What if risk factors influenced the variability of health outcomes as well as the mean? Evidence and implications
What if risk factors influenced the variability of health outcomes as well as the mean? Evidence and implications
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如果风险因素影响健康结果的变异性以及平均值怎么办？
DOI：
10.1101/2021.03.30.21254645
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Bann D
中科院分区：
文献类型：
--
作者：
Bann D
风险因素可能会影响健康结果的变异性和平均值。理解这一点可以帮助病因学的理解和公共卫生翻译，因为改变结果均值和减少变异性的干预措施比那些只影响均值的干预措施更可取。然而，很少有统计工具定期测试变异性的差异。我们使用GAMLSS（位置、规模和形状的广义加性模型）来研究多个风险因素（性别、儿童社会阶层和中年身体活动不足）与健康结果均值和变异性差异的关系。使用1970年英国出生队列研究，以体重指数（BMI; N = 6，025）和心理健康（Warwick-Edinburgh Mental Wellbeing Scale; N = 7，128）作为结果。对于BMI，男性的平均值比女性高2%，但变异性低28%。较低的社会阶层和缺乏身体活动与较高的平均值和较高的变异性（分别为6%和13%）相关。对于心理健康，性别与平均值无关，而男性的变异性低4%。较低的社会阶层和缺乏身体活动与较低的平均值和较高的变异性相关（分别为-7%和11%）。这为风险因素可以减少或增加健康结果的可变性这一概念提供了经验支持。这些发现可以解释为每次暴露的因果效应的异质性，其他（通常未测量）变量的影响，和/或测量误差。这种未充分利用的方法来分析连续分布的结果可能在流行病学，医学和心理科学中具有更广泛的实用性。
Risk factors may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are preferable to those which affect only the mean. However, few statistical tools routinely test for differences in variability. We used GAMLSS (Generalised Additive Models for Location, Scale and Shape) to investigate how multiple risk factors (sex, childhood social class and midlife physical inactivity) related to differences in health outcome mean and variability. The 1970 British birth cohort study was used, with body mass index (BMI; N = 6,025) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7,128) as outcomes. For BMI, males had a 2% higher mean than females yet 28% lower variability. Lower social class and physical inactivity were associated with higher mean and higher variability (6% and 13% respectively). For mental wellbeing, gender was not associated with the mean while males had 4% lower variability. Lower social class and physical inactivity were associated with lower mean yet higher variability (−7% and 11% respectively). This provides empirical support for the notion that risk factors can reduce or increase variability in health outcomes. Such findings may be explained by heterogeneity in the causal effect of each exposure, by the influence of other (typically unmeasured) variables, and/or by measurement error. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiological, medical, and psychological sciences.
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