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一种无假设方法用于识别学年中相对年龄的潜在影响:英国生物银行中的一项工具变量全表型关联研究

A hypothesis-free approach to identifying potential effects of relative age in school year: an instrumental variable phenome-wide association study in the UK Biobank.

作者信息

de Lange Melanie A, Davies Neil M, Millard Louise A C, Tilling Kate

机构信息

MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

出版信息

Am J Epidemiol. 2025 Jun 3;194(6):1673-1680. doi: 10.1093/aje/kwae331.

Abstract

A child's relative age within their school year ("relative age") is associated with educational attainment and mental health. However, hypothesis-driven studies often re-examine the same outcomes and exposure, potentially leading to confirmation and reporting biases and missing unknown effects. Hypothesis-free outcome-wide analyses can potentially overcome these limitations. We conducted a hypothesis-free investigation of the effects of relative age within school year. We performed an instrumental variable (IV) phenome-wide association study in the UK Biobank (participants aged 40-69 years at baseline), using the PHESANT software package. We created 2 IVs for relative age: being born in September vs August (n = 64 075) and week of birth (n = 383 309). Outcomes passing the Bonferroni-corrected P value threshold for either instrument were plotted to identify a discontinuity at the school year transition. Thirteen traits associated with at least 1 of the instruments showed a discontinuity. Previously identified effects included those with a younger relative age being less likely to have educational qualifications and more likely to have started smoking at a younger age. We detected a few associations not explored by previous studies. For example, those of younger relative age had better lung function as adults. Hypothesis-free approaches could help address confirmation and reporting biases in epidemiology.

摘要

儿童在学年内的相对年龄(“相对年龄”)与教育成就和心理健康相关。然而,假设驱动的研究往往会重新审视相同的结果和暴露因素,这可能导致确认偏差和报告偏差,并遗漏未知效应。无假设的全结果分析有可能克服这些局限性。我们对学年内相对年龄的影响进行了一项无假设调查。我们在英国生物银行(基线年龄为40 - 69岁的参与者)中使用PHESANT软件包进行了一项工具变量(IV)全表型关联研究。我们为相对年龄创建了两个工具变量:9月出生与8月出生(n = 64075)以及出生周数(n = 383309)。绘制通过任一工具的Bonferroni校正P值阈值的结果,以识别学年过渡时的不连续性。与至少一个工具变量相关的13个性状显示出不连续性。先前确定的影响包括相对年龄较小的人获得教育资格的可能性较小,以及在较年轻时开始吸烟的可能性较大。我们发现了一些先前研究未探讨的关联。例如,相对年龄较小的人成年后肺功能更好。无假设方法有助于解决流行病学中的确认偏差和报告偏差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/12133288/73d896af2116/kwae331f1.jpg

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