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  • Temporal change in minimum mortality temperature under changing climate: A multicountry multicommunity observational study spanning 1986-2015
    Publication . Yang, Daewon; Hashizume, Masahiro; Tobías, Aurelio; Honda, Yasushi; Roye, Dominic; Oh, Jaemin; Dang, Tran Ngoc; Kim, Yoonhee; Abrutzky, Rosana; Guo, Yuming; Tong, Shilu; Coelho, Micheline de Sousa Zanotti Stagliorio; Saldiva, Paulo Hilario Nascimento; Lavigne, Eric; Correa, Patricia Matus; Ortega, Nicolás Valdés; Osorio, Samuel; Kyselý, Jan; Urban, Aleš; Orru, Hans; Indermitte, Ene; Jaakkola, Jouni; Ryti, Niilo; Pascal, Mathilde; Huber, Veronika; Schneider, Alexandra; Katsouyanni, Klea; Analitis, Antonis; Entezari, Alireza; Mayvaneh, Fatemeh; Goodman, Patrick; Zeka, Ariana; Michelozzi, Paola; de'Donato, Francesca; Alahmad, Barrak; Diaz, Magali Hurtado; la Cruz Valencia, César De; Overcenco, Ala; Houthuijs, Danny; Ameling, Caroline; Rao, Shilpa; Nunes, Baltazar; Madureira, Joana; Holo-Bâc, Iulian Horia; Scovronick, Noah; Acquaotta, Fiorella; Kim, Ho; Lee, Whanhee; Íñiguez, Carmen; Forsberg, Bertil; Vicedo-Cabrera, Ana Maria; Ragettli, Martina S; Guo, Yue-Liang Leon; Pan, Shih Chun; Li, Shanshan; Sera, Francesco; Zanobetti, Antonella; Schwartz, Joel; Armstrong, Ben; Gasparrini, Antonio; Chung, Yeonseung
    Background: The minimum mortality temperature (MMT) or MMT percentile (MMTP) is an indicator of population susceptibility to nonoptimum temperatures. MMT and MMTP change over time; however, the changing directions show region-wide heterogeneity. We examined the heterogeneity of temporal changes in MMT and MMTP across multiple communities and in multiple countries. Methods: Daily time-series data for mortality and ambient mean temperature for 699 communities in 34 countries spanning 1986-2015 were analyzed using a two-stage meta-analysis. First, a quasi-Poisson regression was employed to estimate MMT and MMTP for each community during the designated subperiods. Second, we pooled the community-specific temporally varying estimates using mixed-effects meta-regressions to examine temporal changes in MMT and MMTP in the entire study population, as well as by climate zone, geographical region, and country. Results: Temporal increases in MMT and MMTP from 19.5 °C (17.9, 21.1) to 20.3 °C (18.5, 22.0) and from the 74.5 (68.3, 80.6) to 75.0 (71.0, 78.9) percentiles in the entire population were found, respectively. Temporal change was significantly heterogeneous across geographical regions (P < 0.001). Temporal increases in MMT were observed in East Asia (linear slope [LS] = 0.91, P = 0.02) and South-East Asia (LS = 0.62, P = 0.05), whereas a temporal decrease in MMT was observed in South Europe (LS = -0.46, P = 0.05). MMTP decreased temporally in North Europe (LS = -3.45, P = 0.02) and South Europe (LS = -2.86, P = 0.05). Conclusions: The temporal change in MMT or MMTP was largely heterogeneous. Population susceptibility in terms of optimum temperature may have changed under a warming climate, albeit with large region-dependent variations.
    2024-10-30Journal article Open access Show more
  • Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities
    Publication . Masselot, Pierre; Kan, Haidong; Kharol, Shailesh K; Bell, Michelle L.; Sera, Francesco; Lavigne, Eric; Breitner, Susanne; das Neves Pereira da Silva, Susana; Burnett, Richard T.; Gasparrini, Antonio; Brook, Jeffrey R.; MCC Collaborative Research Network
    Background: Fine particulate matter (PM2.5) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM2.5, it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM2.5 toxicity. Methods: The PMCI is constructed as an index spanning seven different pollutants, relative to the PM2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI. Results: We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM2.5 or its composition. Conclusions: The PMCI represents an efficient and simple predictor of local PM2.5-related mortality, providing evidence that PM2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.
    2024-10-30Research article Open access Show more