| Summary: | Recurrent data are widely encountered in many applications. This thesis work focuses on how the recurrent hospital admissions relate to the air pollutants. In particular, we consider the data for two major cities in Saskatchewan. The study period ranges from January 1, 2005 to December 30, 2011 and involves 20,284 patients aged 40 years and older. The hospital admission data is from the Canadian Institute for Health Information (CIHI). The air pollutants data is from the National Air Pollution Surveillance Program (NAPS)
from Environment Canada. The data set has been approved by the Biomedical Research Ethics Board, University of Saskatchewan. The gaseous pollutants included in this study are carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), as well as particulate matter PM2:5 (tiny particles in the air that are 2:5 microns in width).
In the data analysis, we applied three
different existing models to all respiratory diseases and asthma, respectively. The three models are the Poisson process model (also called
Andersen-Gill model), the Poisson process model with the number of previous events as a covariate and the Poisson process model with shared gamma distributed frailties (random
effects). For all respiratory diseases, the Poisson process model with random effects provides
the best t in comparison to the other two models. The model output suggests that the increased risk of hospital readmission is significantly associated with increased CO and O3.
For asthma, the Poisson process model provides the best t in comparison to the other
two models. We found that only CO and O3 have significant effects on recurrent hospital
admissions due to asthma. We concluded this thesis with the discussion on the current and
potential future work.
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