||This work included four separate studies that each led to a publication. These studies shared a common relationship between economy or lifestyle and health while employing different research methods using data integration. Each study addressed certain aspects of public health informatics. The cost-effectiveness of incorporating a Clinical Decision Support System (CGSS) in a multidimensional rural community intervention aimed to reduce inappropriate antibiotic prescription with calculated during the first study. The three additional studies integrated different levels of ecological and individual data in order to described relationship between economy and health. First, ecological data integration was utilized to create a dataset containing countries' health and economic indicators in order to develop a prediction model of HIV sero-prevalence across Africa. Second, individual data integration was used to assess the role of a single lifestyle indicator (alcohol dependency) on health outcome. Third, ecological and individual data integration was used in order to measure the association between global economy and individual data. The first study demonstrated the cost-effectiveness of using a clinical decision support system in public health interventions. The three additional studies demonstrated, respectively, 1) the possibility to combine multivariate modeling and spatial clustering for predicting HIV sero-prevalence in different countries in sub-Saharan Africa; 2) that alcohol dependency at the time of end-stage renal disease onset is a risk factor for rental graft failure and recipient death; and 3) that, beyond 3 years post transplantation, when some recipients lose Medicare benefits, economic downturns might negatively affect the kidney graft and recipient survival. These studies provide diverse and rigorous research experiences related to public health, epidemiology, and informatics. Further development of the methods used would certainly help explain the relationship between the macroeconomic situation, population health, and individual health as it would facilitate data integration crossing temporal, geographic, and sciences.