Socioeconomic inequalities in teenage pregnancy in Nigeria: evidence from Demographic Health Survey

BMC Public Health, Volume 22, No. 1, Year 2022

Interpretação

Background: Despite the high rate of teenage pregnancy in Nigeria and host of negative medical, social and economic consequences that are associated with the problem, relatively few studies have examined socioeconomic inequality in teenage pregnancy. Understanding the key factors associated with socioeconomic inequality in teenage pregnancy is essential in designing effective policies for teenage pregnancy reduction. This study focuses on measuring inequality and identifying factors explaining socioeconomic inequality in teenage pregnancy in Nigeria. Methods: This is a cross sectional study using individual recode (data) file from the 2018 Nigeria Demographic Health Survey. The dataset comprises a representative sample of 8,423 women of reproductive age 15 – 19 years in Nigeria. The normalized Concentration index (Cn) was used to determine the magnitude of inequalities in teenage pregnancy. The Cn was decomposed to determine the contribution of explanatory factors to socioeconomic inequalities in teenage pregnancy in Nigeria. Results: The negative value of the Cn (-0.354; 95% confidence interval [CI] = -0.400 to -0.308) suggests that pregnancy is more concentrated among the poor teenagers. The decomposition analysis identified marital status, wealth index of households, exposure to information and communication technology, and religion as the most important predictors contributing to observed concentration of teenage pregnancy in Nigeria. Conclusion: There is a need for targeted intervention to reduce teenage pregnancy among low socioeconomic status women in Nigeria. The intervention should break the intergenerational cycle of low socioeconomic status that make teenagers’ susceptible to unintended pregnancy. Economic empowerment is recommended, as empowered girls are better prepared to handle reproductive health issues. Moreover, religious bodies, parents and schools should provide counselling, and guidance that will promote positive reproductive and sexual health behaviours to teenagers. The study area is Nigeria, with an estimated population of 198 million in 2018 [20]. About 70 percent of the population resides in rural areas while only about 30 percent lives in urban areas [21]. With 32.4 percent of the population below the age of 18 years and over 23% adolescents/teenagers [22, 23], Nigeria has a large youth population. Administratively, the country is divided into six geopolitical zones viz., North-Central, North-East, North-West, South-East, South-West, and South-South. Of the six geopolitical zones in Nigeria, southern states had the highest youth literacy rate while northern states had the least youth literacy rate [24]. Approximately 21.3 percent of youths, aged 15–19 had never been to school [24]. The dataset for the analysis comprises women of reproductive age of 15–19 years in the six geopolitical zones of Nigeria. Data were obtained from the latest Nigeria Demographic Health Survey (NDHS), conducted between August 14, 2018 and December 29, 2018. DHS is conducted every five years with common questionnaires and/or variables that are generalizable to over 90 low- and middle-income countries [13]. The NDHS data is a representative of Nigerian population with a response rate of 99%. The study used Individual (women’s) Recode data file that collected information on women’s background characteristics, reproductive history, household asset ownership, etc. The NDHS uses a multistage sampling procedure, standardized tools and well-trained interviewers to collect reliable data on maternal and child health. The details of the survey are explained elsewhere [13]. The sample size for the study was limited to 8,423 women (currently or ever pregnant) of reproductive age 15–19 years in Nigeria. As per DHS recommendation, sample weight was applied to get the representative sample size. The sample focused on the variable ‘currently or ever pregnant’ and “teenage current age” rather than “teenage age at first birth”. The outcome variable in the study is teenage pregnancy. The variable is a dummy variable coded 1 if a teenager (aged 15–19 years) currently or ever pregnant, 0 otherwise. The socioeconomic status of a teenager was measured using wealth index as an indicator of socioeconomic status. Since information on individuals’ expenditure or income are often difficult to collect [25–27], the NDHS constucts a wealth index, as a measure of SES, using easy-to-collect data on a household ownership of selected assets (e.g., car, televisions and bicycles), materials used in housing construction, type of water access, and sanitation facilities [26]. A principal component analysis (PCA) technique was used to construct households’ wealth index scores based on the