Dida Nagasa, Birhanu Zewdie, Gerbaba Mulusew, Tilahun Dejen, Morankar Sudhakar
College of Public Health and Medical Sciences, Department of Health Education and Behavioral Sciences, Jimma University, Jimma, Ethiopia.
College of Public Health and Medical Sciences, Department of Population and Family Health, Jimma University, Jimma, Ethiopia.
Afr Health Sci. 2014 Jun;14(2):288-98. doi: 10.4314/ahs.v14i2.3.
Although ante natal care and institutional delivery is effective means for reducing maternal morbidity and mortality, the probability of giving birth at health institutions among ante natal care attendants has not been modeled in Ethiopia. Therefore, the objective of this study was to model predictors of giving birth at health institutions among expectant mothers following antenatal care.
Facility based cross sectional study design was conducted among 322 consecutively selected mothers who were following ante natal care in two districts of West Shewa Zone, Oromia Regional State, Ethiopia. Participants were proportionally recruited from six health institutions. The data were analyzed using SPSS version 17.0. Multivariable logistic regression was employed to develop the prediction model.
The final regression model had good discrimination power (89.2%), optimum sensitivity (89.0%) and specificity (80.0%) to predict the probability of giving birth at health institutions. Accordingly, self efficacy (beta=0.41), perceived barrier (beta=-0.31) and perceived susceptibility (beta=0.29) were significantly predicted the probability of giving birth at health institutions.
The present study showed that logistic regression model has predicted the probability of giving birth at health institutions and identified significant predictors which health care providers should take into account in promotion of institutional delivery.
尽管产前保健和机构分娩是降低孕产妇发病率和死亡率的有效手段，但在埃塞俄比亚，尚未对接受产前保健的孕妇在医疗机构分娩的概率进行建模。因此，本研究的目的是对接受产前保健的孕妇在医疗机构分娩的预测因素进行建模。
在埃塞俄比亚奥罗米亚州西谢瓦区的两个地区，对322名连续入选的接受产前保健的母亲进行了基于机构的横断面研究设计。参与者按比例从六个医疗机构招募。使用SPSS 17.0版对数据进行分析。采用多变量逻辑回归建立预测模型。
最终回归模型在预测医疗机构分娩概率方面具有良好的区分能力（89.2%）、最佳敏感性（89.0%）和特异性（80.0%）。因此，自我效能感（β=0.41）、感知障碍（β=-0.31）和感知易感性（β=0.29）显著预测了在医疗机构分娩的概率。
本研究表明，逻辑回归模型预测了在医疗机构分娩的概率，并确定了医疗保健提供者在促进机构分娩时应考虑的重要预测因素。
