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 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.
Highlights
Socioeconomic inequalities in health status are observed in all countries [1]
It can be observed that higher intercept values for Limiting Long Term Illness (LLTI) are located in the North, the North East of the city. This spatial trend implies that once spatial variations in the three explanatory variables in the model have been accounted for, rates of LLTI are higher in the North East of Liverpool
Mapping the intercepts from the Geographically weighted regression (GWR) demonstrates that higher intercept values for LLTI are located in the North, the North East of the city
Summary
Socioeconomic inequalities in health status are observed in all countries [1]. In Europe, lower socioeconomic position and measures of social and material deprivation are associated with greater morbidity and mortality [2,3,4]. Research in health geography, epidemiology and public health, has led to an increasing recognition among practitioners and policymakers that space and health are not mutually exclusive; people and their health are shaped by the places in which they live and inhabit on a regular basis [8,9]. This is in part because people with similar characteristics, such as income level, age and employment status, cluster together [10] and in part because individuals living in the same neighbourhood are subject to common contextual influences [10,11,12]
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