Abstract
Objective
To develop a model to predict annual hospital costs for patients with established renal failure, taking into account the effect of patient and treatment characteristics of potential relevance for conducting an economic evaluation, such as age, comorbidities and time on treatment. The analysis focuses on factors leading to variations in inpatient and outpatient costs and excludes fixed costs associated with dialysis, transplant surgery and high cost drugs.
Methods
Annual costs of inpatient and outpatient hospital episodes for patients starting renal replacement therapy in England were obtained from a large retrospective dataset. Multiple imputation was performed to estimate missing costs due to administrative censoring. Two-part models were developed using logistic regression to first predict the probability of incurring any hospital costs before fitting generalised linear models to estimate the level of cost in patients with positive costs. Separate models were developed to predict inpatient and outpatient costs for each treatment modality.
Results
Data on hospital costs were available for 15,869 incident dialysis patients and 4511 incident transplant patients. The two-part models showed a decreasing trend in costs with increasing number of years on treatment, with the exception of dialysis outpatient costs. Age did not have a consistent effect on hospital costs; however, comorbidities such as diabetes and peripheral vascular disease were strong predictors of higher hospital costs in all four models.
Conclusion
Analysis of patient-level data can result in a deeper understanding of factors associated with variations in hospital costs and can improve the accuracy with which costs are estimated in the context of economic evaluations.
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Acknowledgments
This article presents independent research commissioned by the National Institute for Health Research (NIHR) under the Programme Grant for Applied Research (RP-PG-0109-10116) entitled Access to Transplantation and Transplant Outcome Measures (ATTOM). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. JF was funded by a Kidney Research UK Clinical Training Fellowship. The ATTOM Study Group comprises: J. Andrew Bradley, Clare Bradley, John Cairns, Heather Draper, Chris Dudley, John L. Forsythe, Damian G. Fogarty, Rachel J. Johnson, Geraldine Leydon, Wendy Metcalfe, Gabriel C. Oniscu, Rommel Ravanan, Paul Roderick, Charles R. Tomson and Christopher Watson. The authors are grateful to the UK Renal Registry and Hospital Episode Statistics for the linked dataset. Hospital Episode Statistics: Copyright 2014, re-used with the permission of the Health and Social Care Information Centre. All rights reserved.
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A full list of members of the Access to Transplantation and Transplant Outcome Measures (ATTOM) Study Group is provided in the Acknowledgments.
Appendix: A worked example to predict hospital costs based on the final two-part model
Appendix: A worked example to predict hospital costs based on the final two-part model
To estimate annual inpatient costs for a 55-year-old male patient with diabetes who has been on haemodialysis for 3 years:
Part 1: probability of incurring any inpatient cost >£0
Taking the natural log of the odds ratios in Table 3, calculate log odds of incurring any inpatient cost
Calculate probability from log odds
Part 2: estimate level of inpatient cost based on coefficients in Table 5
Combine parts 1 and 2: multiply estimated level of inpatient cost by probability of incurring any cost
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Li, B., Cairns, J., Fotheringham, J. et al. Predicting hospital costs for patients receiving renal replacement therapy to inform an economic evaluation. Eur J Health Econ 17, 659–668 (2016). https://doi.org/10.1007/s10198-015-0705-x
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DOI: https://doi.org/10.1007/s10198-015-0705-x