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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: Yes

Reviewer #3: Partly

**********

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

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: N/A

**********

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: 1. The study presents the results of original research. Yes

2. Results reported have not been published elsewhere. No

3. Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail. No- needs revision as its not clearly identifiable by the reader the 6 indicators and the relationships studied

4. Conclusions are presented in an appropriate fashion and are supported by the data. conclusion section… somewhat… but should be supported by the findings

5. The article is presented in an intelligible fashion and is written in standard English. Yes/ somewhat. But as it is in a descriptive format the indicators and findings are not clear except the two results from the abstract related to urban areas and car ownerships shows easy access compared to rural area and insufficient public transport facilities

6. The research meets all applicable standards for the ethics of experimentation and research integrity. Ethical statement is not clearly identified in the articles

7. The article adheres to appropriate reporting guidelines and community standards for data availability. Yes

This article is timey given the ageing population in Japan and to improve accessibility to healthcare resources or facilities —pharmacies, hospitals, and clinic, to reduce regional disparities as identified across 11 secondary healthcare regions, the official planning units under the Medical Care Act ( ref?).

It is not clear from the abstract or in the paper clearly identified in a table the 6 Accessibility indicators, which the authors compared across regions and age groups.

After figure-1 should be background to the Aichi Prefecture

1. Literature review section is missing. Similar studies could be mentioned from other developing and developed countries with ageing population and healthcare access issues in rural regional areas such as Canada and Australia, similar to Japan.

2. GIS Method used is appropriate, but needs to be clear if any other studies have used such methods in the literature section before moving to the method section. The 6 indicators are still not clearly identifiable. Geographical area for population sampling, 3 types of institution, data source used, is identified.

3. Accessibility indicators ( 6 of them? ) population coverage rate, elderly population coverage rate which are the other 4 indicators? And analytical methods…this could be presented in a table format for clarity ?

4. Study aimed to elucidate regional disparities in accessibility to medical institutions in 67 Aichi Prefecture, Japan, a region characterized by both densely populated urban areas and sparsely 68 populated mountainous areas.

5. And employed Geographic Information Systems (GIS) to quantitatively evaluate the spatial distribution of healthcare resources and accessibility to medical institutions across different regions. The analysis also examined relationships between accessibility and regional characteristics such as population density and the aging rate………….these relationships that the study aims to find...should be mentioned or listed in hypothesis form.

H1- examined relationships between accessibility and regional characteristics such as population density and the aging rate

H2 - between the distribution of healthcare resources and regional characteristic

Any previous studies which included…these two indicators for accessibility – that is population coverage rate and elderly population coverage rate? It could be mentioned .

Authors conducted a comprehensive analysis of accessibility to medical institutions in each 174-region based on indicators such as population coverage rates, population density, aging

rates 175 (percentage of people aged 65 and over), and the distribution of medical resources

Suggestion only: form hypothesis for the 6 indicators ? what are they? Please clarify for the reader, so that the study findings could be generalised and applied to other countries setting with ageing population and regional remote areas, access and transportation issues.

H1- accessibility to medical institutions is positively or negatively related to population coverage rates,

H2-accessibility to medical institutions is positively related to population density,

H3- accessibility to medical institutions is positively related to Pop aging rates

H4- accessibility to medical institutions is positively related to Distribution of medical resources

Please summarise all the findings in a table format as to which indicators ( Accessibility indicators) were compared across regions and age groups were positively or negatively related in terms of accessibility of medical institutions by elderly to Three types of facilities—pharmacies, hospitals, and clinics

The results: These results show that 1- urban areas benefit from concentrated healthcare resources and extensive transit networks, compared to rural and mountainous regions.

2- Although high car ownership may help, but elderly residents remain vulnerable due to mobility restrictions and insufficient public transportation.

Which of the UN- SDGs these findings may help to meet? Please mention in the discussion section.

