, 1–2, 3, and ≥4 doses) for each variable reported.
Thank you very much for your constructive feedback.

Reviewer 5 Report

Comments and Suggestions for Authors

The authors have addressed my comments and made some amendments to the manuscript.

I have no further suggestions.

Thank you for your collaboration.

Author Response

Thank you very much for your constructive feedback.
We appreciate your support and are pleased that the revised manuscript meets your expectations. We have also corrected a few typos during the second revision process.