Survival analysis and prognostic factors of timing of first childbirth among women in Nigeria

BMC Pregnancy and Childbirth, Volume 16, No. 1, Year 2016

Interpretation

Background: First childbirth in a woman’s life is one of the most important events in her life. It marks a turnaround when she might have to drop roles of career building and education, for motherhood and parenthood. The timing of the commencement of these roles affects the child bearing behavior of women as they progress in their reproductive ages. Prevalent early first childbirth in Nigeria has been reported as the main cause of high population growth and high fertility, mortality and morbidity among women, but little has been documented on the progression into first birth as well as factors affecting it in Nigeria. This paper modelled timing of first birth among women in Nigeria and determined socio-demographic and other factors affecting its timing. Methods: We hypothesized that background characteristics of a woman will influence her progression into having first birth. We developed and fitted a survival analysis model to understand the timing of first birth among women in Nigeria using a national representative 2013 NDHS data. Women with no children were right censored as of the date of the survey. The Kaplan Meier survival function was used to estimate the probabilities of first birth not occurring until certain ages of women while Cox proportional hazard regression was used to model the timing of first births at 5 % significance level. Results: About 75.7 % of the respondents had given birth in the Northern region of Nigerian compared with 63.8 % in the South. Half (50.1 %) of the first childbirth occurred within the 15-19 years age bracket and 38.1 % within 20-29 years. The overall median survival time to first birth was 20 years (North 19, South 22), 27 years among women with higher education and 18 years for those with no formal education. The adjusted hazard of first birth was higher in the Northern region of Nigeria than in the South (aHR = 1.24, 95 % CI: 1.20-1.27), and higher in rural areas than in urban areas (aHR = 1.15, 95 % CI: 1.12-1.19). Also, hazard of earlier first birth tripled among women with no education (aHR = 3.36, 95 % CI: 3.17-3.55) compared to women with higher education. The significant factors affecting age at first birth are education, place and zone of residence, age at first marriage, religion, ethnicity and use of contraceptives. Conclusions: This study showed that progression into early first birth is most affected by the education standing of women as well as age at first marriage. Delay of first childbirths as a strategy for fertility reduction and maternal health improvement can be achieved if women are empowered early in life with quality education. Stakeholders should therefore, give adequate attention to educating the girl child. Adverse socio-cultural norms of betrothing and marrying young girls should be abrogated, while health education and promotion of need to delay child bearing must be intensified especially among rural dwellers and also in Northern Nigeria. Data from the 2013 Nigeria Demographic and Health Survey (NDHS) [1], a cross-sectional national representative data, was used for this study. The National Population Commission (Nigeria) and ICF International, United States gave access and authorized the use of the data. The survey used clusters as the primary sampling unit based on the EAs from the 2006 census frame and sampled respondents using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas across the six zones, 36 states, and the Federal Capital Territory, Abuja. A total of 39,902 women aged 15–49 years were identified as eligible for individual interviews, and 98 % of them were successfully interviewed. We extracted information on the women’s background characteristics, sexual and reproductive history and knowledge, source and use of contraception. The dependent variable in this study was age at first child birth while region and geographical zones of residence, education, religion, residence and ethnicity were the independent variables. Included also as independent variables are responses on; if the woman ever smoked, whether she had terminated a pregnancy or not and whether she has ever used something to prevent pregnancy. We collapsed the six zones into two regions: The North Central, North East and North West constituted the “North” while South East, South South and South West formed the “South”. We used women’s current educational attainment as a proxy for education as of the time of first child birth. This is justified, because the educational status does not change for persons who had none or primary education throughout their lifetime since primary education is mostly attained at age 12. “Ever smoked” was used as a proxy for peer pressure. We used survival analysis to model the determinants of age at first birth. Survival analysis is analysis of history of events which uses statistical procedures