What are the policy and managerial implication for practice ? in terms of providing easy access to public transportation, and pharmacies, hospitals, and clinics to reduce this regional disparity and access to healthcare facilities and resources, not just for ageing population , but for remote regional areas.

A Major revision is required as mentioned for clarity.

Reviewer #2: Title: Accessibility to medical institutions in Aichi Prefecture: A GIS-based analysis.

Manuscript id: PONE-D-25-46566

reviewer comments

Areal weighting assumes a uniform distribution of population within each source polygon (here: chō-chō-aza units). Large, heterogeneous polygons (e.g., many forest/field areas) will “contribute” population to the buffer simply because of their large area, even though residences are actually far from facilities. As a result, coverage appears higher in rural/mountainous areas with large chō-chō-aza. Conversely, in cities (small polygons, dense buildings), areal weighting tends to be more accurate because the distribution is actually more even on a small scale.

How did the author anticipate edge effects? The author calculated coverage only within the administrative boundaries of secondary healthcare regions (SHRs). Operationally, the facility map was “masked” per SHR, then the population in each SHR was checked to see if it was within a buffer (e.g., 400 m/2 km) of facilities also located in the same SHR. As a result, residents living right on the edge of an SHR, who may be geometrically/distance-wise closer to a facility in a neighboring SHR, will be classified as “uncovered” because the nearest facility is “not counted”.

Data sources have different years (e.g., 2020 census; 2022 bus stops; 2023 railways; 2025 pharmacy list). This can cause time-lag bias in relation to actual current access conditions. When each layer of data comes from a different year, the author is “pasting” together a picture of the city from four different points in time to assess access today. Changes that occur between years, such as population growth, the opening/closing of facilities, bus stop relocations, and the opening of new rail segments, cause the results to be systematically off from actual conditions. How does the author respond to this?

The interpretation that high car ownership “compensates” for limited access is potentially over-optimistic for elderly people with low mobility; the narrative does acknowledge the vulnerability of the elderly, but the authors do not conduct a quantitative analysis of actual modes of transport.

I strongly recommend testing how the results change if the buffer is altered (e.g., 300/500 m; 1/3 km) or if network travel time is used; without this, the robustness of the indicators is not apparent.

The definition of “coverage” = proximity, not “ease of obtaining services.” Without doctor capacity/density, the indicator could be “almost 100% covered” but patients would still wait a long time. The limits of its interpretation need to be emphasized in the abstract and conclusion. (Currently, this is partially acknowledged as a limitation, but it is not emphasized at the beginning).

The claim of “a basis for inferring national trends” is too much of a leap from evidence based on one prefecture (Aichi) to the national level. Inferentially, external validity/transportability is not guaranteed because (i) spatial structure, transport networks, age demographics, and healthcare service arrangements differ between prefectures; (ii) the indicators used (Euclidean distance, fixed buffer, no capacity) may behave differently in metropolitan areas vs. remote rural areas; (iii) there are time lags between data layers that are not uniform.

Reviewer #3: Dear Authors,

This manuscript addresses an important and timely issue, particularly considering aging populations and increasing concern over spatial equity in healthcare access. The manuscript is relatively well structured, the methodology is transparent, and the results effectively demonstrate regional disparities—especially when comparing highly serviced areas such as Nagoya–Owari Central, where 2 km coverage approaches 100%, with regions like Higashimikawa Northern, where access remains significantly limited.

While the manuscript offers valuable empirical insight, several areas require further development before it can be considered for publication.

One key point relates to the theoretical positioning of the study. The introduction references relevant prior work; however, it does not yet fully engage with more recent international literature examining health accessibility inequalities in aging contexts. Over the past five years, this topic has received growing attention, particularly in Western countries where older populations tend to remain in geographically isolated regions as younger populations consolidate in urbanized centers. For example, in the study "Travel-time accessibility and adaptive spatial planning solutions for the healthcare system" (npj Health Systems) analyze similar accessibility gaps and propose adaptive planning strategies in response to demographic aging. Likewise, in the study "Mapping population dynamics at local scales using spatial networks"(Complexity) the authors document emerging dual settlement patterns—urban concentration versus rural aging—which closely parallel the dynamics observed in Aichi Prefecture. Positioning the manuscript more explicitly within this evolving global discussion would significantly enhance its relevance.