aforementioned information collected in the survey [13]. The first principal component of a set of variables captures the largest amount of information that is common to all the variables [25–27]. Households’ wealth index scores were used to categorise individuals into five SES quintile, starting with the poorest to the richest. In line with previous literature [2, 3, 6, 12], the following variables were used as predictors of teenage pregnancy:, teenage education level, marital status, religion, occupation, place of residence, geopolitical zone, wealth index quintiles, and exposure to information and communication technology (ICT) (frequency of watching television and use of internet). Table Table11 presents description of variables used in the study. Description of variables used in the study We used the concentration index (C) to measure socioeconomic inequality in teenage pregnancy. The C is measured based on the Concentration curve, which plots the cumulative share of health variables in horizontal axis against the cumulative share of population in ascending order of SES in the vertical axis. Twice the area between the Concentration curve and line of perfect equality (i.e., 45-degree line) indicate the magnitude of the C. If the Concentration curve lies above (or below) the line of perfect equality, it suggests that health outcome is concentrated among the poor (or rich). The C was calculated using a convenient regression method as follows [28, 29]: where σr2 is the variance of the fractional rank, h is the healthcare variable of interest (i.e., teenage pregnancy) of i th teenage girl, μ is the mean of the health variable of interest, h, for the whole population, and ri=1N is the fractional rank of the i th teenage girl in the distribution of socioeconomic position, with i=1 for the poorest and i=N for the richest teenager. The C is calculated as the ordinary least squares (OLS) estimate of β [29, 30]. The C ranges from -1 to + 1, for continuous health outcomes. Since our health outcome variable of interest is binary, the minimum and maximum of the C are not between -1 and + 1 and depend on μ [31]. The C can be normalized by multiplying the estimated C by 11-μ to overcome this issue. We used the normalized Concentration index (Cn) to quantify socioeconomic inequalities in teenage pregnancy. If the value of the Cn is zero, it suggests that there is no socioeconomic inequality in health outcomes. A negative (or positive) value of the Cn indicates a higher concentration of the health variable among the poor (or rich) [28]. A higher value of the Cn corresponds to higher socioeconomic inequality in health. In order to identify the contribution of each explanatory variable to socioeconomic inequality in teenage pregnancy, we decomposed the Cn using the Wagstaff, et al. approach [29]. Assume that we have a linear regression model to link our outcome variable (i.e., teenage pregnancy) h, to a set of k explanatory factors, xκ such as: where α is the intercept and β denotes parameter that measure the relationship between each explanatory factor x and the teenage pregnancy, and ε is error term. A Wagstaff, E Van Doorslaer and N Watanabe [29] showed that the C of h can be decomposed into the contribution of determinants that explain the teenage pregnancy as follows: where, x¯ k is the mean of xk, and Ck denotes the C forxk, a contributing factor. The GCε denotes the generalized C of the error term,εi. Equation 3 shows that the overall inequality in the teenage pregnancy has two components. The first term (βkxk¯μ)CK denotes the contribution of factor k to socioeconomic inequality in the teenage pregnancy. It constitutes the deterministic or explained component of the teenage pregnancy of the C. The second term GCεμ represents the unexplained component [28]. Based on Eq. 3, the product of the elasticity of each factor and its Ck gives the contribution of that factor to the inequality. The negative (or positive) contribution of a predictor to the Cn suggests that the socioeconomic distribution of the predictor and the association between the predictor and the teenage pregnancy leads to an increase in the concentration of teenage pregnancy among the poor (or rich). A zero value of either elasticity or the Ck leads to the zero contribution of the factor to C [28]. Applying the A Wagstaff [31] normalization approach to the decomposition of the C can yield: The dataset was weighted using the sampling weight provided in the NDHS to obtain estimates that are representative of all teenagers in Nigeria. Logit model estimation and marginal effects were conducted before the decomposition analysis. Chi-square was used to test associations between explanatory factors and teenage pregnancy. The predictors of teenage pregnancy were considered statistically significant at p < 0.05. All data analyses were conducted using Stata/SE-13 software [32].

