A discrete-time Markov pipeline model of neurosurgical workforce attrition and retention rates across Africa through 2030

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OBJECTIVE: The aim of this study was to project the total neurosurgical workforce across African countries by 2030, quantify the impact of policy interventions, and assess capacity and infrastructure constraints on advanced subspecialization across the continent. METHODS: The authors utilized a discrete-time Markov pipeline model to track prospective candidates from medical graduation through training and retention. Data were sourced from the World Federation of Neurosurgical Societies, WHO, and pan-African surveys for workforce counts, attrition rates, and training capacity. The equipment readiness index (ERI) was developed to link infrastructure to the feasibility of subspecialization practice. Projections were simulated under 4 scenarios, including a policy pack intervention (scholarships, mentorship, equipment), with uncertainty quantified via Monte Carlo methods. A fractional logit model assessed the policy pack's effect on the projected female share of the workforce. RESULTS: The median baseline neurosurgeon count was 10 per country. Annually, at least 231 medical students express interest in neurosurgery; however, limited residency spots cause waiting lists to grow by approximately 154 applicants annually. ERI scores showed extreme variability; only Egypt (0.81) surpassed the highest subspecialization threshold, while 21 countries scored below 0.10, severely curtailing advanced practice feasibility if there is no intervention. Under the baseline scenario, the workforce is projected to reach 5888 by 2030 (43.1% increase). The policy pack scenario yielded 8809 neurosurgeons (a 49.6% increase over baseline). The policy pack positively impacted the female share (median OR 1.28, 95% CI 1.01-1.61; p = 0.04); however, higher ERI was not a significant predictor. CONCLUSIONS: The African neurosurgical workforce faces binding constraints from limited training capacity and poor infrastructure readiness. Targeted policy interventions can power equitable growth.

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