Evidence map›Paper›PMID 34764174›Full record

ArticleBMJ open2021

Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia.

Ninh Thi Ha, Susannah Maxwell, Max K Bulsara, Jenny Doust, Donald Mcrobbie, Peter O'Leary, John Slavotinek, Rachael Moorin

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 31% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Patterns of computed tomography utilisation in injury management: latent classes approach using linked administrative data in Western Australia.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2023
    Observational
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 at 5 institutions in 1 country.

Ninh Thi HaHealth Economics & Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University - Bentley Campus, Perth, Western Australia, Australia ninh.ha05@gmail.com.ORCID 0000-0002-2789-5604
Susannah MaxwellHealth Economics & Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University - Bentley Campus, Perth, Western Australia, Australia.
Max K BulsaraInstitute for Health and Rehabilitation Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia.ORCID 0000-0002-8033-6123
Jenny DoustFaculty of Medicine and Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia.
Donald McrobbieSchool of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
Peter O'LearyFaculty of Health Sciences, Curtin University, Perth, Australian Capital Territory, Australia.
John SlavotinekSouth Australia Medical Imaging, Flinders Medical Centre, Bedford Park, South Australia, Australia.
Rachael MoorinHealth Economics & Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University - Bentley Campus, Perth, Western Australia, Australia.ORCID 0000-0001-8742-7151
The University of Western Australia · AUCurtin University · AUFlinders University · AUThe University of Adelaide · AUThe University of Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWhile CT scanning plays a significant role in healthcare, its increasing use has raised concerns about inappropriate use. This study investigated factors driving the changing use of CT among people admitted to tertiary hospitals in Western Australia (WA). DESIGN AND

settingA repeated cross-sectional study of CT use in WA in 2003-2005 and 2013-2015 using linked administrative heath data at the individual patient level.

participantsA total of 2 375 787 tertiary hospital admissions of people aged 18 years or older.

main outcome measureRate of CT scanning per 1000 hospital admissions.

methodsA multivariable decomposition model was used to quantify the contribution of changes in patient characteristics and changes in the probability of having a CT over the study period.

resultsThe rate of CT scanning increased by 112 CT scans per 1000 admissions over the study period. Changes in the distribution of the observed patient characteristics were accounted for 62.7% of the growth in CT use. However, among unplanned admissions, changes in the distribution of patient characteristics only explained 17% of the growth in CT use, the remainder being explained by changes in the probability of having a CT scan. While the relative probability of having a CT scan generally increased over time across most observed characteristics, it reduced in young adults (-2.8%), people living in the rural/remote areas (-0.8%) and people transferred from secondary hospitals (-0.8%).

conclusionsOur study highlights potential improvements in practice towards reducing medical radiation exposure in certain high risk population. Since changes in the relative probability of having a CT scan (representing changes in scope) rather than changes in the distribution of the patient characteristics (representing changes in need) explained a major proportion of the growth in CT use, this warrants more in-depth investigations in clinical practices to better inform health policies promoting appropriate use of diagnostic imaging tests.

Indexed as

Radiation ExposureTomography, X-Ray ComputedCross-Sectional StudiesHumansTertiary Care CentersWestern AustraliaYoung Adultcomputed tomographydiagnostic radiologyhealth services administration & managementpublic health

Identifiers

PMID34764174
PMCPMC8587703
OpenAlexW3212187970

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.