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Introduction: Skilled health professional assisted delivery is an effective strategy to reduce maternal and newborn mortality. Skilled assistant delivery can prevent about 16–33% of maternal and newborn mortality. Despite the commitments of the government to assure home free delivery, majority of the births in Sub-Saharan Africa are attended by traditional birth attendants. As to our search of the literature, there is limited evidence on the prevalence and determinants of skilled delivery in East African countries. Therefore, this study aimed to estimate the pooled prevalence and determinants of skilled birth attendant delivery in East Africa Countries. Methods: Pooled analysis was done based on Demographic and Health Surveys conducted in the 12 East African countries from 2008 to 2017. A total weighted sample of 141,483 women who gave birth during the study period was included in the study. The pooled prevalence of skilled birth attendance was estimated using STATA version 14. Intra-class Correlation Coefficient, Median Odds Ratio, Proportional Change in Variance, and deviance were used for model fitness and comparison. The multilevel multivariable logistic regression model was fitted to identify determinants of skilled birth attendance in the region. Adjusted Odds Ratio with its 95% Confidence Interval was used to declare significant determinants of skilled birth attendants. Results: The pooled prevalence of skilled birth attendant in East African countries were 67.18% (95% CI:66.98, 67.38) with highest skilled birth attendant in Rwanda (90.68%) and the lowest skilled birth attendant in Tanzania (11.91%). In the Multilevel multivariable logistic regression model; age 15–24 (Adjusted Odds Ratio (AOR) = 1.14, 95%CI:1.09, 1.18), age 25–49(AOR = 1.16, 95%CI:1.10,1.23), primary women education (AOR = 1.57, 95%CI:1.51,1.63), secondary and above women education (AOR = 2.85, 95%CI:1.73,3.01), primary husband education (AOR = 1.11, 95%CI = 1.07,1.15), secondary and above husband education (AOR = 1.46, 95%CI = 1.40,1.53), middle wealth index (AOR = 1.43, 95%CI = 1.38,1.49),rich wealth index (AOR = 2.38, 95%CI = 2.28,2.48), had ANC visit (AOR = 1.68, 95%CI = 1.62,1.73),multiple gestation (AOR = 2.06, 95%CI = 1.90,2.25), parity 2–4(AOR = 0.65, 95%CI = 0.61,0.69), parity 5 + (AOR = 0.44, 95%CI = 0.41,0.47), accessing health care not big problem (AOR = 1.32, 95%CI = 1.28,1.36), residence (AOR = 0.43, 95%CI = 0.41,0.45) and being Burundi resident (AOR = 0.77, 95%CI = 0.70,0.85) were significantly associated with skilled assisted delivery. Conclusion: Skilled birth attendance at birth in the East Africa countries was low. Maternal age, women and husband education, wealth index, antenatal care visit, multiple gestations, parity, accessing health care, residence, and living countries were major determinants of skilled attendant delivery. Strategies to increase the accessibility and availability of healthcare services, and financial support that targets mothers from poor households and rural residents to use health services will be beneficial. Health education targeting mothers and their partner with no education are vital to increasing their awareness about the importance of skilled birth attendance at birth.
The data was obtained from the measure DHS program at www.measuredhs.com after prepared concept notes about the project. The Demographic and Health Survey (DHS) data were pooled from the 12 East Africa Countries from 2008 to 2017. The recent DHS of Country-specific dataset was extracted during the specified period. The 12 East Africa Countries in which data extracted include Burundi, Ethiopia, Kenya, Comoros, Madagascar, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe (Table 1). There 20 countries in WHO regions of East Africa. In history, only 14 countries had DHS data. For this study 12 countries were included (Fig. 1). The DHS program adopts standardized methods involving uniform questionnaires, manuals, and field procedures to gather the information that is comparable across countries in the world. DHSs are nationally representative household surveys that provide data from a wide range of monitoring and impact evaluation indicators in the area of population, health, and nutrition with face to face interviews of women age 15 to 49. The surveys employ a stratified, multi-stage, random sampling design. Information was obtained from eligible women aged 15 to 49 years in each country. Detailed survey methodology and sampling methods used in gathering the data have been reported elsewhere [24]. The DHS years of study and study participants of the skilled birth attendant in the 12 East African Countries from 2008 to 2017 Schematic diagram of selection of study countries among East African countries The response (outcome) variable of this study was a skilled birth attendant. The response variable was generated from the question asked to the women who gave birth within 5 years preceding the survey question “who assisted the delivery?” The response was dichotomized as a health professional and another person. Health professionals include doctors, nurses, nurse/midwife, auxiliary midwife, and others (health officer and health extension workers). Other persons include traditional birth attendance (TBA), traditional health volunteer, community/village health volunteer, neighbors/friends, relatives, others. If a women delivery were assisted by health professional coded as “1”, otherwise coded as “0”. Based on the literature, the independent variables included in this were two types of variables. Individual-level and community-level variables. Community-level variables include country and residence. The individual-level variables are age group, marital status, maternal and husband educational status, occupational status, wealth index, parity, ANC visit, wanted pregnancy, number of gestation, accessing health care wealth index, and birth interval. Accessing health care: most studies have isolated the travel time and transport cost when looking at access to health facilities. In the DHS data, women were asked whether a range of factors would be a big problem for them in accessing health care. We generated a composite variable using each country DHS standard questions. The questions included: If women face at least one or more of the problems (money, distance, companionship, and permission) we considered as there is health care accessing problem that was our primary interest and we coded as 1 and If they reported no health care accessing out of four (money, distance, companionship, and permission) we code 0. Wealth index is calculated by using principal components analysis (PCA) that involves assigning scores on the indicator variables. In the dataset, the index has five quintiles such as; the lowest quintile (poorest), 2nd quintile (poorer), 3rd quintile (middle), 4th quintile (wealthier), and the 5th quintile (wealthiest). In this study for ease of analysis this variable was recategorized as ‘poorest’ and ‘poorer’ were coded as (1) ‘poor’, the middle was coded as (2) ‘middle’, and ‘wealthier’ and ‘wealthiest’ were coded as (3) ‘rich” [25]. Auxiliary midwife:-is a village-level female health worker who is known as the first contact. The data was cleaned by STATA version 14.1 software. Sample weighting was done for further analysis. Since the outcome variable was binary two-level mixed-effects logistic regression analysis was employed. Sampling weight was applied as part of a complex survey design using primary sampling unit, strata, and women’s individual weight (V005). The individual and community level variables associated with skilled birth attendant were checked independently in the bi-variable multilevel logistic regression model and variables which were statistically significant at p-value 0.20 in the bi-variable multilevel mixed-effects logistic regression analysis were considered for the final individual and community level model adjustments. In the multivariable multilevel analysis, variables with a p-value≤0.05 were declared as significant determinants of skilled assistance delivery. Four models were fitted. The first was the null model containing no exposure variables which was used to check variation in community and provide evidence to assess random effects at the community level. Then model I was the multivariable model adjustment for individual-level variables and model II was adjusted for community-level factors. In model III, possible candidate variables from both individual and community-level variables were fitted with the outcome variable. The fixed effects (a measure of association) were used to estimate the association between the likelihood of skilled birth attendant and explanatory variables at both community and individual level and were expressed as odds ratio with 95% confidence interval. Regarding the measures of variation (random-effects), Community-level variance with standard deviation, intracluster correlation coefficient (ICC), Proportional Change in Community Variance (PCV), and median odds ratio (MOR) was used. The aim of the median odds ratio (MOR) is to translate the area level variance in the widely used odds ratio (OR) scale, which has a consistent and intuitive interpretation. The MOR is defined as the median value of the odds ratio between the area at the highest risk and the area at the lowest risk when randomly picking out two areas. The MOR can be conceptualized as the increased risk that (in median) would have if moving to another area with a higher risk. It is computed by; MOR = exp[√(2×Va)×0.6745] [26]. Where; VA is the area level variance, and 0.6745 is the 75th centile of the cumulative distribution function of the normal distribution with mean 0 and variance 1. See elsewhere for a more detailed explanation [24]. Whereas the proportional change in variance is calculated as [27] Where; where VA = variance of the initial model, and VB = variance of the model with more terms.
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