Practical issues in handling data input and uncertainty in a budget impact analysis

Research output: Contribution to journalArticle

Abstract

The objective of this paper was to address the importance of dealing systematically and comprehensively with uncertainty in a budget impact analysis (BIA) in more detail. The handling of uncertainty in health economics was used as a point of reference for addressing the uncertainty in a BIA. This overview shows that standard methods of sensitivity analysis, which are used for standard data set in a health economic model (clinical probabilities, treatment patterns, resource utilisation and prices/tariffs), cannot always be used for the input data for the BIA model beyond the health economic data set for various reasons. Whereas in a health economic model, only limited data may come from a Delphi panel, a BIA model often relies on a majority of data taken from a Delphi panel. In addition, the dataset in a BIA model also includes forecasts (e.g. annual growth, uptakes curves, substitution effects, changes in prescription restrictions and guidelines, future distribution of the available treatment modalities, off-label use). As a consequence, the use of standard sensitivity analyses for BIA data set might be limited because of the lack of appropriate distributions as data sources are limited, or because of the need for forecasting. Therefore, scenario analyses might be more appropriate to capture the uncertainty in the BIA data set in the overall BIA model.

Details

Authors
  • M. J. C. Nuijten
  • T. Mittendorf
  • Ulf Persson
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Health Care Service and Management, Health Policy and Services and Health Economy

Keywords

  • * Model * Budget impact * Data source
Original languageEnglish
Pages (from-to)231-241
JournalEuropean Journal of Health Economics
Volume12
Issue number3
Publication statusPublished - 2011
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Division of Health Economics and Forensic Medicine (Closed 2012) (013040050)