to deal with analysis of time duration, until one or more events of interest happen. It is usual in a follow up study, such as the current study, for some participants not to have experienced the event of interest at the end of the study or some participants were “lost to follow up” or some might have withdrawn during the study. Bias may be introduced if these categories of participants were excluded in further analysis as they could possess unique characteristics that could be useful in answering the research question. In such cases, the length of time the participants stayed in the study would be recorded as their study time and marked as “censored”. Two quantitative terms are important in survival analysis. They are the survivor function S(t) and hazard function h(t). In relation to the present study, the survivor function gives the probability that a woman “survives” longer than some specified time t without a birth, while the hazard function gives the instantaneous potential per unit time to have a first childbirth after time t, given that the individual had not had a first childbirth up to time t. Survival and hazard function are mathematically denoted by and In contrast to the survivor function (S(t)) which describes the probability of not failing before time t, hazard function (h(t)) addresses the failure rate at time t among those individuals who are alive at time t. Also two variables are compulsory in survival analysis; they are survival time and the censoring index. The “survival time” or “follow-up time”, is assumed to be a discrete random variable that takes on only positive integer. In this study, the population at risk are all women involved in the study since they are all likely to give birth one time or the other. The “survival time” for age at first childbirth is the age of the women at first birth while the survival time for those with no birth as of the time of the survey was their current age at the time of the survey. Thus their censoring index were coded “1” and “0” respectively. The usual logistic regression techniques therefore, become unsuitable in a follow up study such as the current study where the follow up time could be determined and used in explaining the event of interest. Kaplan-Meier method, developed for scenarios where survival time is measured on a continuous scale whereby only intervals containing an event contribute to the estimate, was used to compute the survival estimates. The Kaplan-Meier estimates of S(t) were obtained from equation (3) where n j is the number of subject observed at time tj and dj is the number of subject that experienced the event of interest at time tj. We applied the Cox-proportional Hazard model to the age at first birth. The model assumed that proportion of hazards are constant from time to time. In proportional hazard model, the effect of a unit increase in covariate is multiplicative with respect to hazard rate. The Cox model gives an expression for the hazard at time t for an individual with a given specification of a set of independent variables denoted by X to predict individuals’ hazard. The model assumes the relationship for one covariate where ho(t) is the baseline hazard function, xi are the covariates and β i are the coefficients. We also determined Cox regression estimates for all levels of each of the covariates. In which case, the hazard at time t for a subject in group is assumed to be The coefficients are assumed to be the same, regardless of group, but the baseline hazard can be group specific. The sign of the coefficient indicates how a covariate affects the hazard rate. The hazard ratio (HR), expressed as the exponentials of the coefficients, implies more exposure to event of interest if >1, HR < 1 means low exposure while HR = 1 has no effect on the exposure. The statistical significance of the coefficient indicates whether these changes in the expected duration will be statistically significant or not. In the stratified Cox analysis, we tested whether the proportional-hazards assumption was violated using the significance of the hazard ratios, the log likelihood tests and the Wald chi square statistics. The significant variables in the independent Cox regression were plugged into the multiple Cox regression so as to control for the effects of other variables. We fitted four models. The first model involved the two most significant independent variables at the bivariate logistic level, Model II involved the other socio-cultural characteristics of the women (place and regions of residence, ethnicity and religion). In model III we added education and age at marriage to the socio-cultural factors while model IV is the full model. The log likelihood tests and the Wald chi square statistics were used to select the best model. The data was weighted to adjust for differences in population in each state and FCT. Statistical significance was determined at p-value = 0.05. We used the Stata (version 13) statistical analysis software for all the analysis.

