Exploring spatial variability in the relationship between long term limiting illness and area level deprivation at the city level using geographically weighted regression
Morrissey, K
Date: 31 July 2015
Journal
AIMS Public Health
Publisher
AIMS Press
Publisher DOI
Abstract
Ecological influences on health outcomes are associated with the spatial stratification of
health. However, the majority of studies that seek to understand these ecological influences utilise
aspatial methods. Geographically weighted regression (GWR) is a spatial statistics tool that expands
standard regression by allowing for spatial ...
Ecological influences on health outcomes are associated with the spatial stratification of
health. However, the majority of studies that seek to understand these ecological influences utilise
aspatial methods. Geographically weighted regression (GWR) is a spatial statistics tool that expands
standard regression by allowing for spatial variance in parameters. This study contributes to the
urban health literature, by employing GWR to uncover geographic variation in Limiting Long Term
Illness (LLTI) and area level effects at the small area level in a relatively small, urban environment.
Using GWR it was found that each of the three contextual covariates, area level deprivation scores,
the percentage of the population aged 75 years plus and the percentage of residences of white
ethnicity for each LSOA exhibited a non-stationary relationship with LLTI across space.
Multicollinearity among the predictor variables was found not to be a problem. Within an
international policy context, this research indicates that even at the city level, a “one-size fits all”
policy strategy is not the most appropriate approach to address health outcomes. City “wide” health
polices need to be spatially adaptive, based on the contextual characteristics of each area.
Institute of Health Research
Collections of Former Colleges
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