Presentation (CTI)

OBJECTIVES: Disease severity is often considered by health technology assessment (HTA) bodies as part of reimbursement decisions, either qualitatively, or quantitively within cost-effectiveness models (CEMs). Quantitative methods typically use absolute or proportional shortfall (AS or PS) as measures of disease severity. Greater weight is often placed on health gains in more severe diseases in line with societal preference to prioritise health gains for those with greater illness or disability. This research compares the impact of using different approaches to quantitatively assess disease severity.
METHODS: Severity weighting methods adopted by NoMA, ZIN and NICE (HTA bodies in Norway, the Netherlands and England respectively), were applied in an oncology CEM for three subgroups with varying prognosis. ZIN recommends using PS estimates with weightings of 1-4. NoMA recommends using AS estimates with weightings of 1-3. NICE considers the highest of AS and PS estimates with weightings of 1-1.7. Uncertainty in shortfall estimates was explored, with deterministic and probabilistic weightings generated for each methodology.
RESULTS: While each subgroup qualified for a unique NICE severity weighting (1, 1.2 and 1.7), all subgroups qualified for a modifier of 4 (ZIN) and 1.8 (NoMA). Probabilistic results indicated the subgroup with best prognosis qualified for a NICE weighting of 1.2 in 49.2% of simulations, despite a deterministic weighting of 1. 51.5% of simulations for the subgroup with poorest prognosis qualified for a NoMA weighting of 2.2, despite a deterministic weighting of 1.8.
CONCLUSIONS: Different approaches to quantitatively assess disease severity produce highly variable severity weightings. The method used by ZIN calculated the highest weighting across all subgroups, with the method used by NICE calculating the lowest weightings. There is often more than one weighting well-represented in probabilistic simulations, with the most common modifier sometimes differing from the deterministic value, highlighting the importance of quantifying uncertainty in severity weightings.