AI Digest

Study Justification:

– The timing of a woman’s first childbirth is a significant event in her life and can impact her career, education, and overall reproductive behavior.
– Early first childbirth in Nigeria has been linked to high population growth, fertility rates, and maternal morbidity and mortality.
– However, there is limited research on the progression and factors influencing the timing of first childbirth in Nigeria.
– This study aims to fill this research gap and provide insights into the socio-demographic factors affecting the timing of first childbirth among women in Nigeria.

Study Highlights:

– The study used data from the 2013 Nigeria Demographic and Health Survey, which is a nationally representative dataset.
– Survival analysis was used to model the timing of first childbirth, and Cox proportional hazard regression was used to identify significant factors.
– The study found that 50.1% of first childbirths occurred within the 15-19 years age bracket, and 38.1% occurred within the 20-29 years age bracket.
– The median survival time to first birth was 20 years overall, with variations between the Northern and Southern regions of Nigeria.
– Factors such as education, place and zone of residence, age at first marriage, religion, ethnicity, and use of contraceptives were found to significantly affect the age at first birth.

Recommendations for Lay Readers:

– The study highlights the importance of empowering women with quality education early in life to delay first childbirth and improve maternal health.
– Stakeholders should prioritize educating the girl child and work towards abolishing socio-cultural norms that promote early marriage and childbirth.
– Health education and promotion campaigns should be intensified, especially in rural areas and Northern Nigeria, to raise awareness about the benefits of delaying childbearing.

Recommendations for Policy Makers:

– Policy makers should prioritize investments in quality education for girls, as it has a significant impact on delaying first childbirth and reducing fertility rates.
– Efforts should be made to address socio-cultural norms that promote early marriage and childbirth, including legislation and awareness campaigns.
– Health education programs should be expanded, particularly in rural areas and Northern Nigeria, to promote the importance of delaying childbearing for maternal health.

Key Role Players:

– Ministry of Education: Responsible for implementing policies and programs to improve access to quality education for girls.
– Ministry of Women Affairs: Tasked with promoting gender equality and advocating for the rights of women, including efforts to abolish early marriage.
– Ministry of Health: Responsible for implementing health education and promotion campaigns, particularly in rural areas and Northern Nigeria.
– Non-Governmental Organizations (NGOs): Play a crucial role in implementing educational and health programs targeting girls and women.

Cost Items for Planning Recommendations:

– Education infrastructure: Funding for schools, classrooms, teachers, and educational materials.
– Scholarships and grants: Financial support to enable girls from disadvantaged backgrounds to access quality education.
– Awareness campaigns: Budget for designing and disseminating information materials on the importance of delaying childbearing.
– Health education programs: Funding for training healthcare workers, developing educational materials, and organizing community outreach activities.
Please note that the cost items provided are general examples and may 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 relatively strong, but there are some areas for improvement. The study used a national representative dataset and applied survival analysis to model the timing of first birth among women in Nigeria. The abstract provides information on the methodology, results, and conclusions of the study. However, it would be helpful to include more specific details on the sample size, data collection methods, and statistical analysis techniques used. Additionally, providing information on the limitations of the study and suggestions for future research would further strengthen the evidence.

