Using graphic modelling to identify modifiable mediators of the association between area-based deprivation at birth and offspring unemployment.
Using graphic modelling to identify modifiable mediators of the association between area-based deprivation at birth and offspring unemployment.
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DOI：
10.1371/journal.pone.0249258
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Pell JP
中科院分区：
文献类型：
--
作者：
Bogie J;Fleming M;Cullen B;Mackay D;Pell JP
贫困可以代代相传；然而，致病途径尚不清楚。有向无环图（DAG）与中介分析可以帮助阐明和量化复杂的途径，以确定目标干预的可修改因素。我们连接了10个苏格兰范围的数据库（6个健康数据库和4个教育数据库），得出了2009年至2013年间在苏格兰学校就读的217,226名学生的队列。DAG包括23个可能影响出生时地域剥夺与后代“未受教育、就业或培训”状况之间关系的因素，包括孕产妇、产前、围产期和儿童健康、学校参与和教育因素。使用改进的g计算进行分析。出生时的贫困与“未受教育、未就业或未接受培训”的后代增加7.3%有关。这种关联的主要中介因素是怀孕期间吸烟（自然间接效应为0.016,95% CI为0.013,0.019）和缺课（自然间接效应为0.021,95% CI为0.018,0.024），分别解释了总效应的22%和30%。通过解决这些因素，怀孕期间吸烟可能消除的关联比例为19%（设置为非吸烟者0.058时的直接控制影响；95% CI 0.053, 0.063），学校缺勤为38%（设置为无缺勤时的直接控制影响0.043;95% CI 0.037, 0.049）。将DAG与中介分析相结合，有助于理清一个复杂的公共卫生问题，并量化了可以作为干预目标的产妇吸烟和缺课等可修改因素。这项研究还证明了dag在理解复杂公共卫生问题方面的一般效用。
Deprivation can perpetuate across generations; however, the causative pathways are not well understood. Directed acyclic graphs (DAG) with mediation analysis can help elucidate and quantify complex pathways in order to identify modifiable factors at which to target interventions. We linked ten Scotland-wide databases (six health and four education) to produce a cohort of 217,226 pupils who attended Scottish schools between 2009 and 2013. The DAG comprised 23 potential mediators of the association between area deprivation at birth and subsequent offspring ‘not in education, employment or training’ status, covering maternal, antenatal, perinatal and child health, school engagement, and educational factors. Analyses were performed using modified g-computation. Deprivation at birth was associated with a 7.3% increase in offspring ‘not in education, employment or training’. The principal mediators of this association were smoking during pregnancy (natural indirect effect of 0·016, 95% CI 0·013, 0·019) and school absences (natural indirect effect of 0·021, 95% CI 0·018, 0·024), explaining 22% and 30% of the total effect respectively. The proportion of the association potentially eliminated by addressing these factors was 19% (controlled direct effect when set to non-smoker 0·058; 95% CI 0·053, 0·063) for smoking during pregnancy and 38% (controlled direct effect when set to no absences 0·043; 95% CI 0·037, 0·049) for school absences. Combining a DAG with mediation analysis helped disentangle a complex public health problem and quantified the modifiable factors of maternal smoking and school absence that could be targeted for intervention. This study also demonstrates the general utility of DAGs in understanding complex public health problems.
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