Evidence map›Paper›PMID 40701597›Full record

ArticleBMJ open2025

An assessment of data quality and sociodemographic variation in health service utilisation of general practice, emergency department and admitted services in a New South Wales linked health data asset: a retrospective cohort study of Lumos.

Tristan Bouckley, Rimma Myton-Katieva, David Peiris, Devaki Nambiar, Samuel Prince, Simon Bishop, Damien Cordery, Flynn Robert Hill, Patricia Correll, Anne-Marie Feyer and 2 more

Abstract read
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Article in BMJ open, 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

12 authors.

Tristan BouckleyFaculty of Medicine and Health, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia tbouckley@georgeinstitute.org.au.ORCID http://orcid.org/0000-0003-2220-615X
Rimma Myton-KatievaFaculty of Medicine and Health, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.
David PeirisFaculty of Medicine and Health, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0002-6898-3870
Devaki NambiarFaculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Samuel PrinceFaculty of Medicine and Health, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.
Simon BishopSystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, New South Wales, Australia.
Damien CorderySystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, New South Wales, Australia.
Flynn Robert HillSystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, New South Wales, Australia.
Patricia CorrellSystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, New South Wales, Australia.
Anne-Marie FeyerSystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, New South Wales, Australia.
Gill SchierhoutFaculty of Medicine and Health, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.
Anna CampainFaculty of Medicine and Health, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to (1) assess Lumos data quality, a New South Wales (NSW) statewide linked health data asset; and (2) determine sociodemographic variation in health service utilisation of general practice, emergency department and admitted services.

designA retrospective cohort study using Lumos, a linked health data asset.

settingA representative statewide sample population of NSW, Australia.

participantsPeople residing within NSW with an electronic health record at a Lumos participating general practice between January 2010 and June 2023. PRIMARY AND SECONDARY OUTCOME MEASURES: Data quality indicators of Lumos including completeness, representativeness against NSW population data, consistency and timeliness. Furthermore, variation in general practice visits, emergency department presentations and hospital admission rates stratified by age, sex, rurality and Index of Relative Socio-economic Disadvantage (IRSD)-a measure of socioeconomic status used in Australia, where lower values represent greater relative disadvantage across a range of metrics such as education and income.

resultsAt the time of analysis, Lumos included records from 5.2 million unique patients, representing half (49.7%) of the NSW resident population. Limiting data to 2022, the Lumos population distribution broadly aligned with the 2021 Census except for IRSD quintile four and five which were under-represented (15.0% vs 20.4% (standardised difference -0.14)), and over-represented (29.7% vs 19.9% (standardised difference 0.23)), respectively. Age and greater relative disadvantage were associated with higher rates of general practice visits and hospital admissions. Greater relative disadvantage was also associated with higher rates of emergency department presentations.

conclusionsLumos's ability to overcome historical limitations of separately managed health data in Australia and its demonstrated data quality present an opportunity to enhance health system policy and planning in NSW. The variation in service utilisation across primary and tertiary care by population and geography apparent in Lumos reinforces the need for tailored service planning.

Indexed as

Emergency Service, HospitalGeneral PracticeHospitalizationPatient Acceptance of Health CareAdolescentAdultAgedAged, 80 and overChildChild, PreschoolElectronic Health RecordsFemaleHumansInfantInfant, NewbornMaleElectronic Health RecordsHealth policyHealth Services

Identifiers

PMID40701597
PMCPMC12306357

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Registered trials

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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.