A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking
A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking
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DOI：
10.1093/aje/kww071
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发表时间：
2016-10-15
影响因子：
5
通讯作者：
Merlo, Juan
中科院分区：
文献类型：
--
作者：
Chaix, Basile;Kestens, Yan;Merlo, Juan
环境健康研究以个人为统计单位，考察了环境与健康之间的联系。然而，研究人员一直无法调查瞬时接触情况，而且这类研究往往容易受到个人偏好等因素的影响。我们提出了一种基于全球定位系统(GPS)的方法，将个人的观察期划分为对地方的访问和旅行，从而能够进行新颖的生命段调查和病例交叉分析，以改进推理。我们分析了建筑环境和旅行中步行之间的关系。参与者使用GPS接收器和加速计进行了7天的跟踪，并使用基于网络的地图应用程序调查了他们每次旅行期间的交通方式(2012-2013年，法国居住环境和冠心病(RECORD)GPS研究；227名参与者进行了6,313次旅行)。对住所以及旅行的起点和目的地的背景因素进行了评估。条件Logistic回归模型被用来估计环境因素与步行或加速度计评估的旅行步数之间的关联。在案例交叉分析中，当出行起点位于服务密度的第四(与第一)四分位数时，出行中步行的概率是1.37(95%可信区间：1.23，1.61)倍；当出行目的地处于服务密度的第四(与第一)四分位数时，在旅行中行走的概率(95%可信区间：1.23，1.68)是1.47(95%可信区间：1.23，1.68)倍。旅行起点和目的地的绿地也与个人内部、旅行之间的步行差异有关。我们建议的方法使用GPS和基于网络的调查，使新的生命段流行病学调查成为可能。
Environmental health studies have examined associations between context and health with individuals as statistical units. However, investigators have been unable to investigate momentary exposures, and such studies are often vulnerable to confounding from, for example, individual-level preferences. We present a Global Positioning System (GPS)-based methodology for segmenting individuals' observation periods into visits to places and trips, enabling novel life-segment investigations and case-crossover analysis for improved inferences. We analyzed relationships between built environments and walking in trips. Participants were tracked for 7 days with GPS receivers and accelerometers and surveyed with a Web-based mapping application about their transport modes during each trip (Residential Environment and Coronary Heart Disease (RECORD) GPS Study, France, 2012-2013; 6,313 trips made by 227 participants). Contextual factors were assessed around residences and the trips' origins and destinations. Conditional logistic regression modeling was used to estimate associations between environmental factors and walking or accelerometry-assessed steps taken in trips. In case-crossover analysis, the probability of walking during a trip was 1.37 (95% confidence interval: 1.23, 1.61) times higher when trip origin was in the fourth (vs. first) quartile of service density and 1.47 (95% confidence interval: 1.23, 1.68) times higher when trip destination was in the fourth (vs. first) quartile of service density. Green spaces at the origin and destination of trips were also associated with within-individual, trip-to-trip variations in walking. Our proposed approach using GPS and Web-based surveys enables novel life-segment epidemiologic investigations.