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SPOILT FOR CHOICE: IMPLICATIONS OF USING ALTERNATIVE METHODS OF COSTING HOSPITAL EPISODE STATISTICS
Author(s) -
Geue Claudia,
Lewsey James,
Lorgelly Paula,
Govan Lindsay,
Hart Carole,
Briggs Andrew
Publication year - 2012
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.1785
Subject(s) - activity based costing , regression analysis , linear regression , statistics , health economics , cost database , descriptive statistics , econometrics , sample size determination , sample (material) , cost driver , population , actuarial science , cost estimate , health care , operations management , medicine , economics , accounting , mathematics , environmental health , chemistry , management , chromatography , economic growth
SUMMARY In the absence of a ‘gold standard’ to estimate the economic burden of disease, a decision about the most appropriate costing method is required. Researchers have employed various methods to cost hospital stays, including per diem or diagnosis‐related group (DRG)‐based costs. Alternative methods differ in data collection and costing methodology. Using data from Scotland as an illustrative example, costing methods are compared, highlighting the wider implications for other countries with a publicly financed healthcare system. Five methods are compared using longitudinal data including baseline survey data (Midspan) linked to acute hospital admissions. Cost variables are derived using two forms of DRG‐type costs, costs per diem, costs per episode—using a novel approach that distinguishes between variable and fixed costs and incorporates individual length of stay (LOS), and costs per episode using national average LOS. Cost estimates are generated using generalised linear model regression. Descriptive analysis shows substantial variation between costing methods. Differences found in regression analyses highlight the magnitude of variation in cost estimates for subgroups of the sample population. This paper emphasises that any inference made from econometric modelling of costs, where the marginal effect of explanatory variables is assessed, is substantially influenced by the costing method. Copyright © 2011 John Wiley & Sons, Ltd.