Menu
Menu
Menu
Menu
Background: In developing countries, short birth interval is one of the major public health issues. It is one of the leading cause’s adverse birth outcomes in the worldwide. Despite the fact that ending maternal and perinatal morbidity and mortality is one of the Sustainable Development Goals (SDG), the burden of the problem continues to be a huge concern in developing countries, including high fertility countries. Thus, this study aimed to determine the short birth interval and its predictors in ten high fertile sub-Saharan African countries. Methods: Data for this study was obtained from the most recent Demographic and Health Surveys (DHS). A total of weighted sample of 303,979 women of childbearing age group (15– 49) who had at least two alive consecutive children was included. A multilevel mixed-effect binary logistic regression model was fitted to identify the associated factors of short birth interval. As a final step, the Adjusted Odds Ratio (AOR) was used with a confidence interval of 95% in determining statistical significance. Results: Overall prevalence of short birth interval in high fertile sub Saharan Africa was 58.74% (52.32%, 65.17%).The factors significantly associated with the short birth interval were women’s educational status; primary education (AOR = 0.88; 95% CI: 0.86,0.91), secondary and higher (AOR = 0.10; 95% CI: 0.09, 0.11), working (AOR = 0.91; 95% CI: 0.88, 0.93), classified as rich wealth index level (AOR = 0.90; 95% CI: 0.88, 0.93),having six and above ideal number of children (AOR = 2.25; 95% CI: 2.22, 2.30), preferred waiting time two years and above to give birth (AOR = 0.83; 95% CI: 0.76, 0.89), contraceptive non users (AOR = 3.01; 95% CI: 2.93, 3.07), community level education (AOR = 1.97; 95% CI: 1.54, 2.08), rural residency (AOR = 2.17; 95% CI: 2.13, 2.22), and country Chad (AOR = 1.37; 95% CI: 1.22, 1.54). Conclusion: The prevalence of short birth interval in the top ten high fertile sub Saharan African countries is still optimally high. Therefore, the government of each country should work on the access to family planning and education in rural parts of the countries.
The study was a cross-sectional assessment of data from recent Demographic and Health Surveys (DHSs) conducted between January 2010 and December 2018 of ten countries in SSA. As a result of high fertility rates in some countries, the interval between births can be short, causing poor fetal and maternal health outcomes [32–34]. So, our study examined the time between the deliveries of one child to the delivery of the next child in top ten high fertility sub Saharan African countries (Niger, Democratic Republic Congo, Mali, Chad, Angola, Burundi, Nigeria, Gambia, and Burkina Faso were included in this study). These countries were selected because they are the top ten countries with high fertility rates in SSA with fertility rates above 5.0, a higher value than the rate of 4.44 in SSA and 2.47 worldwide [35]. One country (Somalia) with no DHS data was excluded from the analysis. The data for these countries were obtained from the official database of the DHS program, www.measuredhs.com after authorization was allowed via online request by explaining the purpose of our study. We used the woman record (BR file) data set and extracted the dependent and independent variables. The DHS is a nationally representative household survey that uses face-to-face interviews on a wide range of population, health, nutrition tracking, and effect assessment measures. A two-stage stratified sampling procedure was used to identify study participants. In the first step, enumeration areas (EAs) were chosen at random, while households were chosen in the second stage [36]. The current study included individual-level data for 303,979 married women who had at least two live births during the five years preceding years. Women who had never married were not included in the study (Table (Table11). Description of Surveys and sample size characteristics in high fertility countries in SSA (n = 303,979) In this study, the outcome variable was a SBI, which was dichotomized into “yes = 1” and “no = 0”. A SBI is defined as an interval of less than 33 months between two successive live births. A preceding birth interval greater than 33 months was defined as a non-SBI, in accordance with WHO recommendations [37]. The birth interval was calculated by subtracting the birth date of the first child from the date of the second child [38]. All of the independent variables were chosen after a thorough examination of the literature [28, 31, 39–41] and individual-level factors and community-level variables were used to categorize the independent variables. Individual-level variables were age at first marriage, educational status of respondents (no formal education, primary education, secondary and above), occupation (not working, working), wealth status, media exposure, ideal number of children (less than 6 years, 6 years and above), husband education (no formal education, primary education, secondary and above), and husband occupation (not working, working), preferred waiting time to birth (less than 2 years, 2 years and above), and contraceptive use (yes, no). Of the community level variables, residence (rural, urban) were directly accessed from DHS data sets. However, community level poverty (low, high) and community level education (uneducated, educated) were constructed by aggregating individual-level characteristics at the cluster level [42–44]. They were classified as high or low based on the distribution of proportion values generated for each community after using the histogram to check the distribution. Because the aggregate variable was not normally distributed, the median value was chosen as a classification cut-off point. The variable wealth index was re-categorized as “Poor”, “Middle”, and “Rich” categories by merging poorest with poorer and richest with richer [42, 45, 46]. Media exposure was calculated by aggregating TV watching, radio listening, and reading newspapers and woman who has exposure to either of the media sources was categorized as having media exposure and the rest considered as having no media exposure [30]. Categorized as those who wish to wait 2 years and above and those who wish to wait less than 2 years before another pregnancy [28]. Categorized into those who need six or more children and those who need fewer than six [28]. For data analysis Stata version 16 software was used. Throughout the analyses, sampling weight was used to adjust for the unequal probability of sample selection and the differences in response rates. Before data analysis, the data were weighted to ensure that the DHS sample was representative and to provide reliable estimates and standard errors. Due to the hierarchical nature of the DHS data (i.e., mothers are nested inside clusters), a multivariable multilevel logistic regression analysis was used to estimate the effects of each SBI predictor. The equation used for fitting the multilevel logistic regression model was as follows: Where, πiϳ: the probability of short birth interval, 1- πiϳ: the probability of no short birth interval, β1xiϳ: individual and community level variables for the ith individual in group j, respectively. The ß’s are fixed coefficients indicating a unit increase in X can cause a ß unit increase in probability short birth interval. While the ß0 is intercept that is the effect on short birth interval when the effect of all explanatory variables are absent. The u0j shows the random effect (effect of the community on the women’s short birth interval) for the jth community [47, 48]. Four models were fitted in this study. Model 0 (Empty model) was used to assess random variability in the intercept and determine the intra-class correlation coefficient (ICC) and Proportion Change in Variance (PCV). Model I assessed the effects of individual-level predictors. Model II explored the effects of community-level predictors, while Model III (Full model) investigated the effects of both individual and community-level features at the same time. Model III was the best-fitted model since it had the lowest deviance. Variables having a p-value less than 0.2 in bivariable analysis were used for multivariable analysis [49–51]. Finally, in the multivariable analysis, adjusted odds ratios with 95% confidence intervals and a p-value of less than 0.05 were used to identify factors of SBI.
N/A
