Evidence map›Paper›PMID 41413067›Full record

ArticleScientific data2025

Travel times and distances to health services in Australia.

Michaela Filipcikova, Louisa Jorm, Sebastiano Barbieri

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Michaela FilipcikovaThe Hopkins Centre, Griffith University, Brisbane, QLD, Australia. m.filipcikova@griffith.edu.au.ORCID http://orcid.org/0000-0002-0537-5405
Louisa JormCentre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.
Sebastiano BarbieriCentre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Geographic accessibility, the ability to physically reach healthcare services, is a critical determinant of healthcare equity and outcomes. In Australia, over 42,000 individuals residing in inner regional, outer regional, remote, or very remote areas lack access to any primary healthcare service within a 60-minute drive. Such limited access is associated with reduced service utilisation and poorer health outcomes. This study examined national patterns of healthcare accessibility by estimating average driving times and distances between geographic areas (Mesh Blocks, Statistical Areas Level 2, Postal Areas and Modified Monash Model areas within each Australian State and Territory) and selected healthcare services. Using the Open Source Routing Machine, we calculated travel times to the nearest public and private hospitals, emergency departments, general practitioners, bulk-billing general practitioners, and pharmacies. The resulting dataset enables consistent healthcare accessibility comparisons across service types and geographic regions in Australia. The Python code used in this analysis is publicly available and can be adapted to generate similar datasets for other countries.

Indexed as

Health Services AccessibilityTravelAustraliaDatasets as TopicHumans

Identifiers

PMID41413067
PMCPMC12820261

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.