Graduation Year


Document Type




Degree Granting Department

Public Health

Major Professor

Yehia Y. Hammad, Sc.D.

Committee Member

Noreen D. Poor, Ph.D.

Committee Member

Raymond D. Harbison, Ph.D.

Committee Member

Thomas J. Mason, Ph.D.

Committee Member

Getachew A. Dagne, Ph.D.


Asthma epidemiology, Environmental triggers, Socioeconomic deprivation index, Spatial analysis, Non-linear multiple regression model


Despite an improved understanding of the disease, the prevalence of asthma and asthma-related morbidity continue to rise, particularly among minority and inner-city populations. Despite the growing epidemic of asthma, the surveillance of disease at the state or even local levels is very limited. Such information is very important to identify high-risk population groups and to design more effective community-based preventive interventions or risk management programs that may modify these trends.

The study provided important information about spatial differences by the geographical area of residence and changes in asthma hospital admissions over time in the selected area. Environmental exposure to ambient air pollution by ambient particles, sulfur dioxide and ozone was a significant factor to explain the increase in asthma hospitalizations in simple regression analysis, but was not significant after the adjustment to area socioeconomic status characteristics. Sulfur dioxide was the only significant independent variable in a multiple adjusted regression model of hospitalizations for childhood asthma, however, more detailed environmental exposure assessment by calendar quarter suggested that ambient air pollution by sulfur dioxide is not significant variable in the multiple regression model. Future asthma prevention interventions and risk management programs should address population groups described by such socioeconomic status characteristics as poverty, unskilled workers, single parent families with children, families having no vehicle available, people living in less crowded households or socially excluded conditions without adequate family members or relatives support, and also people residing in houses heated by fuel. Developed complex area socioeconomic deprivation index was shown to be a significant predictor of hospital admissions for childhood and adult asthma by zip code area of residence. Predictive loglinear regression model for asthma hospitalizations was further validated by using standard statistical model validation techniques to estimate the accuracy of prediction with new independent dataset outside of our study area. Increase in complex area socioeconomic deprivation index by 1 extra unit could explain the increase by 7.9% in childhood and 7.5% in adult asthma hospitalization in 1997, 8.3% in childhood and 7.2% in adult asthma hospitalizations in 1998, and 7.7% in childhood and 6.7% in adult asthma hospitalizations in 1999 respectively. Predictive log-linear regression model could be successfully applied to develop more effective asthma prevention interventions and risk management programs and to address more sensitive population groups within specific high risk geographical areas.