Trends and determinants of teenage childbearing in Ethiopia: Evidence from the 2000 to 2016 demographic and health surveys

Italian Journal of Pediatrics, Volume 45, No. 1, Year 2019

Interpretação

Background: Teenage childbearing among adolescents aged 15 to 19 is a common sexual and reproductive health (SRH) issue among young people, particularly in developing countries. It is associated with higher maternal and neonatal complications. Almost half (47%) of the population in Ethiopia are young people under 15 years old. Therefore, a clear understanding of the trend and determinants of teenage childbearing is essential to design proper intervention programs. Methods: Secondary analysis of the 2000 to 2016 Ethiopia Demographic and Health Survey (DHS) data were conducted. A total of 3710 (DHS 2000), 3266 (DHS 2005), 4009 (DHS 2011) and 3381 (DHS 2016) adolescents (aged 15 to 19 years old) were included from the four surveys. The main outcome variable of this study was teenage childbearing, and independent variables were categorized into individual- and community-level factors. The 2016 DHS was used to identify the factors associated with teenage childbearing. Multi-level logistic regression analysis technique was used to identify the factors associated with teenage childbearing. The analysis was adjusted for different individual- and community- level factors affecting teenage childbearing. Data analysis was conducted using STATA software. Results: The prevalence of adolescents who started childbearing reduced from 16.3% in 2000 DHS to 12.5% in 2016 DHS, p-value = < 0.0001. From the 2016 DHS, the percentage of adolescents who have had a live birth was 10.1%, and the percentage of adolescents who were currently pregnant was 2.4%. The highest percentage of teenage childbearing was in Affar region (23.4%), and the lowest was in Addis Ababa city (3%). The odds of teenage childbearing was higher among adolescents in the age range of 18-19 years old (AOR = 2.26; 95% CI: 1.29, 3.94, p-value < 0.01), those who started sexual intercourse before their eighteenth birthday (AOR = 12.74; 95% CI: 4.83, 33.62, p-value < 0.001), who were married or living together (AOR = 8.98; 95% CI: 2.49, 32.41, p-value < 0.01), and among those who were widowed, divorced or separated (AOR = 4.89; 95% CI: 1.36, 17.61, p-value < 0.05). Conclusions: One in ten teenage girls have already started childbearing in Ethiopia. Variations were observed in the percentage of teenage childbearing across different sociodemographic- and economic variables. Factors like age, early sexual initiation before 18 years of age, ever married, and geographical region were significant factors associated with teenage childbearing. School- and community- based intervention programs aimed at prevention of early marriage and early sexual intercourse is essential to reduce teenage childbearing and its complications. According to the World Bank, Ethiopia is the second most populous country in Africa, with a population of 105 million in 2017 [35]. According to the 2016 EDHS report, almost half (47%) of Ethiopian population are young people under 15 years old [21]. Ethiopia is structured into nine regions and two city administrations. The regions include: Tigray, Affar, Amhara, Oromiya, Somali, Benishangul-Gumuz, Southern Nations Nationalities and People (SNNP), Gambela, and Harari. Administrative cities include; Addis Ababa and Dire Dawa [8, 21]. This study used data from the four Ethiopia Demographic and Health Surveys- the 2000 [7], 2005 [30], 2011 [8], and 2016 [21] for the descriptive statistics and to identify the trend of teenage childbearing. For the second objective, to determine factors associated with teenage childbearing, data from the 2016 DHS were used. All surveys collected data on household characteristics, women aged 15–49, and men aged 15–59 [7, 8, 21, 30]. The current study used data from the women’s questionnaire, particularly data of adolescent women (aged 15–19) were extracted from all national surveys. The sample for all DHS surveys were designed to represent all regions and administrative cities in the country. The survey participants were selected using stratified and two stage sampling methods: enumeration areas (EAs) in the first stage and households in the second stage. Each region was stratified into urban and rural areas. Then probability proportional allocation to sample size was made. For the 2016 DHS, 645 enumeration areas (EAs) were selected. From this, 202 EAs were from urban and 443 were from rural areas [21]. The 2011 DHS included 624 EAs (187 from urban and 437 from rural areas) [8]. The 2005 DHS included 540 EAs (145 from urban and 395 from rural areas) [30], and 539 EAs (138 from urban ad 401 from rural) were included in the 2000 DHS [7]. A representative sample of 14,072 households were successfully interviewed in the 2000 DHS (response rate 96%) [7], 13,721 households in 2005 DHS (response rate 99%) [30], 16,702 households in 2011 DHS (response rate 98%) [8], and 16,650 households (response rate 98%) were interviewed in the 2016 DHS [21]. The number of adolescents aged 15–19 included in the 2000 DHS were 3710, in 2005 DHS were 3266, in 2011 were 4009, and 3381 adolescents participated in the 2016 DHS [7, 8, 21, 30]. The DHS uses three core questionnaires adapted from the MEASURE DHS project. These questionnaires include the household, women’s and men’s questionnaires [7, 8, 21, 30]. Additional questionnaires include: the biomarker questionnaire and the health facility questionnaire. This study used data from the women’s questionnaire of the surveys. The data collection tool was first prepared in English and then translated in to the three main languages in the country, Amharic, Oromiffa, and Tigrigna languages for the 2005 to 2016 DHS [8, 21, 30]. The 2000 DHS also used additional Somaligna and Afarigna languages [7]. Pretest was conducted before the data collection period, and training was provided for all data collectors, supervisors, and quality controllers involved in the field work [7, 8, 21, 30]. The 2000 DHS was conducted from February to May, 2000 [7], the 2005 DHS from April 27 to August 30, 2005 [30], and the 2011 DHS survey data collection was conducted from December 27, 2010 to June 3, 2011 [8]. The data collection period for 2016 DHS was from January 18, 2016 to June 27, 2016 [21]. The main outcome variable of this study was teenage childbearing. It is defined as the percentage of teenagers who are mothers, pregnant with their first child, and have begun childbearing [36]. These included all women between the age of 15 to 19 years old at the time of interview. The percentage of adolescent women who are mothers was calculated by dividing the number of adolescent women who have had a birth by the total number of teenage women including those women without a birth. Percentage of women that are pregnant with first child was calculated by dividing the number of women that have not had a birth but who are pregnant at the time of data collection by the total number of teenage women including those women without a birth. The percentage of women who have begun childbearing was calculated by adding the number of women who either have had a birth or who are pregnant at the time of interview and dividing by the total number of teenage women including those women without a birth [36]. The independent variables were categorized into two level factors: individual-level, and community-level factors. The individual-level factors include: age of respondents, educational status, wealth status, occupational status, marital status, sex of household head, early sexual initiation (sexual intercourse before 18 years old), Khat chewing, and knowledge towards contraceptive methods. Community-level factors include: place of residence (urban vs rural) and geographic region (Fig (Fig11). Analysis framework for factors associated with teenage childbearing After data collection, completed DHS questionnaires were carefully coded, entered, and edited [8]. Data analysis used the weighted samples to ensure the survey results were representatives of the national and regional level findings. Data analysis was conducted using STATA software (version 14; StataCorp, College Station, TX). Descriptive statistics like frequency and percentage were used. The demographic characteristics of respondents and outcome variables were compared across the four surveys. Except the 2000 DHS, all other surveys reported wealth index. To estimate the wealth index for the 2000 DHS, Principal Component Analysis was used from the household possession of items, floor and roof materials, type of toilet facility, and type of water source. The trend analysis of teenage childbearing was assessed using the Extended Mantel-Haenszel chi square test for linear trend using the OpenEpi (Version 3.01)- Dose Response program [37]. A p-value less than 0.05 was used to declare a 95% significant probability of existence of trend. Multi-level logistic regression analysis technique was used to identify the factors associated with teenage childbearing. A total of four modellings were conducted. The first model was an empty model, which was conducted to estimate the random variability in the intercept. The second model was conducted to estimate the effect of individual- level factors on teenage childbearing. The third model assessed the effect of community- level factors on teenage childbearing. Finally, model four estimated the effect of both individual- and community-level factors on teenage childbearing. The Intra-Cluster Correlation (ICC) was calculated to show between-cluster correlation within a model. The Proportional Change in Variance (PCV) was also calculated to determine the power of variables included in each model in predicting teenage childbearing. The model with the highest PCV value was considered to identify the factors associated with teenage childbearing. Variables with p-value less than 0.05 were taken as significant factors. All Ethiopian Demographic and Health Surveys were conducted after obtaining ethical clearance from Ethiopia Health and Nutrition Research Institute Review Board, the Ministry of Science and Technology, Institutional Review Board of ICF International, and the CDC. The overall process of the survey, including coordination of activities, questionnaire design, training of data collectors, supervisors and all people involved in the process and report writing were strictly followed. Data were collected after taking informed consent, and all information was kept confidential [8]. For this specific research, permission was given by the Demographic and Health Surveys Program to access EDHS data after review of the submitted brief descriptions of the study to the DHS program. The datasets were treated with utmost confidentially.

