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The importance of household composition in epidemiological analyses of sleep: Evidence from the Understanding Society longitudinal panel survey

机译:家庭构成在睡眠流行病学分析中的重要性:来自理解协会纵向面板调查的证据

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Aims: To establish the relationship between household composition and sleep, we: 1) used latent class analysis (LCA) to classify households; 2) examined the reliability and stability of household composition classes over time; 3) conducted multinomial logistic regression analyses to determine the relationship between household class and the self-reported sleep duration and quality of adults. Methods: Data were sourced from Waves 1 and 2 of the United Kingdom “Understanding Society” (USoc) longitudinal panel survey. LCA was used to classify household composition as a categorical latent construct using data on the number and ages of household occupants and the number of rooms used for sleeping. The Bayesian Information Criterion assessed model fit and identified the optimum number of latent classes. Multi-nomial logistic regression was used to investigate cross-sectional relationships between the household classes and self-reported sleep duration and quality amongst adults, after adjustment for confounders. Results: Household composition was best defined by 7 latent classes in data from Wave 1 of USoc. This finding was confirmed in Wave 2. Compared to the reference class (households with no children and no overcrowding), there was a higher risk of short sleep (≤5 hours) versus 7-8 hours sleep for latent household composition classes that included children (RR: 1.56; 95% CI: 1.29-1.89) and for those with both children and overcrowding (RR: 1.57; 95% CI: 1.31-1.88). Similarly the risk of “very bad” versus “fairly good” quality sleep was significantly higher in those household classes with overcrowding, particularly those with extended (RR: 1.75; 95% CI: 1.34-2.29) and large (RR: 1.51; 95% CI: 1.21-1.87) households. Conclusion: These analyses of a recent, nationally representative cohort from the UK, demonstrated that latent household composition classes are reliable over time; and that these latent household composition classes are important correlates of self-reported sleep amongst adult occupants. We showed that household composition is an important contextual variable to consider in most epidemiological studies of sleep.
机译:目的:建立家庭组成与睡眠之间的关系,我们:1)使用潜在类别分析(LCA)对家庭进行分类; 2)研究了一段时间内家庭组成类别的可靠性和稳定性; 3)进行了多项Logistic回归分析,以确定家庭类别与自我报告的睡眠时间和成年人的质量之间的关系。方法:数据来自英国“了解社会”(USoc)纵向面板调查的第一波和第二波。 LCA使用有关住户的人数和年龄以及用于睡觉的房间数的数据,将家庭构成分类为潜在的构造。贝叶斯信息准则评估模型拟合并确定潜在类别的最佳数量。在对混杂因素进行调整之后,采用多项逻辑回归分析研究了家庭类别与成年人自我报告的睡眠时间和质量之间的横断面关系。结果:在USoc的Wave 1数据中,家庭组成最好由7个潜在类别定义。在第2浪中证实了这一发现。与参考班(没有孩子且没有人满为患的家庭)相比,潜伏家庭作息班​​(包括孩子)的短期睡眠(≤5小时)的风险要高出7-8小时。 (RR:1.56; 95%CI:1.29-1.89),以及有小孩和人满为患的人群(RR:1.57; 95%CI:1.31-1.88)。同样,在人满为患的家庭中,特别是长期(RR:1.75; 95%CI:1.34-2.29)和大家庭(RR:1.51;儿童:过度拥挤),“非常差”与“非常好”质量睡眠的风险明显更高。 95%CI:1.21-1.87)的家庭。结论:这些对英国最近的全国代表性人群的分析表明,潜在的家庭组成类别随着时间的推移是可靠的。并且这些潜在的家庭组成类别是成年人居住者自我报告的睡眠的重要关联。我们表明,家庭构成是大多数睡眠流行病学研究中要考虑的重要背景变量。

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