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Record W2771726152 · doi:10.1016/s2542-5196(17)30158-4

Carbon footprinting in health systems: one small step towards planetary health

2017· article· en· W2771726152 on OpenAlexaboutno aff
Tim Taylor, P. Mackie

Bibliographic record

VenueThe Lancet Planetary Health · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsScopusPublic healthCarbon footprintHealth careEnvironmental healthClimate changeBusinessMedicineGreenhouse gasEnvironmental resource managementGeographyPolitical scienceEconomic growthMEDLINENursingEnvironmental scienceEcologyEconomicsBiology

Abstract

fetched live from OpenAlex

Climate change is without doubt one of the major threats facing public health. While we are already experiencing extreme weather events worldwide, the longer term impacts on health will include increased heat-related mortality, increased food-borne disease, and increased risk of vector-borne and water-borne disease.1Haines A Kovats RS Campbell-Ledrum D Corvalan C Climate change and human health: Impacts, vulnerability and public health.Public Health. 2006; 120: 585-596Crossref PubMed Scopus (564) Google Scholar Coupled with increasing—and mobile—populations and antimicrobial resistance, the pressures on health systems will be substantial. Is it surprising then that the Paris Agreement formally linked human and planetary health so clearly and sought to harness leadership from the health sector to achieve robust change? It is in this light that efforts to assess the carbon footprinting of different elements of health care are needed. Bottom-up studies on this have included efforts to assess the carbon footprints of renal care,2Connor A Lillywhite R Cook MW The carbon footprint of a renal service in the United Kingdom.Q J Med. 2010; 103: 965-975Crossref Scopus (46) Google Scholar intensive care,3Pollard A Paddle J Taylor T Tillyard A The carbon footprint of acute care: how energy intensive is critical care?.Public Health. 2014; 128: 771-776Crossref PubMed Scopus (20) Google Scholar dentistry4Duane B Taylor T Stahl-Timmins W Hyland J Mackie P Pollard A Carbon mitigation, patient choice and cost reduction—triple bottom line optimisation for healthcare planning.Public Health. 2014; 128: 920-924Crossref Scopus (10) Google Scholar and, now in The Lancet Planetary Health, operating theatres.5MacNeill A Lillywhite R Brown C The impact of surgery on global climate: a carbon footprinting study of operating theatres in three health systems.Lancet Planet Health. 2017; 1: e381-e388Summary Full Text Full Text PDF PubMed Scopus (144) Google Scholar Andrea MacNeill and colleagues report the carbon footprint of surgical suites in three academic quaternary-care hospitals in Canada, the UK, and the USA to be between 3 218 907 kg and 5 187 936 kg of CO2e over a 1 year period. Substantial contributions come from the use of anaesthetic gases and energy consumption. Ground truthing of the environmental impact of processes is needed, so that appropriate cost-effective action can be taken to reduce emissions in the short term and protect human health in the longer term. Globally health-care systems are stretched with managers and clinicians under pressure to deal with increasing demand from service users, with limited budgets. The potential exists for those responsible to focus on addressing immediate needs as climate change might seem a distant threat. However, studies have shown the potential for triple bottom line solutions in health-care settings: with carbon mitigation options being able to offer improved economic performance in terms of efficiency gains; social gains through improved patient choice; and environmental dividends in terms of reduced carbon emissions.4Duane B Taylor T Stahl-Timmins W Hyland J Mackie P Pollard A Carbon mitigation, patient choice and cost reduction—triple bottom line optimisation for healthcare planning.Public Health. 2014; 128: 920-924Crossref Scopus (10) Google Scholar As an added benefit, climate change mitigation can also lead to improved health through improved air quality. There exists a need for a better understanding of climate change and mitigation in the health sector. Carbon footprinting studies are just a first step. Options need to be evaluated in terms of their cost-effectiveness and many could be win–win in terms of energy savings. Better decision making around carbon is needed at all levels; from decisions about the location of health services by high level managers to even everyday decisions on the choices of what to put on the menu in the hospital canteen. Understanding the carbon footprint of health care involves everyone in that system; from the patients to the managers, from the porters to the surgeons. Patient care is obviously paramount, but carbon management can no longer be ignored. Shared learning across institutions and across international borders is needed; the lessons drawn from critically comparing management practices, building designs, and care pathways in terms of carbon intensity might lead to a more critical approach overall. The co-benefits of mitigation and adaptation might be particularly important in the health and health-care setting. Active travel could promote good physical health, reduce air pollution, and lead to carbon reductions. However, care is also needed as there can be unintended consequences. For example, reducing the temperature of hot water in systems could reduce carbon, but might increase the risks elsewhere (eg, risk of Legionnaires' disease).6Pollard A Taylor T Fleming L Stahl-Timmins W Depledge M Osborne N Mainstreaming carbon management in health care systems: a bottom-up modelling approach.Environ Sci Technol. 2012; 47: 678-686Crossref Scopus (14) Google Scholar Consideration needs to be given to the reasons behind for certain standards—some might be arbitrary—others are not. Lessons from work in Scotland suggests that there is openness from those working in the health sector to reduce carbon. However, better decision support tools might be needed to assist in identifying triple bottom line solutions. Modelling of the carbon intensity of different care pathways and considering alternatives requires substantial effort in terms of time. Assessing potential outcomes requires a multidisciplinary approach, cutting across environmental science, systems modelling, and economics. Carbon footprinting alone will not lead to better decisions, this only enables an understanding of the scale of the mountain to climb to reduce emissions. However, it does provide insights into where efforts could be targeted first. For improved planetary health to be realised it is important that sectors that are impacted by environmental change, such as health, take a proactive role in understanding their own environmental impact. Describing the carbon footprint of different elements of health care represents a small step in this direction. It is a necessary step, but we should be under no illusions. It is no longer sufficient to simply quantify the problems we face; health-care systems need to be much more effective stewards of the resources placed at their disposal. TT acknowledges funding from the European Union's Horizon 2020 Programme for research, technological development and demonstration under grant agreement number 690105 (Integrated Climate forcing and Air pollution Reduction in Urban Systems [ICARUS]). This work reflects only the authors' views and the European Commission is not responsible for any use that may be made of the information it contains. PM is Co-Editor in Chief of the journal, Public Health. TT declares no competing interests. The impact of surgery on global climate: a carbon footprinting study of operating theatres in three health systemsOperating theatres are an appreciable source of greenhouse gas emissions. Emissions reduction strategies including avoidance of desflurane and occupancy-based ventilation have the potential to lessen the climate impact of surgical services without compromising patient safety. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.165
GPT teacher head0.336
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations17
Published2017
Admission routes1
Has abstractyes

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