Many studies have reported a relationship between urban air pollution levels and respiratory health problems. However, there are notable variations in results, depending on modeling approach, covariate selection, period of analysis, etc. To help clarify these factors we compare and apply two estimation approaches: model selection and Bayesian model averaging, to a new data base on 11 large Canadian cities spanning 1974 to 1994. Our data allow us to compare monthly hospital admission rates for all lung diagnostic categories to ambient levels of five common air contaminants, while controlling for income, smoking and meteorological covariates. Only in restricted models on the later sample are we able to replicate a link between hospital admissions and pollution. In the most general specifications we find the health effects of air pollution are insignificant, and those that are significant run opposite to conventional expectations. Income effects are robust across specifications, suggesting that a simultaneous reduction in income and pollution could have a negative net effect on lung-related health.
- air pollution
- environmental modelling