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预测巴西最需要支持的孕妇的早期儿童发育情况的因素。

Predictors of early child development for screening pregnant women most in need of support in Brazil.

机构信息

Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.

Human Development and Violence Research Centre, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.

出版信息

J Glob Health. 2024 Aug 23;14:04143. doi: 10.7189/jogh.14.04143.

Abstract

BACKGROUND

Home visiting programmes can support child development and reduce inequalities, but failure to identify the most vulnerable families can undermine such efforts. We examined whether there are strong predictors of poor child development that could be used to screen pregnant women in primary health care settings to target early interventions in a Brazilian population. Considering selected predictors, we assessed coverage and focus of a large-scale home visiting programme named Primeira Infância Melhor (PIM).

METHODS

We undertook a prospective cohort study on 3603 children whom we followed from gestation to age four years. We then used 27 potential socioeconomic, psychosocial, and clinical risk factors measurable during pregnancy to predict child development, which was assessed by the Battelle Developmental Inventory (BDI) at the age of four years. We compared the results from a Bonferroni-adjusted conditional inference tree with exploratory linear regression and principal component analysis (PCA), and we conducted external validation using data from a second cohort from the same population. Lastly, we assessed PIM coverage and focus by linking 2015 cohort data with PIM databases.

RESULTS

The decision tree analyses identified maternal schooling as the most important variable for predicting BDI, followed by paternal schooling. Based on these variables, a group of 214 children who had the lowest mean BDI (BDI = -0.48; 95% confidence interval (CI) = -0.63, -0.33) was defined by mothers with ≤5 years and fathers with ≤4 years of schooling. Maternal and paternal schooling were also the strongest predictors in the exploratory analysis using regression and PCA, showing linear associations with the outcome. However, their capacity to explain outcome variance was low, with an adjusted R of 5.3% and an area under the receiver operating characteristic curve of 0.62 (95% CI = 0.60, 0.64). External validation showed consistent results. We also provided an online screening tool using parental schooling data to support programme's targeting. PIM coverage during pregnancy was low, but the focus was adequate, especially among families with longer enrolment, indicating families most in need received higher dosage.

CONCLUSIONS

Information on maternal and paternal schooling can improve the focus of home visiting programmes if used for initial population-level screening of pregnant women in Brazil. However, enrolment decisions require complementary information on parental resources and direct interactions with families to jointly decide on inclusion.

摘要

背景

家庭访视项目可以支持儿童发展并减少不平等,但未能识别最脆弱的家庭可能会破坏这些努力。我们研究了是否存在可以用来筛选初级保健环境中孕妇的不良儿童发育的强预测因子,以针对巴西人口进行早期干预。考虑到选定的预测因子,我们评估了一项名为 Primeira Infância Melhor (PIM) 的大规模家庭访视计划的覆盖范围和重点。

方法

我们对 3603 名儿童进行了前瞻性队列研究,从妊娠到 4 岁对他们进行了随访。然后,我们使用了 27 种在怀孕期间可测量的潜在社会经济、心理社会和临床风险因素来预测儿童发育,4 岁时使用 Battelle 发育量表 (BDI) 进行评估。我们将经过 Bonferroni 调整的条件推理树与探索性线性回归和主成分分析 (PCA) 的结果进行了比较,并使用来自同一人群的第二个队列的数据进行了外部验证。最后,我们通过将 2015 年队列数据与 PIM 数据库相关联,评估了 PIM 的覆盖范围和重点。

结果

决策树分析确定母亲的受教育程度是预测 BDI 的最重要变量,其次是父亲的受教育程度。基于这些变量,我们定义了一个由母亲受教育程度≤5 年和父亲受教育程度≤4 年的 214 名儿童组成的组,他们的平均 BDI 最低(BDI=-0.48;95%置信区间[CI]:-0.63,-0.33)。在使用回归和 PCA 进行的探索性分析中,母亲和父亲的受教育程度也是最强的预测因子,与结果呈线性关联。然而,它们解释结果方差的能力很低,调整后的 R 为 5.3%,受试者工作特征曲线下面积为 0.62(95%CI:0.60,0.64)。外部验证显示出一致的结果。我们还提供了一个使用父母教育数据的在线筛查工具,以支持该计划的针对性。怀孕期间 PIM 的覆盖率较低,但重点是适当的,特别是在入组时间较长的家庭中,表明最需要的家庭接受了更高的剂量。

结论

如果用于对巴西孕妇进行初始人群水平筛查,母亲和父亲的受教育程度信息可以改善家庭访视项目的针对性。但是,入组决策需要关于父母资源的补充信息,并与家庭直接互动,以共同决定入组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/972b/11341113/e713b546fbdb/jogh-14-04143-F1.jpg

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