Non-optimum temperatures led to labour productivity burden by causing premature deaths: A multi-country study

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dc.coverageDOI: 10.1016/j.envint.2024.109096
dc.creatorWen, Bo
dc.creatorAdemi, Zanfina
dc.creatorWu, Yao
dc.creatorXu, Rongbin
dc.creatorYu, Pei
dc.creatorLiu, Yanming
dc.creatorYu, Wenhua
dc.creatorYe, Tingting
dc.creatorHuang, Wenzhong
dc.creatorYang, Zhengyu
dc.creatorZhang, Yiwen
dc.creatorZhang, Yuxi
dc.creatorJu, Ke
dc.creatorHales, Simon
dc.creatorLavigne, Eric
dc.creatorHilario Nascimento Sadiva, Paulo
dc.creatorde Sousa Zanotti Stagliorio Coêlho, Micheline
dc.creatorMatus, Patricia
dc.creatorKim, Ho
dc.creatorTantrakarnapa, Kraichat
dc.creatorKliengchuay, Wissanupong
dc.creatorCapon, Anthony
dc.creatorBi, Peng
dc.creatorJalaludin, Bin
dc.creatorHu, Wenbiao
dc.creatorGreen, Donna
dc.creatorZhang, Ying
dc.creatorArblaster, Julie
dc.creatorPhung, Dung
dc.creatorGuo, Yuming
dc.creatorLi, Shanshan
dc.date2024
dc.date.accessioned2025-11-18T19:55:44Z
dc.date.available2025-11-18T19:55:44Z
dc.description<p>Background: Non-optimum temperatures are associated with a considerable mortality burden. However, there is a lack of evaluation of labour productivity losses related to premature deaths due to non-optimum temperatures. This study aimed to quantify the labour productivity burden associated with premature deaths related to non-optimum temperatures and explore the potential socio-economic vulnerabilities. Methods: Daily all-cause mortality data were collected from 1,066 locations in 7 countries (Australia, Brazil, Canada, Chile, New Zealand, South Korea, and Thailand). Productivity-Adjusted Life-Year (PALY) loss due to each premature death was calculated to measure the labour productivity loss, by multiplying the years of working life lost by the proportion of the equivalent full-time (EFT) workers. A two-stage times series design and the generalized linear regression model with a quasi-Poisson family were applied to assess the association between non-optimum temperatures and the PALY loss due to premature deaths. Results: We observed a U-shaped relationship between temperature and PALY lost due to premature mortality. We estimated that 2.51% (95% eCI: 2.05%, 2.92%) of PALY losses could be attributed to non-optimal temperatures, with cold-related deaths contributing 1.26% (95% eCI: 0.94%, 1.54%) and heat-related deaths contributing 1.25% (95% eCI: 0.96%, 1.51%). Cold temperature contributed to the most PALYs lost in those aged 45–54 and 55–64, while heat-related losses predominated among the 15–44 age group. We also observed that the fractions of PALY lost attributed to extreme heat were positively associated with the relative deprivation index, while negatively associated with GDP per capita. Conclusion: This multi-country study highlights that non-optimum temperatures led to a considerable labour productivity loss and socioeconomically disadvantaged communities experience greater losses.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/09a7a2d3-ffd6-468e-b778-e45215948d62
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/59414
dc.languageeng
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourcevol.193 (2024)
dc.subjectSDG 3 - Good Health and Well-being
dc.titleNon-optimum temperatures led to labour productivity burden by causing premature deaths: A multi-country studyeng
dc.typeArticleeng
dc.typeArtículospa
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