Abstract
Guidance on resource allocation decisions in health care is increasingly relying on cost-effectiveness evidence. Whilst economic evaluation methods assist in such decisions, they fail to provide information on the affordability of particular forms of health care. This is likely to be a concern for policy-makers concerned with guidance implementation, as there may be a positive link between afford-ability and implementation.
Methods known as Budget Impact Analyses have been developed to address affordability questions (Chambers et al. 2002). These are now being used in a number of countries to inform decision-making (Trueman et al. 2001). The Australian Pharmaceutical Benefits Advisory Committee, the National Institute for Health and Clinical Excellence (NICE) in the UK and the Sickness Funds Council in the Netherlands all recommend that health care system costs should be estimated in any reimbursement submissions.
Previous estimates of budget impact for NICE have largely been undertaken using deterministic methods (NICE 2001). It is unclear how reliable or uncertain these estimates are. However, using probabilistic sensitivity analysis methods (Briggs 2000), quantification of the uncertainty around budget impact estimates is also possible using regression modeling of cost data (Barber and Thompson 2004)
Methods known as Budget Impact Analyses have been developed to address affordability questions (Chambers et al. 2002). These are now being used in a number of countries to inform decision-making (Trueman et al. 2001). The Australian Pharmaceutical Benefits Advisory Committee, the National Institute for Health and Clinical Excellence (NICE) in the UK and the Sickness Funds Council in the Netherlands all recommend that health care system costs should be estimated in any reimbursement submissions.
Previous estimates of budget impact for NICE have largely been undertaken using deterministic methods (NICE 2001). It is unclear how reliable or uncertain these estimates are. However, using probabilistic sensitivity analysis methods (Briggs 2000), quantification of the uncertainty around budget impact estimates is also possible using regression modeling of cost data (Barber and Thompson 2004)
| Original language | English |
|---|---|
| Pages (from-to) | 61-68 |
| Number of pages | 7 |
| Journal | Swiss Journal of Economics and Statistics |
| Volume | S10 |
| Publication status | Published - 2006 |
Bibliographical note
Acknowledgements:Financial support for this study was provided in part by a grant from the Wellcome Trust. We also acknowledge the financial support of the Institute of Applied Health Sciences, University of Aberdeen, and the Chief Scientist’s Office (CSO) of the Scottish Executive Health Department. The views expressed in the paper however do not necessarily represent those of the CSO or any other funding body.