Methodologically, although the use of straight-line buffers (400 m and 2 km) is understandable for an initial exploratory assessment, the rationale behind selecting these specific thresholds would benefit from deeper justification. Recent research suggests that accessibility thresholds are most meaningful when connected to travel-time criteria rather than linear distance. For instance, In the previos study the authors propose a 30-minute travel-time threshold as a critical standard, aligned with prior work in spatial accessibility assessment across multiple health systems. Connecting the selected distances to either mobility behavior, national planning benchmarks, or established international standards would strengthen methodological clarity and comparability.

The discussion section could also be further developed. As the authors note, Aichi Prefecture has the highest rate of private vehicle ownership in Japan, which may temporarily compensate for reduced spatial accessibility in rural areas. However, this finding invites deeper interpretation: given the elderly population’s declining driving capacity, it is reasonable to question whether car dependency remains a sustainable long-term assumption. The observed discrepancy in Table 4—where elderly coverage in rural areas is consistently lower—makes this issue particularly relevant.

In addition, the policy implications, though mentioned, could be made more concrete. The results strongly support the relevance of targeted interventions such as telemedicine deployment, integration of mobile health services, expansion of rural transportation schemes, and adaptive spatial planning approaches tailored to aging settlement structures.

Finally, I would like to highlight a technical concern: several figures appear in low resolution and would benefit from being resubmitted with higher visual clarity. Because the maps are central to the paper’s main argument, improving their readability—including clearer legends, labels, and scale indicators—will substantially enhance the manuscript’s accessibility and scientific value.

Thank you again for the opportunity to review this work.

Warm regards,

The revisor

**********

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

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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Dear Dr. Nakamura,

Please submit your revised manuscript by May 12 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Lingye Yao, Ph.D.

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy

Reviewer #1: No

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

Reviewer #1: Given the aging population in Japan and many developed countries there is inequality in access to the three healthcare facilities in regional, rural and remote areas.

This study investigated the geographic accessibility of medical institutions in Aichi Prefecture, Japan, using Geographic Information Systems (GIS), in three facilities—pharmacies, hospitals, and clinics—with a focus on Accessibility & Catchment areas

The study results showed that urban areas benefit from concentrated healthcare resources and extensive transit networks, compared to rural and mountainous regions which remained

underserved.

Authors need to show in a framework as to how geographic and demographic perspectives can be integrated into healthcare policy to support community

care and sustainable resource allocation for senior citizens

Discussion needs to be elaborated, and authors need to come up with recommendation and a framework to show how rural-urban disparities in access to

three healthcare institutions—pharmacies, hospitals, and clinics—can be improved via better policy and resource allocation for the elderly.

What is the future research direction in terms of asking/interviewing the elderly through interviews and personally administered survey with the elderly population living in this 11 geographical areas, as to what they need to make life /old aged people conformable in context of medical facilities access, availability, and affordability as well as other elderly needs for real policy impact.

Reviewer #3: After reviewing the revised version of the manuscript and the authors’ detailed responses to the previous round of comments, I consider that the main concerns raised during the initial review have been satisfactorily addressed. The authors have made substantive revisions that improve the clarity, methodological transparency, and overall robustness of the study.

In my view, the manuscript has significantly improved and now presents a clearer and more solid contribution. I therefore recommend that the manuscript be considered suitable for publication.

**********

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Reviewer #1: No

Reviewer #3: No

**********

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To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Dear Dr. Nakamura,

Please submit your revised manuscript by Jul 18 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only  the individual author can complete the verification step; PLOS staff cannot  verify ORCID iDs on behalf of authors.

