Takramah Wisdom Kwami, Aheto Justice Moses K
Department of Epidemiology and Biostatistics, School of Public Health University of Health and Allied Sciences Ho Ghana.
Department of Biostatistics, School of Public Health University of Ghana Accra Ghana.
Health Sci Rep. 2021 Feb 15;4(1):e248. doi: 10.1002/hsr2.248. eCollection 2021 Mar.
One of the priorities and important current problem in public health research globally is modeling of neonatal mortality and its risk factors in using the appropriate statistical methods. It is believed that multiple risk factors interplay to increase the risk of neonatal mortality. To understand the risk factors of neonatal mortality in Ghana, the current study carefully evaluated and compared the predictive accuracy and performance of two classification models.
This study reviewed the birth history data collected on 5884 children born in the 5 years preceding the 2014 Ghana Demographic and Health Survey (GDHS). The 2014 GDHS is a cross-sectional nationally representative household sample survey. The relevant variables were selected using leaps-and-bounds method, and the area under curves were compared to evaluate the predictive accuracy of unweighted penalized and weighted single-level multivariable logistic regression models for predicting neonatal mortality using the 2014 GDHS data.
The study found neonatal mortality prevalence of 2.8%. A sample of 4514 children born in the 5 years preceding the 2014 GDHS was included in the inferential analysis. The results of the current study show that for the unweighted penalized single-level multivariable logistic model, there is an increased risk of neonatal death among babies born to mothers who received prenatal care from non-skilled worker [OR: 3.79 (95% CI: 2.52, 5.72)], multiple births [OR: 3.10 (95% CI: 1.89, 15.27)], babies delivered through caesarian section [OR: 2.24 (95% CI: 1.30, 3.85)], and household with 1 to 4 members [OR: 5.74 (95% CI: 3.16, 10.43)], respectively. The predictive accuracy of the unweighted penalized and weighted single-level multivariable logistic regression models was 82% and 80%, respectively.
The study advocates that prudent and holistic interventions should be institutionalized and implemented to address the risk factors identified in order to reduce neonatal death and, by large, improve child and maternal health outcomes to achieve the SDG target 3.2.
全球公共卫生研究的重点和当前重要问题之一是运用适当的统计方法对新生儿死亡率及其风险因素进行建模。人们认为多种风险因素相互作用会增加新生儿死亡风险。为了解加纳新生儿死亡的风险因素,本研究仔细评估并比较了两种分类模型的预测准确性和性能。
本研究回顾了在2014年加纳人口与健康调查(GDHS)之前5年出生的5884名儿童的出生史数据。2014年GDHS是一项具有全国代表性的横断面家庭抽样调查。使用逐步回归法选择相关变量,并比较曲线下面积,以评估使用2014年GDHS数据的非加权惩罚单水平多变量逻辑回归模型和加权单水平多变量逻辑回归模型预测新生儿死亡的预测准确性。
研究发现新生儿死亡率为2.8%。2014年GDHS之前5年出生的4514名儿童样本纳入了推断分析。本研究结果表明,对于非加权惩罚单水平多变量逻辑模型,母亲接受非技术工人产前护理的婴儿[比值比:3.79(95%置信区间:2.52,5.72)]、多胞胎[比值比:3.10(95%置信区间:1.89,15.27)]、剖宫产分娩婴儿[比值比:2.24(95%置信区间:1.30,3.85)]以及1至4口之家的婴儿[比值比:5.74(95%置信区间:3.16,10.43)]的新生儿死亡风险增加。非加权惩罚单水平多变量逻辑回归模型和加权单水平多变量逻辑回归模型的预测准确性分别为82%和80%。
该研究主张应将审慎且全面的干预措施制度化并实施,以应对已确定的风险因素,从而降低新生儿死亡,并在很大程度上改善儿童和孕产妇健康结局,以实现可持续发展目标3.2。