AI Digest

Study Justification:

– Teenage pregnancy is a significant issue in Nigeria with negative medical, social, and economic consequences.
– There is a lack of research on socioeconomic inequality in teenage pregnancy in Nigeria.
– Understanding the factors contributing to socioeconomic inequality is crucial for designing effective policies to reduce teenage pregnancy.

Study Highlights:

– The study used data from the 2018 Nigeria Demographic Health Survey.
– The concentration index (Cn) was used to measure socioeconomic inequality in teenage pregnancy.
– The study found that teenage pregnancy is more concentrated among poor teenagers.
– Factors such as marital status, household wealth, exposure to information and communication technology, and religion were identified as important predictors of teenage pregnancy.

Study Recommendations:

– Targeted interventions are needed to reduce teenage pregnancy among low socioeconomic status women in Nigeria.
– Interventions should focus on breaking the intergenerational cycle of low socioeconomic status that makes teenagers susceptible to unintended pregnancy.
– Economic empowerment is recommended to better prepare girls to handle reproductive health issues.
– Religious bodies, parents, and schools should provide counseling and guidance to promote positive reproductive and sexual health behaviors among teenagers.

Key Role Players:

– Government agencies responsible for health and education policies
– Non-governmental organizations working on reproductive health and teenage pregnancy prevention
– Religious leaders and organizations
– Schools and educational institutions
– Community leaders and influencers

Cost Items for Planning Recommendations:

– Funding for targeted interventions and programs
– Training and capacity building for healthcare providers, educators, and counselors
– Awareness campaigns and information dissemination
– Development and distribution of educational materials
– Monitoring and evaluation of interventions
– Research and data collection on teenage pregnancy and its determinants

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study provides a clear description of the methods used, including the dataset and statistical analysis. The negative value of the Concentration index suggests that teenage pregnancy is more concentrated among the poor in Nigeria. The study also identifies important predictors contributing to the observed concentration of teenage pregnancy. However, the abstract could be improved by providing more specific details about the sample size, the variables used, and the statistical significance of the predictors. Additionally, it would be helpful to include information about potential limitations of the study and suggestions for future research.

