Neighborhood Greenspace and Socioeconomic Risk are Associated with Diabetes Risk at the Sub-neighborhood Scale: Results from the Prospective Urban and Rural Epidemiology (PURE) Study

Abstract

Greenspace and socioeconomic status are known correlates of diabetes prevalence, but their combined effects at the sub-neighborhood scale are not yet known. This study derives, maps, and validates a combined socioeconomic/greenspace index of individual-level diabetes risk at the sub-neighborhood scale, without the need for clinical measurements. In two Canadian cities (Vancouver and Hamilton), we computed 4 greenspace variables from satellite imagery and extracted 11 socioeconomic variables from the Canadian census. We mapped 5125 participants from the Prospective Urban and Rural Epidemiology Study by their residential address and used age- and sex-dependent walking speeds to estimate individual exposure zones to local greenspace and socioeconomic characteristics, which were then entered into a principal component analysis to derive a novel diabetes risk index (DRI-GLUCoSE). We mapped index scores in both study areas and validated the index using fully adjusted logistic regression models to predict individual diabetes status. Model performance was then compared to other non-clinical diabetes risk indices from the literature. Diabetes prevalence among participants was 9.9%. The DRI-GLUCoSE index was a significant predictor of diabetes status, exhibiting a small non-significant attenuation with the inclusion of dietary and physical activity variables. The final models achieved a predictive accuracy of 75%, the highest among environmental risk models to date. Our combined index of local greenspace and socioeconomic factors demonstrates that the environmental component of diabetes risk is not sufficiently explained by diet and physical activity, and that increasing urban greenspace may be a suitable means of reducing the burden of diabetes at the community scale.

Environmental Microsegregation: Urban Renewal and the Political Ecology of Health

“…In this context, Böhme et al (2015Böhme et al ( , 2019, for example, point out that health and social data are subject to special protection and very little in the way of such data is available at a small-scale level. Most recently, remote sensing has become a valuable tool both in environmental justice research and in urban health as it facilitates the displaying of micro-level environmental conditions (Walker et al, 2022;Weigand et al, 2019). However, following Moretti's (2013) distinction between "distant reading" and "close reading," critical GIS studies have emphasised the necessity of ground-truthing and triangulation of data, avoiding the positivist notion and epistemological shortcomings of GIS (e.g., Burns, 2021; Schuurman & Pratt, 2002;Schuurman et al, 2020).…”

Geographical Research in the Digital Humanities

“…For example, stateimposed measures to contain the virus accentuate the differentiation between private and public spaces, the latter being labelled "unsafe", whilst categorisations of various sites' functions reproduce perceptions and experiences of anxiety bound to sites considered to be high-risk, e.g., grocery stores, schools, and restaurants (Stadlmeier et al 2022). Associations between health risk and the built environment are certainly rooted in the literature (Vialard et al 2019;Walker et al 2020a;Walker et al 2022) and provide important space for critical analysis and methodological development (Walker & Schuurman 2015). It is therefore becoming increasingly important for the research agenda to consider how digital geographical information plays a role in society's confrontation with the epidemiological landscape (Taglioni 2019).…”

Neighborhood Access to the Built Environment and Allostatic Load: A Systematic Review of the Use of Geographic Information Systems

“…The 23 included studies were conducted in 14 countries including: Australia [26], Canada [27][28][29][30], France [31], Greece [32], Japan [33], Portugal [34], South Africa [35], the United Kingdom [36], and the United States (US) [37][38][39][40][41][42][43][44][45][46][47][48].…”

“…Area level demographic % Dwelling Ownership [29] Density All but 2 GIS measures of area-level demographics were measures of density, with the other 2 being a combination of density measures within an index score. These measures typically accounted for population size within administrative units, with existing boundaries being used to aggregate measures of exposure.…”

“…Measures used in the hot-spot analysis study were mapped using a 1.5 miles distance band based on Getis-Ord General G statistic, although sensitivity analysis was conducted to test the effects of using other distance bands. Two studies used factor analysis to create census-tract level measures of socioeconomic status using a range of area-level demographic measures adjusted for population size % Dwellings Rented [29] Density Crime rate [45] Density Education -no degree (%) [29] Density Estimated age-adjusted diabetes prevalence [38] Density Frequency of diabetes-specific emergency visits [38] Density Frequency of diabetes-specific inpatient hospitalizations [38] Density Labour force participation rate (%) [29] Density Lone Parent Families (%) [29] Density Prevalence of Low Income (%) [29] Density Proportion of Hispanic residents [38] Density Proportion of minority residents [38] Density Proportion of non-Hispanic black residents [38] Density Proportion of residents below poverty level [38] Density Proportion of residents < high school degree or equivalent [38] Density Socioeconomic Status of Area (4 components) [39] Index Tract SES factor score (3 Components) [45] Index Unemployment rate (weighted) [27] Density X Unemployment rate (%) [29] Density Food Environment Change in supermarket presence [43] Density X Measures of the food environment essentially represented a specific form of built environment land use. Ten measures were density measures, 2 were proximity measures, and one was an index score.…”