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Using a novel strategy to investigate the spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and mortality and the attributable burden: A case study in the Sichuan Basin, China

Ecotoxicology and environmental safety, 2023-10, Vol.264, p.115405-115405, Article 115405 [Peer Reviewed Journal]

2023 The Authors ;ISSN: 0147-6513 ;EISSN: 1090-2414 ;DOI: 10.1016/j.ecoenv.2023.115405

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  • Title:
    Using a novel strategy to investigate the spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and mortality and the attributable burden: A case study in the Sichuan Basin, China
  • Author: Wang, Wei ; Zeng, Jing ; Li, Xuelin ; Liao, Fang ; Li, Sheng ; Tian, Xinyue ; Yin, Fei ; Zhang, Tao ; Deng, Ying ; Ma, Yue
  • Subjects: Fine particulate matter ; Mortality ; Spatial autocorrelation ; Spatial cluster
  • Is Part Of: Ecotoxicology and environmental safety, 2023-10, Vol.264, p.115405-115405, Article 115405
  • Description: Due to the lack of statistical methods, few studies have investigated the spatial autocorrelated distribution in the association between short-term exposure to PM2.5 and mortality and used a statistical manner to explore the association-clustered regions, which play important roles in identifying high-sensitivity/susceptibility regions. The Sichuan Basin (SCB) is one of the most PM2.5-polluted areas, and the extreme economic imbalance may cause considerable spatial heterogeneity and clustering in PM2.5-mortality association. In this work, we used a recently proposed strategy by us to investigate the spatially autocorrelated and clustered association between daily PM2.5 and cardiorespiratory mortality from 2015 to 2019 in 130 counties of the SCB. First, generalized additive models were independently constructed to obtain the county-level association estimations. Then, an estimation-error-based spatial scan statistic was used to detect the association-clustered regions. Third, multivariate conditional meta autoregression was used to obtain the spatially autocorrelated association distribution, based on which the attributable deaths were mapped and their inequality was evaluated using the Gini coefficient and Lorenz curve. Results showed that two significantly association-clustered regions were detected. One is mainly located in the megacity Chengdu where PM2.5 presented a significantly stronger association with no threshold effect at low-level PM2.5 but a threshold at high-level PM2.5. In the other cluster, a threshold effect at low-level PM2.5 but no threshold at high-level PM2.5 were found. The mortality risk at low/middle-level PM2.5 decreased from Chengdu as the center to the surrounding areas. A total of 29,129 (2.0 %) deaths were attributable to the excess PM2.5 exposure. The attributable deaths also decreased from Chengdu as the center to the surrounding areas with Gini coefficients of 0.43 and 0.3 for absolute and relative attributable deaths, respectively. This novel strategy provided a new epidemiological perspective regarding the association and implicated that Chengdu is significantly deserving of more attention regarding PM2.5-related health loss. •Two significantly association-clustered regions were detected in the SCB.•PM2.5 presented a stronger association in Chengdu than in the other regions.•A significant inequality for PM2.5-attributable deaths was examined in the SCB.•The novel strategy can provide a new perspective regarding the association.
  • Publisher: Elsevier Inc
  • Language: English
  • Identifier: ISSN: 0147-6513
    EISSN: 1090-2414
    DOI: 10.1016/j.ecoenv.2023.115405
  • Source: DOAJ Directory of Open Access Journals
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