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Background. Early initiation of breastfeeding (EIBF) is a costless practice with numerous neonates’ survival benefits. Thus, any disparity results in an unacceptably high neonatal death rate but socioeconomic disparities on EIBF have not been well explored in Ethiopia. Therefore, this study is aimed at assessing the socioeconomic inequalities of EIBF in Ethiopia from 2000 to 2016. Methods. The Ethiopian demographic and health survey data and the World Health Organization’s Health Equity Assessment Toolkit were used to investigate the inequalities in EIBF across the wealth quintile, education, residence, and subnational region. Difference, ratio, slope index inequality (SII), relative index inequality (RII), and population attributable risk (PAR) were used as equity summary measures. Results. In Ethiopia, EIBF practice was 47.4% in 2000, 66.2% in 2005, 51.5% in 2011, and 73.3% in 2016. Wealth-related inequality was observed in the 2000, 2005, and 2011 survey years with SII of -7.1%, -8.8%, and 8.7%, respectively, whereas educational-related inequality was observed in 2005 and 2011 with SII of -11.7% and 6.5%, respectively. However, significant change in wealth-, education-, and residence-related inequalities was detected in 2011. Regional inequality on EIBF was observed in all survey years with a difference of 35.7%, 38.0%, 29.1%, and 48.5% in the 2000, 2005, 2011, and 2016 survey years, respectively. But a significant change in regional inequality was noted in 2016 with a PAR of 17.2%. Conclusions. In Ethiopia, the wealth-, residence-, and educational-related inequalities of EIBF increased significantly between the years 2000 and 2011. However, regional inequality persistently increased from 2000 to 2016. Overall, one-sixth of the national level EIBF was decreased due to regional disparity in 2016. The northern regions of Ethiopia (Tigray, Afar, and Amhara) poorly performed compared to the peer regions. Therefore, interventions targeting them would significantly improve the national level of EIBF.
Ethiopia is the second highly populated country in Africa, containing 116,831,357 inhabitants with a per capita income of US$850 in 2019 [25, 26]. For administrative purposes, Ethiopia has 11 regions, namely, Tigray, Amhara, Oromia, Southern Nation Nationalities and Peoples Region (SNNPR), Afar, Somalia, Gambela, Benishangul, Dire Dawa, Addis Ababa, and Harari. The country has a three-tiered healthcare system with its health policy prioritizing disease prevention with a special focus on maternal and child health [27]. The primary level includes the primary hospitals, the health centres, and the health posts in which essential and nonspecialized health services are provided. The secondary level contains the general hospitals that provide curative services, and the tertiary level consists of the comprehensive specialized hospitals that offer superspecialist care [27]. Besides, for the past two decades, the country implemented the health extension program to reach the highly remote areas and the rural residents of Ethiopia under the primary level of health care [28]. Though most maternal and child health services are exempted health services in Ethiopia [29], there are observed socioeconomic and area-based inequalities towards the uptake of maternal and child health services in favour of the advantageous subgroups [30, 31]. The secondary data used in this study were from four nationally representative cross-sectional Ethiopian Demographic and Health Surveys (EDHS) conducted in 2000, 2005, 2011, and 2016. These surveys provide data on key demographic and health indicators including maternal and child health. The EDHS was collected using a two-stage stratified sampling technique. In the first stage, independent selection was employed in each sampling enumeration area after classifying the country into two enumeration areas with a proportional probability depending on the population size of the enumeration area. In the second stage of selection, a systematic selection of the newly created household listing from a fixed number of households per cluster was selected with an equal probability after a household listing operation was carried out in all selected enumeration areas. A total of 3680, 3528, 4037, and 3861 women aged 15 to 49 years who gave birth two years preceding 2000, 2005, 2011, and 2016 survey years, respectively, were used in this study [32–35]. Early initiation of breastfeeding was the outcome variable for which inequality was measured. According to the WHO definitions for assessing infant and young child feeding [36], EIBF was calculated as the ratio of women with live birth and puts their newborn to the breast within the first one hour of delivery to the total number of women with a live birth in the two years before the survey. The inequality is disaggregated by educational status, place of residence, economic status, and subnational regions. Educational status was classified as no education, primary education, and secondary education and above. The economic status was categorized into five quintiles, from the poorest (quintile 1) to the richest (quintile 5) sequentially. The place of residence was classified as rural and urban, and the subnational regions included the nine regions and two city administrations. The place of residence and subnational region did not show up in the sequential presentation of the study participants. The trend on the socioeconomic inequality of EIBF was presented using tables and figures. The disaggregation included the computed point estimates with a corresponding 95% uncertainty interval (UI). The data were obtained as part of WHO’s Health Equity Assessment Toolkit (HEAT) software [37]. The 2021 updated online version (version 4.0) of HEAT software was used for this study. More than 30 critical health indicators on reproductive, maternal, and child health were included in the updated version. Besides, six inequality dimensions (age, sex, economic status measured as wealth decile or wealth quintile, education, place of residence, and subnational region) were included to perform inequality assessment for more than 450 international household surveys conducted in 115 countries between 1991 and 2018. The HEAT software’s essential purpose was to run country’s health equity assessment and compare its trend over time and with other countries’ inequality. The software allows to perform the summary measure of health inequality and segregate the data across the different dispersion measures. The HEAT software is a comprehensible, interactive, and easy-access software to compare health inequality [37]. The measure of inequality can be performed through relative and absolute inequality measures, which can be simple or complex [38, 39]. The criteria for selecting the type of measurement of inequality depend on the type of variable (ordering or nonordering) that the disparity is segregated. In this study, Difference (D), Ratio ®, Relative Index of Inequality (RII), Slope Index of Inequality (SII), and Population Attributable Risk (PAR) were used as a summary measure of dispersion for the EIBF trend in Ethiopia. These summary measures were selected due to their more comprehensive application to the inequality assessment [40–42]. “Difference” is the simple and absolute measure of inequality calculated as the mean percentage of EIBF in the one group subtracted from the mean percentage of EIBF in the other subgroup, whereas “Ratio” is the simple and relative measure of inequality calculated as the percentage of EIBF percentage in one subgroup to the mean percentage of EIBF in the other subgroup. The two main limitations of simple measures of inequality were the ignorance of the middle subgroups and not considering population size [39, 43]. On the other hand, “slope index inequality” is the complex and absolute measure of inequality that applies to natural ordering subgroups like education and wealth. It performs inequality measures by ranking from the disadvantaged subgroup to the advantageous subgroup and subtracting from the advantageous subgroup to the disadvantageous subgroup; thus, a positive value shows that the EIBF is more prevalent in the advantageous subgroup. The negative value shows the EIBF is more prevalent in disadvantageous subgroups. Besides, “relative index inequality” is a complex and relative measure of inequality determined by dividing the predicted EIBF from the highest rank to the lowest rank of the entire distribution for nonordering stratifies like urban, subnational region, and sex. The complex measure of inequality addresses the limitation of the simple measure of inequality by producing a single value expressing the disparity across the subgroups considering population’s size [44]. Population attributable risk is the absolute measure of inequality that shows how much the disparity is eliminated by improving the EIBF in the population relative to the best-performing subgroup, keeping the improvement rate constant as the reference subgroup. It is calculated as the difference between the estimate for the reference subgroup and the national level [44]. The trend of EIBF was assessed across the four equity stratifies for each of the four survey years from 2000 to 2016 EDHS. The point estimate of the proportion of EIBF in each survey year was computed with the 95% uncertainty interval (UI). To declare a statistically significant disparity in Difference, SII, and PAR, the 95% UI should not include zero, and in Ratio and RII, the 95% UI should not include one. Whereas to declare a significant change in inequality over time, the UIs of the summary measure must not be overlapped [42]. Moreover, this paper was prepared according to the guideline for Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) as a meant for logical and scientific representations of the study findings [45]. This study does not need ethical clearance as the data were available publicly and uploaded as part of the WHO HEAT software. The institution that conducted the survey completed all the necessary ethical procedures. Besides, the Institutional Review Board of Ethiopia and the Inner-City Fund international approved the EDHS.
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