Spatial Environmental Factors Predict Cardiovascular and All-Cause Mortality: Results of the Space Study Publisher Pubmed



Hadley MB¹ ; Nalini M^{2, 3} ; Adhikari S⁴ ; Szymonifka J⁴ ; Etemadi A⁵ ; Kamangar F⁶ ; Khoshnia M⁷ ; Mcchane T¹ ; Pourshams A² ; Poustchi H² ; Sepanlou SG² ; Abnet C⁵ ; Freedman ND⁵ ; Boffetta P^{8, 9} Show All Authors Show Affiliations
Source: PLoS ONE Published:2022

Abstract

Background Environmental exposures account for a growing proportion of global mortality. Large cohort studies are needed to characterize the independent impact of environmental exposures on mortality in low-income settings. Methods We collected data on individual and environmental risk factors for a multiethnic cohort of 50,045 individuals in a low-income region in Iran. Environmental risk factors included: ambient fine particular matter air pollution; household fuel use and ventilation; proximity to traffic; distance to percutaneous coronary intervention (PCI) center; socioeconomic environment; population density; local land use; and nighttime light exposure. We developed a spatial survival model to estimate the independent associations between these environmental exposures and all-cause and cardiovascular mortality. Findings Several environmental factors demonstrated associations with mortality after adjusting for individual risk factors. Ambient fine particulate matter air pollution predicted all-cause mortality (per μg/m3, HR 1.20, 95% CI 1.07, 1.36) and cardiovascular mortality (HR 1.17, 95% CI 0.98, 1.39). Biomass fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.23, 95% CI 0.99, 1.53) and cardiovascular mortality (HR 1.36, 95% CI 0.99, 1.87). Kerosene fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.09, 95% CI 0.97, 1.23) and cardiovascular mortality (HR 1.19, 95% CI 1.01, 1.41). Distance to PCI center predicted all-cause mortality (per 10km, HR 1.01, 95% CI 1.004, 1.022) and cardiovascular mortality (HR 1.02, 95% CI 1.004, 1.031). Additionally, proximity to traffic predicted all-cause mortality (HR 1.13, 95% CI 1.01, 1.27). In a separate validation cohort, the multivariable model effectively predicted both all-cause mortality (AUC 0.76) and cardiovascular mortality (AUC 0.81). Population attributable fractions demonstrated a high mortality burden attributable to environmental exposures. Interpretation Several environmental factors predicted cardiovascular and all-cause mortality, independent of each other and of individual risk factors. Mortality attributable to environmental factors represents a critical opportunity for targeted policies and programs. Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.