Area Social Deprivation and Public Health: Analyzing the Spatial Non-stationary Associations Using Geographically Weighed Regression

Public health in linkage to land use: Theoretical framework, empirical evidence, and critical implications for reconnecting health promotion to land use policy

“…The incidence rate (p) of each disease is calculated for 57 districts (the basic unit of census) by using the following Eq. (1) (Coggon et al, 1997;Su et al, 2016;Weng et al, 2016). General descriptive statistics are shown in Table 1.…”

Spatial analysis of inequalities in fetal and infant mortality due to avoidable causes

“…Hence, indicators for social deficits in association with spatial analysis techniques have become a methodological alternative for mapping intraurban differences through measuring the life conditions (17)(18)(19) . This allows the identification of risk conditions originating from adverse socioeconomic circumstances within communities, and the correlation of these conditions to spatial units (20)(21) . Composite indices are practical tools for investigating inequalities in healthcare and socioeconomic conditions, and allow the concentration of interventions and resources in areas or groups in greater need (19)(20)(21)(22) .…”

“…This allows the identification of risk conditions originating from adverse socioeconomic circumstances within communities, and the correlation of these conditions to spatial units (20)(21) . Composite indices are practical tools for investigating inequalities in healthcare and socioeconomic conditions, and allow the concentration of interventions and resources in areas or groups in greater need (19)(20)(21)(22) .…”

Type 2 diabetes mellitus and neighborhood deprivation index: A spatial analysis in Zhejiang, China

“…Due to the absence of fine‐level socioeconomic data, studies on the relationship between SES and type 2 diabetes mellitus at the neighborhood level in China are sparse. Two pilot studies attempted to explore the neighborhood SES–health relationship within a city region, but their findings were not convincing, as there were just 57 districts (samples) included in the studies. With the development of remote sensing technology, increasing data (e.g., night‐time light data and built environment data) could be aggregated for constructing the neighborhood deprivation index at a fine geographical resolution (sub‐districts and townships level), which could improve the neighborhood deprivation measures and the understanding of the neighborhood SES–health relationship in China.…”