Harris S A, Purdham J T, Corey P N, Sass-Kortsak A M
Department of Public Health Sciences, Faculty of Medicine, University of Toronto, Ontario, Canada.
AIHAJ. 2000 Sep-Oct;61(5):649-57. doi: 10.1080/15298660008984574.
The accurate quantification of the absorbed dose of pesticides following occupational exposures generally requires complete 24-hour urine collections, often over extended periods of time. Difficulty in obtaining volunteer cooperation may result in incomplete urine collections. Traditionally, 24-hour urinary creatinine has been used to identify incomplete urine samples and has been used to standardize pesticide and other chemical dose estimates. More recently, the use of creatinine to standardize dose estimates has been questioned, as has its utility in the identification of incomplete urine collections. This research evaluates the use of personal observation, statistical methods, and published models to predict creatinine excretion to identify and adjust for incomplete urine collections. Based on the use of published creatinine prediction models, an evaluation of the day-to-day creatinine excretion within subjects, and personal observation, a small number of suspected urine samples were identified. Although it is likely that these samples were incomplete, correction of these urine volumes based on the published models did little to improve pesticide dose prediction. Further, results indicate that subjects who report missed urine samples may be able to estimate the missing volumes with some accuracy. In future pesticide exposure studies, the use of self-reported missed volumes may help to increase the accuracy of dose prediction when there is strong cooperation with collection procedures. A statistical model to predict creatinine excretion in professional turf applicators was developed to provide a preliminary screening for urinary completeness for future studies in which compliance with urinary collection is thought to be insufficient.
职业接触农药后准确量化吸收剂量通常需要完整收集24小时尿液,而且往往需要持续较长时间。获得志愿者合作存在困难可能导致尿液收集不完整。传统上,24小时尿肌酐一直用于识别不完整的尿液样本,并用于标准化农药和其他化学物质的剂量估计。最近,使用肌酐来标准化剂量估计受到了质疑,其在识别不完整尿液收集方面的效用也受到了质疑。本研究评估了使用个人观察、统计方法和已发表的模型来预测肌酐排泄,以识别和校正不完整的尿液收集。基于已发表的肌酐预测模型的使用、对受试者每日肌酐排泄的评估以及个人观察,识别出了少量可疑尿液样本。尽管这些样本很可能不完整,但根据已发表的模型对这些尿量进行校正对改善农药剂量预测作用不大。此外,结果表明,报告遗漏尿液样本的受试者可能能够较为准确地估计遗漏的尿量。在未来的农药暴露研究中,当与收集程序有良好合作时,使用自我报告的遗漏尿量可能有助于提高剂量预测的准确性。开发了一个预测专业草坪施药人员肌酐排泄的统计模型,以便为未来那些认为尿液收集依从性不足的研究提供尿液完整性的初步筛查。