Lecture 1.1.2 | Costing Methods | Masters in Health Economics
Why It Matters
Accurate costing determines whether costly health technologies are financially viable, influencing hospital budgets, reimbursement policies, and ultimately patient access to innovative care.
Key Takeaways
- •Top‑down costing suits rapid budgeting; uses aggregate patient data.
- •Bottom‑up micro‑costing captures detailed resource use for technology assessments.
- •Six‑step algorithm ensures consistent cost object definition and inventory.
- •Shadow pricing adds opportunity cost of unpaid care to total cost.
- •Sensitivity analysis tests robustness of cost estimates against price fluctuations.
Summary
The lecture introduces health‑economics costing methods, contrasting top‑down aggregate approaches with bottom‑up micro‑costing. Using a new $2 million robotic surgery unit as a case study, the instructor shows why simple division of purchase price by procedure count is insufficient, highlighting hidden overheads such as surgeon training, electricity, and opportunity costs. Key insights include the definition of top‑down costing—allocating aggregated costs across patient groups for rapid, high‑level budgeting—and bottom‑up micro‑costing, which tracks every minute of staff time, consumable, and capital use to achieve granular precision. The session walks through a six‑step algorithm: defining the cost object, identifying resources, building an inventory, valuing resources, calculating total cost, and performing uncertainty/sensitivity analysis. It also explains unit‑cost conversion, market versus shadow pricing, and allocation of overheads like heating and IT. Illustrative examples feature multiple‑choice questions on resource identification, the use of shadow pricing to value unpaid caregiver time in a vaccination program, and the impact of depreciation on long‑lived equipment. The robot scenario demonstrates how training hours, specialized rooms, and electricity dramatically inflate per‑procedure cost beyond the capital purchase price. The implications are clear: selecting the appropriate costing method shapes budget forecasts, technology adoption decisions, and policy evaluations. Bottom‑up micro‑costing, though data‑intensive, yields the precision needed for health‑technology assessments, while top‑down methods serve quick, macro‑level planning. Sensitivity analysis ensures that cost estimates remain robust amid price volatility, guiding stakeholders toward evidence‑based resource allocation.
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