ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland, Australia.
School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.
PLoS One. 2019 Nov 13;14(11):e0223956. doi: 10.1371/journal.pone.0223956. eCollection 2019.
Organochlorine pesticides (OCPs) are toxic chemicals that persist in human tissue. Short and long term exposure to OCPs have been shown to have adverse effects on human health. This motivates studies into the concentrations of pesticides in humans. However these studies typically emphasise the analysis of the main effects of age group, gender and time of sample collection. The interactions between main effects can distinguish variation in OCP concentration such as the difference in concentrations between genders of the same age group as well as age groups over time. These are less studied but may be equally or more important in understanding effects of OCPs in a population. The aim of this study was to identify interactions relevant to understanding OCP concentrations and utilise them appropriately in models. We propose a two stage analysis comprising of boosted regression trees (BRTs) and hierarchical modelling to study OCP concentrations. BRTs are used to discover influential interactions between age group, gender and time of sampling. Hierarchical models are then employed to test and infer the effect of the interactions on OCP concentrations. Results of our analysis show that the best fitting model of an interaction effect varied between OCPs. The interaction between age group and gender was most influential for hexachlorobenzene (HCB) concentrations. There was strong evidence of an interaction effect between age group and time for β-hexachlorocyclohexane (β-HCH) concentrations in >60 year olds as well as an interaction effect between age group and gender for HCB concentrations for adults aged >45 years. This study highlights the need to consider appropriate interaction effects in the analysis of OCP concentrations and provides further insight into the interplay of main effects on OCP concentration trends.
有机氯农药 (OCPs) 是有毒化学物质，会在人体组织中残留。短期和长期接触 OCPs 已被证明对人类健康有不良影响。这促使人们研究人类体内的农药浓度。然而，这些研究通常强调分析年龄组、性别和样本采集时间的主要影响。主要影响之间的相互作用可以区分 OCP 浓度的变化，例如同一年龄组不同性别之间的浓度差异以及随时间推移的年龄组之间的差异。这些相互作用研究较少，但在理解人群中 OCP 的影响方面可能同样重要，甚至更为重要。本研究的目的是确定与理解 OCP 浓度相关的相互作用，并在模型中适当地利用它们。我们提出了一种两阶段分析方法，包括增强回归树 (BRT) 和分层建模，以研究 OCP 浓度。BRT 用于发现年龄组、性别和采样时间之间有影响力的相互作用。然后使用分层模型来测试和推断相互作用对 OCP 浓度的影响。我们的分析结果表明，相互作用的最佳拟合模型因 OCP 而异。年龄组和性别之间的相互作用对六氯苯 (HCB) 浓度的影响最大。在 60 岁以上人群中，β-六氯环己烷 (β-HCH) 浓度的年龄组和时间之间存在强烈的相互作用证据，而 HCB 浓度在 45 岁以上成年人中也存在年龄组和性别之间的相互作用证据。这项研究强调了在分析 OCP 浓度时需要考虑适当的相互作用效应，并进一步深入了解主要效应对 OCP 浓度趋势的相互作用。
