Estimating System-Wide Healthcare Costs Using a Health System Model: Application to the Thanzi La Onse Model of...

Modelling approaches that consider system-wide delivery platforms rather than single diseases can be instrumental in economic evaluation and forward-looking policy formulation. This study develops a costing approach tailored to the Thanzi La Onse (TLO) model of Malawi's healthcare system, with general applicability to other health system models.

We developed a mixed-method costing approach to estimate the total cost of healthcare delivery (excluding high-level administrative costs) in Malawi using the TLO model, from a healthcare provider perspective. Through iterative adjustments of key parameters, we aligned model-based estimates as closely as possible with real-world expenditure and budget data. Costs were projected for 2023-2030 under alternative scenarios of health system capacity.

A comparison with expenditure and budget data suggests our costing method is broadly reliable for the conditions captured by the model, though some mismatches remain owing to data limitations and definitional inconsistencies. Under current system capacity, total healthcare delivery costs for 2023-2030 were estimated at 2.83 billion US dollars [95% uncertainty interval (UI), $2.80-$2.87 billion], excluding non-medical infrastructure and administrative costs, averaging $390.98 million [$385.92-$396.71 million] annually or $16.89 [$16.75-$17.08] per capita. Scenario analysis highlighted strong interdependencies within the health system. Improving consumable availability alone increased consumables costs by 4.63%, while expanding human resources for health (HRH) alone increased them by 1.43%. When both HRH and consumable availability were expanded together, consumable costs rose by 5.93%, a combined effect larger than either change alone, illustrating how bottlenecks in one component constrain the impact of improvements in another.

Mixed-method costing using health system models is a feasible and robust method to estimate and forecast healthcare delivery costs. Clarifying assumptions and limitations can improve their utility for economic analyses and evidence-based planning in the health sector.