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Latency analysis in epidemiologic studies of occupational exposures: application to the Colorado Plateau uranium miners cohort.

机译:职业暴露流行病学研究中的延迟分析:在科罗拉多高原铀矿工队列中的应用。

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BACKGROUND: Latency effects are an important factor in assessing the public health implications of an occupational or environmental exposure. Usually, however, latency results as described in the literature are insufficient to answer public health related questions. Alternative approaches to the analysis of latency effects are warranted. METHODS: A general statistical framework for modeling latency effects is described. We then propose bilinear and exponential decay latency models for analyzing latency effects as they have parameters that address questions of public health interest. Methods are described for fitting these models to cohort or case-control data; statistical inference is based on standard likelihood methods. APPLICATION: A latency analysis of radon exposure and lung cancer in the Colorado Plateau uranium miners cohort was performed. We first analyzed the entire cohort and found that the relative risk associated with exposure increases for about 8.5 years and thereafter decreases until it reaches background levels after about 34 years. The hypothesis that the relative risk remains at its peak level is strongly rejected (P < 0.001). Next, we investigated the variation in the latency effects over subsets of the cohort based on attained age, level and rate of exposure, and smoking. Age was the only factor for which effect modification was demonstrated (P = 0.014). We found that the decline in effect is much steeper at older ages (60+ years) than younger. CONCLUSION: The proposed methods can provide much more information about the exposure-disease latency effects than those generally used.
机译:背景:延迟效应是评估职业或环境暴露对公共健康影响的重要因素。然而,通常,文献中描述的潜伏期结果不足以回答与公共卫生有关的问题。分析等待时间影响的替代方法是必要的。方法:描述了建模潜伏期影响的一般统计框架。然后,我们提出双线性和指数衰减潜伏期模型,用于分析潜伏期影响,因为它们具有解决公共卫生问题的参数。描述了使这些模型适合队列或病例对照数据的方法。统计推断基于标准似然法。应用:在科罗拉多高原铀矿工队列中进行了exposure暴露和肺癌的潜伏期分析。我们首先分析了整个队列,发现与暴露相关的相对风险在大约8.5年内增加,然后降低,直到大约34年后达到背景水平。相对风险保持在峰值水平的假设被强烈拒绝(P <0.001)。接下来,我们根据所达到的年龄,暴露水平和暴露率以及吸烟情况,研究了队列中子集的潜伏期影响的变化。年龄是唯一可以证明效果改善的因素(P = 0.014)。我们发现,老年人(60岁以上)的影响下降比年轻人小得多。结论:所提出的方法可以提供比通常使用的方法更多的有关暴露-疾病潜伏期影响的信息。

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