Resumo

The study area is Nigeria, with an estimated population of 198 million in 2018 [20]. About 70 percent of the population resides in rural areas while only about 30 percent lives in urban areas [21]. With 32.4 percent of the population below the age of 18 years and over 23% adolescents/teenagers [22, 23], Nigeria has a large youth population. Administratively, the country is divided into six geopolitical zones viz., North-Central, North-East, North-West, South-East, South-West, and South-South. Of the six geopolitical zones in Nigeria, southern states had the highest youth literacy rate while northern states had the least youth literacy rate [24]. Approximately 21.3 percent of youths, aged 15–19 had never been to school [24]. The dataset for the analysis comprises women of reproductive age of 15–19 years in the six geopolitical zones of Nigeria. Data were obtained from the latest Nigeria Demographic Health Survey (NDHS), conducted between August 14, 2018 and December 29, 2018. DHS is conducted every five years with common questionnaires and/or variables that are generalizable to over 90 low- and middle-income countries [13]. The NDHS data is a representative of Nigerian population with a response rate of 99%. The study used Individual (women’s) Recode data file that collected information on women’s background characteristics, reproductive history, household asset ownership, etc. The NDHS uses a multistage sampling procedure, standardized tools and well-trained interviewers to collect reliable data on maternal and child health. The details of the survey are explained elsewhere [13]. The sample size for the study was limited to 8,423 women (currently or ever pregnant) of reproductive age 15–19 years in Nigeria. As per DHS recommendation, sample weight was applied to get the representative sample size. The sample focused on the variable ‘currently or ever pregnant’ and “teenage current age” rather than “teenage age at first birth”. The outcome variable in the study is teenage pregnancy. The variable is a dummy variable coded 1 if a teenager (aged 15–19 years) currently or ever pregnant, 0 otherwise. The socioeconomic status of a teenager was measured using wealth index as an indicator of socioeconomic status. Since information on individuals’ expenditure or income are often difficult to collect [25–27], the NDHS constucts a wealth index, as a measure of SES, using easy-to-collect data on a household ownership of selected assets (e.g., car, televisions and bicycles), materials used in housing construction, type of water access, and sanitation facilities [26]. A principal component analysis (PCA) technique was used to construct households’ wealth index scores based on the aforementioned information collected in the survey [13]. The first principal component of a set of variables captures the largest amount of information that is common to all the variables [25–27]. Households’ wealth index scores were used to categorise individuals into five SES quintile, starting with the poorest to the richest. In line with previous literature [2, 3, 6, 12], the following variables were used as predictors of teenage pregnancy:, teenage education level, marital status, religion, occupation, place of residence, geopolitical zone, wealth index quintiles, and exposure to information and communication technology (ICT) (frequency of watching television and use of internet). Table Table11 presents description of variables used in the study. Description of variables used in the study We used the concentration index (C) to measure socioeconomic inequality in teenage pregnancy. The C is measured based on the Concentration curve, which plots the cumulative share of health variables in horizontal axis against the cumulative share of population in ascending order of SES in the vertical axis. Twice the area between the Concentration curve and line of perfect equality (i.e., 45-degree line) indicate the magnitude of the C. If the Concentration curve lies above (or below) the line of perfect equality, it suggests that health outcome is concentrated among the poor (or rich). The C was calculated using a convenient regression method as follows [28, 29]: where σr2 is the variance of the fractional rank, h is the healthcare variable of interest (i.e., teenage pregnancy) of i th teenage girl, μ is the mean of the health variable of interest, h, for the whole population, and ri=1N is the fractional rank of the i th teenage girl in the distribution of socioeconomic position, with i=1 for the poorest and i=N for the richest teenager. The C is calculated as the ordinary least squares (OLS) estimate of β [29, 30]. The C ranges from -1 to + 1, for continuous health outcomes. Since our health outcome variable of interest is binary, the minimum and maximum of the C are not between -1 and + 1 and depend on μ [31]. The C can be normalized by multiplying the estimated C by 11-μ to overcome this issue. We used the normalized Concentration index (Cn) to quantify socioeconomic inequalities in teenage pregnancy. If the value of the Cn is zero, it suggests that there is no socioeconomic inequality in health outcomes. A negative (or positive) value of the Cn indicates a higher concentration of the health variable among the poor (or rich) [28]. A higher value of the Cn corresponds to higher socioeconomic inequality in health. In order to identify the contribution of each explanatory variable to socioeconomic inequality in teenage pregnancy, we decomposed the Cn using the Wagstaff, et al. approach [29]. Assume that we have a linear regression model to link our outcome variable (i.e., teenage pregnancy) h, to a set of k explanatory factors, xκ such as: where α is the intercept and β denotes parameter that measure the relationship between each explanatory factor x and the teenage pregnancy, and ε is error term. A Wagstaff, E Van Doorslaer and N Watanabe [29] showed that the C of h can be decomposed into the contribution of determinants that explain the teenage pregnancy as follows: where, x¯ k is the mean of xk, and Ck denotes the C forxk, a contributing factor. The GCε denotes the generalized C of the error term,εi. Equation 3 shows that the overall inequality in the teenage pregnancy has two components. The first term (βkxk¯μ)CK denotes the contribution of factor k to socioeconomic inequality in the teenage pregnancy. It constitutes the deterministic or explained component of the teenage pregnancy of the C. The second term GCεμ represents the unexplained component [28]. Based on Eq. 3, the product of the elasticity of each factor and its Ck gives the contribution of that factor to the inequality. The negative (or positive) contribution of a predictor to the Cn suggests that the socioeconomic distribution of the predictor and the association between the predictor and the teenage pregnancy leads to an increase in the concentration of teenage pregnancy among the poor (or rich). A zero value of either elasticity or the Ck leads to the zero contribution of the factor to C [28]. Applying the A Wagstaff [31] normalization approach to the decomposition of the C can yield: The dataset was weighted using the sampling weight provided in the NDHS to obtain estimates that are representative of all teenagers in Nigeria. Logit model estimation and marginal effects were conducted before the decomposition analysis. Chi-square was used to test associations between explanatory factors and teenage pregnancy. The predictors of teenage pregnancy were considered statistically significant at p < 0.05. All data analyses were conducted using Stata/SE-13 software [32].

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Solutions: Develop and implement mobile applications or SMS-based platforms that provide pregnant women with information on prenatal care, nutrition, and safe delivery practices. These platforms can also send reminders for antenatal visits and provide access to teleconsultations with healthcare providers.

2. Community Health Workers: Train and deploy community health workers to provide maternal health education, counseling, and basic healthcare services to pregnant women in rural areas. These workers can also facilitate referrals to healthcare facilities for more specialized care.

3. Telemedicine: Establish telemedicine networks that connect healthcare providers in urban areas with pregnant women in remote or underserved areas. This allows for remote consultations, diagnosis, and monitoring of high-risk pregnancies, reducing the need for women to travel long distances for healthcare.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with subsidized or free access to essential maternal health services, including antenatal care, delivery, and postnatal care. These vouchers can be distributed through community health centers or outreach programs.

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

6. Public-Private Partnerships: Foster collaborations between the government, private healthcare providers, and non-profit organizations to improve the availability and quality of maternal health services. This can involve initiatives such as public-private clinics, training programs for healthcare providers, and infrastructure development.

7. Health Financing Schemes: Develop innovative health financing schemes, such as community-based health insurance or microfinance programs, to help pregnant women afford the costs associated with maternal healthcare. This can reduce financial barriers and increase access to quality services.

