Prevalence and determinants of unintended pregnancy in sub-Saharan Africa: A multi-country analysis of demographic and health surveys

PLoS ONE, Volume 14, No. 8, Year 2019

Interpretation

Introduction Approximately 14 million unintended pregnancies are recorded annually in sub-Saharan Africa (SSA). We sought to investigate the prevalence and determinants of unintended pregnancies among women in sub-Saharan Africa. Materials and methods The study pooled data from current Demographic and Health Surveys (DHS) conducted from January 1, 2010 to December 31, 2016 from 29 countries in SSA. Logistic regression analysis was used to examine the factors that influence unintended pregnancies in SSA. Results were presented using odds ratios (OR). Results We found overall unintended pregnancy prevalence rate of 29%, ranging from 10.8% in Nigeria to 54.5% in Namibia. As compared to women aged 15–19 years, women of all other age categories had higher odds of unintended pregnancies. Married women were 6 times more probable to report unintended pregnancy as compared to women who had never married (OR = 6.29, CI = 5.65–7.01). The phenomenon had higher odds among rural residents as compared to urban residents (OR = 1.08, CI = 1.01–1.16). Women with primary (OR = 0.74, CI = 0.69–0.80) and secondary (OR = 0.71, CI = 0.65–0.77) levels of education had less chances of unintended pregnancies, compared to those with no education. Again, women in all other wealth categories had less probability of unintended pregnancy, as compared to women with poorest wealth status. Conclusion Our study contributes substantially towards the discourse of maternal wellbeing by unveiling the prevalence and determinants of unintended pregnancy across the SSA region. There is the need for SSA countries with high prevalence of unintended pregnancies to consider past and present successful interventions of other countries within the region such as health education, counselling, skills-building, comprehensive sex education and access to contraception. Much of these efforts rest with the governments of SSA countries. The study made use of pooled data from current Demographic and Health Surveys (DHS) conducted from January 1, 2010 and December 31, 2016 in 29 countries in sub-Saharan Africa. The countries are Angola, Benin, Burkina Faso, Burundi, Congo DR, Congo, Côte d’Ivoire, Cameroon, Chad, Comoros, Ethiopia, Gabon, Ghana, Gambia, Guinea, Kenya, Liberia, Lesotho, Mali, Malawi, Namibia, Nigeria, Rwanda, Sierra Leone, Senegal, Togo, Uganda, Zambia and Zimbabwe. These 29 countries were included in the study because they had current DHS data and also all the variables of interest for this study. Our study included these 29 countries under the DHS program in order to provide a holistic and in-depth evidence of unintended pregnancy in SSA. DHS is a nationwide survey collected every five-year period across low- and middle-income countries. The survey is representative of each of these countries. Women’s files were used for our study and these files possess the responses by women aged 15 to 49. The survey targets core maternal and child health indicators such as unintended pregnancy, contraceptive use, skilled birth attendance, immunisation among under-fives and intimate partner violence. The DHS survey employs stratified two-stage sampling technique in order to ensure national representativeness [29]. As described in detail previously [30] the first-stage constituted the development of a sampling frame consisting of a list of primary sampling units (PSUs) or enumeration areas (EAs) which cover the entire country and are usually developed from the available latest national census. Each PSU or EA is further subdivided into standard size segments of about 100–500 households per segment. In this stage, a sample of predetermined segments is selected randomly with probability proportional to the EA’s measure of size (number of households in EA). In the second stage, DHS survey personnel select households systematically from a list of previously enumerated households in each selected EA segment, and in-person interviews are conducted in selected households with target populations: women aged 15–49 and men aged 15–64. The number of selected households per EA is variable and ranges from 30 to 40 households/women per rural cluster and from 20 to 25 households/women per urban