Proximate and socio-economic determinants of under-five mortality in Benin, 2017/2018

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Study Justification:
– Under-five mortality remains a critical public health problem in Benin, a sub-Saharan African country.
– There is a lack of empirical information using nationally representative data to explain the factors contributing to under-five mortality in Benin.
Study Highlights:
– The study analyzed data from the 2017 to 2018 Benin Demographic and Health Surveys.
– The study found an under-five mortality rate of 96 deaths per 1000 live births in Benin.
– Socio-economic factors such as region, rural areas, and birth rank and interval were associated with higher risk of under-five mortality.
– Proximate factors such as lack of postnatal check-up visits were also associated with higher risk of under-five mortality.
Study Recommendations:
– Implement socio-economic interventions to address the disparities in access and utilization of healthcare services in Benin.
– Implement proximate interventions, particularly related to postnatal check-up visits, to reduce under-five mortality.
– Develop comprehensive, long-term public health interventions to address the determinants of under-five mortality in Benin.
Key Role Players:
– Ministry of Health: Responsible for implementing and coordinating interventions to reduce under-five mortality.
– Healthcare providers: Involved in delivering healthcare services, including postnatal check-up visits.
– Community leaders and organizations: Engage in community-based interventions and awareness campaigns.
– Non-governmental organizations (NGOs): Provide support and resources for implementing interventions.
Cost Items for Planning Recommendations:
– Healthcare infrastructure: Investment in healthcare facilities and equipment.
– Healthcare workforce: Training and hiring healthcare professionals.
– Health education and awareness campaigns: Funding for community-based interventions and public health campaigns.
– Access to healthcare services: Improving transportation and infrastructure to ensure access to healthcare facilities.
– Monitoring and evaluation: Establishing systems to track progress and measure the impact of interventions.
Please note that the provided cost items are general categories and not actual cost estimates. Actual cost planning would require a detailed analysis and budgeting process.

Background Globally, under-five mortality has declined significantly, but still remains a critical public health problem in sub-Saharan African countries such as Benin. Yet, there is no empirical information in the country using a nationally representative data to explain this phenomenon. The aim of this study was to examine how proximate and socio-economic factors are associated with mortality in under-five children in Benin. Methods We analysed data of 5977 under-five children using the 2017 to 2018 Benin Demographic and Health Surveys. Multivariable hierarchical logistic regression modelling technique was applied to investigate the factors associated with under-five mortality. The fit of the models were assessed using variance inflation factor and Pseudo R 2. Results were reported as adjusted odds ratios (aORs). All comparisons were considered to be statistically significant at p2 years of birth interval (aOR=1.52; 95% CI: 1.07 to 2.17). Among the proximate determinants, we found the probability of death to be higher in children whose mothers had no postnatal check-up (PNC) visits after delivery (aOR=1.79; 95% CI: 1.22 to 2.63), but there was no significant association between individual-level/household-level factors and under-five mortality. Conclusion This study has established that socio-economic and proximate factors are important determinants of under-five mortality in Benin. Our findings have shown the need to implement both socio-economic and proximate interventions, particularly those related to PNC visits when planning on under-five mortality. To achieve this, a comprehensive, long-term public health interventions, which consider the disparity in the access and utilisation of healthcare services in Benin are key.

Data from the 2017 to 2018 Benin Demographic and Health Surveys (BDHS) was used in this study. Specifically, the birth recode file, which contains data of all births, was used. Data of 5977 under-five children, which formed the unit of analysis in this study, were obtained by interviewing women who had given birth within 5 years to the survey. The BDHS used a multistage, stratified sampling design in selecting all eligible women and men for interviews from households that were considered as sampling units.23 This study adapted Mosley and Chen’s conceptual framework of child survival in developing countries (figure 1)22 as its conceptual framework. The framework helped in selecting variables available in the 2017 to 2018 BDHS datasets for the analyses. The adapted constructs of the conceptual framework are shown in figure 1. Conceptual framework of determinants of under-five mortality Source.22 PNC, postnatal check-up. The outcome variable was under-five mortality, defined as the death of a child within the first 5 years of life. We re-coded it into a binary variable as (0=No and 1=Yes). The explanatory variables considered in this study include community-level and household-level/individual-level socio-economic variables and proximate determinants variables. The community-level socio-economic variables include region and place of residence. The household-level and individual-level socio-economic variables were wealth index, mother’s ethnicity, mother’s religion, maternal education and occupation, and partner’s education and occupation. The proximate determinants include sex of child, birth size, birth rank and birth interval, mother’s age at childbirth, ANC visit, tobacco use, place of delivery, type of assistance during delivery and postnatal check-up (PNC) visits. The various categories for these determinants can be found in table 1. U5MR (per 1000 live births) and uOR by explanatory variables (n=5977, weighted) *p<0.05; **p<0.01; and ***p<0.001. U5MR, under-five mortality rate; uOR, unadjusted OR. Descriptive and multiple regression analyses were performed in this study. The first step of the analyses involved the use of frequency tabulations to describe the proportions of all the explanatory variables, followed by a distribution of under-five mortality per the explanatory variables, with their respective CIs. Then, we conducted a bivariate logistic regression analysis with each of the explanatory variables and the outcome variable (under-five mortality) to assess the link between all the potential determinants and deaths in under-five children without adjusting for the effect of other covariates. This was followed by a multicollinearity test on all the explanatory variables to determine if there was evidence of multicollinearity. Using the variance inflation factor (VIF), the multicollinearity test results indicated no collinearity among the explanatory variables (mean VIF=1.49, max VIF=1.66 and minimum=1.01). Next, we performed a multivariable hierarchical logistic regression analysis in three stages. First, community-level socio-economic determinants variables were fitted in the first model to assess their association with under-five mortality (Model I). This was followed by the inclusion of household-level/individual-level socio-economic variables (Model II). Proximate determinants variables were added in the final model to also examine their association with deaths in under-five children (Model III). Categories of the explanatory variables that had the lowest under-five mortality rates were used as reference categories. The goodness-of-fit of the logistic models was assessed using Pseudo R2. Data processing and analysis were performed using Stata, V.14.2 (Stata Corp, College Station, Texas, USA). We further applied sample weight (v005/1 000 000) to correct for oversampling and undersampling. The SVY command in Stata was used to account for the complex survey design and generalisability of the findings. We further used the ‘syncmrates’ command to calculate the under-five mortality rates using the synthetic cohort probability.15 Patients and the public were not involved in the design and conduct of this research.

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 pregnant women and new mothers with access to important health information, appointment reminders, and personalized care plans. These apps can also facilitate communication with healthcare providers and enable remote monitoring of maternal and fetal health.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals via video calls. This can help overcome geographical barriers and provide timely access to prenatal care and medical advice.

3. Community Health Workers: Train and deploy community health workers who can provide maternal health education, conduct regular check-ups, and assist with referrals for higher-level care. These workers can bridge the gap between healthcare facilities and communities, particularly in rural areas.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access essential maternal health services, such as antenatal care, skilled birth attendance, and postnatal care. These vouchers can help reduce financial barriers and increase utilization of maternal health services.

5. Maternity Waiting Homes: Establish maternity 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 comfortable environment for women to stay during the final weeks of pregnancy, ensuring timely access to skilled birth attendance.

6. Transportation Support: Develop transportation support systems, such as ambulances or community transport services, to facilitate the transportation of pregnant women to healthcare facilities for antenatal care, delivery, and postnatal care. This can address transportation challenges and ensure timely access to maternal health services.

7. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, pharmacies, and technology companies to expand service delivery and reach underserved populations.

8. Maternal Health Education Programs: Implement comprehensive maternal health education programs that target women, families, and communities. These programs can raise awareness about the importance of antenatal care, skilled birth attendance, and postnatal care, as well as promote healthy behaviors during pregnancy and childbirth.

9. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to enhance the provision of maternal health services. This can involve training healthcare providers, improving infrastructure and equipment, and implementing evidence-based clinical guidelines.

