Maternal Health Planning and Prioritization in Chad: Developing a supportive tool

The Republic of Chad has one of the highest rates of maternal mortality in the world. With scarce resources to respond to competing demands, pragmatic evidence-based planning tools are needed to aid planning and support priority setting. This action research aimed to develop a tool to support maternal health (MH) planning and prioritization decisions and identify priority regions/provinces for intervention in Chad based on aggregate MH coverage gap scores (Target-Coverage = Coverage Gap). A rapid review was conducted to identify key indicators and relevant national targets. The 2019 Multiple Indicator Cluster Survey and other national surveys were the data sources for selected indicators at the provincial level. Aggregate MH coverage gaps were calculated and displayed using geographic information system software to visualize variations by province. Eleven key informant interviews (KIIs) and six focus group discussions (FGDs) were conducted with clinicians and administrators to understand existing MH planning, prioritization, and maternal mortality risks in Chad. Wide provincial variation in aggregate MH coverage gaps was identified (mean score 374.3, SD: 77.4). Indicators contributing the most to coverage gaps include emergency obstetric care, adolescent births, tetanus vaccination, and delivery by skilled health personnel. Two weighting scenarios for the coverage gap scores are also considered. KIIs and FGDs revealed that existing MH planning in Chad differs provincially and by health system level, with no clear prioritization processes identified. Main themes regarding MH risks reported by stakeholders included challenges relating to the health system, policy landscape, country and population-specific factors, along with specific MH threats. Current centralized planning approaches may benefit from greater consideration of provincial differences to support more efficient and equitable resource distribution. This multi-indicator assessment offers an adaptable approach for evidence-based MH resource allocation to prioritize subnational areas with worst health indicators in resource-limited settings, although further research is needed to test its impact.