Health systems Performance in Health Outcomes, Health Financing and COVID-19 pandemic: lessons from 31 countries

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This manuscript evaluates and ranks the health system performance (HSP) of 31 countries across multiple dimensions—health outcomes, financial indicators, and COVID-19-related metrics—using the TOPSIS method and hierarchical clustering. The study highlights substantial variation in performance, with Luxembourg, Switzerland, and Qatar ranking highest, while several low-income or politically unstable countries rank lowest.

The topic is both important and timely, addressing global health system performance, especially in the context of the COVID-19 pandemic. The combination of TOPSIS and entropy weighting, along with hierarchical clustering, offers a structured, quantitative approach that is of interest to researchers and policymakers. The multi-dimensional framework—integrating health, financial, and pandemic indicators—adds to its comprehensiveness.

Justification for Country Selection

The selection criteria for the 31 countries need a clearer explanation. Although the authors mention purposive sampling, they should provide transparent inclusion/exclusion criteria, especially regarding the omission of some OECD or LMIC countries.

Weighting Approach

The use of a combined Three-Scale and Entropy-Weighting method is innovative but requires further clarification. How were the weights validated or benchmarked? The manuscript should specify if Ci (similarity index) ranges between 0 and 1 and clarify the scale in Table 2 to ensure it is self-explanatory.

Comparability of Countries

The manuscript aims to compare Iran’s health system with others. For meaningful comparisons, countries with similar settings (e.g., population size, health system structure, economy) should be selected. Comparing countries like Norway and Sweden is appropriate due to similar systems, but comparing Norway with Uganda is less informative due to fundamentally different contexts. Given that Iran's Ci score is close to countries like Syria, Paraguay, Kenya, and Nigeria, comparisons should focus on such peers. There will be more information when compared to similar countries like Iran, rather than high CI score countries, which have a very different system from the Iranian health care system.

Performance Categorization

Based on Table 2, Luxembourg, Switzerland, and Qatar clearly form the high-performance group. The middle-performance group could reasonably extend at least up to Bahrain, while the remaining countries would fall into a low-performance category. The current clustering requires reconsideration or stronger justification. It does not seem appropriate to categorize countries such as Singapore, the UK, Canada, and Australia as low performers alongside countries like Afghanistan, Nigeria, and Uganda, given the substantial differences in their health system structures, resources, and outcomes.

I recommend either revising the clustering approach or introducing an additional category (e.g., creating four performance levels instead of three) to more accurately reflect these differences. Table 2 shows that countries from the USA to Bahrain have relatively higher Ci scores, while countries from Chile to Yemen have scores that are very close to zero, which could justify labeling them as “very low performers” or “lowest performers.”

Additionally, there appears to be an inconsistency between Section 4.2 (page 16), where these countries are described as high performers, and Table 1, where they are categorized as low-performance countries. This discrepancy should be addressed to ensure clarity and alignment between the analysis and interpretation.

Use of Data Years

Table 3 uses data from 2000 and 2019 for MMR and NMR. The authors should explain why more recent data (if available) weren’t used to enhance the study’s relevance.

Minor Suggestions

Improve clarity and grammar throughout the manuscript.

Enhance figures/tables with clearer legends, axis labels, and explanations.

The manuscript provides valuable insights into global health system performance using a rigorous methodological approach. However, addressing the above recommendations would strengthen its clarity, methodological transparency, and policy relevance.

Reviewer #2: This manuscript evaluates the performance of health systems in 31 countries using the TOPSIS method and hierarchical clustering. It ranks and categorizes these systems based on their performance in health, health financing, and response to COVID-19. The study is timely and relevant; however, several comments are provided to enhance the manuscript.

Sample Selection Transparency:

While the manuscript mentions purposive sampling based on GDP, population size, COVID-19 indices, and healthcare expenditure, the specific inclusion/exclusion criteria for the 31 countries are not clearly detailed. It is recommended to provide justification for the selection/exclusion of countries and discuss potential selection bias.

Data Timeliness and Consistency:

Some indicators (e.g., financial and health metrics) use data from different years (e.g., 2019 vs. 2023 for COVID-19). Discuss how this temporal mismatch may have influenced country rankings and consider sensitivity analyses or at least acknowledge this as a limitation in more depth.

Regarding vaccination against COVID-19, there was competition for access to the vaccine, so not all countries had easy access to the vaccine at the same time. Assess whether this would also be a potential bias in the study.

Data Availability and Research Reproducibility:

The authors state that "all data are fully available without restriction"; however, the current statement indicates that data is available upon request due to privacy/ethical reasons. For transparency and reproducibility, the authors should consider making de-identified datasets and analysis code publicly available in a suitable repository, following PLOS ONE guidelines (Authors must follow standards and practice for data deposition in publicly available resources including those created for gene sequences, microarray expression, structural studies, and similar kinds of data. Failure to comply with community standards may result in rejection. For more information about PLOS ONE requirements for data sharing and deposition in public databases, see our data availability policy https://journals.plos.org/plosone/s/data-availability).

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes:  Montasir Ahmed

Reviewer #2: Yes:  Abel Silva de Meneses

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Submitted filename: lessons from 31 countries.docx

Health systems Performance in Health Outcomes, Health Financing and COVID-19 pandemic: lessons from 31 countries

PONE-D-25-18510R1

Dear Dr. Azari,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. The authors addressed all the recommendation notes made by the reviewers, and it is ready for publication.

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Kind regards,

André Luis C Ramalho, PhD

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

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Reviewer #2: (No Response)

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: Yes:  Abel Silva de Meneses

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