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Background Low birth weight (LBW) is one of the major determinants of perinatal survival, infant morbidity, and mortality, as well as the risk of developmental disabilities and illnesses in future lives. Though studies were conducted to assess the magnitude and associated factors of low birth weight, most of the studies were at a single center and little information on the regional level. Hence, this study assessed the prevalence and associated factors of low birth weight in Sub-Saharan countries. Method This study was based on secondary data sources from 35 Sub-Saharan countries’ Demography and Health Survey (DHS). For this study, we used the Kids Record (KR file) data set. In the KR file, all under-five children who were born in the last five years preceding the survey in the selected enumeration area who had birth weight data were included for the study. To identify determinants of low birth weight multivariable mixed-effect logistic regression model fitted. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤0.05 in the multivariable model were used to declare significant factors associated with low birth weight at birth. Result The pooled prevalence of newborn babies’ low birth weight measured at birth in Sub-Saharan Africa was 9.76% with (95% CI: 9.63% to 9.89%). Female child, women not participated in healthcare decision making, and wider birth intervals, divorced/ separated women, and twin pregnancies associated with increased occurrences of low birth weight, while some level of woman and husband education, antenatal care visits, older maternal age, and multiparity associated with reduced occurrence low birth weight. Conclusion This study revealed that the magnitude of low birth weight was high in sub-Saharan Africa countries. Therefore, the finding suggests that more emphasis is important for women with a lack of support, multiples, and healthcare decision-making problems.
Secondary data analysis was done based on the most recent Demographic and Health Surveys (DHS) conducted in the 35 Sub-African (SSA) countries. Southern Region of Africa (Lesotho, Namibia and South Africa), Central Region of Africa(Angola, DR Congo, Congo, Cameroon, Gabon, Sao Tome & Principe, and Chad), Eastern Region of Arica (Burundi, Ethiopia, Kenya, Comoros, Madagascar, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe), Western Region of Africa (Burkina-Faso, Benin, Cote d’Ivoire, Ghana, Gambia, Guinea, Liberia, Mali, Nigeria, Niger, Sierra Leone, Senegal, and Togo). Each country’s sampling procedure was the same or homogeneity across countries [17] (Table 1). These datasets were appended together to investigate mothers’ perceived birth size for the prediction of low birth weight babies in SSA. The DHS is a nationally representative survey that collects data on basic health indicators like mortality, morbidity, family planning service utilization, fertility, maternal and child health. The data were derived from the measure DHS program. The DHS has different datasets (men, women, children, birth, and household datasets). For this study, we used the Kids Record (KR file) data set. In the KR file, all under-five children who were born in the last five years preceding the survey in the selected enumeration area who had birth weight data were included for the study. The DHS used two stages of stratified sampling technique to select the study participants. We pooled the DHS surveys conducted in the 35 Sub-African countries, and a total weighted sample of 202,878 under-five children was included in the study (Fig 1). The main outcome variable of this study was birth weight. Data on children’s birth weight were collected from mothers who gave birth within five years before the survey of each Sub-Saharan Africa country either by accessing birth weight through record review or by the mother’s report by recalling the measured weight of the child at birth. The births without recorded birth weight were excluded from the study. Finally, LWB was defined as a birth weight <2.5kg, and those 2.5 kgs were considered normal and above normal birthweights. Potential risk factors for LBW were included based on the literature review [4,11,12,16,18,19], we included two types of variables in the analysis. Level one variable (individual-level variables) that is maternal and husband education (has no education, primary education and secondary and above, no education means respondents cannot read and write or had no any formal education), maternal age, mother marital status, household wealth index, maternal occupation status, women health care decision making autonomy, media exposure, number of antenatal care (ANC) visit, preceding birth interval, parity(recoded as 1–2,3–5 and 6+), sex of the child, type of birth and iron supplementation. Level two variables (community-level variables) included in this study were region (recoded as West Africa, East Africa, Central Africa, and South Africa), residence, and country. The wealth variable was generated from the wealth index for the households. In the dataset, the index has five quintiles, such as; the lowest quintile (poorest), second quintile (poorer), third quintile (middle), four quintiles (wealthier), and the fifth quintile (wealthiest). In this study for ease of analysis, this variable was categorized 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”. Defines as women health care decision-making capacity for a woman to achieve well-being and decision making a role. A respondent said to be media exposed if they listen/read at least one media in the week (Radio or TV or Newspapers) We pooled the data from the 35 Sub-Saharan African countries together after extracting the variables based on literature. Before any statistical analysis, the data were weighted using sampling weight, primary sampling unit, and strata to restore the survey’s representativeness and take sampling design when calculating standard errors and reliable estimates. Cross tabulations and summary statistics were done using STATA version 14 software. A meta-analysis was done using the “meta-prop” Stata command. A fixed-effect meta-analysis was done to estimate the pooled prevalence of LBW in SSA. Pooled analysis was done for both SSA regions and sub-level regions. The pooled prevalence of low birth weight at birth with the 95% Confidence Interval (CI) was reported using a forest plot. For the determinants factors, the DHS data had a hierarchical structure; this violates the independence of observations and equal variance assumption of the traditional logistic regression model. Hence, children are nested within a cluster, and we expect that children within the same cluster may be more similar to each other than women in the rest of the country. This implies that there is a need to take into account the between cluster variability by using advanced models. Therefore, a mixed effect logistic regression model (both fixed and random effect) was fitted. Since the outcome variable was binary, standard logistic regression and Generalized Linear Mixed Models (GLMM) were fitted. Model comparison and fitness were made based on the Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR) test, Median Odds Ratio (MOR), and deviance (-2LLR) values since the models were nested. The model with the lowest deviance was chosen. Accordingly, the mixed-effect logistic regression model was the best-fitted model. Variables with a p-value <0.2 in the bi-variable analysis were considered in the multivariable mixed-effect logistic regression model. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤0.05 in the multivariable model were used to declare significant factors associated with low birth weight at birth. Permission for data access was obtained from a major demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifier.
