4.7 Article

An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles County, California

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 425, Issue -, Pages 110-118

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2012.02.015

Keywords

Los Angeles, CA; Air pollutants; Asthma; MCMC; Temporal random effects

Funding

  1. National Institutes of Health (NIH) [NIH/NIGMS 1-RC1-GM092400-01]
  2. National Science Foundation [NSF-DMS-1106609]
  3. Division Of Mathematical Sciences
  4. Direct For Mathematical & Physical Scien [1106609] Funding Source: National Science Foundation

Ask authors/readers for more resources

There is now a large body of literature supporting a linkage between exposure to air pollutants and asthma morbidity. However, the extent and significance of this relationship varies considerably between pollutants, location, scale of analysis, and analysis methods. Our primary goal is to evaluate the relationship between asthma hospitalizations, levels of ambient air pollution, and weather conditions in Los Angeles (LA) County, California, an area with a historical record of heavy air pollution. County-wide measures of carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O-3), particulate matter < 10 mu m (PM10), particulate matter < 2.5 mu m (PM2.5), maximum temperature, and relative humidity were collected for all months from 2001 to 2008. We then related these variables to monthly asthma hospitalization rates using Bayesian regression models with temporal random effects. We evaluated model performance using a goodness of fit criterion and predictive ability. Asthma hospitalization rates in LA County decreased between 2001 and 2008. Traffic-related pollutants, CO and NO2, were significant and positively correlated with asthma hospitalizations. PM2.5 also had a positive, significant association with asthma hospitalizations. PM10, relative humidity, and maximum temperature produced mixed results, whereas O-3 was non-significant in all models. Inclusion of temporal random effects satisfies statistical model assumptions, improves model fit, and yields increased predictive accuracy and precision compared to their non-temporal counterparts. Generally, pollution levels and asthma hospitalizations decreased during the 9 year study period. Our findings also indicate that after accounting for seasonality in the data, asthma hospitalization rate has a significant positive relationship with ambient levels of CO, NO2, and PM2.5. (C) 2012 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available