8. Maternal Health Education Campaigns: Launch targeted awareness campaigns to educate communities about the importance of maternal health and the available services. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience.

9. Strengthening Health Systems: Invest in strengthening the overall health system by improving infrastructure, ensuring the availability of essential medical supplies and equipment, and training healthcare providers in maternal health care. This will contribute to better access and quality of care for pregnant women.

10. Data-driven Decision Making: Utilize data from surveys, research studies, and health information systems to identify gaps and prioritize interventions. This can help policymakers and healthcare providers make informed decisions and allocate resources effectively to improve maternal health outcomes.

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 Nigerian healthcare system.

AI Innovations Description

Based on the information provided, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Targeted Intervention: Implement targeted interventions to reduce teenage pregnancy among low socioeconomic status women in Nigeria. This could involve providing comprehensive reproductive health education and services specifically tailored to the needs of this population. The interventions should focus on empowering girls and breaking the intergenerational cycle of low socioeconomic status that makes teenagers more susceptible to unintended pregnancy.

2. Economic Empowerment: Promote economic empowerment among teenage girls as a means to reduce teenage pregnancy. This can be done by providing vocational training, entrepreneurship programs, and access to financial resources. Empowered girls are better prepared to handle reproductive health issues and make informed decisions about their future.

3. Collaboration with Religious Bodies, Parents, and Schools: Engage religious bodies, parents, and schools in promoting positive reproductive and sexual health behaviors to teenagers. This can be achieved through counseling, guidance, and the inclusion of comprehensive sexuality education in school curricula. By involving key stakeholders, a supportive environment can be created to address the issue of teenage pregnancy.

4. Access to Information and Communication Technology (ICT): Improve access to information and communication technology, such as television and internet, among teenagers. This can be done by providing affordable and reliable internet services, promoting digital literacy, and leveraging technology platforms to disseminate accurate and age-appropriate reproductive health information.

By implementing these recommendations, it is possible to improve access to maternal health and reduce teenage pregnancy rates among low socioeconomic status women in Nigeria.

AI Innovations Methodology

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

1. Increase access to reproductive health education: Implement comprehensive and age-appropriate reproductive health education programs in schools and communities to provide teenagers with accurate information about contraception, sexually transmitted infections, and pregnancy prevention.

2. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, particularly in rural areas where access to maternal health services is limited. This includes ensuring the availability of skilled healthcare providers, essential medical supplies, and equipment for safe deliveries.

3. Promote economic empowerment: Implement programs that focus on empowering teenage girls from low socioeconomic backgrounds by providing vocational training, entrepreneurship opportunities, and access to microfinance. Economic empowerment can help reduce the intergenerational cycle of low socioeconomic status and make teenagers more resilient to unintended pregnancies.

4. Enhance access to family planning services: Increase the availability and affordability of contraceptives, including long-acting reversible contraceptives, through public health facilities and community-based distribution programs. This can help teenagers make informed choices about their reproductive health and prevent unintended pregnancies.

5. Strengthen community support systems: Engage religious bodies, parents, and schools in promoting positive reproductive and sexual health behaviors among teenagers. This can be done through counseling, guidance, and creating safe spaces for open discussions about reproductive health.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that measure access to maternal health, such as the percentage of teenage girls receiving antenatal care, the percentage of deliveries attended by skilled birth attendants, and the rate of teenage pregnancies.

2. Collect baseline data: Gather baseline data on the selected indicators from reliable sources, such as national surveys or health facility records. This will provide a starting point for comparison.

3. Develop a simulation model: Create a simulation model that incorporates the potential recommendations and their expected impact on the selected indicators. This model should consider factors such as population demographics, geographic distribution, and existing healthcare infrastructure.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of each recommendation on the selected indicators. Adjust the parameters of each recommendation to reflect realistic implementation scenarios.

5. Analyze results: Analyze the simulation results to determine the potential improvements in access to maternal health resulting from the implementation of the recommendations. Compare the simulated outcomes with the baseline data to assess the effectiveness of each recommendation.

6. Refine and validate the model: Continuously refine and validate the simulation model by incorporating new data and feedback from stakeholders. This will help improve the accuracy and reliability of the simulations over time.

By using this methodology, policymakers and stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions about resource allocation and program implementation.

Autor

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1186/s12889-022-14146-0

Research Areas:

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

Study Countries:

Nigeria

Study Design:

Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female

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