Dey Arnab K, Dehingia Nabamallika, Bhan Nandita, Thomas Edwin Elizabeth, McDougal Lotus, Averbach Sarah, McAuley Julian, Singh Abhishek, Raj Anita
Center on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Joint Doctoral Program-Public Health, San Diego State University and University of California San Diego, San Diego, CA, USA.
SSM Popul Health. 2022 Sep 29;19:101234. doi: 10.1016/j.ssmph.2022.101234. eCollection 2022 Sep.
Intra-uterine devices (IUDs) are a safe and effective method to delay or space pregnancies and are available for free or at low cost in the Indian public health system; yet, IUD uptake in India remains low. Limited quantitative research using national data has explored factors that may affect IUD use. Machine Learning (ML) techniques allow us to explore determinants of low prevalence behaviors in survey research, such as IUD use. We applied ML to explore the determinants of IUD use in India among married women in the 4th National Family Health Survey (NFHS-4; N = 499,627), which collects data on demographic and health indicators among women of childbearing age. We conducted ML logistic regression (lasso and ridge) and neural network approaches to assess significant determinants and used iterative thematic analysis (ITA) to offer insight into related variable constructs generated from a series of regularized models. We found that couples' shared family planning (FP) goals were the strongest determinants of IUD use, followed by receipt of FP services and desire for no more children, higher wealth and education, and receipt of maternal and child health services. Findings highlight the importance of male engagement and family planning services for IUD uptake and the need for more targeted efforts to support awareness of IUD as an option for spacing, especially for those of lower SES and with lower access to care.
宫内节育器（IUDs）是一种安全有效的延迟或间隔怀孕的方法，在印度公共卫生系统中可免费或以低成本获得；然而，印度宫内节育器的使用率仍然很低。利用国家数据进行的有限定量研究探讨了可能影响宫内节育器使用的因素。机器学习（ML）技术使我们能够在调查研究中探索低流行行为的决定因素，例如宫内节育器的使用。我们应用机器学习来探索在第四次全国家庭健康调查（NFHS - 4；N = 499,627）中印度已婚妇女使用宫内节育器的决定因素，该调查收集育龄妇女的人口和健康指标数据。我们进行了机器学习逻辑回归（套索和岭回归）和神经网络方法来评估重要的决定因素，并使用迭代主题分析（ITA）来深入了解从一系列正则化模型中生成的相关变量结构。我们发现，夫妻共同的计划生育（FP）目标是宫内节育器使用的最强决定因素，其次是获得计划生育服务、不想再生育、更高的财富和教育水平，以及获得母婴健康服务。研究结果强调了男性参与和计划生育服务对宫内节育器使用的重要性，以及需要做出更有针对性的努力来提高对宫内节育器作为间隔生育选择的认识，特别是对于社会经济地位较低且获得医疗服务机会较少的人群。
