Introduction: Early identification and management of postpartum complications through timely visit for postnatal care is one of the key interventions in reducing maternal mortality. A community-based national level representative dataset was necessary to inform decision makers in achieving the sustainable development goal (SDG) target of reducing maternal mortality by 70%.  This study aims to determine the individual and community level predictors of timely utilization of maternal postnatal care service in Ethiopia.

Methods: A community based nationally representative survey of Ethiopian Demographic and Health Survey (EDHS) in 2016 was utilized. A total of 3,948 women aged 15-49 giving birth in the two years before the survey were included. A two-level mixed-effects logistic regression analysis was employed.

Result: Among women aged 15-49 years giving birth in the 2 years before the survey, 17% had a postnatal check during the first 2 days after birth in Ethiopia. Institutional delivery AOR 2.14 [95% C.I 1.70, 2.0] and giving birth by caesarean section AOR 1.66 [95% CI 1.10, 2.50] were found to be an individual level determinants, whereas administrative regions(Oromia 69%, Somali 56%, Benishangul 55%, SNNPR 43%, Gambela 66%, Afar 50% and Dire Dawa 55% which less likely to utilize postnatal care utilization as compared to Addis Ababa city administrative area), Higher community level wealth AOR 1.44 [95% C.I 1.08, 1.2], antenatal care coverage AOR 1.52 [95% C.I 1.19, 1.96] and perceived distance of the health facility as a big problem AOR 0.78[95% C.I 0.60, 0.99] were the community level predictors of timely utilization of post-natal care in Ethiopia.

Conclusion: Less than one in five women utilized postnatal care in the first 2 days after birth in Ethiopia. Individual level factors (being gave birth at health facility and caesarean delivery) and community level factors (administrative regions, community level wealth, antenatal care and perception of distance to health facility as a big problem) were found to be significant predictors.