Dataset Information

Long-term exposure to wildfire smoke PM_2.5 and mortality in the contiguous United States.

ABSTRACT: Despite the growing evidence on the health effects of short-term exposure to wildfire smoke fine particles (PM_2.5), the impacts of long-term wildfire smoke PM_2.5 exposure remain unclear. We investigated the association between long-term exposure to wildfire smoke PM_2.5 and all-cause mortality and mortality from a wide range of specific causes in all 3,108 counties in the contiguous U.S., 2007-2020. Monthly county-level mortality data were collected from the National Center for Health Statistics. Wildfire smoke PM_2.5 concentration was derived from a 10×10 km² resolution spatiotemporal model. Controlling for non-smoke PM_2.5, air temperature, and unmeasured spatial and temporal confounders, we found a non-linear association between 12-month moving average concentration of smoke PM_2.5 and monthly all-cause mortality rate. Relative to a month with the long-term smoke PM_2.5 exposure below 0.1 μg/m³, all-cause mortality increased by 0.40-1.54 and 3.65 deaths per 100,000 people per month when the 12-month moving average of PM_2.5 concentration was of 0.1-5 and 5+ μg/m³, respectively. Cardiovascular, ischemic heart disease, digestive, endocrine, diabetes, mental, suicide, and chronic kidney disease mortality were all found to be associated with long-term wildfire smoke PM_2.5 exposure. Smoke PM_2.5 contributed to approximately 30,180 all-cause deaths/year (95% CI: 21,449, 38,910) in the contiguous U.S. Higher smoke PM_2.5-related increases in mortality rates were found for people aged 65 above and racial minority populations. Positive interaction effects with extreme heat were also observed. Our study identified the detrimental effects of long-term exposure to wildfire smoke PM_2.5 on a wide range of mortality outcomes, underscoring the need for public health actions and communication to prepare communities and individuals to mitigate smoke exposure.

SUBMITTER: Ma Y

PROVIDER: S-EPMC9915814 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Long-term exposure to wildland fire smoke PM2.5 and mortality in the contiguous United States.

Ma Yiqun Y Zang Emma E Liu Yang Y Wei Jing J Lu Yuan Y Krumholz Harlan M HM Bell Michelle L ML Chen Kai K

medRxiv : the preprint server for health sciences 20240611

Despite the substantial evidence on the health effects of short-term exposure to ambient fine particles (PM2.5), including increasing studies focusing on those from wildland fire smoke, the impacts of long-term wildland fire smoke PM2.5 exposure remain unclear. We investigated the association between long-term exposure to wildland fire smoke PM2.5 and non-accidental mortality and mortality from a wide range of specific causes in all 3,108 counties in the contiguo ...[more]

PMID: 36778437

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Project description:BackgroundSubstantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM2.5 compared with the number of studies focusing on total PM2.5 exposure.ObjectivesWe estimated the associations between cardiorespiratory acute events and exposure to smoke PM2.5 in Colorado using a novel exposure model to separate smoke PM2.5 from background ambient PM2.5 levels.MethodsWe obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM2.5 and non-smoke PM2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM2.5 and non-smoke PM2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease.ResultsPer 1 μg/m3 increase in fire smoke PM2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM2.5. Associations with non-smoke PM2.5 were null for all outcomes. Positive age-specific associations related to smoke PM2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults.DiscussionThis is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM2.5 exposure in a changing climate.

Project description:Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.

Project description:It has been posited that populations being exposed to long-term air pollution are more susceptible to COVID-19. Evidence is emerging that long-term exposure to ambient PM2.5 (particulate matter with aerodynamic diameter 2.5 μm or less) associates with higher COVID-19 mortality rates, but whether it also associates with the speed at which the disease is capable of spreading in a population is unknown. Here, we establish the association between long-term exposure to ambient PM2.5 in the United States (US) and COVID-19 basic reproduction ratio R0- a dimensionless epidemic measure of the rapidity of disease spread through a population. We inferred state-level R0 values using a state-of-the-art susceptible, exposed, infected, and recovered (SEIR) model initialized with COVID-19 epidemiological data corresponding to the period March 2-April 30. This period was characterized by a rapid surge in COVID-19 cases across the US states, implementation of strict social distancing measures, and a significant drop in outdoor air pollution. We find that an increase of 1 μg/m3 in PM2.5 levels below current national ambient air quality standards associates with an increase of 0.25 in R0 (95% CI: 0.048-0.447). A 10% increase in secondary inorganic composition, sulfate-nitrate-ammonium, in PM2.5 associates with ≈10% increase in R0 by 0.22 (95% CI: 0.083-0.352), and presence of black carbon (soot) in the ambient environment moderates this relationship. We considered several potential confounding factors in our analysis, including gaseous air pollutants and socio-economical and meteorological conditions. Our results underscore two policy implications - first, regulatory standards need to be better guided by exploring the concentration-response relationships near the lower end of the PM2.5 air quality distribution; and second, pollution regulations need to be continually enforced for combustion emissions that largely determine secondary inorganic aerosol formation.

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Long-term exposure to wildfire smoke PM_2.5 and mortality in the contiguous United States.

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Long-term exposure to wildland fire smoke PM<sub>2.5</sub> and mortality in the contiguous United States.

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Long-term exposure to wildfire smoke PM2.5 and mortality in the contiguous United States.

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Long-term exposure to wildland fire smoke PM<sub>2.5</sub> and mortality in the contiguous United States.

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OmicsDI is part of the ELIXIR infrastructure

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Long-term exposure to wildfire smoke PM_2.5 and mortality in the contiguous United States.