Trend and decomposition analysis of risk factors of childbirths with no one present in Nigeria, 1990-2018

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Study Justification:
– The study aimed to assess the trend and determinants of childbirths with no one present (NOP) at birth in Nigeria.
– This is an important topic as childbirths with NOP can pose significant risks to both the mother and the newborn.
– Understanding the factors contributing to NOP can help inform interventions and policies to improve maternal and newborn health outcomes.
Highlights:
– The prevalence of childbirths with NOP in Nigeria decreased significantly over the studied period, from 27% in 1990 to 11% in 2018.
– There were wide variations in NOP across different states in Nigeria, with the highest prevalence in Zamfara, Kano, and Katsina.
– The decomposition analysis showed that differences in women’s characteristics (endowment) and effects (coefficient) accounted for 85.4% and 14.6% of the changes in NOP, respectively.
– The most significant contributors to the changes were the decision-maker of healthcare utilization (49%) and women’s educational status (24%).
– Only Gombe experienced a significant increase in the level of NOP between 2003 and 2018.
Recommendations:
– To achieve zero prevalence of NOP at delivery in Nigeria, a special focus should be placed on healthcare utilization, enhancing maternal education, and empowering women in healthcare decision-making.
– Interventions should be tailored to address the specific needs and challenges in different states, particularly those with higher prevalence of NOP.
– Strengthening healthcare infrastructure and services, improving access to skilled birth attendants, and promoting community awareness and support for safe childbirth practices are essential.
Key Role Players:
– Ministry of Health: Responsible for policy development, coordination, and implementation of interventions to reduce NOP at delivery.
– Healthcare Providers: Including doctors, nurses, midwives, and community health workers who play a crucial role in providing skilled assistance during childbirth.
– Community Leaders and Traditional Birth Attendants: Engaging these stakeholders can help promote safe childbirth practices and encourage the use of skilled birth attendants.
– Women’s Education and Empowerment Organizations: Working to improve educational opportunities for women and empower them to make informed decisions about their healthcare.
Cost Items for Planning Recommendations:
– Healthcare Infrastructure: Budget for improving and expanding healthcare facilities, including maternity wards and delivery rooms.
– Skilled Birth Attendants: Allocate funds for training and capacity building of healthcare professionals to ensure an adequate number of skilled birth attendants.
– Education Programs: Invest in educational initiatives targeting women, including awareness campaigns, health education, and vocational training.
– Community Engagement: Allocate resources for community outreach programs, workshops, and support groups to promote safe childbirth practices and encourage community involvement.
Please note that the cost items provided are general categories and not actual cost estimates. Actual budget planning would require a detailed analysis and assessment of specific needs and resources available.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is cross-sectional and based on nationally representative data, which adds credibility to the findings. The authors used a multivariate decomposition analysis to identify the factors contributing to the changes in childbirths with no one present (NOP) at delivery. However, the abstract lacks specific details about the sampling methodology and the statistical analysis techniques used. To improve the strength of the evidence, the authors should provide more information about the sampling methodology, including the sampling frame and the sampling units at each stage. Additionally, they should describe the statistical analysis techniques in more detail, including the models used in the decomposition analysis and any assumptions made. Providing this information would enhance the transparency and replicability of the study.

Objectives To assess the trend and decompose the determinants of delivery with no one present (NOP) at birth with an in-depth subnational analysis in Nigeria. Design Cross-sectional. Setting Nigeria, with five waves of nationally representative data in 1990, 2003, 2008, 2013 and 2018. Participants Women with at least one childbirth within 5 years preceding each wave of data collection. Primary and secondary outcome measures The outcome of interest is giving birth with NOP at delivery defined as childbirth assisted by no one. Data were analysed using Χ 2 and multivariate decomposition analyses at a 5% significance level. Results The prevalence of having NOP at delivery was 15% over the studied period, ranges from 27% in 1990 to 11% in 2018. Overall, the prevalence of having NOP at delivery reduced significantly by 35% and 61% within 2003-2018 and 1990-2018, respectively (p<0.001). We found wide variations in NOP across the states in Nigeria. The highest NOP practice was in Zamfara (44%), Kano (40%) and Katsina (35%); while the practice was 0.1% in Bayelsa, 0.8% in Enugu, 0.9% in Osun and 1.1% in Imo state. The decomposition analysis of the changes in having NOP at delivery showed that 85.4% and 14.6% were due to differences in women's characteristics (endowment) and effects (coefficient), respectively. The most significant contributions to the changes were the decision-maker of healthcare utilisation (49%) and women educational status (24%). Only Gombe experienced a significant increase (p<0.05) in the level of having NOP between 2003 and 2018. Conclusion A long-term decreasing secular trend of NOP at delivery was found in Nigeria. NOP is more prevalent in the northern states than in the south. Achieving zero prevalence of NOP at delivery in Nigeria would require a special focus on healthcare utilisation, enhancing maternal education and healthcare utilisation decision-making power.

