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Background: Despite the extensive research on fertility desires among women the world over, there is a relative dearth of literature on the desire for more children in sub-Saharan Africa (SSA). This study, therefore, examined the desire for more children and its predictors among childbearing women in SSA. Methods: We pooled data from 32 sub-Saharan African countries’ Demographic and Health Surveys. A total of 232,784 married and cohabiting women with birth history, who had complete information on desire for more children made up the sample for the study. The outcome variable for the study was desire for more children. Multilevel logistic regression analysis was conducted. Results were presented using adjusted odds ratios (aOR), with their corresponding 95% confidence intervals (CI). Results: The overall prevalence of the desire for more children was 64.95%, ranging from 34.9% in South Africa to 89.43% in Niger. Results of the individual level predictors showed that women aged 45–49 [AOR = 0.04, CI = 0.03–0.05], those with higher education [AOR = 0.80, CI = 0.74–0.87], those whose partners had higher education [AOR = 0.88; CI = 0.83–0.94], women with four or more births [AOR = 0.10, CI = 0.09–0.11], those who were using contraceptives [AOR = 0.68, CI = 0.66–0.70] and those who had four or more living children [AOR = 0.09 CI = 0.07–0.12] were less likely to desire for more children. On the other hand, the odds of desire for more children was high among women who considered six or more children as the ideal number of children [AOR = 16.74, CI = 16.06–17.45] and women who did not take decisions alone [AOR = 1.58, CI = 1.51–1.65]. With the contextual factors, the odds of desire for more children was high among women who lived in rural areas compared to urban areas [AOR = 1.07, CI = 1.04–1.13]. Conclusions: This study found relatively high prevalence of women desiring more children. The factors associated with desire for more children are age, educational level, partners’ education, parity, current contraceptive use, ideal number of children, decision-making capacity, number of living children and place of residence. Specific public health interventions on fertility control and those aiming to design and/or strengthen existing fertility programs in SSA ought to critically consider these factors.
We pooled data from the DHS of 32 sub-Saharan African countries. Specifically, we used data from the women’s file of the various countries. The DHS focuses on essential maternal and child health markers, including fertility preference [27]. The DHS employs a two-stage stratified sampling technique, which makes the survey data nationally representative [28]. A total of 232,784 married and cohabiting women with birth history who had complete information on desire for more children made up the sample for the study. Details of the methodology adopted by the DHS have been reported elsewhere [28]. Table 1 gives a detailed description of the study sample. Detailed description of the study sample Desire for more children was the outcome variable. This was derived from the question “Would you like to have a (another) child with your husband/partner, or would you prefer not to have any more children with him?” It had five responses: “want a (another) child,” “want no more,” “cannot get pregnant,” “undecided,” and “don’t know.” Our outcome variable was computed from two of these responses, namely “want a (another) child,” coded as 1 and “want no more,” coded as 0. Hence, women who responded that they want another child were considered as having a desire for more children while those who responded that they want no more were considered as not having a desire for more children. Women who provided any other response (“cannot get pregnant,” “undecided,” and “don’t know”) were excluded because their responses were unclear about their fertility preference. The study used eleven independent variables, grouped into individual level and contextual level factors. The individual level factors included age, highest educational level, partner’s highest educational level, parity, current use of contraceptives, exposure to media (radio, television and newspaper/magazine), ideal number of children, decision making autonomy (decision on healthcare, decision on large household purchase and decision on visits to family or relatives), and number of living children. The contextual level factors were place of residence and wealth status. These variables were considered because of their statistically significant relationships with desire for more children in previous studies [2, 29, 30]. Details of how each of these variables were coded can be found in Table 2. Based on the findings of previous studies [2, 12, 21–26], we hypothesized that older women would be less likely to desire for more children compared to younger women; women with higher levels of education would be less likely to desire for more children compared to those with no formal education; women whose partners have higher levels of education would have lower odds of desiring for more children compared to those whose partners have no formal education. Other hypotheses that guided the analysis and results of the study were that the odds of desire for more children would decrease with increasing parity, wealth quintile, higher number of living children, contraceptive use and exposure to media. Women who consider 6 + as the ideal number of children, those who do not take decisions alone, and those who live in rural areas would be more likely to desire for more children. Desire for more children by explanatory variables (n = 232,784 weighted) *** = p < 0.001; ** = p < 0.01 and * = p < 0.05, cOR crude Odds Ratio, CI Confidence Interval Stata version 14.0 was used to process and analyse the data. The analysis began with a computation of the prevalence of desire for more children in SSA using bar chart. After this, we pooled the datasets and calculated the proportions of desire for more children for each of the explanatory variables. We then used a bivariate logistic regression to assess the association between the explanatory variables and desire for more children. This was done to identify significant explanatory variables for the next part of the analysis, which involved multilevel logistic regression. For the multilevel logistic regression, a two-stage approach was employed, where women were nested within clusters and clusters were considered as random effects to cater for the unexplained variability at the contextual level [31]. Four models were generated from the multilevel modelling, consisting of the empty model (Model 0), Model I, Model II, and Model III. Model 0 showed the variance in desire for more children attributed to the distribution of the primary sampling units (PSUs) in the absence of the explanatory variables. Model I had the individual level factors and desire for more children while Model II contained the contextual level factors and desire for more children. The final model (Model III) was the complete model that had the individual and contextual level factors and desire for more children. Model comparison was done using the log-likelihood ratio (LLR) and Akaike’s Information Criterion (AIC) tests. Odds ratio and associated 95% confidence intervals (CIs) were presented for all the models apart from Model 0. To ensure non-existence of correlation between the significant explanatory variables, we ran a multicollinearity test, using the variance inflation factor (VIF), and the results showed no evidence of collinearity among the explanatory variables (Mean VIF = 1.71, Maximum VIF = 2.93 and Minimum VIF = 1.03). Statistical significance was declared at p < 0.05. Sample weight (v005/1,000,000) was applied to correct for over- and under-sampling while the SVY command was used to account for the complex survey design and generalizability of the findings. According to Hatt and Waters [32], pooling data can reveal broader results that are ‘‘often obscured by the noise of individual data sets.’’ To calculate the pooled values, an additional adjustment is needed to account for the variability in the number of individuals sampled in each country. This is accomplished using the weighting factor 1/(A*nc/nt), where A is the number of countries asked a particular question, nc is the number of respondents for the country c, and nt is the total number of respondents over all countries asked the question [33]. The DHSs obtained ethical clearance from the Ethics Committee of ORC Macro Inc. as well as Ethics Boards of partner organisations of the various countries such as the Ministries of Health. During each of the surveys, either written or verbal consent was provided by the women. This was a secondary analysis of data and, therefore, we did not need further approval for this study since the data is available in the public domain. However, we sought permission from MEASURE DHS website and access to the data was provided after our intent for the request was assessed and approved on 3rd April, 2019. Further information about the DHS data usage and ethical standards is available at http://goo.gl/ny8T6X
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