Life cycle environmental emissions and health damages from the Canadian healthcare system: An economic-environmental-epidemiological analysis
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Notice bibliographique
Résumé
BACKGROUND: Human health is dependent upon environmental health. Air pollution is a leading cause of morbidity and mortality globally, and climate change has been identified as the single greatest public health threat of the 21st century. As a large, resource-intensive sector of the Canadian economy, healthcare itself contributes to pollutant emissions, both directly from facility and vehicle emissions and indirectly through the purchase of emissions-intensive goods and services. Together these are termed life cycle emissions. Here, we estimate the extent of healthcare-associated life cycle emissions as well as the public health damages they cause. METHODS AND FINDINGS: We use a linked economic-environmental-epidemiological modeling framework to quantify pollutant emissions and their implications for public health, based on Canadian national healthcare expenditures over the period 2009-2015. Expenditures gathered by the Canadian Institute for Health Information (CIHI) are matched to sectors in a national environmentally extended input-output (EEIO) model to estimate emissions of greenhouse gases (GHGs) and >300 other pollutants. Damages to human health are then calculated using the IMPACT2002+ life cycle impact assessment model, considering uncertainty in the damage factors used. On a life cycle basis, Canada's healthcare system was responsible for 33 million tonnes of carbon dioxide equivalents (CO2e), or 4.6% of the national total, as well as >200,000 tonnes of other pollutants. We link these emissions to a median estimate of 23,000 disability-adjusted life years (DALYs) lost annually from direct exposures to hazardous pollutants and from environmental changes caused by pollution, with an uncertainty range of 4,500-610,000 DALYs lost annually. A limitation of this national-level study is the use of aggregated data and multiple modeling steps to link healthcare expenditures to emissions to health damages. While informative on a national level, the applicability of these findings to guide decision-making at individual institutions is limited. Uncertainties related to national economic and environmental accounts, model representativeness, and classification of healthcare expenditures are discussed. CONCLUSIONS: Our results for GHG emissions corroborate similar estimates for the United Kingdom, Australia, and the United States, with emissions from hospitals and pharmaceuticals being the most significant expenditure categories. Non-GHG emissions are responsible for the majority of health damages, predominantly related to particulate matter (PM). This work can guide efforts by Canadian healthcare professionals toward more sustainable practices.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle