Assessing Individuals' Exposure to Environmental Conditions Using Residence-based Measures, Activity Location-based Measures, and Activity Path-based Measures.
Assessing Individuals' Exposure to Environmental Conditions Using Residence-based Measures, Activity Location-based Measures, and Activity Path-based Measures.
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使用基于居住地的措施、基于活动地点的措施和基于活动路径的措施来评估个人对环境条件的暴露程度。
DOI：
10.1097/ede.0000000000000940
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Wiebe,DouglasJ
中科院分区：
文献类型：
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作者：
Morrison,ChristopherN;Byrnes,HilaryF;Miller,BrendaA;Kaner,Emily;Wiehe,SarahE;Ponicki,WilliamR;Wiebe,DouglasJ
背景：希望测量个人暴露于环境条件的情况的研究人员可以使用许多方法。不同的方法可能会产生与健康结果关联的不同估计。以青少年在酒精商店的暴露情况为例，我们的目的是 (1) 比较暴露测量值，以及 (2) 评估暴露测量值是否与饮酒存在差异相关。 方法：我们使用全球定位系统 (GPS) 在 2015/2016 年对旧金山湾区 231 名 14-16 岁青少年进行了为期 4 周的跟踪。参与者每周收到六次生态瞬时评估调查短信，包括酒精消耗量评估。我们使用 GPS 数据来计算酒精销售点的暴露情况，采用三种方法类型：基于居住地（例如，在家庭人口普查区内）、基于活动位置（例如，在经常去的地方的缓冲距离内）和基于活动路径（例如，在 GPS 路线的缓冲距离内每小时的平均销售点）。 Spearman 相关性比较了暴露测量，单独的 Tobit 模型评估了与饮酒的生态瞬时评估反应呈阳性的比例之间的关联。结果：测量值在方法类型内 (ρ≥ 0.7) 大多呈强相关，但在方法类型之间呈弱 (ρ< 0.3) 到中度 (0.3≤ ρ< 0.7) 相关。与饮酒的关联在方法类型内部和之间大多不一致。一些基于居住地的测量（例如，人口普查区：β= 8.3，95% CI= 2.8, 13.8）、基于活动地点的方法和大多数基于活动路径的方法（例如，每小时出口小时数、100 m 缓冲区：β= 8.3、95% CI= 3.3、13.3）与饮酒量相关。结论：有关测量的方法学决策暴露于环境条件的程度可能会影响研究结果。生态研究强调社会和物理环境条件与广泛的健康结果相关。例如，社会劣势指数与芝加哥人口普查区的凶杀案有关1；关闭活禽市场减少了中国四个城市 2 中甲型 H7N9 禽流感病毒的家禽传播；切尔诺贝利核灾难后，放射性碘 131 剂量预测了乌克兰和白俄罗斯定居点的甲状腺癌发病率。 3 评估是否在个体层面观察到诸如此类的人口层面关系是走向因果推理的重要一步。个体层面的研究提供了反对生态谬误的证据（即从汇总数据中错误地推断个体层面的关联）4、5，并支持通过暴露人群将环境条件与健康结果直接联系起来的因果机制。研究人员可以使用多种方法来测量个人暴露于环境条件的情况，并且不同的方法捕获这种暴露的不同方面。然而，目前尚不清楚研究人员在如何测量暴露方面做出的选择如何影响对环境条件与健康结果之间关联的估计。
Background:Many approaches are available to researchers who wish to measure individuals’ exposure to environmental conditions. Different approaches may yield different estimates of associations with health outcomes. Taking adolescents’ exposure to alcohol outlets as an example, we aimed to (1) compare exposure measures and (2) assess whether exposure measures were differentially associated with alcohol consumption.Methods:We tracked 231 adolescents 14–16 years of age from the San Francisco Bay Area for 4 weeks in 2015/2016 using global positioning systems (GPS). Participants were texted ecologic momentary assessment surveys six times per week, including assessment of alcohol consumption. We used GPS data to calculate exposure to alcohol outlets using three approach types: residence-based (eg, within the home census tract), activity location–based (eg, within buffer distances of frequently attended places), and activity path–based (eg, average outlets per hour within buffer distances of GPS route lines). Spearman correlations compared exposure measures, and separate Tobit models assessed associations with the proportion of ecologic momentary assessment responses positive for alcohol consumption.Results:Measures were mostly strongly correlated within approach types (ρ≥ 0.7), but weakly (ρ< 0.3) to moderately (0.3≤ ρ< 0.7) correlated between approach types. Associations with alcohol consumption were mostly inconsistent within and between approach types. Some of the residence-based measures (eg, census tract: β= 8.3, 95% CI= 2.8, 13.8), none of the activity location–based approaches, and most of the activity path–based approaches (eg, outlet–hours per hour, 100 m buffer: β= 8.3, 95% CI= 3.3, 13.3) were associated with alcohol consumption.Conclusions:Methodologic decisions regarding measurement of exposure to environmental conditions may affect study results.Ecologic studies emphasize that social and physical environmental conditions are associated with a wide range of health outcomes. For example, indices of social disadvantage are related to homicide in Chicago census tracts 1; the closure of live poultry markets reduced poultry-to-person transmission of avian influenza A H7N9 virus in four Chinese cities 2; radioactive iodine-131 dose predicted thyroid cancer incidence in Ukrainian and Belarussian settlements following the Chernobyl nuclear disaster. 3 Assessing whether population-level relationships such as these are observed at an individual level is an important step toward causal inference. Individual-level studies provide evidence against the ecologic fallacy (ie, erroneously inferring individual-level associations from aggregated data) 4, 5 and in support of causal mechanisms directly linking environmental conditions to health outcomes through the people who are exposed. Many approaches are available for researchers seeking to measure individuals’ exposure to environmental conditions, and different approaches capture different aspects of this exposure. Nevertheless, it is not clear how the choices researchers make about how to measure exposure affects estimates of associations between environmental conditions and health outcomes.