cluster [30]. The surveys were done in different times due to the variations in the starting points of the DHS in the various countries. The sample frame usually excludes nomadic and institutional groups such as prisoners and hotel occupants. As evidence in other studies combining the DHS in sub-Saharan Africa [30–32], although the starting points of the data surveys are different, this does not defeat the ability to compare the DHS among the countries. Permission to use the data set was sought from MEASURE DHS. The data set is available to the public at https://dhsprogram.com/data/available-datasets.cfm. The dependent variable for the study was “pregnancy intentions” which arose from the question regarding whether women wanted their current pregnancy or not. It had three responses: ‘then’, ‘later’ and ‘not at all’. Following the definition of unintended pregnancy as “pregnancies that are either wanted earlier or later than occurred (mistimed) or not needed (unwanted)” (CDC, 2015) [3], we coded these three responses as follows: then = 0 ‘intended’; ‘later and not at all’ = 1 ‘unintended’. The inclusion criteria was all women (15–49) who had answered this particular question. Eleven explanatory variables were considered in our study. These are age, marriage, place of residence, wealth, parity, occupation, education, religion, contraceptive use intention, knowledge of contraception and country of origin. Apart from country of origin, the rest of the variables were not determined a priori; instead, the selection was based on their significant association with the outcome variable, unintended pregnancy. Additionally, a number of these variables have been reported as predictors of unintended pregnancies [6–8, 19, 20]. Six of these variables were recoded to make them meaningful for analysis and interpretation. Marriage was recoded into ‘never married (0)’, ‘married (1)’, ‘cohabiting (2)’, ‘widowed (3)’ and ‘divorced (4)’. Occupation was captured as ‘not working (0)’, ‘managerial (1)’, ‘clerical (2)’, ‘sales (3)’, ‘agricultural (4)’, ‘household (5)’, ‘services (6)’ and ‘manual (7)’. We recoded parity (birth order) as ‘zero birth (0)’, ‘one birth (1)’, ‘two births (2)’, ‘three births (3)’, and four or more births (4)’. We recoded religion as ‘Christianity (1)’, ‘Islam (2)’, ‘Traditionalist (3)’, and ‘no religion (4)’. Contraceptive knowledge was recoded as ‘knows no method (0)’, ‘knows traditional (1)’, and ‘knows modern (2)’. Finally, intention of contraceptive use was recoded into ‘intends to use (1)’, and ‘does not intend to use (2)’. The analysis began with computation of unintended pregnancy prevalence among the 29 SSA countries. Secondly, we appended the dataset and this generated a total sample of 36,529. After appending, we calculated the overall prevalence and proportions of unintended pregnancy across the socio-demographic characteristics with their significance levels and chi-square (χ2) values. Logistic regression analysis was carried out in a hierarchical order where the first model (Model I) was a bivariate analysis of the effect of country on unintended pregnancies. Angola was chosen as the reference country because previous studies have identified no contraceptive use [33–35], and high unmet need for family planning [34, 36] in the country. In Model II, we adjusted for the effect of the other explanatory variables to ascertain how these variables induce unintended pregnancies using a multivariate analysis. The choice of reference categories for these explanatory variables was similarly informed by propositions of some previous studies [5, 6, 37]. Logistic regression was employed because our dependent variable (unintended pregnancy) was measured as a binary factor. Results for the regression analysis have been presented as odds ratios (OR), with their corresponding 95% confidence intervals (CI) signifying precision and significance of the reported OR. Any OR less than one (1) denotes less odds of unintended pregnancy whereas those higher than one (1) indicate higher odds of unintended pregnancy. The inherent sample weight was applied and all analyses were carried out with STATA version 13.0. The DHS surveys obtain 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. Since the data was not collected by the authors of this paper, 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 27th January, 2019.

