A quantitative approach for estimating exposure to pesticides in the Agricultural Health Study
A quantitative approach for estimating exposure to pesticides in the Agricultural Health Study
复制标题
DOI：
10.1093/annhyg/mef011
复制
发表时间：
2002-03-01
影响因子：
--
通讯作者：
Blair, A
中科院分区：
文献类型：
--
作者：
Dosemeci, M;Alavanja, MCR;Blair, A
我们在一项对北卡罗来纳州和爱荷华州 58000 多名农药施用者进行的大型前瞻性队列研究中开发了一种定量方法来估计长期特定化学农药的暴露量。向施药者发放登记问卷，收集农药暴露的基本时间和强度相关信息，例如混合条件、施药持续时间和频率、施药方法和使用的个人防护装备。此外，还进行了详细的带回家调查问卷，以进一步收集与强度相关的暴露信息，例如混合和应用设备的维护或修理、工作实践和个人卫生。超过 40% 的注册申请者回答了这份详细的带回家的调查问卷。开发了两种算法来识别施用者的暴露场景，分别使用来自登记和带回家的调查问卷的信息来计算特定于个体农药的暴露分数的特定强度。 “通用算法”使用来自登记问卷的四个基本变量（即混合状态、施用方法、设备维修状态和个人防护装备使用）和已发表的农药暴露文献中的测量数据来计算每个施药者对个别农药的估计暴露强度。 “详细”算法基于一般算法中的变量以及带回家的调查问卷中的附加暴露信息，包括所使用的混合系统的类型（即封闭式或开放式）、具有封闭式驾驶室和/或木炭过滤器的拖拉机、使用后清洗设备的频率、更换旧手套的频率、个人卫生和泄漏后更换衣服。两种算法中应用的权重因子都是使用已发表的农药暴露文献中的测量数据和专业判断来估计的。对于每个研究对象，通过结合两种算法独立计算的农药暴露强度和问卷中农药使用的持续时间/频率，得出特定化学品的终生累积农药暴露水平。 2,4-滴和毒死蜱的持续时间、强度和累积暴露水平的分布按州、性别、年龄组和整个登记队列以及对带回家的调查问卷作出答复的施药者子队列的施药者类型（即农民或商业施药者）进行呈现。在两个研究人群中，按州、性别、年龄和施药器类型划分的所有基本暴露指数（即强度、持续时间和累积暴露于 2,4-D 和毒死蜱）的分布模式几乎相同，表明施药者的带回家问卷子队列在暴露方面代表了整个队列。
We developed a quantitative method to estimate long-term chemical-specific pesticide exposures in a large prospective cohort study of more than 58000 pesticide applicators in North Carolina and Iowa. An enrollment questionnaire was administered to applicators to collect basic time- and intensity-related information on pesticide exposure such as mixing condition, duration and frequency of application, application methods and personal protective equipment used. In addition, a detailed take-home questionnaire was administered to collect further intensity-related exposure information such as maintenance or repair of mixing and application equipment, work practices and personal hygiene. More than 40% of the enrolled applicators responded to this detailed take-home questionnaire. Two algorithms were developed to identify applicators' exposure scenarios using information from the enrollment and take-home questionnaires separately in the calculation of subject-specific intensity of exposure score to individual pesticides. The 'general algorithm' used four basic variables (i.e. mixing status, application method, equipment repair status and personal protective equipment use) from the enrollment questionnaire and measurement data from the published pesticide exposure literature to calculate estimated intensity of exposure to individual pesticides for each applicator. The 'detailed' algorithm was based on variables in the general algorithm plus additional exposure information from the take-home questionnaire, including types of mixing system used (i.e. enclosed or open), having a tractor with enclosed cab and/or charcoal filter, frequency of washing equipment after application, frequency of replacing old gloves, personal hygiene and changing clothes after a spill. Weighting factors applied in both algorithms were estimated using measurement data from the published pesticide exposure literature and professional judgment. For each study subject, chemical-specific lifetime cumulative pesticide exposure levels were derived by combining intensity of pesticide exposure as calculated by the two algorithms independently and duration/frequency of pesticide use from the questionnaire. Distributions of duration, intensity and cumulative exposure levels of 2,4-D and chlorpyrifus are presented by state, gender, age group and applicator type (i.e. farmer or commercial applicator) for the entire enrollment cohort and for the sub-cohort of applicators who responded to the take-home questionnaire. The distribution patterns of all basic exposure indices (i.e. intensity, duration and cumulative exposure to 2,4-D and chlorpyrifos) by state, gender, age and applicator type were almost identical in two study populations, indicating that the take-home questionnaire sub-cohort of applicators is representative of the entire cohort in terms of exposure.