Spatial Clusters of Diabetes and Physical Inactivity: Do Neighborhood Characteristics in High and Low Clusters Differ?

Abstract

This study aims to find spatial clusters of diabetes and physical inactivity among a sample population in Kerala, India, and evaluate built environment characteristics within the high and low spatial clusters. Spatial clusters with a higher and lower likelihood of diabetes and physical inactivity were identified using spatial scan statistic at various radii. Built environment characteristics were captured at panchayat level and 1600 m buffer around participant location using Geographical Information Systems. Comparison of sociodemographic and built environment factors was carried out for participants within high and low spatial clusters using t tests. Ten high and 8 low spatial clusters of diabetes and 17 high and 23 low spatial clusters of physical inactivity were identified in urban and rural areas of Kerala. Significant differences in built environment characteristics were consistent for low spatial clusters of diabetes and physical inactivity in the urban scenario. Built environment characteristics were found to be relevant in both urban and rural areas of Kerala. There is an urgent call to explore spatial clustering of non-communicable diseases in Kerala and undo the one-size-fits-all approach for prevention and control of non-communicable diseases.

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

“…Although adjusting for socioeconomic and demographic factors did not modify the distribution or the characteristics of the clusters, we did highlight an older population and a higher proportion of non-workers in low raw MVPA clusters, and a lower neighborhood household income in low MVPA clusters after adjustment for socioeconomic and demographic factors. Huang et al [20], Tamura et al [18], and Valson et al [22] also found inconsistent findings related to socioeconomic determinants and spatial clusters of physical activity.…”

“…Studies that have explored the association of the built environment with physical activity from a geographic perspective have found mixed evidence [18, 19, 21]. However, there is an overall agreement that environments that favor walkability, such as higher population density (urbanicity) and street connectivity, are associated with higher physical activity levels [17, 18, 20, 22]. In contrast, we did not find a positive association between a higher land use area coverage with the spatial clusters of high physical activity [17, 18, 22].…”

“…However, there is an overall agreement that environments that favor walkability, such as higher population density (urbanicity) and street connectivity, are associated with higher physical activity levels [17, 18, 20, 22]. In contrast, we did not find a positive association between a higher land use area coverage with the spatial clusters of high physical activity [17, 18, 22]. This is probably because we were unable to stratify by different types of commercial & industrial land use areas such as food, retails, services, cultural, and physical activity as proposed by Frank et al [37].…”

“…Studies using a spatial methodology to assess this relationship have shown partial associations [15–22]. This mixed evidence may result from the fact that these works analyze the association at an aggregated level (i.e.…”

“…Furthermore, of these studies, only one objectively measured physical activity [17]. Among the research works assessing spatial patterns of physical activity at an individual scale, all were based on self-reported physical activity [20–22], which limits their conclusions.…”