An investigation of time series and case-crossover analyses of air pollution and asthma hospital admission data for children in Toronto (Ontario).

Date of Award


Publication Type

Master Thesis

Degree Name



Mathematics and Statistics

First Advisor

Fung, K.





Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


Air pollution has been a popular topic of study over the years. It causes great harm to our environment (global warming) not to mention our health (cancer, heart disease, respiratory disease, etc.). Many people have investigated the damaging relationship between air pollution and mortality and morbidity, using different methods along the way. The different methods yield results that are not directly comparable with one another because the methods use different strategies. Air pollution data and hospital admissions data for asthma patients aged six to twelve in the Toronto area from January 1, 1981 to December 31, 1993 were gathered and analyzed under a variety of time series and case-crossover designs. The lack of consistency in the results among the techniques led us to perform a simulation in order to choose the most accurate method to analyze this Toronto data. While the time series approach produced fairly accurate results, the bidirectional case-crossover using the exact method of approximation was the overall best technique of analysis.Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .L589. Source: Masters Abstracts International, Volume: 41-04, page: 1098. Adviser: Karen Fung. Thesis (M.Sc.)--University of Windsor (Canada), 2002.