Assessing the health impacts of air pollution: a re-analysis of the Hamilton children's cohort data using a spatial analytic approach.

The objective of this paper was to reassess children's exposure to air pollution as well as investigate the importance of other covariates of respiratory health. We re-examined the Hamilton Children's Cohort (HCC) dataset with enhanced spatial analysis methods, refined in the approximately two decades since the original study was undertaken. Children's exposure to air pollution was first re-estimated using kriging and land-use regression. The land-use regression model performed better, compared to kriging, in capturing local variation of air pollution. Multivariate linear and logistic regression analysis was then applied for the study of potential risk factors for respiratory health. Findings agree with the HCC study-results, confirming that children's respiratory health was associated with maternal smoking, hospitalization in infancy and air pollution. However, results from this study reveal a stronger association between children's respiratory health and air pollution. Additionally, this study demonstrated associations with low-income, household crowding and chest illness in siblings.

Anatomy of heat waves and mortality in Toronto: lessons for public health protection.

BACKGROUND: Periods of unusually hot weather, especially in temperate climates, carry with them a burden of morbidity and mortality, particularly in urban areas. With lessening debate on its origins, and signs of global warming already apparent, it is becoming imperative for public health practitioners to recognize and predict the risks of "heat waves", and to develop protective community responses to them. This study makes use of historical data and a methodology developed previously to examine the pattern of hot weather experienced over the last five decades in the City of Toronto, and to assess the associated burden of mortality. METHODS: Synoptic classification of air masses based on meteorological data for Toronto was used, to assign the annual mean burden of illness (in terms of elevated mortality) associated with hot weather and air pollution. Then, coefficients relating daily mortality risk to historical daily weather and air quality data were determined with a model system that (for each air mass) assessed the factors that contributed to day-to-day variability in mortality. RESULTS: Over the period of study, there were 120 (95% CI: 105-135) heat-related deaths on average per year, with great variability from year to year, reflecting the variability of hot weather. Mortality was greatest in July and August, when the greatest number of multi-day heat episodes occurred. Furthermore, the longer the episode, the greater was the daily risk for mortality. INTERPRETATION: The method can be used to forecast the risk of heat-related mortality, and to facilitate the development of public health responses to mitigate that risk.