ArticleBMJ open2025
Big data in modelling geographical accessibility to healthcare: a scoping review protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionResearch on modelling geographical accessibility to healthcare services has witnessed rapid methodological advancement and refinement. One of the contributing factors is the increasing availability of big data detailing the link between the population in need of care and the health facility such as infrastructure, travel modes and speeds, traffic congestion and the quality of road network. This has allowed more granular computation of geographic access metrics, particularly in low-and-middle income countries where data are scarce. However, there are no reviews providing a comprehensive overview of the availability and use of big data for assessing geographical accessibility to healthcare. This protocol aims to describe a methodological approach that will be used to review the existing literature on the application of big data (past or potential) in evaluating geographical accessibility to healthcare. METHODS AND ANALYSIS: To characterise the big data that can be used to model geographical accessibility to healthcare, a scoping review will be undertaken and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extensions for Scoping Reviews guidelines. We will search seven scientific databases (PubMed, Scopus, Web of Science, EBSCOhost-CINAHL, Cochrane, Embase and MEDLINE via Ovid), grey literature, reference lists of identified publications and conference proceedings. Search engines will be used to identify relevant big data services not yet used in published academic literature. All literature published in English or French will be included, regardless of publication type, geographical location or year of publication provided it describes or mentions big data that may be useful for evaluating geographical accessibility to healthcare. Study selection and data extraction will be performed independently by two researchers with a third resolving any discrepancies. Analysis will be conducted to summarise big data providers, their characteristics and their usefulness in terms of types of spatial accessibility metrics that can be derived. ETHICS AND DISSEMINATION: Formal ethical approval is not required, as primary data will not be collected in this review. Findings will be disseminated through peer-reviewed publication in a journal, conference presentation and condensed summaries for stakeholders through professional networks and social media summaries. REGISTRATION: Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/S496F.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.