Using decision trees to understand the influence of individual- and neighborhood-level factors on urban diabetes and asthma

Objective: To determine the influence of individual and neighborhood factors that combined are associated with asthma and diabetes in a sample of urban Philadelphians using data mining, a novel technique in public health research. Methods: We obtained secondary data collected between May 2011 and November 2014 on individual's health and perception of neighborhood characteristics (N=450) and Philadelphia LandCare Program data that provided relevant environmental data for the analysis (N=676). Rapid Miner open access data mining software was used to perform decision tree analyses. Results: Individual-and neighborhood-level environmental factors were intricately related in the decision tree models, having varying influence on the outcomes of asthma and diabetes. The decision trees had high specificity (95-100) and classified factors that were associated with an absence of disease (diabetes/asthma). Conclusion: Improved neighborhood-level conditions related to social and physical disorder were consistently found to be associated with an absence of both asthma and diabetes in this urban population. Policy implications: This study illustrates the potential utility of applying data mining techniques for understanding complex public health issues.