The carbon footprint of hospital diagnostic imaging in Australia - Scott McAlister; Forbes McGain; Matilde Petersen; David Story; Kate Charlesworth; Glenn Ison; Alexandra Barratt (Elsevier, 2022)
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