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邻里致胖性建筑环境特征(OBCT)指数:实践与理论

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory.

作者信息

Lam Thao Minh, den Braver Nicolette R, Ohanyan Haykanush, Wagtendonk Alfred J, Vaartjes Ilonca, Beulens Joline Wj, Lakerveld Jeroen

机构信息

Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, De Boelelaan 1117, 1081HV, Amsterdam, the Netherlands; Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands; Upstream Team, Amsterdam UMC, VU University Amsterdam, De Boelelaan 1089a, 1081HV, Amsterdam, the Netherlands.

Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, De Boelelaan 1117, 1081HV, Amsterdam, the Netherlands; Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands; Upstream Team, Amsterdam UMC, VU University Amsterdam, De Boelelaan 1089a, 1081HV, Amsterdam, the Netherlands.

出版信息

Environ Res. 2024 Jun 15;251(Pt 1):118625. doi: 10.1016/j.envres.2024.118625. Epub 2024 Mar 11.

Abstract

BACKGROUND

Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments.

OBJECTIVES

We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods.

METHODS

The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome.

RESULTS

The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%.

DISCUSSION

The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

摘要

背景

肥胖是2型糖尿病和心血管疾病等主要慢性疾病的关键风险因素。为了全面描述致胖的建筑环境,我们最近开发了一种新型的致胖建筑环境特征(OBCT)指数,该指数由17个成分组成,涵盖了食物和身体活动(PA)环境。

目的

我们旨在评估全国健康监测中OBCT指数与体重指数(BMI)之间的关联。此外,我们探索了使用无监督和有监督方法改进该指数的可能途径。

方法

为荷兰的12,821个行政街区构建了OBCT指数,并将其与2016年公共卫生监测中符合条件的成年参与者的居住地址相关联。我们将数据随机分为一个训练子集(三分之二;n = 255,187)和一个测试子集(三分之一;n = 127,428)。在训练集中,我们使用非参数受限立方回归样条来评估指数与BMI的关联,并根据个体人口统计学特征进行调整。研究了年龄、性别、社会经济地位(SES)和城市化程度的效应修正。作为改进措施,我们(1)根据地址密度调整食物环境,(2)在指数中加入房价,(3)采用三种加权策略,在训练集中有两种方法由BMI进行监督(变量选择和随机森林)。我们在测试集中通过检验它们以BMI为结果的模型拟合情况来比较这些方法。

结果

在完全调整的模型中,OBCT指数与BMI存在显著的非线性关联(p<0.05),且受年龄、性别、SES和城市化程度的影响。然而,该指数解释的BMI方差较低(<0.05%)。有监督方法比无监督方法增加的解释方差更多,不过总体改进有限,因为最高解释方差仍<0.5%。

讨论

该指数尽管有潜力突出致胖环境中的差异,但与BMI的关联有限。复杂的改进不一定有益,其组成部分应重新运作。

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