We used secondary data extracted from five successive NDHS conducted in 1990, 2003, 2008, 2013 and 2018.18–22 The NDHS is cross-sectional, population-based and nationally representative in design. The respondents were women aged 15–49 years. However, our analysis was restricted to respondents who reported at least one birth delivery within 5 years preceding each of the surveys. Geographically, Nigeria is divided into six geopolitical zones (regions), and these regions are further subdivided into states and Federal Capital Territory (FCT) for administrative purposes. As of 1990, Nigeria has 21 states. These were then divided and grouped into 30 states and the FCT in 1991. Additional six states were created in 1996 which resulted in the present number of 36 states (figure 1). Map of Nigeria showing the 36 states and the Federal Capital Territory, by the geopolitical zones. A multistage cluster sampling technique was used where the clusters are the primary sampling unit. Local government areas (LGAs) were selected from each state and FCT in the first stage. Enumeration areas were then extracted from each LGA at the second stage, and households and household representatives were randomly selected for questioning in the last stage. For further details on the sampling methodology, please visit wwwdhsprogramcom. In all, 8781, 7620, 33 385, 38 984 and 41 821 women participated in 1990, 2003, 2008, 2013 and 2018, respectively.18–22 We used the data on the delivery of the last pregnant by any of these respondents within 5 years preceding the surveys. A total of 4874, 3761, 17 920, 20 100 and 21 792 eligible deliveries for 1990, 2003, 2008, 2013 and 2018 NDHS, respectively, were included in this study. The outcome variable was whether a birth delivery was assisted or not irrespective of who offered the assistance. The reported birth delivery assistants by the respondents are skilled (doctors, nurses and midwives), unskilled (traditional, community health worker, auxiliary nurses, family, friends) and having NOP at delivery.16–22 The outcome was categorised as NOP at delivery versus anyone present. The explanatory variables used in this study consist of individual, household, community and societal factors. They were identified based on extensive literature search and review.16–19 21 22 Andersen behavioural model and healthcare utilisation30 was also used. In addition, we adopted and enlarged the behavioural–ecological framework of healthcare access and navigation to select the explanatory variables in this study.31 The variables are the following: We used descriptive statistics to report the frequency distribution and prevalence of NOP at delivery as well as its percentage changes by the explanatory characteristics and state of residence. We examined trends in NOP at delivery for 1990–2003, 2003–2008, 2008–2013, 2013–2018, 2003–2018 and 1990–2018. The Χ2 analysis for trend was used to identify the significant changes across multiple time points. Multivariate decomposition analysis (MDA) was employed to decompose changes in NOP at delivery between 2003 and 2018. Data management and analysis were conducted using Stata V.16.0, R statistical software and Power BI were used for the visualisations. Samples were weighted using weighting factors included in the NDHS data to account for unequal group sizes, and all significance tests were at 5%. The MDA allows the quantification of the contributions of different factors to changes in outcome measurements over two time points or among two groups of people with different outcomes. Unlike the logistic regressions that identify the odds of an event occurring, the MDA uses different models including the logistic regression to identify the contributions of explanatory variables to the differentials in the probability of events occurring in different groups. In which case, the groups are mutually exclusive. In the decomposition analysis, we excluded 1990 data and considered only 2003–2018, as there were only 19 states in Nigeria as of 1990 and thereby would disallow full comparison across the current 36 states in Nigeria. The difference in respondents’ NOP at delivery is the response variable, 2003 constituted a ‘group’ while 2018 is another ‘group’, while predictor effects were partitioned into differences in characteristics (endowment) and differences in the effects (coefficient) in the regression decomposition.35 This enables the identification of the root of changes in NOP between 2003 and 2018 and evaluates how changes in NOP at delivery were affected by the explanatory characteristics. The MDA technique is an improvement of the Oaxaca-Blinder decomposition,36 37 which has been extended to handle non-linear models including logit and probit models.35 38 In this study, the decomposition of the difference in the factors influencing NOP at delivery is a function of a linear combination of the predictors and regression coefficients and can be in general, additively decomposed into: where Y is the n by 1 vector of the dependent variable, 0≤p≤1, X is the n by k matrices of the independent variables and β is the k by 1 vector of the regression coefficients in equation (1). The difference in the proportion of respondents with NOP was decomposed in equation (2) into two parts. In equation (3), the component {F(XPβP)–F(X1-PβP)} is the differential attributable to differences in endowment (otherwise called the explained component), while {F(X1-PβP)–F(X1-Pβ1-P)} is the differential attributable to differences in coefficients (unexplained component). Also, YP denotes the proportion of respondents with NOP at delivery (comparison group), while Y1-P denotes the proportion of respondents with someone present at delivery (reference group). The method has been used elsewhere.39

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

1. Telemedicine and Telehealth: Implementing telemedicine and telehealth services can provide remote access to healthcare professionals, allowing pregnant women in remote or underserved areas to receive prenatal care and consultations without having to travel long distances.