AI Digest

Study Justification:

The study aims to investigate the trends and determinants of teenage childbearing in Ethiopia using data from the 2000 to 2016 Demographic and Health Surveys. Teenage childbearing is a significant sexual and reproductive health issue, particularly in developing countries, and is associated with higher maternal and neonatal complications. With almost half of the Ethiopian population being young people under 15 years old, understanding the trend and determinants of teenage childbearing is crucial for designing effective intervention programs.

Highlights:

1. Prevalence of teenage childbearing: The study found that the prevalence of adolescents who started childbearing reduced from 16.3% in 2000 to 12.5% in 2016.
2. Regional variations: The highest percentage of teenage childbearing was observed in the Affar region (23.4%), while the lowest was in Addis Ababa city (3%).
3. Factors associated with teenage childbearing: The study identified several significant factors, including age (18-19 years old), early sexual initiation before 18 years of age, being married or living together, and being widowed, divorced, or separated.

Recommendations:

1. School-based intervention programs: Implementing comprehensive sexual education programs in schools to prevent early marriage and early sexual intercourse among adolescents.
2. Community-based intervention programs: Engaging communities in promoting awareness and providing support to prevent teenage childbearing.
3. Access to contraception: Ensuring availability and accessibility of contraceptive methods to prevent unintended pregnancies among teenagers.

Key Role Players:

1. Ministry of Health: Responsible for developing and implementing intervention programs and policies related to teenage childbearing.
2. Ministry of Education: Involved in the implementation of school-based sexual education programs.
3. Non-governmental organizations (NGOs): Collaborating with government agencies to provide support and resources for intervention programs.
4. Community leaders and religious leaders: Engaging communities and promoting awareness about the consequences of teenage childbearing.

Cost Items for Planning Recommendations:

1. Program development and implementation: Budget for designing and implementing school-based and community-based intervention programs.
2. Training and capacity building: Funds for training teachers, healthcare providers, and community leaders to effectively deliver intervention programs.
3. Contraceptive services: Budget for ensuring the availability and accessibility of contraceptive methods for teenagers.
4. Monitoring and evaluation: Allocation of resources for monitoring and evaluating the effectiveness of intervention programs.
Please note that the provided information is based on the description of the study and may not include all details.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on secondary analysis of data from the 2000 to 2016 Ethiopia Demographic and Health Surveys. The study used a large sample size and conducted multi-level logistic regression analysis to identify factors associated with teenage childbearing. The prevalence of teenage childbearing decreased over time, and significant factors such as age, early sexual initiation, marital status, and geographic region were identified. To improve the evidence, the abstract could provide more information on the sampling methods used in the surveys and the response rates. Additionally, it would be helpful to include information on the limitations of the study and recommendations for future research.

