Disparities in pregnancy-related deaths: Spatial and Bayesian network analyses of maternal mortality ratio in 54 African countries

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Study Justification:
– Maternal mortality remains a significant public health problem, particularly in Africa.
– Despite global efforts, the number of pregnancy-related deaths in Africa is still high.
– This study aims to examine the spatial distribution of maternal mortality in Africa and explore the influence of social determinants of health (SDoH) on this distribution.
– By understanding the factors contributing to maternal mortality, effective strategies can be designed to address the issue.
Study Highlights:
– The average prevalence of maternal mortality ratio (MMR) in Africa was found to be 415 per 100,000 live births.
– Spatial analysis revealed clusters (hotspots) of high MMR in countries within the Middle and West Africa regions.
– Cold spot clusters, with significantly low MMR, were observed in South Africa and Namibia.
– Gender inequities and the proportion of skilled birth attendants were identified as the strongest social determinants associated with maternal mortality in Africa.
Recommendations for Lay Reader and Policy Maker:
– Design effective strategies to address gender inequalities and the shortage of health professionals to reduce maternal mortality in Africa.
– Focus interventions on countries within the Middle and West Africa regions, where the prevalence of maternal mortality is highest.
– Improve access to emergency healthcare during childbirth, antenatal care, and skilled birth attendants.
– Promote sexual and reproductive health through family planning, reducing adolescent birth rates, and preventing child marriage.
Key Role Players:
– Ministries of Health in African countries
– International organizations (e.g., World Health Organization, United Nations)
– Non-governmental organizations (NGOs) working in maternal health
– Health professionals (doctors, nurses, midwives)
– Community health workers
– Researchers and academics in the field of maternal health
Cost Items for Planning Recommendations:
– Training and capacity building for healthcare professionals
– Infrastructure development for healthcare facilities
– Health education and awareness campaigns
– Supply of essential medical equipment and medications
– Monitoring and evaluation systems
– Research and data collection on maternal health indicators
– Collaboration and coordination efforts between stakeholders

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on an ecological study using aggregated country-level data from multiple databases. The study includes descriptive analyses, spatial analysis, regression analysis, and Bayesian network analysis. While the study provides valuable insights into the spatial distribution and social determinants of maternal mortality in Africa, there are some limitations that could be addressed to improve the strength of the evidence. These include: 1) The use of aggregated country-level data may mask individual-level variations and limit the generalizability of the findings. Collecting individual-level data would provide a more comprehensive understanding of the factors influencing maternal mortality. 2) The reliance on secondary data sources introduces the possibility of data inaccuracies and inconsistencies. Collecting primary data through rigorous research methods would enhance the reliability of the findings. 3) The study does not mention any measures taken to control for confounding variables, which could affect the validity of the results. Conducting multivariable analyses and adjusting for potential confounders would strengthen the evidence. 4) The abstract does not provide information on the sample size or representativeness of the included countries, which makes it difficult to assess the generalizability of the findings. Including this information would improve the transparency of the study. Overall, addressing these limitations would enhance the strength of the evidence and provide more robust insights into the factors contributing to maternal mortality in Africa.

Background Maternal mortality remains a public health problem despite several global efforts. Globally, about 830 women die of pregnancy-related death per day, with more than two-third of these cases occurring in Africa. We examined the spatial distribution of maternal mortality in Africa and explored the influence of SDoH on the spatial distribution. Methods We used country-level secondary data of 54 African countries collected between 2006 and 2018 from three databases namely, World Development Indicator, WHO’s Global Health Observatory Data and Human Development Report. We performed descriptive analyses, presented in tables and maps. The spatial analysis involved local indicator of spatial autocorrelation maps and spatial regression. Finally, we built Bayesian networks to determine and show the strength of social determinants associated with maternal mortality. Results We found that the average prevalence of maternal mortality ratio (MMR) in Africa was 415 per 100 000 live births. Findings from the spatial analyses showed clusters (hotspots) of MMR with seven countries (Guinea-Bissau, Guinea, Sierra Leone, Cote d’Ivoire, Chad and Cameroon, Mauritania), all within the Middle and West Africa. On the other hand, the cold spot clusters were formed by two countries; South Africa and Namibia; eight countries (Algeria, Tunisia, Libya, Ghana, Gabon and Congo, Equatorial Guinea and Cape Verde) formed low-high clusters; thus, indicating that these countries have significantly low MMR but within the neighbourhood of countries with significantly high MMR. The findings from the regression and Bayesian network analysis showed that gender inequities and the proportion of skilled birth attendant are strongest social determinants that drive the variations in maternal mortality across Africa. Conclusion Maternal mortality is very high in Africa especially in countries in the middle and western African subregions. To achieve the target 3.1 of the sustainable development goal on maternal health, there is a need to design effective strategies that will address gender inequalities and the shortage of health professionals.