2. Mobile Health (mHealth) Applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take control of their own health and make informed decisions.

3. Community Health Workers: Training and deploying community health workers who can provide basic prenatal care, health education, and referrals to pregnant women in their communities can help bridge the gap in access to maternal health services, especially in rural areas.

4. Transportation Support: Establishing transportation services or partnerships to ensure that pregnant women have access to reliable and affordable transportation to healthcare facilities for prenatal visits, delivery, and postnatal care.

5. Maternal Health Vouchers: Implementing voucher programs that provide financial assistance to pregnant women, particularly those from low-income backgrounds, to cover the costs of prenatal care, delivery, and postnatal care.

6. Maternity Waiting Homes: Establishing maternity waiting homes near healthcare facilities can provide a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy, ensuring they are close to medical care when labor begins.

7. Health Education Campaigns: Conducting targeted health education campaigns to raise awareness about the importance of prenatal care, safe delivery practices, and postnatal care can help improve maternal health outcomes and encourage women to seek timely healthcare.

8. Strengthening Health Systems: Investing in the overall improvement of health systems, including infrastructure, staffing, and equipment, can enhance the quality and availability of maternal health services.

It’s important to note that the specific context and needs of Nigeria should be taken into consideration when implementing these innovations.
AI Innovations Description
Based on the description provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthen healthcare utilization: Focus on improving access to healthcare services during pregnancy and childbirth. This can be achieved by increasing the number of skilled healthcare providers, ensuring availability of essential medical supplies and equipment, and improving the quality of healthcare facilities.

2. Enhance maternal education: Implement programs and initiatives that promote education for women, particularly in areas with high prevalence of childbirth with no one present. This can include providing scholarships, vocational training, and awareness campaigns to emphasize the importance of education for maternal health.

3. Empower women in healthcare decision-making: Promote women’s involvement in decision-making processes related to their healthcare, including the choice of healthcare provider and place of delivery. This can be done through community engagement, education, and advocacy programs that empower women to make informed decisions about their maternal health.

4. Address regional disparities: Recognize and address the wide variations in childbirth practices across different states in Nigeria. Develop targeted interventions and policies that specifically address the needs and challenges faced by regions with high prevalence of childbirth with no one present.

5. Long-term monitoring and evaluation: Establish a comprehensive monitoring and evaluation system to track the progress and impact of interventions aimed at improving access to maternal health. This will help identify areas of improvement and ensure the sustainability of the implemented innovations.

By implementing these recommendations, it is possible to develop innovative solutions that can effectively improve access to maternal health in Nigeria.
AI Innovations Methodology
Based on the provided description, here are some potential innovations that could improve access to maternal health:

1. Telemedicine and Telehealth: Implementing telemedicine and telehealth services can provide remote access to healthcare professionals, allowing pregnant women in remote or underserved areas to receive prenatal care and consultations without having to travel long distances.

2. Mobile Health (mHealth) Applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take an active role in their own healthcare and make informed decisions.

3. Community Health Workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in their local communities can help bridge the gap in access to maternal health services, especially in rural areas.

4. Transportation Support: Establishing transportation networks or partnerships to provide affordable and reliable transportation options for pregnant women to reach healthcare facilities can address the barriers related to distance and transportation.

5. Maternal Health Vouchers: Introducing voucher programs that cover the cost of maternal health services for low-income women can remove financial barriers and ensure access to quality care.

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 key 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, or the percentage of women delivering in healthcare facilities.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or region.

3. Define the intervention scenarios: Develop different scenarios that represent the implementation of the recommended innovations. For example, one scenario could simulate the impact of implementing telemedicine services, while another scenario could simulate the impact of deploying community health workers.

4. Model the impact: Use statistical or mathematical models to estimate the potential impact of each scenario on the selected indicators. This could involve analyzing historical data, conducting surveys or interviews, and applying appropriate statistical techniques.

5. Compare the scenarios: Compare the projected outcomes of each scenario to determine which innovations are likely to have the greatest impact on improving access to maternal health. Consider factors such as cost-effectiveness, scalability, and feasibility of implementation.

6. Refine and validate the model: Continuously refine the model based on new data and feedback. Validate the model by comparing the projected outcomes with real-world data or conducting pilot studies to assess the actual impact of implementing the recommended innovations.

By following this methodology, policymakers and healthcare providers can make informed decisions about which innovations to prioritize and invest in to improve access to maternal health.

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