Department of Statistics, Banaras Hindu University, Varanasi, India.
Analytics Department, Global IT Center, SBI, Navi Mumbai, India.
Reprod Health. 2020 Jul 8;17(1):106. doi: 10.1186/s12978-020-00955-2.
In India, around 20,000 women die every year due to abortion-related complications. In count data modeling, there is sometimes a prevalence of zero counts. This article is concerned with the estimation of various count regression models to predict the average number of spontaneous abortions among women in Punjab and few northern states in India. The study also assesses the factors associated with the number of spontaneous abortions.
This study includes 27,173 married women of Punjab obtained from the DLHS-4 survey (2012-13) to train the count models. The study predicts the average number of spontaneous abortions using various count regression models, and also identifies the determinants affecting the spontaneous abortions. Further, the best model is validated with other northern states of India using the latest data (NFHS-4, 2015-16).
Statistical comparisons among four estimation methods reveals that the ZINB model provides the best prediction for the number of spontaneous abortions. The study suggests total children born to a woman, antenatal care (ANC) place, place of residence, woman's education, and economic status are the most significant factors affecting the instance of spontaneous abortion.
This article offers a practical demonstration of techniques designed to handle count outcome variables. The statistical comparisons among four estimation models revealed that the ZINB model provides the best prediction for the number of spontaneous abortions, and it suggests policymakers to use this model to predict the number of spontaneous abortions. The study recommends promoting higher education among women in Punjab and other northern states of India. It also suggests that women must receive institutional antenatal care and have a limited number of children.
在印度，每年约有 2 万名妇女因堕胎相关并发症而死亡。在计数数据建模中，有时会出现零计数的情况。本文关注的是各种计数回归模型的估计，以预测印度旁遮普邦和北部几个邦的妇女自然流产的平均数量。该研究还评估了与自然流产数量相关的因素。
本研究包括从 DLHS-4 调查（2012-13 年）中获得的旁遮普邦的 27173 名已婚妇女，用于训练计数模型。该研究使用各种计数回归模型预测自然流产的平均数量，并确定影响自然流产的决定因素。此外，使用最新数据（NFHS-4，2015-16 年），使用其他印度北部邦验证最佳模型。
四种估计方法的统计比较表明，ZINB 模型对自然流产数量的预测最佳。该研究表明，妇女所生子女总数、产前护理（ANC）地点、居住地、妇女教育程度和经济状况是影响自然流产发生率的最重要因素。
本文提供了一种实用的技术演示，旨在处理计数结果变量。四种估计模型的统计比较表明，ZINB 模型对自然流产数量的预测最佳，建议政策制定者使用该模型来预测自然流产数量。该研究建议在旁遮普邦和印度北部其他邦提高妇女的高等教育水平。还建议妇女必须接受机构产前护理，并生育有限数量的子女。