This is an ecological study based on aggregated country-level data extracted from three publicly available databases: World Development Indicator, WHO’s Global Health Observatory Data and Human Development Report (see online supplemental file 1). Data from 54 African countries were included in the study, the data were majorly based on nationally representative cross-sectional surveys such as Demographic and Health Surveys, Household Income and Expenditure Surveys, House Living Standard and Socioeconomic Surveys. The study year for the variables differs, ranging from 2006 to 2018; detailed description of data collection and survey designed are published elsewhere.16–18 bmjgh-2020-004233supp001.pdf MMR was defined and computed as the number of women per 100 000 live births who died from pregnancy-related causes during pregnancy or within 42 days of pregnancy termination. This is computed by dividing recorded or estimated maternal death (non-AIDS women aged 15–49 years) by the total record or estimated live birth within the same period. The data were extracted from the World Bank Development Indicator, which is based on estimations from raw data collection in national representative cross-sectional surveys such as household income and expenditure surveys, house living standard and socioeconomic surveys. Twenty-three independent variables measured at country-level were involved in the study. They were selected based on previous research on maternal mortality in Africa, and to cover the different aspects of SDH framework (see figure 1). As demonstrated in figure 1, we operationalised the selected independent variables using the social determinant models to highlight the possible relationships between MMR and the selected variables. The country-level demographic, socioeconomic and sociocultural factors examined include income inequality, Gross National Income (GNI) per capita, Gender Inequality Index (GII), urban residence, poverty rate, crude birth rate and female educational status. Behavioural factors involved are the prevalence of adult female currently smoking and total alcohol per capita consumption in female adult. We included prevalence of anaemia during pregnancy, the prevalence of obesity among female adults (age-standardised); underweight among female adults (as a proxy for maternal nutrition), the prevalence of hypertension among adult females (age-adjusted) and prevalence of diabetes mellitus in female adults (age-adjusted). Finally, we included factors associated with healthcare service coverage; the percentage of deliveries by caesarean section (CS, as a proxy for access to emergency healthcare during childbirth), antenatal care (ANC) coverage—at least four visits, skilled birth attendants during delivery, and proportion of women of reproductive age who have their need satisfied for family planning, adolescent birth rate and prevalence of child marriage—proxies for sexual and reproductive health. We hypothesised that the overarching determinants in the outermost box of the framework adapted from the WHO will influence factors in inner boxes, and this will further modify MMR in Africa. We conducted multicollinearity test and removed highly correlated variable by picking most relevant one maternal mortality research. Social determinants framework for maternal death. We conducted a descriptive analysis to explore the distribution of the dependent and independent factors examined in the social determinants of health (SDoH) framework. We adapted the WHO framework to build our models in three main domains (socioeconomic/cultural, healthcare resources and maternal conditions) for easy interpretations and policy implications. The values were expressed as mean with SD or median with IQR. We used GeoDa v. 1.14 software to perform the spatial analysis, it is more suitable because it explicitly handles spatial data and allows statistical test findings as desired in this study.19 Country was used as a unit scale for spatial analysis, the distance-based threshold of 4272 km (Arc distance) was generated with X and Y coordinates from shapefile downloaded from Intergovernmental Action on Development Climate Prediction and Application Centre (ICPAC) GeoPortal (http://geoportal.icpac.net/). The appropriateness of this spatial weight was confirmed by assessing the symmetry of the connectivity histogram. The connectivity map also showed that all the 54 countries interlinked which is necessary to ascertain spatial dependency. We generated quantile cluster map to show the spatial distribution of the MMR in Africa descriptively. We also performed Global Moran’s I analysis to examine if spatial autocorrelation occurs at local level. Local indicator of spatial autocorrelation (LISA) cluster maps was also generated to statistically show the hot and cold spots spatial clusters of neighbouring countries with high and low MMR, respectively. Local spatial autocorrelation was measured with Local Moran’s I index which ranges from −1 to +1; with positive (+) values indicating strong clustering and negative (–) values indicating dispersion. Finally, we conducted spatial regression based on approaches developed by Anselin20 to investigate the association between MMR and independent variables. Six models were built with the first five covering a different aspect of SDoH framework and the final model only contained significant variables from each model. A pair-wise correlation was used to deal with missing data which were <2%. Ordinary least square (OLS) diagnostic was examined for each model, where spatial dependency was indicated, the model was fitted with spatial error or spatial lag regression as appropriate; best-fit model was determined using R-squared, Log likelihood and Akaike information criterion. We also tested for multicollinearity of independent variables, value <30 was used;20 999 Monte Carlo permutation was used for randomisation to ensure p-value<0.05. We used the Bayesian network analysis to examine the strength of the relationship between MMR and the determinants. A machine learning R package called ‘bnlearn’ was used for the Bayesian network modelling;21 22 it is based on GeNie software. By using the variables that showed significant association in the final model of the spatial regression, we built the structure of the network from the domain knowledge of the authors on temporal precedence of the variables, using score-based structure algorithms. We introduced geographical regions based on the United Nation’s categorisation as a variable to deal with possible residual cofounding and accounted for missing values by using the expectation–maximisation algorithm. All the variables were continuous data, we discretised them by their median values to show the diagnostic conditional probability distribution. We used supervised machine learning techniques to predict the joint conditional probability of significant independent variables from the Bayesian network when countries with higher MMR are reduced to 70 MMR per 100 000; the target for the 2030 SDG for MMR. Finally, we used k-fold cross-validation (at k=10) to compare and examine the Bayesian model’s goodness of fit; log-likelihood loss was used as the loss function, therefore, the lower the value, the better the fit.23 No patients or the public were directly involved in the design, conduct, reporting or dissemination plans of this research.

