Modelling complex maternal healthcare provision discontinuities in urban Sub-Saharan Africa: A system dynamics and geospatial lens.

Every day, more than 800 women and 14,000 babies die due to largely preventable causes related to lack of timely access to good quality of care during pregnancy and childbirth. Two-thirds of maternal deaths occur in sub-Saharan Africa, reaching nearly 200,000 deaths annually (1). The highest risk of maternal and perinatal deaths is between the onset of labour and shortly after childbirth. The most important causes of maternal deaths include severe bleeding, high blood pressure disorders, and sepsis. Perinatal deaths are predominantly caused by complications of prolonged labour and prematurity (2).

Most deaths and morbidities can be prevented if women give birth at the right level of care that has indeed the capacity to function as designed to manage complications (4). Delays in accessing health facilities and the poor integration of health service delivery platforms hinder effective health service delivery (5,6). Traditionally, research about these challenges focused on rural contexts of low- and middle-income countries with high levels of mortality. This is because historically, urban areas are centres of relatively good infrastructure, hubs for innovation, and many others. However, rapid urbanisation has strained these resources leading to overcrowded and under-resourced facilities, negatively impacting the quality of care and the wellbeing of women and newborns in urban settings (8).

Two-thirds of the world's population will live in urban settings by 2050, and the fastest rates of urbanisation are in Africa and Asia9. As cities grow, the demand for healthcare services has surged, leading to more private healthcare providers. This poses governance challenges, such as ensuring compliance with care standards, especially in areas with weak regulatory frameworks. Private healthcare can exacerbate inequalities, with higher-income individuals accessing better services. Integrating private providers into public health strategies is essential but challenging. Robust governance, effective regulations, and strong public-private partnerships are crucial to ensure private healthcare contributes positively to overall health outcomes in urban environments (10, 11).

Continued high maternal and perinatal mortality in cities is an excellent example of a “complex and intractable” problem in healthcare. It is happening despite the increasing availability of resources and health facilities, and relatively short geographic distances between women and health facilities. There are many factors contributing to the eco-system of maternal health in cities beyond healthcare provision, including infrastructure, security, national policies, and global health priorities (12). To improve the survival and well-being of pregnant women and babies, we urgently need a model of how these various factors operate and interact. Such a model predicting maternal and perinatal mortality would enable a more rapid and accurate assessment of various potential interventions (13). System dynamics (SD) modelling is a powerful tool for understanding and managing complex systems. SD examines the structure of systems and their dynamic behaviour and encompasses not only the physical aspect of processes but also the crucial policies. SD explores and gives insights into complex systems, using data from earlier research to inform the analysis and development of causal loops and dynamic models. It captures and analyses feedback loops, making it ideal for systems where behaviour changes over time due to internal and external influences, making them suitable for studying urban maternal health, where various factors interact dynamically (13).

Therefore, this project will synthesise existing research on system dynamic modelling to inform the development of a comprehensive conceptual model and evaluate the feasibility of this comprehensive model for urban maternal health, capturing complex interactions and discontinuities in Lubumbashi (DRC) using primary and secondary data, comparing these models to identify common elements, and validating a generalised model and to determine how well it would simulate the urban maternal healthcare system dynamics by applying it to other 2 to 3 cities where data is available, e.g. Conakry (Guinea), to capture the most critical bottlenecks preventing women and newborns from accessing life-saving care on a timely basis.

References: 1. WHO 20231, 2. Kassebaum, N.J., et al., 2013, 4. Birabwa, C., et al., 2024, 5. hou, D., et al., Bmj, 2015. 351., 6. Bagade, T., et al., 2024., 8. Matthews, Z., et al., 2010, 9. United Nations 2019, 10. Burris, S. and V. Lin, 2021, 11. Debie, A., R.B. Khatri, and Y. Assefa 2022, 12. Harpham, T., et al., 2022, 13. Sooka, C. and A. Rwashana-Semwanga, 2011