Evidence map›Paper›PMID 36697052›Full record

ArticleBMJ open2023

Understanding compensable and non-compensable patient profiles, pathways and physical outcomes for transport and work-related injuries in Queensland, Australia through data linkage.

Kirsten Vallmuur, Victoria McCreanor, Angela Watson, Cate Cameron, Susanna Cramb, Shannon Dias, Shahera Banu, Jacelle Warren

Abstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  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

8 authors.

Kirsten VallmuurAustralian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia k.vallmuur@qut.edu.au.ORCID 0000-0002-3760-0822
Victoria McCreanorAustralian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0002-0589-8521
Angela WatsonAustralian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.
Cate CameronAustralian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.
Susanna CrambAustralian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0001-9041-9531
Shannon DiasJamieson Trauma Institute, Metro North Health, Brisbane, Queensland, Australia.
Shahera BanuJamieson Trauma Institute, Metro North Health, Brisbane, Queensland, Australia.
Jacelle WarrenAustralian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn many jurisdictions, people experiencing an injury often pursue compensation to support their treatment and recovery expenses. Healthcare costs form a significant portion of payments made by compensation schemes. Compensation scheme regulators need accurate and comprehensive data on injury severity, treatment pathways and outcomes to enable scheme modelling, monitoring and forecasting. Regulators routinely rely on data provided by insurers which have limited healthcare information. Health data provide richer information and linking health data with compensation data enables the comparison of profiles, patterns, trends and outcomes of injured patients who claim and injured parties who are eligible but do not claim. METHODS AND ANALYSIS: This is a retrospective population-level epidemiological data linkage study of people who have sought ambulatory, emergency or hospital treatment and/or made a compensation claim in Queensland after suffering a transport or work-related injury, over the period 1 January 2011 to 31 December 2021. It will use person-linked data from nine statewide data sources: (1) Queensland Ambulance Service, (2) Emergency Department, (3) Queensland Hospital Admitted Patients, (4) Retrieval Services, (5) Hospital Costs, (6) Workers' Compensation, (7) Compulsory Third Party Compensation, (8) National Injury Insurance Scheme and (9) Queensland Deaths Registry. Descriptive, parametric and non-parametric statistical methods and geospatial analysis techniques will be used to answer the core research questions regarding the patient's health service use profile, costs, treatment pathways and outcomes within 2 years postincident as well as to examine the concordance and accuracy of information across health and compensation databases. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Royal Brisbane and Women's Hospital Human Research Ethics Committee, and governance approval was obtained via the Public Health Act 2005, Queensland. The findings of this study will be used to inform key stakeholders across the clinical, research and compensation regulation area, and results will be disseminated through peer-reviewed journals, conference presentations and reports/seminars with key stakeholders.

Indexed as

Occupational InjuriesAustraliaFemaleHealth Care CostsHumansInformation Storage and RetrievalPalliative CareQueenslandRetrospective StudiesWorkers' Compensationpublic healthstatistics & research methodstrauma management

Identifiers

PMID36697052
PMCPMC9884851

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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.