Resumo

According to the World Bank, Ethiopia is the second most populous country in Africa, with a population of 105 million in 2017 [35]. According to the 2016 EDHS report, almost half (47%) of Ethiopian population are young people under 15 years old [21]. Ethiopia is structured into nine regions and two city administrations. The regions include: Tigray, Affar, Amhara, Oromiya, Somali, Benishangul-Gumuz, Southern Nations Nationalities and People (SNNP), Gambela, and Harari. Administrative cities include; Addis Ababa and Dire Dawa [8, 21]. This study used data from the four Ethiopia Demographic and Health Surveys- the 2000 [7], 2005 [30], 2011 [8], and 2016 [21] for the descriptive statistics and to identify the trend of teenage childbearing. For the second objective, to determine factors associated with teenage childbearing, data from the 2016 DHS were used. All surveys collected data on household characteristics, women aged 15–49, and men aged 15–59 [7, 8, 21, 30]. The current study used data from the women’s questionnaire, particularly data of adolescent women (aged 15–19) were extracted from all national surveys. The sample for all DHS surveys were designed to represent all regions and administrative cities in the country. The survey participants were selected using stratified and two stage sampling methods: enumeration areas (EAs) in the first stage and households in the second stage. Each region was stratified into urban and rural areas. Then probability proportional allocation to sample size was made. For the 2016 DHS, 645 enumeration areas (EAs) were selected. From this, 202 EAs were from urban and 443 were from rural areas [21]. The 2011 DHS included 624 EAs (187 from urban and 437 from rural areas) [8]. The 2005 DHS included 540 EAs (145 from urban and 395 from rural areas) [30], and 539 EAs (138 from urban ad 401 from rural) were included in the 2000 DHS [7]. A representative sample of 14,072 households were successfully interviewed in the 2000 DHS (response rate 96%) [7], 13,721 households in 2005 DHS (response rate 99%) [30], 16,702 households in 2011 DHS (response rate 98%) [8], and 16,650 households (response rate 98%) were interviewed in the 2016 DHS [21]. The number of adolescents aged 15–19 included in the 2000 DHS were 3710, in 2005 DHS were 3266, in 2011 were 4009, and 3381 adolescents participated in the 2016 DHS [7, 8, 21, 30]. The DHS uses three core questionnaires adapted from the MEASURE DHS project. These questionnaires include the household, women’s and men’s questionnaires [7, 8, 21, 30]. Additional questionnaires include: the biomarker questionnaire and the health facility questionnaire. This study used data from the women’s questionnaire of the surveys. The data collection tool was first prepared in English and then translated in to the three main languages in the country, Amharic, Oromiffa, and Tigrigna languages for the 2005 to 2016 DHS [8, 21, 30]. The 2000 DHS also used additional Somaligna and Afarigna languages [7]. Pretest was conducted before the data collection period, and training was provided for all data collectors, supervisors, and quality controllers involved in the field work [7, 8, 21, 30]. The 2000 DHS was conducted from February to May, 2000 [7], the 2005 DHS from April 27 to August 30, 2005 [30], and the 2011 DHS survey data collection was conducted from December 27, 2010 to June 3, 2011 [8]. The data collection period for 2016 DHS was from January 18, 2016 to June 27, 2016 [21]. The main outcome variable of this study was teenage childbearing. It is defined as the percentage of teenagers who are mothers, pregnant with their first child, and have begun childbearing [36]. These included all women between the age of 15 to 19 years old at the time of interview. The percentage of adolescent women who are mothers was calculated by dividing the number of adolescent women who have had a birth by the total number of teenage women including those women without a birth. Percentage