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Ha NT, Harris M, Bulsara M, Doust J, Kamarova S, McRobbie D, O'Leary P, Parizel PM, Slavotinek J, Wright C, Youens D, Moorin R. Patterns of computed tomography utilisation in injury management: latent classes approach using linked administrative data in Western Australia. Eur J Trauma Emerg Surg 2023; 49:2413-2427. [PMID: 37318517 PMCID: PMC10728237 DOI: 10.1007/s00068-023-02303-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/03/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE Whilst computed tomography (CT) imaging has been a vital component of injury management, its increasing use has raised concern regarding ionising radiation exposure. This study aims to identify latent classes (underlying patterns) of CT use over a 3-year period following the incidence of injury and factors predicting the observed patterns. METHOD A retrospective observational cohort study was conducted in 21,544 individuals aged 18 + years presenting to emergency departments (ED) of four tertiary public hospitals with new injury in Western Australia. Mixture modelling approach was used to identify latent classes of CT use over a 3-year period post injury. RESULTS Amongst injured people with at least one CT scan, three latent classes of CT use were identified including a: temporarily high CT use (46.4%); consistently high CT use (2.6%); and low CT use class (51.1%). Being 65 + years or older, having 3 + comorbidities, history with 3 + hospitalisations and history of CT use before injury were associated with consistently high use of CT. Injury to the head, neck, thorax or abdomen, being admitted to hospital after the injury and arriving to ED by ambulance were predictors for the temporarily high use class. Living in areas of higher socio-economic disadvantage was a unique factor associated with the low CT use class. CONCLUSIONS Instead of assuming a single pattern of CT use for all patients with injury, the advanced latent class modelling approach has provided more nuanced understanding of the underlying patterns of CT use that may be useful for developing targeted interventions.
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Affiliation(s)
- Ninh T Ha
- Health Economics and Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia.
| | - Mark Harris
- School of Accounting, Economics and Finance, Faculty of Business and Law, Curtin University, Perth, Western Australia, Australia
| | - Max Bulsara
- Institute for Health Research, University of Notre Dame, Fremantle, WA, Australia
- Centre for Health Services Research, School of Population and Global Health, The University of Western Australia, Crawley, Australia
| | - Jenny Doust
- Australian Women and Girls' Health Research Centre, School of Public Health, University of Queensland, Brisbane, Australia
| | - Sviatlana Kamarova
- Health Economics and Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia
- School of Health Sciences, University of Sydney, Camperdown, New South Wales, Australia
- Nepean Blue Mountains Local Health District, Kingswood, New South Wales, Australia
| | - Donald McRobbie
- School of Physical Sciences, University of Adelaide, Adelaide, Australia
| | - Peter O'Leary
- Health Economics and Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia
- Obstetrics and Gynaecology Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, WA, Australia
- PathWest Laboratory Medicine, QE2 Medical Centre, Nedlands, WA, Australia
| | - Paul M Parizel
- Medical School, University of Western Australia, Perth, WA, Australia
- Department of Radiology, Royal Perth Hospital, Victoria Square, Perth, WA, 6000, Australia
| | - John Slavotinek
- SA Medical Imaging, SA Health and College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Cameron Wright
- Health Economics and Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia
- Fiona Stanley Hospital, 11 Robin Warren Dr, Murdoch, WA, Australia
- Division of Internal Medicine, Medical School, Faculty of Health and Medical Sciences, University of Western, Perth, Australia
- School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - David Youens
- Health Economics and Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia
- Centre for Health Services Research, School of Population and Global Health, The University of Western Australia, Crawley, Australia
| | - Rachael Moorin
- Health Economics and Data Analytics, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia
- Centre for Health Services Research, School of Population and Global Health, The University of Western Australia, Crawley, Australia
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