Development of a neighborhood obesogenic built environment characteristics index for the Netherlands

Abstract

Objective Environmental factors that drive obesity are often studied individually, whereas obesogenic environments are likely to consist of multiple factors from food and physical activity (PA) environments. This study aimed to compose and describe a comprehensive, theory‐based, expert‐informed index to quantify obesogenicity for all neighborhoods in the Netherlands. Methods The Obesogenic Built Environment CharacterisTics (OBCT) index consists of 17 components. The index was calculated as an average of componential scores across both food and PA environments and was scaled from 0 to 100. The index was visualized and summarized with sensitivity analysis for weighting methods. Results The OBCT index for all 12,821 neighborhoods was right‐skewed, with a median of 44.6 (IQR = 10.1). Obesogenicity was lower in more urbanized neighborhoods except for the extremely urbanized neighborhoods (>2500 addresses/km 2 ), where obesogenicity was highest. The overall OBCT index score was moderately correlated with the food environment (Spearman ρ = 0.55, p <0.05) and with the PA environment (ρ = 0.38, p <0.05). Hierarchical weighting increased index correlations with the PA environment but decreased correlations with the food environment. Conclusions The novel OBCT index and its comprehensive environmental scores are potentially useful tools to quantify obesogenicity of neighborhoods.

Identifying obesogenic environment through spatial clustering of body mass index among adults

“…However, the complex relationship between environment and obesity varies across populations. In Netherlands, the association of obesogenic environment was stronger in younger adults, females, high income and those who live in highly urbanized areas and with high neighbourhood socioeconomics [ 93 ]. Similarly, adults living in the highest tertile of obesogenicity was associated with higher odds of developing cardiovascular disease [ 94 ].…”

“…Past literatures had documented a generally very small effect of environment on weight status, with high risk of bias [ 92 , 97 ]. In a large cross-sectional population survey in the Netherlands, the obesogenic index explained only 0.05% variance in BMI with a largely inverse and non-linear association, mostly driven by the food environment measures [ 93 ]. The association between fast-food environment studies and health outcome were inconsistent, where positive, null and negative associations were found among different studies conducted in Netherlands [ 98 – 101 ].…”

“…However, other studies have demonstrated that a larger obesogenic environment index with more components and more parameters might not always explain more variance than a smaller index with fewer components. Food environment components had approximately the same explained variance with the overall obesogenic index [ 93 ]. In fact, the sub-indices of the index perform better than the overall index in terms of explaining health outcomes [ 102 ; 103 ].…”

“…Dalmat and colleagues also revealed that simple walkability proxies such as population density could reasonably predict walking compared to more complex composite indicators [ 104 ]. Housing price as an indicator of area socioeconomic status improved the direction of association and model fit of obesogenic index, performing better than a composite index with 17 components [ 93 ]. Concordantly, area socio-economy status explained most of the geographical variation in obesity prevalence in Western countries after accounting for population density, race/ethnicity and age [ 54 ; 105 ].…”

A positive neighborhood walkability is associated with a higher magnitude of leisure walking in adults upon COVID-19 restrictions: a longitudinal cohort study

“…improved sidewalks, addition of a park), which often results in variable findings [ 15 – 17 ]. The advantage of using a composite approach, such as the WI, has been increasingly deployed and is shown to be useful and reliable to study the association between walking activity and the built environment [ 34 , 36 , 50 – 53 ]. Transportation walking, when defined as walking trips towards nearby shops and services, have more frequently published a positive correlation with walkability, compared to leisure walking [ 54 ].…”