  In public health, various data are rigorously collected and published with
open access. These data reflect the environmental and non-environmental
characteristics of heterogeneous neighborhoods in cities. In the present study,
we aimed to study the relations between these data and disease risks in
heterogeneous neighborhoods. A flexible framework was developed to determine
the key factors correlated with diseases and find the most relevant combination
of 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 two
non-environmental factors (immigrants, and mental health visits) were
identified in the relational analysis of all of the target neighborhoods. To
determine the influence of the heterogeneity of the neighborhoods, they were
clustered into three different classes. In the most severe class, two
additional factors related to dwellings were determined to be involved, which
increased the observation's deviance from 48.1% to 80%. The factors determined
in this study may assist governments in improving public health policies.
