Method for costing a health system using a Health Systems Model
Mohan, S.; Manthalu, G.; Mangal, T. D.; Mfutso-Bengo, J.; Molaro, M.; Nkhoma, D.; She, B.; Tafesse, W.; Twea, P. D.; Walker, S.; Chalkley, M.; Colbourn, T.; Hallett, T. B.; Phillips, A. N.; Revill, P.
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ObjectivesModelling approaches that consider system-wide delivery platforms rather than single diseases are increasingly recognized as crucial for the economic evaluation of policy and investment choices and can be instrumental in forward-looking policy formulation. This paper develops a costing approach tailored to one such model, the Thanzi La Onse (TLO) model of Malawis healthcare system, to estimate healthcare delivery costs under varying health system assumptions. MethodsWe developed a mixed-method costing approach to estimate the total cost of healthcare delivery in Malawi using the TLO model, from a healthcare provider perspective. Through an iterative adjustment of key costing parameters, we attempted to align our model-based estimates with real-world expenditure and budget data. Costs were estimated over an 8-year period (2023-2030) under alternative scenarios of health system capacity, including supply chain performance and the size of the health workforce. ResultsA detailed comparison of our cost estimates against expenditure and budget data demonstrates the reliability of our costing method and assumptions, for the conditions and resources captured by the model. Under current health system capacity, the total cost of healthcare delivery in Malawi between 2023 and 2030 was estimated at $1.53 billion [95% confidence interval, $1.51b -$1.54b], which translates to an average annual cost of $309.83 million [$306.17m -$313.56m]. The estimation of costs under alternative scenarios demonstrates the importance of capturing feedback effects to correctly forecast healthcare costs. ConclusionMixed-method costing used within health system models, such as TLO, is a feasible and robust method for estimating healthcare delivery costs. This approach can provide valuable insights for health sector planning and resource allocation.
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