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Spatial environmental factors predict cardiovascular and all-cause mortality: Results of the SPACE study

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...

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Detalles Bibliográficos
Autores principales: Hadley, Michael B., Nalini, Mahdi, Adhikari, Samrachana, Szymonifka, Jackie, Etemadi, Arash, Kamangar, Farin, Khoshnia, Masoud, McChane, Tyler, Pourshams, Akram, Poustchi, Hossein, Sepanlou, Sadaf G., Abnet, Christian, Freedman, Neal D., Boffetta, Paolo, Malekzadeh, Reza, Vedanthan, Rajesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231727/
https://www.ncbi.nlm.nih.gov/pubmed/35749347
http://dx.doi.org/10.1371/journal.pone.0269650
Descripción
Sumario: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/m(3), 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.