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

1. Telemedicine: Implementing telemedicine 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 information and resources on maternal health, such as prenatal care guidelines, nutrition advice, and appointment reminders, can empower women to take control of their own health and access necessary information easily.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities can help bridge the gap in access to healthcare services, particularly in rural or marginalized areas.

4. Transportation services: Establishing transportation services specifically for pregnant women, especially in areas with limited transportation options, can ensure that women can reach healthcare facilities for prenatal check-ups, delivery, and emergency care in a timely manner.

5. Maternal health clinics: Setting up dedicated maternal health clinics that offer comprehensive prenatal care, delivery services, and postnatal care can provide a centralized and specialized approach to maternal healthcare, making it more accessible and convenient for women.

6. Financial incentives: Implementing financial incentives, such as cash transfers or subsidies, for pregnant women to seek prenatal care and deliver in healthcare facilities can help overcome financial barriers and encourage women to access the necessary care.

7. Public awareness campaigns: Conducting public awareness campaigns to educate communities about the importance of maternal health, the available services, and the benefits of seeking timely care can help reduce cultural and social barriers that prevent women from accessing healthcare.

8. Partnerships with non-governmental organizations (NGOs): Collaborating with NGOs that specialize in maternal health can leverage their expertise and resources to improve access to quality care, especially in resource-constrained settings.

9. Task-shifting: Training and empowering midwives and other healthcare professionals to take on additional responsibilities traditionally performed by doctors can help alleviate the shortage of skilled birth attendants and increase access to safe deliveries.

10. Strengthening health systems: Investing in the overall strengthening of healthcare systems, including infrastructure, equipment, and training of healthcare professionals, can improve the quality and availability of maternal health services.

It’s important to note that the specific context and needs of each country or region should be taken into account when considering these innovations.
AI Innovations Description
Based on the research findings and recommendations from the study titled “Disparities in pregnancy-related deaths: Spatial and Bayesian network analyses of maternal mortality ratio in 54 African countries,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Design effective strategies to address gender inequalities: Gender inequities were identified as one of the strongest social determinants driving variations in maternal mortality across Africa. To improve access to maternal health, innovative interventions can be developed to address gender inequalities, such as promoting women’s empowerment, ensuring equal access to education and economic opportunities, and challenging harmful gender norms and practices.

