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.
摘要：
绿地和社会经济地位是已知的糖尿病患病率的相关因素，但它们在次邻域尺度上的综合影响尚不清楚。这项研究得出，地图,并在亚邻域尺度上验证个体水平糖尿病风险的社会经济/绿色空间综合指数，不需要临床测量。在加拿大的两个城市（温哥华和汉密尔顿），我们从卫星图像中计算了4个绿地变量，并从加拿大人口普查中提取了11个社会经济变量。我们通过居住地址绘制了来自前瞻性城市和农村流行病学研究的5125名参与者的地图，并使用年龄和性别依赖性的步行速度来估计个人对当地绿地和社会经济特征的暴露区域。然后将其输入主成分分析，以得出新的糖尿病风险指数（DRI-GLUCoSE）。我们绘制了两个研究领域的指数得分，并使用完全调整的逻辑回归模型验证了该指数，以预测个体糖尿病状况。然后将模型性能与文献中的其他非临床糖尿病风险指数进行比较。参与者的糖尿病患病率为9.9%。DRI-GLUCoSE指数是糖尿病状态的重要预测因子，在包含饮食和身体活动变量的情况下，表现出小的非显著衰减。最终模型实现了75%的预测准确率，是迄今为止环境风险模型中最高的。我们对当地绿地和社会经济因素的综合指数表明，饮食和身体活动不足以解释糖尿病风险的环境因素。增加城市绿地可能是减轻社区糖尿病负担的合适手段。