We look forward to receiving your revised manuscript.

Kind regards,

Lingye Yao, Ph.D.

Academic Editor

PLOS One

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

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

Reviewer #4: Yes

Reviewer #5: Partly

**********

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

Reviewer #4: Yes

Reviewer #5: N/A

**********

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

The PLOS Data policy

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

Reviewer #4: In this revision, the authors have addressed all concerns and the manuscript can be accepted for publication.

Reviewer #5: The paper uses GIS buffer analysis (400m walk / 2km drive) with areal weighting on 2020 Census chō-chō-aza units to measure population and elderly population coverage rates for three facility types (pharmacies, hospitals, clinics) across 11 secondary healthcare regions in Aichi Prefecture, Japan. Also measures public transit accessibility via 400m buffers around bus stops and train stations. Sensitivity analyses test buffer radii from 300m to 3km. Simple regression tests correlations between coverage rates and (a) population density and (b) aging rate. Software: ArcGIS Pro 3.4.0.

Criterion 1 — Original research

The simultaneous analysis of three facility types under both walking and driving assumptions, using elderly population coverage rate as an indicator, has not been done for Aichi Prefecture (or apparently the broader region). The authors' novelty claim is reasonable and not overstated.

Criterion 2 — Not published elsewhere

Appears to be original work from the Drug Informatics lab at Gifu Pharmaceutical University.

Criterion 3 — Technical standard and methodological detail

Partially met — several issues require attention (see Major and Minor Concerns below).

Core methodology (Euclidean buffer analysis + areal weighting + census data) is appropriate and well-described. Data sources are fully cited and publicly accessible. Sensitivity analysis across six buffer distances is a genuine strength. However:

- Regression analyses use only N=11 data points (one per secondary healthcare region), giving very low statistical power and high leverage for individual outliers.

- Euclidean buffers are cited against the Walk Score 5-min walk standard, but Walk Score uses pedestrian network distances; this is methodologically inconsistent.

- Geocoding success rate for pharmacy addresses is unreported.

- Hospital/clinic location data is from 2020 (MLIT) while pharmacy data is from 2025, creating a 5-year temporal asymmetry within the same analysis.

Criterion 4 — Conclusions supported by data

Mostly met. The core descriptive findings (urban-rural disparity, elderly coverage gap, transit coverage gap) are directly supported by the data and clearly presented. The regression findings are appropriately cautious regarding the aging rate correlation. One concern: the policy recommendations (mobile clinics, transport subsidies, etc.) are imported from the literature rather than derived from this study's empirical findings; this should be more explicitly framed as narrative synthesis rather than evidence-based conclusions from the present data.

Criterion 5 — Standard English

Mostly met. Writing is generally clear and accessible to non-specialists. No notable language issues.

Criterion 6 — Ethics

N/A

Criterion 7 — Reporting guidelines and data availability

Partially met — minor gaps.

Data availability statement ("All relevant data are within the manuscript and its Supporting Information files") is acceptable given that all source data come from public repositories (e-Stat, MLIT, Tokai-Hokuriku Bureau). However, the geocoded pharmacy point layer (the processed output from the CSV Address Matching Service) is a derived dataset that does not exist in any public repository. Ideally this derived dataset should be deposited. Supporting Tables S1-S3 cover the coverage rates and correlations.

No formal reporting checklist (e.g., STROBE for observational studies) is provided. While STROBE is primarily designed for epidemiological studies, an ecological GIS study using population data falls within its scope; at least, the authors should check and confirm whether any relevant checklist applies to this study type.

