Drivers of desire for more children among childbearing women in sub-Saharan Africa: implications for fertility control

BMC Pregnancy and Childbirth, Volume 20, No. 1, Year 2020

Interpretation

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

AI Digest

Study Justification:

This study aimed to address the relative lack of literature on the desire for more children among childbearing women in sub-Saharan Africa (SSA). By examining the predictors of this desire, the study provides valuable insights into fertility control in the region. Understanding the factors associated with the desire for more children is crucial for designing effective public health interventions and strengthening existing fertility programs in SSA.

Highlights:

– The overall prevalence of the desire for more children among childbearing women in SSA was found to be 64.95%, with significant variations across countries.
– Individual level predictors of the desire for more children included age, educational level (both for women and their partners), parity, current contraceptive use, ideal number of children, decision-making autonomy, and number of living children.
– Contextual factors such as place of residence (rural vs. urban) were also associated with the desire for more children.
– The study identified important factors that policy makers and public health practitioners should consider when addressing fertility control in SSA.

Recommendations:

Based on the study findings, the following recommendations are proposed:

1. Develop targeted interventions to address the desire for more children among women aged 45-49, as they were found to be less likely to desire more children.
2. Implement educational programs to increase awareness and access to contraception, particularly among women with higher education and their partners.
3. Strengthen existing fertility programs by considering the ideal number of children as a factor influencing the desire for more children.
4. Promote women’s decision-making autonomy and empowerment to reduce the desire for more children.
5. Tailor interventions to specific geographic areas, considering the higher odds of desire for more children among women living in rural areas.

Key Role Players:

1. Policy makers and government officials responsible for reproductive health programs and policies.
2. Public health practitioners and organizations working in the field of fertility control and family planning.
3. Non-governmental organizations (NGOs) involved in women’s empowerment and education.
4. Health professionals, including doctors, nurses, and midwives, who provide reproductive health services.
5. Researchers and academics specializing in reproductive health and population studies.

Cost Items for Planning Recommendations:

1. Development and implementation of educational programs: This includes costs for curriculum development, training of educators, production of educational materials, and dissemination of information.
2. Access to contraception: Budget items may include procurement and distribution of contraceptives, training of healthcare providers, and awareness campaigns.
3. Strengthening existing fertility programs: This may involve conducting research, evaluating program effectiveness, and implementing changes based on findings.
4. Women’s empowerment initiatives: Costs may include capacity-building workshops, advocacy campaigns, and support for women’s organizations.
5. Geographic-specific interventions: Budget items may include conducting needs assessments, community engagement activities, and targeted interventions in rural areas.
Please note that the above cost items are general categories and the actual costs will vary depending on the specific context and implementation strategies.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a comprehensive description of the study methodology and findings. However, it lacks specific details on the sampling technique and statistical analysis used. To improve the evidence, the authors could provide more information on the sampling technique, such as the sampling frame and sampling units. Additionally, they could describe the statistical tests used to assess the association between the explanatory variables and desire for more children, including the significance level and any adjustments made for multiple comparisons. Providing these details would enhance the transparency and replicability of the study.

Abstract

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

Materials

N/A

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources related to maternal health, including family planning, prenatal care, and postnatal care. These apps can be easily accessible to women in sub-Saharan Africa, even in remote areas with limited healthcare facilities.

2. Telemedicine: Implement telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to specialized care for high-risk pregnancies.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, education, and support to women in their communities. These workers can bridge the gap between formal healthcare facilities and women in remote areas.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, enabling them to access essential maternal health services, such as antenatal care, delivery, and postnatal care.

5. Maternal Health Clinics: Establish dedicated maternal health clinics that offer comprehensive services, including prenatal care, skilled birth attendance, emergency obstetric care, and postnatal care. These clinics can be strategically located to ensure accessibility for women in underserved areas.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of maternal health and family planning. These campaigns can address cultural and social barriers, dispel myths, and promote informed decision-making.

7. Integration of Services: Integrate maternal health services with other healthcare programs, such as HIV/AIDS prevention and treatment, to provide comprehensive care for women during pregnancy and beyond.

8. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources, expertise, and infrastructure to expand healthcare coverage and reach underserved populations.

