Environmental Impacts of Kidney Replacement Therapies: A Comparative Lifecycle Assessment
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Health care delivery is associated with considerable emissions of greenhouse gases and other pollutants. Although the relative health and economic impacts of kidney replacement therapies (KRTs) have been examined, their comparative environmental impacts have been poorly described. This study sought to characterize these impacts, comparing them across types of KRT. STUDY DESIGN: A comparative lifecycle assessment (LCA). SETTING & PARTICIPANTS: Data collection implemented at Vancouver General Hospital in Vancouver, British Columbia, Canada. EXPOSURE: Three KRTs: deceased-donor kidney transplant (KT), automated/cycler peritoneal dialysis (PD), or in-center hemodialysis (HD). OUTCOME: Environmental impacts of KRTs over 1 year were evaluated using the World ReCiPe (H) 2016 method. ANALYTICAL APPROACH: Lifecycle inventory results were transformed into 3 end-point and 18 midpoint environmental impact categories including climate change, air pollution, human toxicity, and water depletion. RESULTS: Across the majority of environmental impact categories, including climate change, air pollution, human toxicity, and water depletion, HD had the highest environmental impact and KT the lowest. The climate impact from a patient receiving HD was 74% and 46% more than from patients receiving KT and PD, respectively. Similarly, HD accounted for 65% of total air pollution impacts, 54% of human toxicity, and 44% of water depletion. The highest impact of PD was on water depletion (41%) and metal depletion (81%). KT demonstrated the lowest impact across all categories except terrestrial ecotoxicity. Within each therapy, patient and staff travel and consumables were the largest contributors to greenhouse gas emissions. LIMITATIONS: Pharmaceuticals were excluded from this study because of a lack of publicly available data. CONCLUSIONS: KT is the most environmentally preferred KRT. PD had fewer environmental impacts than HD. Understanding the relative environmental impacts of KRTs can help inform clinical decision-making in the management of kidney failure. PLAIN-LANGUAGE SUMMARY: The environmental impacts of health care are gaining attention, yet kidney care, and especially kidney replacement therapies (KRTs), have been underexamined. This study was inspired by growing concerns about the environmental consequences of KRTs like hemodialysis, peritoneal dialysis, and transplantation. We used environmental assessment tools to measure emissions and resource use across different KRTs in a clinical setting in Vancouver, Canada. We found that these therapies vary widely in their environmental impacts, with in-center hemodialysis having the greatest negative impact and kidney transplant the least impact. This study also explored the sources of these impacts and can inform health systems and health care policymakers regarding opportunities for more environmentally informed practices in kidney care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".