The carbon footprint of hospital diagnostic imaging in Australia
Summary: Background: Pathology testing and diagnostic imaging together contribute 9% of healthcare's carbon footprint. Whilst the carbon footprint of pathology testing has been undertaken, to date, the carbon footprint of the four most common imaging modalities is unclear. Methods: We performe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | The Lancet Regional Health. Western Pacific |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666606522000748 |
| _version_ | 1818029629773971456 |
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| author | Scott McAlister Forbes McGain Matilde Petersen David Story Kate Charlesworth Glenn Ison Alexandra Barratt |
| author_facet | Scott McAlister Forbes McGain Matilde Petersen David Story Kate Charlesworth Glenn Ison Alexandra Barratt |
| author_sort | Scott McAlister |
| collection | DOAJ |
| description | Summary: Background: Pathology testing and diagnostic imaging together contribute 9% of healthcare's carbon footprint. Whilst the carbon footprint of pathology testing has been undertaken, to date, the carbon footprint of the four most common imaging modalities is unclear. Methods: We performed a prospective life cycle assessment at two Australian university-affiliated health services of five imaging modalities: chest X-ray (CXR), mobile chest X-ray (MCXR), computerised tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US). We included scanner electricity use and all consumables and associated waste, including bedding, imaging contrast, and gloves. Analysis was performed using both attributional and consequential life cycle assessment methods. The primary outcome was the greenhouse gas footprint, measured in carbon dioxide equivalent (CO2e) emissions. Findings: Mean CO2e emissions were 17·5 kg/scan for MRI; 9·2 kg/scan for CT; 0·8 kg/scan for CXR; 0·5 kg/scan for MCXR; and 0·5 kg/scan for US. Emissions from scanners from standby energy were substantial. When expressed as emissions per additional scan (results of consequential analysis) impacts were lower: 1·1 kg/scan for MRI; 1·1 kg/scan for CT; 0·6 kg/scan for CXR; 0·1 kg/scan for MCXR; and 0·1 kg/scan for US, due to emissions from standby power being excluded. Interpretation: Clinicians and administrators can reduce carbon emissions from diagnostic imaging, firstly by reducing the ordering of unnecessary imaging, or by ordering low-impact imaging (X-ray and US) in place of high-impact MRI and CT when clinically appropriate to do so. Secondly, whenever possible, scanners should be turned off to reduce emissions from standby power. Thirdly, ensuring high utilisation rates for scanners both reduces the time they spend in standby, and apportions the impacts of the reduced standby power of a greater number of scans. This therefore reduces the impact on any individual scan, maximising resource efficiency. Funding: Healthy Urban Environments (HUE) Collaboratory of the Maridulu Budyari Gumal Sydney Partnership for Health, Education, Research and Enterprise MBG SPHERE. The National Health and Medical Research Council (NHMRC) PhD scholarship |
| first_indexed | 2024-12-10T05:22:44Z |
| format | Article |
| id | doaj.art-a437f3dbddfc48a2beb0ceaa2b792ef2 |
| institution | Directory Open Access Journal |
| issn | 2666-6065 |
| language | English |
| last_indexed | 2024-12-10T05:22:44Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health. Western Pacific |
| spelling | doaj.art-a437f3dbddfc48a2beb0ceaa2b792ef22022-12-22T02:00:46ZengElsevierThe Lancet Regional Health. Western Pacific2666-60652022-07-0124100459The carbon footprint of hospital diagnostic imaging in AustraliaScott McAlister0Forbes McGain1Matilde Petersen2David Story3Kate Charlesworth4Glenn Ison5Alexandra Barratt6The Centre for Health Policy, The University of Melbourne, Australia, Wiser Healthcare and Faculty of Medicine and Health, The University of Sydney, Australia, and Department of Critical Care, The University of Melbourne, Grattan St, Parkville, VIC 3010, Australia; Corresponding author.Department of Critical Care, The University of Melbourne, Australia and Western Health, Melbourne, AustraliaWiser Healthcare and Faculty of Medicine and Health, The University of Sydney, AustraliaDepartment of Critical Care, The University of Melbourne, AustraliaNorthern Sydney Local Health District, Sydney, AustraliaDepartment of Cardiology, St George Hospital, Sydney, AustraliaWiser Healthcare and Faculty of Medicine and Health, The University of Sydney, AustraliaSummary: Background: Pathology testing and diagnostic imaging together contribute 9% of healthcare's carbon footprint. Whilst the carbon footprint of pathology testing has been undertaken, to date, the carbon footprint of the four most common imaging modalities is unclear. Methods: We performed a prospective life cycle assessment at two Australian university-affiliated health services of five imaging modalities: chest X-ray (CXR), mobile chest X-ray (MCXR), computerised tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US). We included scanner electricity use and all consumables and associated waste, including bedding, imaging contrast, and gloves. Analysis was performed using both attributional and consequential life cycle assessment methods. The primary outcome was the greenhouse gas footprint, measured in carbon dioxide equivalent (CO2e) emissions. Findings: Mean CO2e emissions were 17·5 kg/scan for MRI; 9·2 kg/scan for CT; 0·8 kg/scan for CXR; 0·5 kg/scan for MCXR; and 0·5 kg/scan for US. Emissions from scanners from standby energy were substantial. When expressed as emissions per additional scan (results of consequential analysis) impacts were lower: 1·1 kg/scan for MRI; 1·1 kg/scan for CT; 0·6 kg/scan for CXR; 0·1 kg/scan for MCXR; and 0·1 kg/scan for US, due to emissions from standby power being excluded. Interpretation: Clinicians and administrators can reduce carbon emissions from diagnostic imaging, firstly by reducing the ordering of unnecessary imaging, or by ordering low-impact imaging (X-ray and US) in place of high-impact MRI and CT when clinically appropriate to do so. Secondly, whenever possible, scanners should be turned off to reduce emissions from standby power. Thirdly, ensuring high utilisation rates for scanners both reduces the time they spend in standby, and apportions the impacts of the reduced standby power of a greater number of scans. This therefore reduces the impact on any individual scan, maximising resource efficiency. Funding: Healthy Urban Environments (HUE) Collaboratory of the Maridulu Budyari Gumal Sydney Partnership for Health, Education, Research and Enterprise MBG SPHERE. The National Health and Medical Research Council (NHMRC) PhD scholarshiphttp://www.sciencedirect.com/science/article/pii/S2666606522000748Diagnostic imagingCarbon footprintLife cycle assessmentNet-zero carbonAgingComorbidities |
| spellingShingle | Scott McAlister Forbes McGain Matilde Petersen David Story Kate Charlesworth Glenn Ison Alexandra Barratt The carbon footprint of hospital diagnostic imaging in Australia The Lancet Regional Health. Western Pacific Diagnostic imaging Carbon footprint Life cycle assessment Net-zero carbon Aging Comorbidities |
| title | The carbon footprint of hospital diagnostic imaging in Australia |
| title_full | The carbon footprint of hospital diagnostic imaging in Australia |
| title_fullStr | The carbon footprint of hospital diagnostic imaging in Australia |
| title_full_unstemmed | The carbon footprint of hospital diagnostic imaging in Australia |
| title_short | The carbon footprint of hospital diagnostic imaging in Australia |
| title_sort | carbon footprint of hospital diagnostic imaging in australia |
| topic | Diagnostic imaging Carbon footprint Life cycle assessment Net-zero carbon Aging Comorbidities |
| url | http://www.sciencedirect.com/science/article/pii/S2666606522000748 |
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