AI Digest

Study Justification:

The study aimed to investigate the prevalence and determinants of unintended pregnancies among women in sub-Saharan Africa (SSA). This is important because approximately 14 million unintended pregnancies occur annually in SSA. Understanding the factors that contribute to unintended pregnancies can help inform policies and interventions to reduce their occurrence and improve maternal wellbeing.

Highlights:

– The study found an overall unintended pregnancy prevalence rate of 29% in sub-Saharan Africa, with significant variation across countries.
– Women of all age categories, except those aged 15-19, had higher odds of unintended pregnancies.
– Married women were six times more likely to report unintended pregnancy compared to unmarried women.
– Rural residents had higher odds of unintended pregnancy compared to urban residents.
– Women with primary and secondary levels of education had lower chances of unintended pregnancies compared to those with no education.
– Women in higher wealth categories had lower probability of unintended pregnancy compared to those with the poorest wealth status.

Recommendations for Lay Reader:

Based on the findings, it is recommended that sub-Saharan African countries with high prevalence of unintended pregnancies consider implementing successful interventions from other countries in the region. These interventions may include health education, counseling, skills-building, comprehensive sex education, and improved access to contraception. These efforts primarily rest with the governments of sub-Saharan African countries.

Recommendations for Policy Maker:

Policy makers should prioritize addressing the high prevalence of unintended pregnancies in sub-Saharan Africa. This can be achieved by implementing evidence-based interventions such as health education, counseling, skills-building, comprehensive sex education, and improving access to contraception. Collaboration between governments, healthcare providers, educators, and community organizations is crucial for the successful implementation of these interventions.

Key Role Players:

1. Government officials and policymakers: Responsible for developing and implementing policies and programs to address unintended pregnancies.
2. Healthcare providers: Involved in providing reproductive health services, including counseling, education, and access to contraception.
3. Educators: Play a role in delivering comprehensive sex education in schools and other educational settings.
4. Community organizations: Engage in community outreach and awareness campaigns to promote reproductive health and family planning.
5. Researchers and academics: Conduct further studies to monitor the prevalence and determinants of unintended pregnancies and evaluate the effectiveness of interventions.

Cost Items for Planning Recommendations:

1. Training and capacity building for healthcare providers and educators.
2. Development and dissemination of educational materials and resources.
3. Implementation of health education and counseling programs.
4. Access to affordable and quality contraception methods.
5. Monitoring and evaluation of interventions.
6. Research funding for further studies and data collection.
Please note that the cost items provided are general categories and not actual cost estimates. The specific costs will vary depending on the context and scale of implementation.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a multi-country analysis of demographic and health surveys conducted in 29 countries in sub-Saharan Africa. The study used a large sample size and employed logistic regression analysis to examine the factors influencing unintended pregnancies. The results are presented using odds ratios, which provide a measure of association between the variables. The study also provides actionable steps to improve the prevalence of unintended pregnancies, such as implementing health education, counseling, skills-building, comprehensive sex education, and access to contraception. To improve the evidence, the abstract could include more details about the methodology, such as the specific sampling techniques used and any limitations of the study.

