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In public health, various data are rigorously collected and published withopen access. These data reflect the environmental and non-environmentalcharacteristics of heterogeneous neighborhoods in cities. In the present study,we aimed to study the relations between these data and disease risks inheterogeneous neighborhoods. A flexible framework was developed to determinethe key factors correlated with diseases and find the most relevant combinationof factors to explain observations of diseases through nonlinear analyses.Taking Lung Cancer Premature Mortality Rate (LCPMR) in Toronto as an example,two environmental factors (green space, and industrial pollution) and twonon-environmental factors (immigrants, and mental health visits) wereidentified in the relational analysis of all of the target neighborhoods. Todetermine the influence of the heterogeneity of the neighborhoods, they wereclustered into three different classes. In the most severe class, twoadditional factors related to dwellings were determined to be involved, whichincreased the observation's deviance from 48.1% to 80%. The factors determinedin this study may assist governments in improving public health policies.

An Open-Data Analysis of Heterogeneities in Lung Cancer Premature Mortality Rate and Associated Factors among Toronto Neighborhoods