Dataset Information

Application of machine learning to understand child marriage in India.

ABSTRACT:

Background

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.

Methods

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.

Findings

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.

Interpretation

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.

SUBMITTER: Raj A

PROVIDER: S-EPMC7732880 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Application of machine learning to understand child marriage in India.

Raj Anita A Dehingia Nabamallika N Singh Abhishek A McDougal Lotus L McAuley Julian J

SSM - population health 20201205

<h4>Background</h4>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 ...[more]

PMID: 33335970

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Project description:BackgroundThere has been much speculation about the role of inequitable gender norms and early marriage in mental health and suicide risks in girls and young women, but no prospective study has yet investigated this relationship. Understanding these links has become particularly important in the context of the COVID-19 pandemic which has led to increased risk of child marriage in the most vulnerable girls.MethodsWe examined the association between early marriage and mental health in girls using data from Understanding the Lives of Adolescents and Young Adults (UDAYA), a longitudinal study in adolescents in Uttar Pradesh and Bihar, India. The study included girls who were unmarried at wave 1 (2015-2016) and participated at wave 2 data collection (2018-2019). Information on mental health (Patient Health Questionnaire-9 (PHQ-9)), suicidal thoughts, plans and attempts were collected at both waves. Logistic regression with survey weights was used to estimate the association between marrying between the two waves and mental health.FindingsBetween waves 1 and 2, 1825 (23%) participants (n = 7864) married. Unmarried girls with depressive symptoms (PHQ score≥9) at wave 1 had greater odds of transitioning into marriage by wave 2 than those without (adjusted-OR 1.5; 95% CI 1.1 to 2.0). The odds of wave 2 depressive symptoms were higher in newly married vs unmarried girls (adjusted-OR 2.0; 95% CI 1.6-2.5). Among newly married girls, the odds of depressive symptoms were higher for those who experienced any abuse than those who did not (adjusted-OR 1.6; 95% CI 1.2-2.2). This effect was larger for girls who had not given birth (adjusted-OR 2.2; 95% CI 1.4-3.3).InterpretationOur findings show poor mental health preceded and was a consequence of child marriage. Mental health should be considered in policies and programming aimed at reducing early marriage; equally the mental health of young brides should be a focus for community and maternal health services.FundingBill and Melinda Gates Foundation and David and Lucile Packard Foundation.

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Dataset Information

Application of machine learning to understand child marriage in India.

Background

Methods

Findings

Interpretation

Publications

Application of machine learning to understand child marriage in India.

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