School of Science and Aerospace Studies, Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya.
Department of Paediatrics, University of Nairobi, Nairobi, Kenya.
PLoS One. 2021 Feb 8;16(2):e0246269. doi: 10.1371/journal.pone.0246269. eCollection 2021.
Infant morbidity is a topic of interest because it is used globally as an indicator of the status of health care in a country. A large body of evidence supports an association between bacterial vaginosis (BV) and infant morbidity. When estimating the relationship between the predictors and the estimated variable of morbidity severity, the latter exhibits imbalanced data, which means that violation of symmetry is expected. Two competing methods of analysis, that is, (1) probit and (2) logit techniques, can be considered in this context and have been applied to model such outcomes. However, these models may yield inconsistent results. While non-normal modeling approaches have been embraced in the recent past, the skewed logit model has been given little attention. In this study, we exemplify its usefulness in analyzing imbalanced longitudinal responses data.
While numerous non-normal methods for modeling binomial responses are well established, there is a need for comparison studies to assess their usefulness in different scenarios, especially under a longitudinal setting. This is addressed in this study. We use a dataset from Kenya about infants born to human immunodeficiency virus (HIV) positive mothers, who are also screened for BV. We aimed to investigate the effect of BV on infant morbidity across time. We derived a score for morbidity incidences depending on illnesses reported during the month of reference. By adjusting for the mother's BV status, the child's HIV status, sex, feeding status, and weight for age, we estimated the standard binary logit and skewed logit models, both using Generalized Estimating Equations.
Results show that accounting for skewness in imbalanced binary data can show associations between variables in line with expectations documented by the literature. In addition, an in-depth analysis accounting for skewness has shown that, over time, maternal BV is associated with multiple health conditions in infants.
Maternal BV status was positively associated with infant morbidity incidences, which highlights the need for early intervention in cases of HIV-infected pregnant women.
婴儿发病率是一个备受关注的话题，因为它被全球用作衡量一个国家医疗保健状况的指标。大量证据支持细菌性阴道病（BV）与婴儿发病率之间存在关联。当估计预测因子与发病率严重程度的估计变量之间的关系时，后者表现出数据不平衡，这意味着预计会违反对称性。在这种情况下，可以考虑两种竞争分析方法，即（1）概率单位和（2）对数技术，并已应用于此类结果模型。然而，这些模型可能会产生不一致的结果。虽然最近已经采用了非正态建模方法，但倾斜对数模型很少受到关注。在这项研究中，我们举例说明了它在分析不平衡纵向反应数据中的有用性。
虽然已经建立了许多用于对二项式反应进行建模的非正态方法，但需要进行比较研究来评估它们在不同情况下的有用性，尤其是在纵向设置下。本研究解决了这个问题。我们使用了来自肯尼亚的一组关于艾滋病毒（HIV）阳性母亲所生婴儿的数据集，这些母亲也接受了细菌性阴道病筛查。我们旨在调查细菌性阴道病对婴儿发病率随时间的影响。我们根据参考月份报告的疾病，为发病率得出了一个评分。通过调整母亲的细菌性阴道病状况、孩子的 HIV 状况、性别、喂养方式和体重与年龄的关系，我们使用广义估计方程估计了标准二进制对数和倾斜对数模型。
结果表明，在不平衡的二项数据中考虑偏度可以显示出与文献记录的预期相符的变量之间的关联。此外，深入分析考虑偏度表明，随着时间的推移，母亲的细菌性阴道病与婴儿的多种健康状况有关。
母亲的细菌性阴道病状况与婴儿发病率呈正相关，这突出表明需要对感染 HIV 的孕妇进行早期干预。
