Understanding the social determinants of child mortality in Latin America over the last two decades: a machine learning approach.
Understanding the social determinants of child mortality in Latin America over the last two decades: a machine learning approach.
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DOI：
10.1038/s41598-023-47994-w
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发表时间：
2023-11-27
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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作者：
降低儿童死亡率仍然是一项重大的全球公共卫生挑战，特别是在拉丁美洲等高度不平等的区域。我们使用机器学习（ML）算法来探索巴西，厄瓜多尔和墨西哥20年来社会决定因素与5岁以下儿童死亡率（U5MR）之间的关系。我们创建了一个2000年至2019年的小学水平队列，并训练了一个随机森林模型（RF）来估计社会决定因素在预测五岁以下儿童死亡率方面的相对重要性。我们进行了敏感性分析，训练了另外两个ML模型，并给出了均方误差、均方根误差和绝对偏差的中位数。我们的研究结果表明，贫困、文盲和基尼指数是根据RF预测5岁以下儿童死亡率的最重要变量。此外，非线性关系主要是基尼指数和五岁以下儿童死亡率。我们的研究表明，降低拉丁美洲五岁以下儿童死亡率的长期公共政策应侧重于减少贫困、文盲和社会经济不平等。这项研究为拉丁美洲社会决定因素与儿童死亡率之间的关系提供了重要见解。使用ML算法，结合大型纵向数据，使我们能够比传统模型更仔细地评估社会决定因素对健康的影响。
The reduction of child mortality rates remains a significant global public health challenge, particularly in regions with high levels of inequality such as Latin America. We used machine learning (ML) algorithms to explore the relationship between social determinants and child under-5 mortality rates (U5MR) in Brazil, Ecuador, and Mexico over two decades. We created a municipal-level cohort from 2000 to 2019 and trained a random forest model (RF) to estimate the relative importance of social determinants in predicting U5MR. We conducted a sensitivity analysis training two more ML models and presenting the mean square error, root mean square error, and median absolute deviation. Our findings indicate that poverty, illiteracy, and the Gini index were the most important variables for predicting U5MR according to the RF. Furthermore, non-linear relationships were found mainly for Gini index and U5MR. Our study suggests that long-term public policies to reduce U5MR in Latin America should focus on reducing poverty, illiteracy, and socioeconomic inequalities. This research provides important insights into the relationships between social determinants and child mortality rates in Latin America. The use of ML algorithms, combined with large longitudinal data, allowed us to evaluate the effects of social determinants on health more carefully than traditional models.
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影响因子：
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作者：
Bitew, Fikrewold H.;Nyarko, Samuel H.;Sparks, Corey S.
通讯作者：
Sparks, Corey S.
影响因子：
4.5
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH
DOI：
10.3390/ijerph18031315
发表时间：
2021-02-01
影响因子：
--
作者：
Bizzego A;Gabrieli G;Bornstein MH;Deater-Deckard K;Lansford JE;Bradley RH;Costa M;Esposito G
通讯作者：
Esposito G