The authors have declared that no competing interests exist.
SA conceived of the study, participated in its design and coordination, drafted the manuscript, initiated the research, carried out the statistical analysis, interpreted the results and drafted the final manuscript. WL, UK and ZM participated in the design and coordination of the study, revised the proposal and guided the statistical analysis and write up of the manuscript. All authors read and approved the final manuscript.
Expanded program on immunization is one of the most successful and cost effective public health interventions that protect children against vaccine preventable diseases. The full childhood immunization coverage in many parts of Ethiopia is far from optimal. Hence, the main objective of this study was to assess factors associated with childhood full immunization in Ethiopia.
The data source for this study was the 2011 Ethiopian Demographic and Health Survey. Multilevel regression analysis techniques were used to conduct the analysis. Accordingly a two level multilevel regression analysis model was built with individuals (level 1) nested with in communities (level 2).
A total of 4983 children aged 12–59 months nested within 520 clusters were included in the analysis. According to the analysis results, in the year 2011, 26 % of children less than 5 years old were fully immunized in Ethiopia. Being born at health institutions, higher level of maternal education, media exposure, region of residence and residing in communities possessing higher maternal antenatal care services utilization were positively associated with childhood full immunization. In contrary to this, the number children aged less than 5 years in the household was negatively associated with childhood full immunization. The random effect results indicated that 21 % of the variation among the communities was due to community level factors.
It was found that various individual and contextual factors were associated with childhood full immunization. In addition, significant community level variation remains after having controlled individual and community level factors which is an indicative of a need for further research on community level factors. Hence, utilizing multilevel modeling in determining the effect of both individual and contextual level factors simultaneously had brought an important output which may help planners, policy and decision makers to emphasize on both individuals and communities in which they live.