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健康研究中空间行为模式的评估:其社会人口学决定因素以及与交通方式的关联(RECORD队列研究)

Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study).

作者信息

Perchoux Camille, Kestens Yan, Thomas Frédérique, Van Hulst Andraea, Thierry Benoit, Chaix Basile

机构信息

Department of Social and Preventive Medicine, Université de Montréal, Canada; Montreal University Hospital Research Center (CRCHUM), Canada; Sorbonne Universités, UPMC Université, Paris 06, France; INSERM, UMR_S 1136, France.

Department of Social and Preventive Medicine, Université de Montréal, Canada; Montreal University Hospital Research Center (CRCHUM), Canada.

出版信息

Soc Sci Med. 2014 Oct;119:64-73. doi: 10.1016/j.socscimed.2014.07.026. Epub 2014 Jul 11.

Abstract

Prior epidemiological studies have mainly focused on local residential neighborhoods to assess environmental exposures. However, individual spatial behavior may modify residential neighborhood influences, with weaker health effects expected for mobile populations. By examining individual patterns of daily mobility and associated socio-demographic profiles and transportation modes, this article seeks to develop innovative methods to account for daily mobility in health studies. We used data from the RECORD Cohort Study collected in 2011-2012 in the Paris metropolitan area, France. A sample of 2062 individuals was investigated. Participants' perceived residential neighborhood boundaries and regular activity locations were geocoded using the VERITAS application. Twenty-four indicators were created to qualify individual space-time patterns, using spatial analysis methods and a geographic information system. Three domains of indicators were considered: lifestyle indicators, indicators related to the geometry of the activity space, and indicators related to the importance of the residential neighborhood in the overall activity space. Principal component analysis was used to identify main dimensions of spatial behavior. Multilevel linear regression was used to determine which individual characteristics were associated with each spatial behavior dimension. The factor analysis generated five dimensions of spatial behavior: importance of the residential neighborhood in the activity space, volume of activities, and size, eccentricity, and specialization of the activity space. Age, socioeconomic status, and location of the household in the region were the main predictors of daily mobility patterns. Activity spaces of small sizes centered on the residential neighborhood and implying a large volume of activities were associated with walking and/or biking as a transportation mode. Examination of patterns of spatial behavior by individual socio-demographic characteristics and in relation to transportation modes is useful to identify populations with specific mobility/accessibility needs and has implications for investigating transportation-related physical activity and assessing environmental exposures and their effects on health.

摘要

先前的流行病学研究主要聚焦于当地居民区以评估环境暴露情况。然而,个体的空间行为可能会改变居民区的影响,预计流动人群的健康影响较弱。通过研究个体的日常出行模式以及相关的社会人口学特征和交通方式,本文旨在开发创新方法,以便在健康研究中考虑日常出行因素。我们使用了2011年至2012年在法国巴黎大都市区收集的RECORD队列研究数据。对2062名个体进行了调查。参与者感知到的居民区边界和常规活动地点通过VERITAS应用程序进行了地理编码。利用空间分析方法和地理信息系统创建了24个指标来描述个体的时空模式。考虑了三个指标领域:生活方式指标、与活动空间几何形状相关的指标以及与居民区在整体活动空间中的重要性相关的指标。主成分分析用于识别空间行为的主要维度。多级线性回归用于确定哪些个体特征与每个空间行为维度相关。因子分析产生了空间行为的五个维度:居民区在活动空间中的重要性、活动量以及活动空间的大小、偏心率和专业化程度。年龄、社会经济地位以及家庭在该地区的位置是日常出行模式的主要预测因素。以居民区为中心且活动量较大的小尺寸活动空间与步行和/或骑自行车作为交通方式相关。按个体社会人口学特征以及与交通方式的关系来考察空间行为模式,有助于识别有特定出行/可达性需求的人群,并且对调查与交通相关的身体活动以及评估环境暴露及其对健康的影响具有重要意义。

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