of women that are pregnant with first child was calculated by dividing the number of women that have not had a birth but who are pregnant at the time of data collection by the total number of teenage women including those women without a birth. The percentage of women who have begun childbearing was calculated by adding the number of women who either have had a birth or who are pregnant at the time of interview and dividing by the total number of teenage women including those women without a birth [36]. The independent variables were categorized into two level factors: individual-level, and community-level factors. The individual-level factors include: age of respondents, educational status, wealth status, occupational status, marital status, sex of household head, early sexual initiation (sexual intercourse before 18 years old), Khat chewing, and knowledge towards contraceptive methods. Community-level factors include: place of residence (urban vs rural) and geographic region (Fig (Fig11). Analysis framework for factors associated with teenage childbearing After data collection, completed DHS questionnaires were carefully coded, entered, and edited [8]. Data analysis used the weighted samples to ensure the survey results were representatives of the national and regional level findings. Data analysis was conducted using STATA software (version 14; StataCorp, College Station, TX). Descriptive statistics like frequency and percentage were used. The demographic characteristics of respondents and outcome variables were compared across the four surveys. Except the 2000 DHS, all other surveys reported wealth index. To estimate the wealth index for the 2000 DHS, Principal Component Analysis was used from the household possession of items, floor and roof materials, type of toilet facility, and type of water source. The trend analysis of teenage childbearing was assessed using the Extended Mantel-Haenszel chi square test for linear trend using the OpenEpi (Version 3.01)- Dose Response program [37]. A p-value less than 0.05 was used to declare a 95% significant probability of existence of trend. Multi-level logistic regression analysis technique was used to identify the factors associated with teenage childbearing. A total of four modellings were conducted. The first model was an empty model, which was conducted to estimate the random variability in the intercept. The second model was conducted to estimate the effect of individual- level factors on teenage childbearing. The third model assessed the effect of community- level factors on teenage childbearing. Finally, model four estimated the effect of both individual- and community-level factors on teenage childbearing. The Intra-Cluster Correlation (ICC) was calculated to show between-cluster correlation within a model. The Proportional Change in Variance (PCV) was also calculated to determine the power of variables included in each model in predicting teenage childbearing. The model with the highest PCV value was considered to identify the factors associated with teenage childbearing. Variables with p-value less than 0.05 were taken as significant factors. All Ethiopian Demographic and Health Surveys were conducted after obtaining ethical clearance from Ethiopia Health and Nutrition Research Institute Review Board, the Ministry of Science and Technology, Institutional Review Board of ICF International, and the CDC. The overall process of the survey, including coordination of activities, questionnaire design, training of data collectors, supervisors and all people involved in the process and report writing were strictly followed. Data were collected after taking informed consent, and all information was kept confidential [8]. For this specific research, permission was given by the Demographic and Health Surveys Program to access EDHS data after review of the submitted brief descriptions of the study to the DHS program. The datasets were treated with utmost confidentially.