Major concerns

M1 — Regression analyses with N=11: insufficient power and outlier sensitivity

The simple regression analyses comparing population coverage rates against population density and aging rate are conducted with N=11 observations (one per secondary healthcare region). At this sample size, the analyses have extremely low statistical power and R² values are easily driven by one or two outliers. The contrast between Higashimikawa Northern (density 50/km², aging rate 38.5%) and Nagoya-Owari Central (density 6,785/km², aging rate 24.2%) is so extreme that these two regions likely account for most of the observed variance.

Required action: The authors should either (a) explicitly acknowledge in the text that the regression results are highly sensitive to outlier regions and should be treated as descriptive rather than inferential, or (b) report the change in R² when the two extreme regions (Higashimikawa Northern and Nagoya-Owari Central) are excluded, to show whether the relationships hold in the mid-range regions. The current text presents R²>0.70 as a "relatively strong positive correlation" without this caveat; this is overconfident given the sample size.

M2 — Euclidean buffer vs. Walk Score network distance: methodological inconsistency

The 400m walking buffer is justified by citing the Walk Score methodology (ref 18: this seems a methodology webpage, not a peer-reviewed source), which assigns full walkability credit up to 400 m (0.25 miles, ~5-min walk) before applying a distance-decay function. Walk Score, however, calculates distances using the actual pedestrian street network, not straight-line (Euclidean) distance. The authors acknowledge Euclidean distance as a general spatial limitation elsewhere in the paper (citing Boscoe et al. 2012 for driving accessibility), but they do not address the specific inconsistency that the citation they invoke to justify the 400m threshold, Walk Score, itself relies on network distances. The two approaches are not interchangeable: the detour index (ratio of network to Euclidean distance) consistently exceeds 1.0 in urban street grids and can be substantially higher in mountainous terrain, meaning the Euclidean 400m buffer overstates walkable coverage relative to what the Walk Score standard actually implies, with the effect most pronounced in the rural areas where access gaps are largest.

Required action: The authors should revise the Walk Score citation language to make clear that their Euclidean buffer approximates but does not replicate the Walk Score methodology, and add a sentence acknowledging this specific inconsistency and its directional effect on coverage estimates in mountainous versus urban settings.

Minor concerns

m1 — Geocoding success rate not reported

Pharmacy locations were derived by geocoding 3,962 addresses (of which 3,628 are in Aichi Prefecture) using the University of Tokyo CSV Address Matching Service. The geocoding success rate (proportion of addresses successfully matched) and the spatial precision of matches are not reported. Failed matches or low-confidence matches would create gaps in the pharmacy coverage map that are invisible in the current analysis.

Required action: Report the geocoding success rate (number/percentage of addresses successfully geocoded) and the precision level of matches (e.g., address-level vs. street-level vs. area-level). If any addresses failed geocoding, state what proportion this represents.

m2 — Temporal mismatch between data layers is asymmetric

The authors acknowledge the time-lag limitation (p.23, lines 406-413), the study combines 2020 Census population data with 2022 bus stop data, 2023 railway data, and a 2025 pharmacy list. This is reasonable given data availability constraints. However, the discussion does not note that this mismatch is directionally asymmetric: pharmacy data is from 2025 (most current), while hospital/clinic data is from 2020 (MLIT). Between 2020 and 2025, Japan's Regional Healthcare Vision has driven national bed restructuring (targeting a 30% reduction in acute-phase hospital beds), and mounting financial pressures on smaller public hospitals have contributed to closures nationally. The hospital layer (321 hospitals in Aichi Prefecture, 2020) may therefore overstate current availability, particularly in lower-density areas where smaller hospitals are more financially vulnerable.

Required action: Add a sentence to the limitations paragraph explicitly noting that the hospital/clinic data (2020) is 5 years older than the pharmacy data (2025), and that hospital restructuring between these years may mean that current hospital coverage rates in rural areas are lower than reported.

m3 — Policy recommendations need clearer framing

The Discussion now includes two new paragraphs (added in response to Reviewer #1) recommending mobile clinics, transport subsidies, and community-based transportation modes (pp.18-21). These are sensible suggestions well-grounded in the referenced literature. However, they are presented as though they follow directly from the study's empirical findings, when they are in fact literature-based recommendations triggered by the geographic patterns observed. The current framing ("it is necessary to strengthen transportation support…") uses normative language that goes beyond what the data support.

