PDF(Pubmed)
Abstract：
Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head\'s education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.
摘要：
尽管低出生体重（LBW）的患病率随着时间的推移有所下降，它作为孟加拉国公共卫生问题的持续重要性仍然显而易见。低出生体重被认为是导致婴儿死亡率的一个因素，长期的健康并发症，以及对非传染性疾病的脆弱性。本研究利用2012-2013年和2019年进行的多指标类集调查（MICS）的全国代表性数据来探讨与出生体重相关的因素。出生体重数据建模考虑了因素之间的相互作用，数据中的聚类，和空间相关性。生成区级地图以识别LBW的高风险区域。平均出生体重略有增加，从2012-2013年的2.93公斤上升到2019年的2.96公斤。这项研究采用了回归树，一种流行的机器学习算法，辨别出生体重潜在决定因素之间的基本相互作用。各种模型的发现，包括固定效应，混合效应，和空间依赖模型，强调产妇年龄等因素的重要性，户主的教育，产前保健,很少有数据驱动的相互作用影响出生体重。特定地区的地图显示，西南地区和选定的北部地区的平均出生体重较低，在两个调查期间坚持。考虑层次结构和空间自相关，提高了模型性能，特别是在拟合最近一轮调查数据时。该研究旨在通过利用机器学习技术和回归模型来识别需要高度关注的弱势儿童群体，从而为地区一级的政策制定和有针对性的干预措施提供信息。