Materiais

N/A

Inovações

Based on the provided information, here are some potential recommendations for innovations to improve access to maternal health in Ethiopia:

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to important health information, appointment reminders, and emergency services. These apps can also include features for tracking prenatal care, vaccinations, and growth milestones.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This can help address the shortage of healthcare providers in rural areas and ensure that women receive timely and accurate medical advice.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal and postnatal care, as well as education on maternal health, in underserved areas. These workers can also serve as a bridge between the community and healthcare facilities, facilitating referrals and follow-up care.

4. Maternal Waiting Homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes can provide a safe and supportive environment for women to stay during the final weeks of pregnancy, ensuring timely access to skilled birth attendants.

5. Mobile Clinics: Set up mobile clinics that travel to remote areas, providing comprehensive maternal health services, including prenatal care, vaccinations, and postnatal care. This can help overcome geographical barriers and reach women who have limited access to healthcare facilities.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of prenatal care, family planning, and safe delivery practices. These campaigns can be conducted through various channels, such as radio, television, community meetings, and social media.

7. Strengthening Health Infrastructure: Invest in improving the infrastructure of healthcare facilities, particularly in rural areas, to ensure that they have the necessary equipment, supplies, and skilled healthcare providers to deliver quality maternal health services.

8. Maternity Waiting Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to cover transportation costs and other expenses associated with accessing maternal health services. This can help reduce financial barriers and increase utilization of healthcare services.

9. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to leverage resources and expertise in improving maternal health. This can involve initiatives such as training programs for healthcare providers, provision of medical equipment, and support for community-based interventions.

10. Data-driven Decision Making: Strengthen the collection and analysis of maternal health data to inform evidence-based decision making and monitor the impact of interventions. This can help identify gaps in service delivery, track progress, and guide resource allocation for maximum impact.

It is important to note that the implementation of these innovations should be context-specific and tailored to the unique needs and challenges of the Ethiopian healthcare system.

AI Innovations Description

The recommendation to improve access to maternal health based on the provided description is to implement school- and community-based intervention programs aimed at preventing early marriage and early sexual intercourse. These programs should focus on educating adolescents about the risks and consequences of teenage childbearing, as well as providing information and access to contraception methods. Additionally, efforts should be made to address socio-economic factors such as poverty and lack of education, which are known determinants of teenage childbearing. By addressing these factors and providing comprehensive sexual and reproductive health education, it is possible to reduce teenage childbearing and its complications, ultimately improving access to maternal health in Ethiopia.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health in Ethiopia:

1. Strengthen comprehensive sex education: Implementing comprehensive sex education programs in schools and communities can provide adolescents with accurate information about sexual and reproductive health, including contraception, pregnancy prevention, and the consequences of early childbearing.

2. Increase availability and accessibility of contraceptives: Expanding the availability and accessibility of contraceptives, including long-acting reversible contraceptives (LARCs), can help prevent unintended pregnancies among adolescents and reduce the risk of teenage childbearing.

3. Improve healthcare infrastructure and services: Investing in healthcare infrastructure, particularly in rural areas, can improve access to maternal health services. This includes increasing the number of health facilities, trained healthcare providers, and essential equipment for safe deliveries and emergency obstetric care.

4. Strengthen community-based interventions: Implementing community-based interventions, such as mobile clinics and outreach programs, can bring maternal health services closer to remote and underserved communities. This can help overcome geographical barriers and increase access to prenatal care, skilled birth attendance, and postnatal care.

5. Address social and cultural norms: Addressing social and cultural norms that contribute to early marriage and early childbearing is crucial. This can be done through community engagement, awareness campaigns, and advocacy to promote gender equality, empower girls, and discourage harmful practices.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the percentage of pregnant women receiving prenatal care, the percentage of births attended by skilled health personnel, and the percentage of women using modern contraceptives.

2. Collect baseline data: Gather existing data on the selected indicators to establish a baseline. This can be obtained from national surveys, health facility records, and other relevant sources.

3. Define the intervention scenarios: Develop different scenarios that represent the implementation of the recommendations. For example, scenario 1 could represent the current situation without any interventions, scenario 2 could represent the implementation of comprehensive sex education, scenario 3 could represent the expansion of contraceptive services, and so on.

4. Simulate the impact: Use statistical modeling techniques, such as regression analysis or simulation models, to estimate the potential impact of each scenario on the selected indicators. This involves analyzing the relationship between the interventions and the indicators, taking into account other contextual factors and potential confounders.

5. Compare the results: Compare the simulated outcomes of each scenario to the baseline data to assess the potential improvements in access to maternal health. This can be done by calculating the percentage change or absolute difference in the selected indicators between the scenarios.

6. Sensitivity analysis: Conduct sensitivity analysis to test the robustness of the results by varying key assumptions or parameters in the simulation model. This helps to understand the uncertainty and potential limitations of the findings.

7. Interpret and communicate the results: Interpret the simulated impact of the recommendations and communicate the findings to policymakers, stakeholders, and the public. Highlight the potential benefits and challenges of each intervention scenario and provide evidence-based recommendations for decision-making.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data in Ethiopia.

Autor

Dima Health

Statistics:

Citations: 12

Identifiers:

DOI: 10.1186/s13052-019-0745-4

ISSN: 17208424

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female

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