10. Data-driven Decision Making: Utilize data from national surveys, such as the Benin Demographic and Health Surveys, to identify gaps and prioritize interventions for improving maternal health. Data analysis can help identify high-risk areas, target resources effectively, and monitor the impact of interventions over time.
AI Innovations Description
Based on the study findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

Implement a comprehensive, long-term public health intervention that addresses both socio-economic and proximate factors contributing to under-five mortality in Benin. This intervention should prioritize improving access to postnatal check-up (PNC) visits for mothers after delivery.

To achieve this, the following steps can be taken:

1. Increase awareness and education: Launch a public awareness campaign to educate mothers and communities about the importance of postnatal check-ups and the potential risks associated with not receiving them. This can be done through various channels such as radio, television, community meetings, and health clinics.

2. Strengthen healthcare infrastructure: Improve the availability and accessibility of healthcare facilities, particularly in rural areas and the Plateau region where the risk of under-five mortality was found to be higher. This can involve building new healthcare centers, equipping existing facilities with necessary resources, and training healthcare providers to deliver quality postnatal care.

3. Enhance transportation services: Address transportation barriers that hinder mothers from accessing postnatal check-ups. This can be done by providing affordable transportation options, such as community-based transportation services or mobile clinics, to ensure that mothers can easily reach healthcare facilities.

4. Promote community engagement: Engage community leaders, local organizations, and community health workers to promote the importance of postnatal check-ups and encourage mothers to seek these services. This can involve conducting community outreach programs, organizing support groups for mothers, and involving community members in decision-making processes related to maternal health.

5. Monitor and evaluate: Establish a monitoring and evaluation system to track the implementation and impact of the intervention. Regularly assess the coverage and quality of postnatal check-ups, as well as the reduction in under-five mortality rates. This will help identify areas for improvement and ensure the sustainability of the intervention.

By implementing these recommendations, it is expected that access to maternal health services, particularly postnatal check-ups, will improve, leading to a reduction in under-five mortality rates in Benin.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase access to postnatal check-up (PNC) visits: Implement strategies to ensure that all mothers receive postnatal check-ups after delivery. This could include improving awareness and education about the importance of PNC visits, providing transportation services for mothers in remote areas, and strengthening healthcare infrastructure to accommodate increased demand for PNC services.

2. Improve healthcare access in rural areas: Develop initiatives to address the disparities in healthcare access between urban and rural areas. This could involve establishing mobile clinics or telemedicine services to reach remote communities, training and deploying more healthcare professionals in rural areas, and providing financial incentives for healthcare providers to work in underserved regions.

3. Enhance maternal education and awareness: Implement programs to improve maternal education and awareness about maternal health. This could include providing comprehensive prenatal and postnatal education, promoting healthy behaviors during pregnancy, and addressing cultural and social barriers that prevent women from seeking maternal healthcare services.

4. Strengthen healthcare infrastructure: Invest in improving healthcare infrastructure, including the construction and renovation of healthcare facilities, ensuring the availability of essential medical equipment and supplies, and enhancing the capacity of healthcare workers to provide quality maternal healthcare services.

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

1. Define indicators: Identify specific indicators that can measure the impact of the recommendations, such as the percentage increase in PNC visits, the reduction in maternal mortality rates, or the improvement in healthcare access in rural areas.

2. Collect baseline data: Gather data on the current state of maternal health access, including the number of PNC visits, maternal mortality rates, and healthcare infrastructure in different regions.

3. Develop a simulation model: Create a simulation model that incorporates the identified indicators and baseline data. This model should consider various factors, such as population demographics, healthcare resources, and geographical distribution.

4. Introduce the recommendations: Input the proposed recommendations into the simulation model and assess their potential impact. This could involve adjusting variables related to PNC visits, healthcare access in rural areas, maternal education, and healthcare infrastructure.

5. Run simulations: Run multiple simulations using different scenarios to evaluate the potential outcomes of implementing the recommendations. This could include varying the intensity of interventions, considering different timeframes for implementation, and assessing the cost-effectiveness of each scenario.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This could involve comparing the outcomes of different scenarios, identifying key drivers of change, and assessing the feasibility and sustainability of the proposed interventions.

7. Refine and validate the model: Continuously refine and validate the simulation model based on new data and feedback from stakeholders. This will ensure that the model accurately reflects the real-world context and can provide reliable predictions for decision-making.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on which interventions to prioritize and implement.

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