African Health Sciences
Makerere University Medical School
Vol. 2, No. 14, 2014, pp. 288-298
Bioline Code: hs14046
Full paper language: English
Document type: Research Article
Document available free of charge
African Health Sciences, Vol. 2, No. 14, 2014, pp. 288-298
© African Health Sciences
Modeling the probability of giving birth at health institutions among pregnant women attending antenatal care in West Shewa Zone, Oromia, Ethiopia: a cross sectional study|
Dida, Nagasa; Birhanu, Zewdie; Gerbaba, Mulusew; Tilahun, Dejen & Morankar, Sudhakar
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Institutional delivery; intention; ANC; probability