Inequities and trends of polio immunisation among children aged 12-23 months in Ethiopia: a multilevel analysis of Ethiopian demographic and health survey

Trends of inequality in DPT3 immunization services utilization in Ethiopia and its determinant factors: Evidence from Ethiopian demographic and health surveys, 2000–2019

by Hailu Fekadu, Wubegzier Mekonnen, Aynalem Adugna, Helmut Kloos, Damen Hailemariam

Low levels of diphtheria, tetanus toxoid, pertussis (DPT3) immunization services utilization and high deaths among under five children are concentrated in economically and socially disadvantaged groups, especially in low and middle-income countries, including Ethiopia. Hence, the aim of this study is to assess levels and trends in DPT3 immunization services utilization in Ethiopia and identify inequalities.

This study used data from 2000, 2005, 2011, 2016, and 2019 Ethiopian Demographic Health Surveys (EDHSs). The 2019 updated version of the world health organization (WHO’s) Health Equity Assessment Toolkit (HEAT) software was used to analyze the data. Six measure of inequality was calculated: ratio (R), differences (D), relative index of inequality (RII), slope index of inequality (SII), population attributable fraction (PAF) and population attributable risk (PAR). The findings were disaggregated by the five equity stratifiers: economic status, education, place of residence, regions and sex of the child.

This study showed an erratic distribution of DPT3 immunization services utilization in Ethiopia. The trends in national DPT3 immunization coverage increased from 21% in (2000) to 62% in (2019) (by 41 percentage points). Regarding economic inequality, DPT3 immunization coverages for the poorest quintiles over 20 years were 15.3% (2000), and 47.7% (2019), for the richest quintiles coverage were 43.1 (2000), and 83.4% (2019). However, the service utilization among the poorest groups were increased three fold compared to the richest groups. Regarding educational status, inequality (RII) show decreasing pattern from 7.2% (2000) to 1.5% in(2019). Concerning DPT3 immunization inequality related to sex, (PAR) show that, sex related inequality is zero in 2000, 2005 and in 2019. However, based on the subnational region level, significance difference (PAR) was found in all surveys: 59.7 (2000), 51.1 (2005), 52.2 (2011), 42.5 (2016) and 30.7 (2019). The interesting point of this finding was that, the value of absolute inequality measures (PAR) and (PAF), are shown a decreasing trends from 2000 to 2019, and the gap among the better of regions and poor regions becoming narrowed over the last 20 years. Concerning individual and community level factors, household wealth index, education of the mother, age of respondent, antenatal care, and place of delivery show statically significant with outcome variable. Keeping the other variables constant the odds of an average child in Amhara Region getting DPT3 immunization was 54% less than for a child who lived in Addis Ababa (OR: 0.46, 95% CI: 0.34 – 0.63). Respondents from households with the richest and richer wealth status had 1.21, and 1.26 times higher odds of DPT3 immunization services utilization compared to their counterpart (OR: 1.21, 95% CI: 1.04 -1.41) and (OR: 1.26, 95% CI: 1.13 – 1.40) respectively.

We conclude that DPT3 immunization coverage shows a growing trend over 20 years in Ethiopia. But inequalities in utilization of DPT3 immunization services among five equality stratifies studied persisted. Reasons for this could be complex and multifactorial and depending on economic, social, maternal education, place of residence, and healthcare context. Therefore, policy has to be structured and be implemented in a ways that address context specific barriers to achieving equality among population sub-groups and regions.

Predicting child development and school readiness, at age 5, for Aboriginal and non-Aboriginal children in Australia’s Northern Territory

by Abel Fekadu Dadi, Vincent He, Georgina Nutton, Jiunn-Yih Su, Steven Guthridge

Positive early development is critical in shaping children’s lifelong health and wellbeing. Identifying children at risk of poor development is important in targeting early interventions to children and families most in need of support. We aimed to develop a predictive model that could inform early support for vulnerable children.

We analysed linked administrative records for a birth cohort of 2,380 Northern Territory children (including 1,222 Aboriginal children) who were in their first year of school in 2015 and had a completed record from the Australian Early Development Census (AEDC). The AEDC measures early child development (school readiness) across five domains of development. We fitted prediction models, for AEDC weighted summary scores, using a Partial Least Square Structural Equation Model (PLS-SEM) considering four groups of factors–pre-pregnancy, pregnancy, known at birth, and child-related factors. We first assessed the models’ internal validity and then the out-of-sample predictive power (external validity) using the PLS_predict procedure.

We identified separate predictive models, with a good fit, for Aboriginal and non-Aboriginal children. For Aboriginal children, a significant pre-pregnancy predictor of better outcomes was higher socioeconomic status (direct, β = 0.22 and indirect, β = 0.16). Pregnancy factors (gestational diabetes and maternal smoking (indirect, β = -0.09) and child-related factors (English as a second language and not attending preschool (direct, β = -0.28) predicted poorer outcomes. Further, pregnancy and child-related factors partially mediated the effects of pre-pregnancy factors; and child-related factors fully mediated the effects of pregnancy factors on AEDC weighted scores. For non-Aboriginal children, pre-pregnancy factors (increasing maternal age, socioeconomic status, parity, and occupation of the primary carer) directly predicted better outcomes (β = 0.29). A technical observation was that variance in AEDC weighted scores was not equally captured across all five AEDC domains; for Aboriginal children results were based on only three domains (emotional maturity; social competence, and language and cognitive skills (school-based)) and for non-Aboriginal children, on a single domain (language and cognitive skills (school-based)).

The models give insight into the interplay of multiple factors at different stages of a child’s development and inform service and policy responses. Recruiting children and their families for early support programs should consider both the direct effects of the predictors and their interactions. The content and application of the AEDC measurement need to be strengthened to ensure all domains of a child’s development are captured equally.

Maternal near-miss prediction model development among pregnant women in Bahir Dar City administration, northwest Ethiopia: a study protocol