Abstract

This study aimed to determine the spatial effects of traffic- and industrial-related pollution on the mortality for lung cancer (LC). We conducted a retrospective cohort study by using the data from LC registry in Jiading District for the period from 2002 to 2012. Standard parametric model with Weibull distribution was used for spatial survival analysis. Shorter distance to highway (adjusted odds ratio (aOR) = 1.15, 95% confidence interval (CI): 1.03-1.30) and higher factory density (aOR = 1.20, 95% CI: 1.05-1.37) were significantly associated with an increased risk of LC death, and there was a spatial difference in the associations between northern and southern areas of Jiading District. The risk was high in suburbs as compared with urban areas. Traffic- and industrial-related pollution were significantly associated with an increased risk of LC death, which showed a spatial variation. Further studies are needed to better understand the current LC status in the suburbs and to reduce health disparities.

Highlights

  • An estimated 19.3 million newly diagnosed cancer cases and 10.0 million cancer deaths occurred in 2020, and among them, lung cancer (LC) remained the leading cause of cancer-related death (Wang et al 2020; Sung et al 2021)

  • Shorter distance to highway and higher factory density were significantly associated with increased risks of LC death, and the associations showed spatial differences in northern and southern areas of Jiading District, respectively

  • Traffic and factory-related pollution was significantly associated with increased risk of LC death with an obvious spatial heterogeneity

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Summary

Methods

We conducted a retrospective cohort study by using the data of registered LC patients in Jiading District from 2002 to 2012. Standard parametric model with weibull distribution was used to explore factors related to LC death and the spatial effect of environmental factors were detected by using spatial survival analysis

Results
Introduction
Study site
The data of LC patients
Road and factory data
Data processing
Exposure classification
Traditional survival analysis
Spatial survival analysis
Characteristics and survival of LC patients
Risk factors associated with LC survival from parametric survival analysis
Discussion
Availability of data and materials
Full Text
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