Abstract

The study made use of pooled data from current Demographic and Health Surveys (DHS) conducted from January 1, 2010 and December 31, 2016 in 29 countries in sub-Saharan Africa. The countries are Angola, Benin, Burkina Faso, Burundi, Congo DR, Congo, Côte d’Ivoire, Cameroon, Chad, Comoros, Ethiopia, Gabon, Ghana, Gambia, Guinea, Kenya, Liberia, Lesotho, Mali, Malawi, Namibia, Nigeria, Rwanda, Sierra Leone, Senegal, Togo, Uganda, Zambia and Zimbabwe. These 29 countries were included in the study because they had current DHS data and also all the variables of interest for this study. Our study included these 29 countries under the DHS program in order to provide a holistic and in-depth evidence of unintended pregnancy in SSA. DHS is a nationwide survey collected every five-year period across low- and middle-income countries. The survey is representative of each of these countries. Women’s files were used for our study and these files possess the responses by women aged 15 to 49. The survey targets core maternal and child health indicators such as unintended pregnancy, contraceptive use, skilled birth attendance, immunisation among under-fives and intimate partner violence. The DHS survey employs stratified two-stage sampling technique in order to ensure national representativeness [29]. As described in detail previously [30] the first-stage constituted the development of a sampling frame consisting of a list of primary sampling units (PSUs) or enumeration areas (EAs) which cover the entire country and are usually developed from the available latest national census. Each PSU or EA is further subdivided into standard size segments of about 100–500 households per segment. In this stage, a sample of predetermined segments is selected randomly with probability proportional to the EA’s measure of size (number of households in EA). In the second stage, DHS survey personnel select households systematically from a list of previously enumerated households in each selected EA segment, and in-person interviews are conducted in selected households with target populations: women aged 15–49 and men aged 15–64. The number of selected households per EA is variable and ranges from 30 to 40 households/women per rural cluster and from 20 to 25 households/women per urban cluster [30]. The surveys were done in different times due to the variations in the starting points of the DHS in the various countries. The sample frame usually excludes nomadic and institutional groups such as prisoners and hotel occupants. As evidence in other studies combining the DHS in sub-Saharan Africa [30–32], although the starting points of the data surveys are different, this does not defeat the ability to compare the DHS among the countries. Permission to use the data set was sought from MEASURE DHS. The data set is available to the public at https://dhsprogram.com/data/available-datasets.cfm. The dependent variable for the study was “pregnancy intentions” which arose from the question regarding whether women wanted their current pregnancy or not. It had three responses: ‘then’, ‘later’ and ‘not at all’. Following the definition of unintended pregnancy as “pregnancies that are either wanted earlier or later than occurred (mistimed) or not needed (unwanted)” (CDC, 2015) [3], we coded these three responses as follows: then = 0 ‘intended’; ‘later and not at all’ = 1 ‘unintended’. The inclusion criteria was all women (15–49) who had answered this particular question. Eleven explanatory variables were considered in our study. These are age, marriage, place of residence, wealth, parity, occupation, education, religion, contraceptive use intention, knowledge of contraception and country of origin. Apart from country of origin, the rest of the variables were not determined a priori; instead, the selection was based on their significant association with the outcome variable, unintended pregnancy. Additionally, a number of these variables have been reported as predictors of unintended pregnancies [6–8, 19, 20]. Six of these variables were recoded to make them meaningful for analysis and interpretation. Marriage was recoded into ‘never married (0)’, ‘married (1)’, ‘cohabiting (2)’, ‘widowed (3)’ and ‘divorced (4)’. Occupation was captured as ‘not working (0)’, ‘managerial (1)’, ‘clerical (2)’, ‘sales (3)’, ‘agricultural (4)’, ‘household (5)’, ‘services (6)’ and ‘manual (7)’. We recoded parity (birth order) as ‘zero birth (0)’, ‘one birth (1)’, ‘two births (2)’, ‘three births (3)’, and four or more births (4)’. We recoded religion as ‘Christianity (1)’, ‘Islam (2)’, ‘Traditionalist (3)’, and ‘no religion (4)’. Contraceptive knowledge was recoded as ‘knows no method (0)’, ‘knows traditional (1)’, and ‘knows modern (2)’. Finally, intention of contraceptive use was recoded into ‘intends to use (1)’, and ‘does not intend to use (2)’. The analysis began with computation of unintended pregnancy prevalence among the 29 SSA countries. Secondly, we appended the dataset and this generated a total sample of 36,529. After appending, we calculated the overall prevalence and proportions of unintended pregnancy across the socio-demographic characteristics with their significance levels and chi-square (χ2) values. Logistic regression analysis was carried out in a hierarchical order where the first model (Model I) was a bivariate analysis of the effect of country on unintended pregnancies. Angola was chosen as the reference country because previous studies have identified no contraceptive use [33–35], and high unmet need for family planning [34, 36] in the country. In Model II, we adjusted for the effect of the other explanatory variables to ascertain how these variables induce unintended pregnancies using a multivariate analysis. The choice of reference categories for these explanatory variables was similarly informed by propositions of some previous studies [5, 6, 37]. Logistic regression was employed because our dependent variable (unintended pregnancy) was measured as a binary factor. Results for the regression analysis have been presented as odds ratios (OR), with their corresponding 95% confidence intervals (CI) signifying precision and significance of the reported OR. Any OR less than one (1) denotes less odds of unintended pregnancy whereas those higher than one (1) indicate higher odds of unintended pregnancy. The inherent sample weight was applied and all analyses were carried out with STATA version 13.0. The DHS surveys obtain 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. Since the data was not collected by the authors of this paper, 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 27th January, 2019.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based systems to provide pregnant women with information on prenatal care, nutrition, and family planning. These platforms can also be used to send reminders for appointments and medication.

2. Telemedicine: Implement telemedicine services to connect pregnant women in remote areas with healthcare providers. This can help overcome geographical barriers and provide access to prenatal consultations and advice.

3. Community Health Workers: Train and deploy community health workers to provide education, counseling, and basic prenatal care to pregnant women in underserved areas. These workers can also help identify high-risk pregnancies and refer women to appropriate healthcare facilities.

4. Task Shifting: Train and empower midwives and nurses to provide comprehensive prenatal care, including antenatal check-ups, basic laboratory tests, and counseling on family planning. This can help alleviate the burden on doctors and increase access to care.

5. Integration of Services: Integrate maternal health services with other healthcare services, such as HIV testing and treatment, to provide comprehensive care for pregnant women. This can improve efficiency and reduce the need for multiple visits to different healthcare facilities.

6. Strengthening Health Systems: Invest in improving healthcare infrastructure, equipment, and supplies in order to provide quality maternal health services. This includes ensuring the availability of skilled healthcare providers, essential drugs, and necessary medical equipment.

7. Financial Support: Implement policies and programs to provide financial support for pregnant women, such as subsidies for prenatal care, transportation vouchers, and cash transfers. This can help reduce financial barriers to accessing maternal health services.

8. Public Awareness Campaigns: Conduct public awareness campaigns to educate communities about the importance of prenatal care, family planning, and the availability of maternal health services. This can help reduce stigma, increase demand for services, and promote early and regular prenatal care.

