Estimate of the carbon footprint of the US Health Care Sector

Section of Hospital Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
JAMA The Journal of the American Medical Association (Impact Factor: 35.29). 11/2009; 302(18):1970-2. DOI: 10.1001/jama.2009.1610
Source: PubMed
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    • "In the USA, healthcare contributes 8% of national greenhouse gas emissions (Chung & Meltzer, 2009). "
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    ABSTRACT: Non-attendance at mental health clinics is an international problem. A survey was conducted in the UK investigating communication methods used by staff to inform and remind patients about appointments. Increased number of communication methods used was associated with a reduced non-attendance rate. A care modelling analysis is provided that explores the healthcare use of three hypothetical patients following clinic non-attendance. The financial and environmental costs of each are then calculated and results discussed. Reducing non-attendance is achievable through the use of multiple communication methods. This small change can improve the sustainability of mental healthcare in different countries by improving quality of care and reducing financial and environmental costs.
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    • "Even on a larger scale, a 5000 m 2 healthcare facility would emit only 500e900 tonnes (t) of CO 2 e annually as a result of onsite fuel and electricity consumption, depending on its location, the services it houses, and its ventilation system (Lomas and Ji, 2009; Murray et al., 2008). Cumulative health sector emissions, on the other hand, are significant: they are estimated to represent 3% of total greenhouse gas emissions in England (Sustainable Development Commission- Stockholm Environment Institute, 2008), and 8% of greenhouse gas emissions in the United States (Chung and Meltzer, 2009), with more than half those emissions arising upstream in the supply chain. As with global emissions, for the health sector to substantially reduce its total greenhouse gas emissions will require the cumulative efforts of hundreds of component sub-sectors each reducing their emissions on what might appear to be relatively small scales. "
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    ABSTRACT: Emergency medical services, or ‘ambulance services’, are a vehicle-intense component of the health sector that could contribute to that sector’s emissions reduction efforts. This analysis uses data from an inventory of ambulance service Scope 1 (arising from direct energy consumption) and Scope 2 (arising from purchased energy consumption) emissions, along with publicly available expenditure data and emissions multipliers derived from economy-wide inputeoutput tables, to estimate the life cycle greenhouse gas emissions of Australian ambulance services. Total emissions are estimated at between 216,369 and 546,688 t CO2e annually, and represent between 1.8% and 4.4% of total Australian health sector emissions. Approximately 20% of ambulance service emissions arise from direct consumption of vehicle fuels (diesel and petrol) and aircraft fuels, with 22% arising from electricity consumption, and 58% arising from Scope 3 (e.g., supply chain; waste disposal) processes. Incorporating alternative fuels and higher efficiency vehicles into Australian ambulance services’ vehicle fleets could reduce their direct greenhouse emissions, but broader efforts targeting reduced electricity consumption, greener electricity generation, and environmentally friendly purchasing practices will be required to substantially reduce their total carbon footprint.
    Journal of Cleaner Production 12/2012; 37:135-141. DOI:10.1016/j.jclepro.2012.06.020 · 3.84 Impact Factor
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    • "Minimising patient travel distance fits with the current sustainability and equality agenda [44]. Greenhouse gas (GHG) emission attributable to health care provision is substantial; it consists of 7% of total GHG emission in the US and 3% in the UK [45,46]. The shortening of commuting times reduces not only travel cost for dialysis patients, but also GHG emission related to their commuting [45,47]. "
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    ABSTRACT: Background Frequent and long-term commuting is a requirement for dialysis patients. Accessibility thus affects their quality of lives. In this paper, a new model for accessibility measurement is proposed in which both geographic distance and facility capacity are taken into account. Simulation of closure of rural facilities and that of capacity transfer between urban and rural facilities are conducted to evaluate the impacts of these phenomena on equity of accessibility among dialysis patients. Methods Post code information as of August 2011 of all the 7,374 patients certified by municipalities of Hiroshima prefecture as having first or third grade renal disability were collected. Information on post code and the maximum number of outpatients (capacity) of all the 98 dialysis facilities were also collected. Using geographic information systems, patient commuting times were calculated in two models: one that takes into account road distance (distance model), and the other that takes into account both the road distance and facility capacity (capacity-distance model). Simulations of closures of rural and urban facilities were then conducted. Results The median commuting time among rural patients was more than twice as long as that among urban patients (15 versus 7 minutes, p < 0.001). In the capacity-distance model 36.1% of patients commuted to the facilities which were different from the facilities in the distance model, creating a substantial gap of commuting time between the two models. In the simulation, when five rural public facilitiess were closed, Gini coefficient of commuting times among the patients increased by 16%, indicating a substantial worsening of equity, and the number of patients with commuting times longer than 90 minutes increased by 72 times. In contrast, closure of four urban public facilities with similar capacities did not affect these values. Conclusions Closures of dialysis facilities in rural areas have a substantially larger impact on equity of commuting times among dialysis patients than closures of urban facilities. The accessibility simulations using thecapacity-distance model will provide an analytic framework upon which rational resource distribution policies might be planned.
    International Journal of Health Geographics 07/2012; 11(1):28. DOI:10.1186/1476-072X-11-28 · 2.62 Impact Factor
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