9. Supply Chain Management: Strengthen supply chain management systems to ensure the availability of essential maternal health commodities, such as contraceptives, prenatal vitamins, and emergency obstetric drugs, in healthcare facilities.

10. Data-driven Decision Making: Utilize data and analytics to identify gaps in maternal health services and inform evidence-based decision making. This can help allocate resources effectively and monitor the impact of interventions.

These innovations, when implemented effectively, have the potential to improve access to maternal health services and contribute to reducing maternal mortality rates in sub-Saharan Africa.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health based on the study findings would be to develop targeted interventions that address the factors associated with the desire for more children in sub-Saharan Africa (SSA). These interventions should focus on the following areas:

1. Education: Promote and provide access to education for women and their partners. Higher levels of education have been associated with a lower desire for more children. By empowering women with education, they are more likely to make informed decisions about their reproductive health and have better access to family planning services.

2. Family planning: Strengthen existing family planning programs and increase awareness about contraceptive methods. Women who were currently using contraceptives were less likely to desire more children. Providing information, counseling, and access to a variety of contraceptive methods can help women and their partners make informed choices about family planning.

3. Fertility programs: Design and implement fertility programs that take into account the ideal number of children desired by women. Women who considered six or more children as the ideal number were more likely to desire more children. Fertility programs should provide information and support to help women and their partners make realistic and informed decisions about family size.

4. Decision-making autonomy: Empower women to have more decision-making autonomy in matters related to their reproductive health. Women who did not take decisions alone were more likely to desire more children. Promoting gender equality and empowering women to have a voice in decision-making processes can help ensure that their reproductive health needs are met.

5. Rural areas: Pay special attention to women living in rural areas. Women living in rural areas were more likely to desire more children. Targeted interventions should be developed to address the unique challenges faced by women in rural areas, such as limited access to healthcare facilities and information.

By implementing these recommendations, it is possible to improve access to maternal health and promote reproductive health choices for women in sub-Saharan Africa.

AI Innovations Methodology

Based on the provided description, the study aims to examine the desire for more children among childbearing women in sub-Saharan Africa (SSA) and identify predictors of this desire. The study used data from the Demographic and Health Surveys (DHS) of 32 sub-Saharan African countries. The methodology involved pooling data from the women’s file of the various countries, which employed a two-stage stratified sampling technique to ensure national representativeness.

The sample for the study consisted of 232,784 married and cohabiting women with birth history who had complete information on desire for more children. The outcome variable was the desire for more children, derived from a question in the survey. The study used eleven independent variables, including age, education level, partner’s education level, parity, contraceptive use, exposure to media, ideal number of children, decision-making autonomy, number of living children, place of residence, and wealth status.

The analysis involved both bivariate and multilevel logistic regression. The bivariate logistic regression assessed the association between the explanatory variables and desire for more children, while the multilevel logistic regression accounted for the unexplained variability at the contextual level. Four models were generated, including an empty model, models with individual level factors, contextual level factors, and a complete model with both levels of factors.

To ensure non-existence of correlation between the significant explanatory variables, a multicollinearity test using the variance inflation factor (VIF) was conducted. The statistical significance was declared at p < 0.05. Sample weight and the SVY command were used to correct for over- and under-sampling and account for the complex survey design.

The study obtained ethical clearance from the Ethics Committee of ORC Macro Inc. and the Ethics Boards of partner organizations in the various countries. Written or verbal consent was obtained from the women during the surveys. As this was a secondary analysis of publicly available data, further approval was not required.

In terms of recommendations to improve access to maternal health based on the study findings, it would be important to consider the factors associated with the desire for more children, such as age, education level, partner’s education level, parity, contraceptive use, ideal number of children, decision-making capacity, number of living children, and place of residence. Tailoring interventions and programs to address these factors could help in promoting reproductive health and family planning services, improving access to contraceptives, and providing education and empowerment opportunities for women. Additionally, addressing contextual factors, such as rural-urban disparities, could help in ensuring equitable access to maternal health services in sub-Saharan Africa.

Author

Dima Health

Statistics:

Citations: 8

Identifiers:

DOI: 10.1186/s12884-020-03470-1

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health

Study Countries:

Multi-Countries, Niger, South Africa

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female