Abstract

Data from the 2013 Nigeria Demographic and Health Survey (NDHS) [1], a cross-sectional national representative data, was used for this study. The National Population Commission (Nigeria) and ICF International, United States gave access and authorized the use of the data. The survey used clusters as the primary sampling unit based on the EAs from the 2006 census frame and sampled respondents using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas across the six zones, 36 states, and the Federal Capital Territory, Abuja. A total of 39,902 women aged 15–49 years were identified as eligible for individual interviews, and 98 % of them were successfully interviewed. We extracted information on the women’s background characteristics, sexual and reproductive history and knowledge, source and use of contraception. The dependent variable in this study was age at first child birth while region and geographical zones of residence, education, religion, residence and ethnicity were the independent variables. Included also as independent variables are responses on; if the woman ever smoked, whether she had terminated a pregnancy or not and whether she has ever used something to prevent pregnancy. We collapsed the six zones into two regions: The North Central, North East and North West constituted the “North” while South East, South South and South West formed the “South”. We used women’s current educational attainment as a proxy for education as of the time of first child birth. This is justified, because the educational status does not change for persons who had none or primary education throughout their lifetime since primary education is mostly attained at age 12. “Ever smoked” was used as a proxy for peer pressure. We used survival analysis to model the determinants of age at first birth. Survival analysis is analysis of history of events which uses statistical procedures to deal with analysis of time duration, until one or more events of interest happen. It is usual in a follow up study, such as the current study, for some participants not to have experienced the event of interest at the end of the study or some participants were “lost to follow up” or some might have withdrawn during the study. Bias may be introduced if these categories of participants were excluded in further analysis as they could possess unique characteristics that could be useful in answering the research question. In such cases, the length of time the participants stayed in the study would be recorded as their study time and marked as “censored”. Two quantitative terms are important in survival analysis. They are the survivor function S(t) and hazard function h(t). In relation to the present study, the survivor function gives the probability that a woman “survives” longer than some specified time t without a birth, while the hazard function gives the instantaneous potential per unit time to have a first childbirth after time t, given that the individual had not had a first childbirth up to time t. Survival and hazard function are mathematically denoted by and In contrast to the survivor function (S(t)) which describes the probability of not failing before time t, hazard function (h(t)) addresses the failure rate at time t among those individuals who are alive at time t. Also two variables are compulsory in survival analysis; they are survival time and the censoring index. The “survival time” or “follow-up time”, is assumed to be a discrete random variable that takes on only positive integer. In this study, the population at risk are all women involved in the study since they are all likely to give birth one time or the other. The “survival time” for age at first childbirth is the age of the women at first birth while the survival time for those with no birth as of the time of the survey was their current age at the time of the survey. Thus their censoring index were coded “1” and “0” respectively. The usual logistic regression techniques therefore, become unsuitable in a follow up study such as the current study where the follow up time could be determined and used in explaining the event of interest. Kaplan-Meier method, developed for scenarios where survival time is measured on a continuous scale whereby only intervals containing an event contribute to the estimate, was used to compute the survival estimates. The Kaplan-Meier estimates of S(t) were obtained from equation (3) where n j is the number of subject observed at time tj and dj is the number of subject that experienced the event of interest at time tj. We applied the Cox-proportional Hazard model to the age at first birth. The model assumed that proportion of hazards are constant from time to time. In proportional hazard model, the effect of a unit increase in covariate is multiplicative with respect to hazard rate. The Cox model gives an expression for the hazard at time t for an individual with a given specification of a set of independent variables denoted by X to predict individuals’ hazard. The model assumes the relationship for one covariate where ho(t) is the baseline hazard function, xi are the covariates and β i are the coefficients. We also determined Cox regression estimates for all levels of each of the covariates. In which case, the hazard at time t for a subject in group is assumed to be The coefficients are assumed to be the same, regardless of group, but the baseline hazard can be group specific. The sign of the coefficient indicates how a covariate affects the hazard rate. The hazard ratio (HR), expressed as the exponentials of the coefficients, implies more exposure to event of interest if >1, HR < 1 means low exposure while HR = 1 has no effect on the exposure. The statistical significance of the coefficient indicates whether these changes in the expected duration will be statistically significant or not. In the stratified Cox analysis, we tested whether the proportional-hazards assumption was violated using the significance of the hazard ratios, the log likelihood tests and the Wald chi square statistics. The significant variables in the independent Cox regression were plugged into the multiple Cox regression so as to control for the effects of other variables. We fitted four models. The first model involved the two most significant independent variables at the bivariate logistic level, Model II involved the other socio-cultural characteristics of the women (place and regions of residence, ethnicity and religion). In model III we added education and age at marriage to the socio-cultural factors while model IV is the full model. The log likelihood tests and the Wald chi square statistics were used to select the best model. The data was weighted to adjust for differences in population in each state and FCT. Statistical significance was determined at p-value = 0.05. We used the Stata (version 13) statistical analysis software for all the analysis.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide information and resources related to maternal health, including pregnancy care, childbirth preparation, and postnatal care. These apps can be easily accessible to women in Nigeria, especially in rural areas, where access to healthcare facilities may be limited.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help address the issue of limited access to healthcare facilities, particularly in remote areas. Pregnant women can receive medical advice, prenatal check-ups, and even emergency consultations through video calls or phone calls.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, education, and support to pregnant women in their communities. These workers can conduct regular check-ups, provide prenatal and postnatal care, and educate women about healthy pregnancy practices.

4. Maternal Health Education Programs: Implement comprehensive maternal health education programs that target women of reproductive age. These programs can provide information on family planning, prenatal care, nutrition, and the importance of delaying early childbirth. They can be conducted in schools, community centers, and through mass media channels.

5. Improved Access to Contraceptives: Increase access to and availability of contraceptives, including both modern methods and traditional methods, to empower women to make informed decisions about family planning and spacing of pregnancies. This can help reduce the prevalence of early first childbirths and improve maternal health outcomes.

