Raj Anita, Dehingia Nabamallika, Singh Abhishek, McAuley Julian, McDougal Lotus
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.
EClinicalMedicine. 2021 Aug 1;39:101046. doi: 10.1016/j.eclinm.2021.101046. eCollection 2021 Sep.
Machine learning techniques can explore low prevalence data to offer insight into identification of factors associated with non-marital sexual violence (NMSV). NMSV in India is a health and human rights concern that disproportionately affects adolescents, is under-reported, and not well understood or addressed in the country.
We applied machine learning methods to retrospective cross-sectional data from India's nationally-representative National Family Health Survey 4, a demographic and health study conducted in 2015-16, which offers 4000+ variables as potential independent variables. We used Least Absolute Shrinkage and Selection Operator (lasso) or L-1 regularized logistic regression models as well as L-2 regularized logistic regression or ridge models; we conducted an iterative thematic analysis (ITA) of variables generated from a series of regularized models.
Thematic analysis of regularized models highlight that past exposure to violence was most predictive of NMSV, followed by geography, sexual behavior, and poor sexual and reproductive health knowledge. After these, indicators largely related to resources and autonomy (e.g., access to health services, and income generating) were associated with NMSV. Exploratory analysis with the subsample of never married adolescents 15-19 years old, a population with higher representation of recent NMSV, further emphasized the role of wealth and mobility as key correlates of NMSV, along with poor HIV knowledge, tobacco use, higher fertility preferences, and attitudes accepting of marital violence.
Findings indicate the validity of machine learning with iterative theme analysis (ITA) to identify factors associated with violence. Findings were consistent with prior work demonstrating associations between NMSV and other violence experiences, but also showed novel correlates such as lower SRH knowledge and service utilization and, for girls, norms and preferences suggesting more restrictive gender norms. Sexual and reproductive health, gender equity and safety focused interventions are important for addressing NMSV in India, particularly for adolescents.
机器学习技术可以探索低发生率数据，以深入了解与非婚姻性暴力（NMSV）相关的因素。印度的非婚姻性暴力是一个健康和人权问题，对青少年的影响尤为严重，报告不足，在该国也未得到充分理解或解决。
我们将机器学习方法应用于印度具有全国代表性的第四次全国家庭健康调查的回顾性横断面数据，该调查是在2015 - 16年进行的一项人口与健康研究，提供了4000多个变量作为潜在自变量。我们使用了最小绝对收缩和选择算子（lasso）或L - 1正则化逻辑回归模型以及L - 2正则化逻辑回归或岭模型；我们对一系列正则化模型生成的变量进行了迭代主题分析（ITA）。
正则化模型的主题分析表明，过去遭受暴力的经历最能预测非婚姻性暴力，其次是地理位置、性行为以及性与生殖健康知识匮乏。在此之后，主要与资源和自主权相关的指标（如获得医疗服务和创收）与非婚姻性暴力有关。对15 - 19岁未婚青少年子样本的探索性分析进一步强调了财富和流动性作为非婚姻性暴力关键相关因素的作用，同时还有艾滋病毒知识匮乏、吸烟、较高的生育意愿以及对婚姻暴力的接受态度。
研究结果表明机器学习与迭代主题分析（ITA）在识别与暴力相关因素方面的有效性。研究结果与先前表明非婚姻性暴力与其他暴力经历之间关联的工作一致，但也显示了新的相关因素，如较低的性与生殖健康知识和服务利用率，以及对于女孩而言，暗示更具限制性性别规范的规范和偏好。以性与生殖健康、性别平等和安全为重点的干预措施对于解决印度的非婚姻性暴力问题很重要，尤其是对青少年而言。
