Raj Anita, Dehingia Nabamallika, Singh Abhishek, McDougal Lotus, McAuley Julian
Center on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Department of Education Studies, Division of Social Sciences, University of California San Diego, San Diego, CA, USA.
SSM Popul Health. 2020 Dec 5;12:100687. doi: 10.1016/j.ssmph.2020.100687. eCollection 2020 Dec.
Prior research documents that India has the greatest number of girls married as minors of any nation in the world, increasing social and health risks for both these young wives and their children. While the prevalence of child marriage has declined in the nation, more work is needed to accelerate this decline and the negative consequences of the practice. Expanded targets for intervention require greater identification of these targets. Machine learning can offer insight into identification of novel factors associated with child marriage that can serve as targets for intervention.
We applied machine learning methods to retrospective cross-sectional survey data from India on demographics and health, the nationally-representative National Family Health Survey, conducted in 2015-16. We analyzed data using a traditional regression model, with child marriage as the dependent variable, and 4000+ variables from the survey as the independent variables. We also used three commonly used machine learning algorithms- Least Absolute Shrinkage and Selection Operator (lasso) or L-1 regularized logistic regression models; L2 regularized logistic regression or ridge models; and neural network models. Finally, we developed and applied a novel and rigorous approach involving expert qualitative review and coding of variables generated from an iterative series of regularized models to assess thematically key variable groupings associated with child marriage.
Analyses revealed that regularized logistic and neural network applications demonstrated better accuracy and lower error rates than traditional logistic regression, with a greater number of features and variables generated. Regularized models highlight higher fertility and contraception, longer duration of marriage, geographic, and socioeconomic vulnerabilities as key correlates; findings shown in prior research. However, our novel method involving expert qualitative coding of variables generated from iterative regularized models and resultant thematic generation offered clarity on variables not focused upon in prior research, specifically non-utilization of health system benefits related to nutrition for mothers and infants.
Machine learning appears to be a valid means of identifying key correlates of child marriage in India and, via our innovative iterative thematic approach, can be useful to identify novel variables associated with this outcome. Findings related to low nutritional service uptake also demonstrate the need for more focus on public health outreach for nutritional programs tailored to this population.
先前的研究表明，在世界各国中，印度未成年结婚的女孩数量最多，这增加了这些年轻妻子及其子女的社会和健康风险。尽管该国童婚率有所下降，但仍需开展更多工作来加速这一下降趋势，并减少这种做法的负面影响。扩大干预目标需要更精准地识别这些目标。机器学习能够为识别与童婚相关的新因素提供见解，这些因素可作为干预目标。
我们将机器学习方法应用于2015 - 16年在印度进行的具有全国代表性的全国家庭健康调查中的人口统计和健康回顾性横断面调查数据。我们使用传统回归模型进行数据分析，将童婚作为因变量，调查中的4000多个变量作为自变量。我们还使用了三种常用的机器学习算法——最小绝对收缩和选择算子（lasso）或L - 1正则化逻辑回归模型；L2正则化逻辑回归或岭模型；以及神经网络模型。最后，我们开发并应用了一种新颖且严谨的方法，该方法涉及专家对从一系列迭代正则化模型生成的变量进行定性审查和编码，以评估与童婚相关的主题关键变量分组。
分析表明，正则化逻辑回归和神经网络应用相较于传统逻辑回归展现出更高的准确性和更低的错误率，且生成了更多的特征和变量。正则化模型突出了较高的生育率和避孕率、较长的婚姻持续时间、地理以及社会经济脆弱性等关键关联因素；这些结果与先前研究一致。然而，我们的新方法，即对迭代正则化模型生成的变量进行专家定性编码以及由此产生的主题分析，明确了先前研究未关注的变量，特别是与母婴营养相关的卫生系统福利未得到利用的情况。
机器学习似乎是识别印度童婚关键关联因素的有效手段，并且通过我们创新的迭代主题方法，有助于识别与这一结果相关的新变量。与低营养服务利用率相关的研究结果也表明，需要更加关注针对这一人群量身定制的营养项目的公共卫生推广。