6. Strengthening Healthcare Infrastructure: Invest in improving healthcare infrastructure, particularly in rural areas, by establishing more healthcare facilities, equipping them with necessary medical equipment and supplies, and ensuring the availability of skilled healthcare professionals. This will help ensure that pregnant women have access to quality maternal healthcare services.

7. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to provide a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy. This can help ensure that women have timely access to skilled birth attendants and emergency obstetric care when needed.

8. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to address the challenges in maternal health. This can involve initiatives such as providing financial support for maternal healthcare services, training healthcare professionals, and implementing innovative solutions to improve access to maternal health.

It is important to note that these recommendations are based on the information provided and may need to be further evaluated and tailored to the specific context and needs of Nigeria.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study is to prioritize and invest in quality education for girls. The study found that the education level of women significantly influenced the timing of their first childbirth. Women with higher education had a longer median survival time to first birth compared to those with no formal education. Therefore, empowering girls with quality education early in life can help delay first childbirths, which can contribute to fertility reduction and improved maternal health.

To implement this recommendation, stakeholders such as governments, NGOs, and educational institutions should focus on providing accessible and quality education for girls, particularly in rural areas and in Northern Nigeria where early first childbirths are more prevalent. This can be achieved through initiatives such as building schools, providing scholarships and financial support, improving teacher training, and promoting awareness about the importance of education for girls.

Additionally, efforts should be made to address socio-cultural norms that promote early marriage and betrothal of young girls. Advocacy campaigns and community engagement can help raise awareness about the negative consequences of early marriage and encourage families and communities to prioritize girls’ education and delay childbearing.

Furthermore, health education programs should be intensified, especially among rural dwellers and in Northern Nigeria, to promote the importance of delaying childbearing for maternal health. These programs can provide information on family planning methods, reproductive health, and the benefits of spacing pregnancies.

Overall, by prioritizing and investing in quality education for girls, addressing socio-cultural norms, and promoting health education, it is possible to improve access to maternal health by delaying first childbirths and reducing fertility rates.

AI Innovations Methodology

Based on the provided description, the study aimed to model the timing of first childbirth among women in Nigeria and determine the socio-demographic and other factors affecting its timing. The study used data from the 2013 Nigeria Demographic and Health Survey (NDHS), which is a cross-sectional national representative data.

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

1. Identify the recommendations: Based on the findings of the study, identify potential recommendations that could improve access to maternal health. For example, one recommendation could be to prioritize early education for girls to delay first childbirth and improve maternal health outcomes.

2. Define the simulation model: Develop a simulation model that captures the key variables and relationships relevant to maternal health access. This model should include factors such as education, age at first marriage, region of residence, and use of contraceptives, which were found to be significant in the study.

3. Set baseline values: Assign baseline values to the variables in the simulation model based on the study findings. For example, set the proportion of women with early first childbirth in the Northern region of Nigeria and rural areas according to the study’s results.

4. Introduce the recommendations: Modify the simulation model to incorporate the recommended interventions. For example, increase the proportion of girls receiving early education in the model.

5. Simulate the impact: Run the simulation model with the baseline values and the introduced recommendations. Analyze the results to determine the impact of the recommendations on improving access to maternal health. This could include measuring changes in the timing of first childbirth, maternal health outcomes, and other relevant indicators.

6. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the results. Vary the input parameters within a plausible range to see how the outcomes change. This can help identify the key drivers of the impact and assess the uncertainty associated with the recommendations.

7. Interpret and communicate the results: Analyze the simulation results and interpret the findings. Summarize the impact of the recommendations on improving access to maternal health and communicate the results to relevant stakeholders, policymakers, and healthcare providers.

By following this methodology, researchers can simulate the potential impact of recommendations on improving access to maternal health based on the findings of the study. This can provide valuable insights for decision-making and policy development in the field of maternal health in Nigeria.

Author

Dima Health

Statistics:

Citations: 19

Identifiers:

DOI: 10.1186/s12884-016-0895-y

Research Areas:

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

Study Countries:

Nigeria

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female