Dataset Information

Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions.

ABSTRACT: Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ?2.5 ?m in diameter (PM2.5) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time-stratified case-crossover design. Hospital admissions aggregated by ZIP code were linked with population-weighted daily average concentrations of smoke PM2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF-Chem) model, a kriged interpolation of PM2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF-Chem, satellite observations of aerosol optical depth, and kriged PM2.5. A 10 ?g/m3 increase in GWR smoke PM2.5 was associated with an 8% increased risk in asthma-related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019-1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM2.5 exposure method: a 10 ?g/m3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026-1.145) and not significant using WRF-Chem (OR: 0.986, 95%CI: 0.931-1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

SUBMITTER: Gan RW

PROVIDER: S-EPMC5580836 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions.

Gan Ryan W RW Ford Bonne B Lassman William W Pfister Gabriele G Vaidyanathan Ambarish A Fischer Emily E Volckens John J Pierce Jeffrey R JR Magzamen Sheryl S

GeoHealth 20170331 3

Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM<sub>2.5</sub>) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evalua ...[more]

PMID: 28868515

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Project description:BackgroundUnderstanding the potential links between extreme weather events and human health in India is important in the context of vulnerability and adaptation to climate change. Research exploring such linkages in India is sparse.ObjectivesWe evaluated the association between extreme precipitation and gastrointestinal (GI) illness-related hospital admissions in Chennai, India, from 2004 to 2007.MethodsDaily hospital admissions were extracted from two government hospitals in Chennai, India, and meteorological data were retrieved from the Chennai International Airport. We evaluated the association between extreme precipitation (? 90th percentile) and hospital admissions using generalized additive models. Both single-day and distributed lag models were explored over a 15-day period, controlling for apparent temperature, day of week, and long-term time trends. We used a stratified analysis to explore the association across age and season.ResultsExtreme precipitation was consistently associated with GI-related hospital admissions. The cumulative summary of risk ratios estimated for a 15-day period corresponding to an extreme event (relative to no precipitation) was 1.60 (95% CI: 1.29, 1.98) among all ages, 2.72 (95% CI: 1.25, 5.92) among the young (? 5 years of age), and 1.62 (95% CI: 0.97, 2.70) among the old (? 65 years of age). The association was stronger during the pre-monsoon season (March-May), with a cumulative risk ratio of 6.50 (95% CI: 2.22, 19.04) for all ages combined compared with other seasons.ConclusionsHospital admissions related to GI illness were positively associated with extreme precipitation in Chennai, India, with positive cumulative risk ratios for a 15-day period following an extreme event in all age groups. Projected changes in precipitation and extreme weather events suggest that climate change will have important implications for human health in India, where health disparities already exist.CitationBush KF, O'Neill MS, Li S, Mukherjee B, Hu H, Ghosh S, Balakrishnan K. 2014. Associations between extreme precipitation and gastrointestinal-related hospital admissions in Chennai, India. Environ Health Perspect 122:249-254;?http://dx.doi.org/10.1289/ehp.1306807.

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:Sickle cell disease is an increasing global health burden. This inherited disease is characterized by a remarkable phenotypic heterogeneity, which can only partly be explained by genetic factors. Environmental factors are likely to play an important role but studies of their impact on disease severity are limited and their results are often inconsistent. This study investigated associations between a range of environmental factors and hospital admissions of young patients with sickle cell disease in London and in Paris between 2008 and 2012. Specific analyses were conducted for subgroups of patients with different genotypes and for the main reasons for admissions. Generalized additive models and distributed lag non-linear models were used to assess the magnitude of the associations and to calculate relative risks. Some environmental factors significantly influence the numbers of hospital admissions of children with sickle cell disease, although the associations identified are complicated. Our study suggests that meteorological factors are more likely to be associated with hospital admissions for sickle cell disease than air pollutants. It confirms previous reports of risks associated with wind speed (risk ratio: 1.06/standard deviation; 95% confidence interval: 1.00-1.12) and also with rainfall (1.06/standard deviation; 95% confidence interval: 1.01-1.12). Maximum atmospheric pressure was found to be a protective factor (0.93/standard deviation; 95% confidence interval: 0.88-0.99). Weak or no associations were found with temperature. Divergent associations were identified for different genotypes or reasons for admissions, which could partly explain the lack of consistency in earlier studies. Advice to patients with sickle cell disease usually includes avoiding a range of environmental conditions that are believed to trigger acute complications, including extreme temperatures and high altitudes. Scientific evidence to support such advice is limited and sometimes confusing. This study shows that environmental factors do explain some of the variations in rates of admission to hospital with acute symptoms in sickle cell disease, but the associations are complex, and likely to be specific to different environments and the individual's exposure to them. Furthermore, this study highlights the need for prospective studies with large numbers of patients and standardized protocols across Europe.

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Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions.

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Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions.

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