Required action: Minor reframing, add a phrase such as "Drawing on evidence from comparable rural healthcare settings…" or "Based on the broader literature…" at the start of the recommendation paragraph to make clear that these are evidence-informed policy suggestions rather than conclusions directly derivable from this study's spatial data.

m4 — Transit proximity does not equal transit connectivity

The public transit accessibility analysis reports the share of population within 400m of a bus stop or train station. This measures 'nearness to a transit node', not 'access to healthcare via transit'. A resident living within 400m of a bus stop is counted as "covered" regardless of whether any route from that stop actually reaches a pharmacy, hospital, or clinic. In the rural regions where the analysis matters most, sparse and infrequent services make this distinction consequential, the 400m proximity metric likely overstates functional transit access to healthcare in these areas.

Required action: Add a sentence to the Limitations section stating explicitly that the transit coverage figures reflect proximity to transit nodes only, and do not capture whether those transit links provide functional connections to healthcare facilities. This framing prevents over-interpretation of transit coverage rates as a proxy for healthcare reachability via public transport.

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Reviewer #4: Yes: Kiki Adhinugraha

Reviewer #5: No

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Accessibility to medical institutions in Aichi Prefecture: A geographic information systems-based analysis

PONE-D-25-46566R3

Dear Dr. Nakamura,

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.

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Additional Editor Comments (optional):

Please ensure that the three minor comments from Reviewer #2 are carefully addressed during the proofreading period prior to official publication.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #4: 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

Reviewer #4: Yes

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

Reviewer #2: Yes

Reviewer #4: Yes

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

The PLOS Data policy

Reviewer #2: (No Response)

Reviewer #4: Yes

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

Reviewer #2: Yes

Reviewer #4: Yes

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Reviewer #2: The manuscript is now much improved, and several of my previous comments have been addressed. The authors now define accessibility more carefully as proximity-based potential access, include sensitivity analyses using different buffer sizes, acknowledge the limitations of areal weighting, discuss the differences in reference years across datasets, provide a more cautious interpretation of car ownership, and better limit the generalization of the findings. In its current form, I believe the manuscript is suitable for publication.

However, I would like to offer a few minor recommendations for the authors’ consideration.

First, please consider removing the claim that the findings from Aichi Prefecture can be used to infer healthcare access trends in other regions. The manuscript itself acknowledges that external validity or transportability is not guaranteed. Therefore, the statement about “inferring trends” elsewhere is not fully consistent with the limitations section. It would be more appropriate to state that the analytical method may be applied in other regions to examine whether similar patterns are found.

Second, please consider replacing the term “public transport accessibility” with “proximity to transit stops and stations,” “transit-node proximity,” or another more precise term. The analysis only measures whether residents live within 400 m of a bus stop or railway station. As the authors have already acknowledged, residents classified as “covered” may not have a route that actually connects them to a medical institution. Therefore, the term “accessibility” remains too broad for this indicator.

Third, please consider further softening the claim that car ownership compensates for limited healthcare access and, where possible, use more appropriate indicators such as household vehicle ownership rates, driving licence ownership, or modal share. Differences in coverage between buffer sizes only show changes in potential geographic reach under distance assumptions. They do not demonstrate that every resident owns, can drive, or has access to a car. The manuscript itself notes the decline in driving ability among older people and that approximately 25% of older adults do not hold a driving licence.

Apart from these minor points, I believe that all sections of the manuscript are now very well developed.

Reviewer #4: In this revision, the authors have addressed all concerns and the manuscript can be accepted for publication.

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Reviewer #2: No

Reviewer #4: No

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