Development of a Community-Based Surgical Demand Forecasting Model for Underserved Local Government Areas in Nigeria

Estimating unmet surgical need at the local government area (LGA) level in Nigeria is essential for planning surgical service expansion, workforce deployment, and mobile surgical outreach but no validated demand forecasting methodology exists for the Nigerian context. This dissertation develops and validates a community-based surgical demand forecasting model for underserved LGAs using primary epidemiological and sociodemographic data. A multi-stage household survey was conducted in 24 LGAs across the North Central, North West, and North East zones between 2021 and 2023, capturing self-reported untreated surgical conditions including hernia, goiter, cataracts, orthopaedic deformities, and abdominal masses. Survey data were linked to facility catchment area mapping and LGA-level demographic and poverty indicators. A Poisson regression demand forecasting model was developed and validated against actual surgical caseloads at 12 sentinel facilities. The model predicted annual surgical demand within 15% of observed caseloads in validation LGAs. Untreated surgical conditions were identified in 4.7% of the surveyed population, with hernias and cataracts predominating. Poverty index and distance from surgical facility were the strongest demand predictors. The model is offered as an open-access planning tool and recommended for adoption by state ministries of health and the Federal Ministry of Health in developing NSOAPs. Keywords: surgical demand forecasting, unmet surgical need, LGA, Nigeria, NSOAP