It is important to note that the specific implementation of these innovations should be tailored to the local context and needs of each country in sub-Saharan Africa.

AI Innovations Description

Based on the study’s findings and recommendations, here is a recommendation that can be developed into an innovation to improve access to maternal health:

Develop a comprehensive maternal health program that focuses on reducing unintended pregnancies in sub-Saharan Africa. This program should include the following components:

1. Health education and counseling: Implement targeted health education campaigns to raise awareness about family planning methods, contraception, and the importance of planned pregnancies. Provide counseling services to women and couples to help them make informed decisions about their reproductive health.

2. Skills-building: Offer training programs to healthcare providers to enhance their skills in providing family planning services and counseling. This will ensure that women have access to accurate information and quality care.

3. Comprehensive sex education: Integrate comprehensive sex education into school curricula to educate young people about reproductive health, contraception, and responsible sexual behavior. This will help reduce unintended pregnancies among adolescents and young adults.

4. Access to contraception: Improve access to a wide range of contraceptive methods, including both traditional and modern methods. This can be achieved by strengthening the supply chain, increasing availability in healthcare facilities, and addressing cultural and social barriers to contraceptive use.

5. Government commitment: Encourage governments in sub-Saharan Africa to prioritize maternal health and allocate sufficient resources to implement and sustain these interventions. This can be done through policy advocacy and collaboration with international organizations.

By implementing this comprehensive maternal health program, it is expected that the prevalence of unintended pregnancies will decrease, leading to improved maternal health outcomes in sub-Saharan Africa.

AI Innovations Methodology

Based on the research study provided, here are some potential recommendations for improving access to maternal health:

1. Strengthen Health Education: Implement comprehensive health education programs that focus on reproductive health, family planning, and the importance of prenatal care. This can help increase awareness and knowledge among women, leading to better decision-making regarding unintended pregnancies.

2. Increase Access to Contraception: Improve availability and affordability of contraception methods, including both traditional and modern methods. This can help women prevent unintended pregnancies and have better control over their reproductive health.

3. Enhance Counseling Services: Provide counseling services that address family planning, pregnancy intentions, and the importance of antenatal care. This can help women make informed choices and receive appropriate support throughout their pregnancy journey.

4. Skills-Building Programs: Offer skills-building programs that empower women to make informed decisions about their reproductive health. This can include training on contraceptive use, pregnancy planning, and self-care during pregnancy.

5. Strengthen Healthcare Infrastructure: Invest in improving healthcare infrastructure, especially in rural areas, to ensure access to quality maternal healthcare services. This can include building and upgrading healthcare facilities, training healthcare providers, and ensuring the availability of essential medical supplies.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define Key Indicators: Identify key indicators that measure access to maternal health, such as the percentage of women receiving prenatal care, the rate of unintended pregnancies, and the availability of contraception methods.

2. Baseline Data Collection: Collect baseline data on the identified indicators from the target population. This can be done through surveys, interviews, or analysis of existing data sources.

3. Introduce Innovations: Implement the recommended innovations in selected regions or communities. This can be done through pilot programs or targeted interventions.

4. Monitor and Evaluate: Continuously monitor and evaluate the impact of the innovations on the identified indicators. This can involve collecting data on the indicators before and after the implementation of the innovations.

5. Analyze Data: Analyze the collected data to assess the changes in the indicators and determine the impact of the innovations on improving access to maternal health. This can be done using statistical analysis techniques, such as regression analysis or comparative analysis.

6. Adjust and Scale-Up: Based on the findings, make adjustments to the innovations if necessary and develop strategies for scaling up successful interventions to reach a larger population.

7. Continuous Improvement: Continuously monitor and evaluate the impact of the scaled-up interventions and make further improvements as needed. This can involve ongoing data collection, analysis, and feedback loops to ensure the effectiveness of the interventions.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of the recommended innovations on improving access to maternal health and make informed decisions on implementing and scaling up these interventions.

Authors & Co-Authors

Statistics:

Citations: 52

Authors: 7

Identifiers:

DOI: 10.1371/journal.pone.0220970

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Violence and Injury

Study Countries:

Angola, Benin, Burkina Faso, Burundi, Cameroon, Chad, Comoros, Congo, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Multi-Countries, Namibia, Nigeria, Rwanda, Senegal, Sierra Leone, Togo, Uganda, Zambia, Zimbabwe

Study Design:

Cross Sectional Study

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Male & Female