2. Address the shortage of health professionals: The study highlighted the shortage of health professionals as a significant factor contributing to maternal mortality. To improve access to maternal health, innovative solutions can be developed to address this shortage, such as training and deploying more skilled birth attendants, midwives, and healthcare workers in areas with high maternal mortality rates. This can include innovative training programs, telemedicine, and task-shifting approaches to expand the healthcare workforce.

3. Strengthen healthcare service coverage: The study identified factors related to healthcare service coverage, such as access to emergency healthcare during childbirth, antenatal care coverage, and access to skilled birth attendants, as important determinants of maternal mortality. Innovations can be developed to improve healthcare service coverage, such as mobile health technologies for remote areas, community-based healthcare models, and strengthening health systems to ensure availability and accessibility of essential maternal health services.

4. Utilize spatial analysis and mapping: The study utilized spatial analysis and mapping techniques to identify hotspots and cold spots of maternal mortality in Africa. Innovations can be developed to leverage these spatial analysis tools and techniques to identify areas with high maternal mortality rates and target interventions and resources accordingly. This can include the development of interactive maps and dashboards that provide real-time data on maternal health indicators and facilitate evidence-based decision-making.

5. Collaborate and share best practices: To improve access to maternal health, it is essential to foster collaboration and knowledge sharing among stakeholders, including governments, healthcare providers, researchers, and international organizations. Innovations can be developed to facilitate collaboration and the exchange of best practices, such as online platforms, networks, and forums where stakeholders can share experiences, lessons learned, and innovative approaches to addressing maternal health challenges.

By implementing these recommendations and developing innovative solutions, it is possible to improve access to maternal health and reduce maternal mortality rates in Africa.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations to improve access to maternal health:

1. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, especially in middle and western African countries where maternal mortality rates are high. This includes increasing the number of healthcare professionals, ensuring the availability of essential medical supplies and equipment, and improving the quality of healthcare services.

2. Address gender inequalities: Implement strategies to address gender inequities that contribute to maternal mortality. This can include promoting women’s empowerment, ensuring access to education and economic opportunities, and addressing cultural and social norms that limit women’s decision-making power and access to healthcare.

3. Increase access to skilled birth attendants: Improve access to skilled birth attendants during delivery, as they play a crucial role in preventing maternal deaths. This can be achieved by training and deploying more skilled healthcare professionals, particularly in remote and underserved areas.

4. Enhance antenatal care coverage: Increase the coverage and quality of antenatal care services, including regular check-ups, screenings, and education for pregnant women. This can help identify and manage potential complications early on, reducing the risk of maternal mortality.

5. Improve access to emergency obstetric care: Ensure that women have access to emergency obstetric care, including timely access to cesarean sections when needed. This requires strengthening referral systems and improving transportation infrastructure to facilitate timely access to healthcare facilities.

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 reflect access to maternal health, such as maternal mortality ratio, antenatal care coverage, skilled birth attendance rate, and availability of emergency obstetric care.

2. Collect baseline data: Gather data on the selected indicators for the target countries or regions. This can be obtained from national health surveys, health facility records, and other relevant sources.

3. Develop a simulation model: Build a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider the interdependencies between different factors and their effects on maternal health outcomes.

4. Input intervention scenarios: Define different intervention scenarios based on the recommendations identified earlier. For example, simulate the impact of increasing the number of skilled birth attendants or improving access to emergency obstetric care.

5. Run simulations: Use the simulation model to run the defined intervention scenarios and assess their impact on the selected indicators. This can be done by comparing the baseline data with the simulated data under different scenarios.

6. Analyze results: Analyze the simulation results to determine the effectiveness of each intervention scenario in improving access to maternal health. This can involve comparing the changes in the selected indicators and identifying the most impactful interventions.

7. Refine and iterate: Based on the simulation results, refine the intervention scenarios and the simulation model if necessary. Repeat the simulation process to further explore the potential impact of different interventions and optimize the recommendations.

By following 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 to prioritize and implement effective strategies.

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