Comparing models of the effect of air pollutants on hospital admissions and symptoms for chronic obstructive pulmonary disease.

Abstract

There is an increasing interest in the use of hospital admission for Chronic obstructive pulmonary disease (COPD) in studies of short-term exposure effects attributed to air pollutants. However, little is known about the effect of air pollutants on COPD symptoms. This study was undertaken to determine whether there was an association between air pollutant levels and both hospital admissions and symptoms for COPD. For model comparison, we present Generalized Linear Model, Generalized Additive Model and a general approach for Bayesian inference via Markov chain Monte Carlo in generalized additive model. Furthermore, for comparing the predictive accuracy, Artificial Neural Networks (ANN) approach is given.

• Authors: Mehmet Ali Cengiz, Yuksel Terzi
• Journal: Central European journal of public health (Cent Eur J Public Health) Vol. 20 Issue 4 Pg. 282-6 (Dec 2012) ISSN: 1210-7778 [Print] Czech Republic
• PMID: 23441395 (Publication Type: Comparative Study, Journal Article)

Topics

• Air Pollution (adverse effects)
• Bayes Theorem
• Hospitalization (statistics & numerical data)
• Humans
• Linear Models
• Monte Carlo Method
• Neural Networks, Computer
• Poisson Distribution
• Predictive Value of Tests
• Pulmonary Disease, Chronic Obstructive (epidemiology, etiology)