There are several methods to cost hospital contacts when estimating the cost effectiveness of a new intervention. In England, the National Insitute for Health and Care Excellence (NICE) recommends the use of diagnosis-related group (DRG)-based costs as a valuable way of costing hospital resource use. There are three main sources of unit costs of a DRG: (i) tariffs as used for reimbursement purposes, (ii) benchmarking finished consultant episode (FCE)-level reference costs and (iii) benchmarking spell-level reference costs.

The purpose of this work is to compare the implications of choosing a particular source of DRG-based unit costs when conducting an economic evaluation.

As a case study, we used a cost-utility model developed to compare secondary fracture prevention models of care for hip fracture patients (1). A Markov model was derived from large primary and hospital care administrative datasets in England. Utilities were informed by a meta-regression of thirty-two studies. Hospital resource use (inpatient, outpatient, critical care and emergency care) was valued using the three different 2014–15 DRG-based unit costs and regression-based costing models were derived from 33,000 hip fracture patients to inform the health states of the model (2). For each source of DRG-based costs, we calculated mean life years, Quality-Adjusted Life Years (QALYs) and costs for a representative male and female associated with three models of care: (i) orthogeriatrician (OG)-led, (ii) nurse-led fracture liaison services and (iii) usual care.

Using the benchmarking FCE-level DRG-based costs, the OG-led model was estimated to be the most effective model of care (1.77 QALYs, 95 percent Confidence Interval, CI 1.56-1.98) at a threshold of GBP30,000/QALY. However, it also resulted in the highest costs per patient. We will report the cost-effectiveness results using the two remaining DRG-based costs.

Choosing a particular hospital costing method may have an impact on economic evaluations. We will reflect on the implications for the estimated hospital costs, decision uncertainty and adoption of models of care.