Budget Impact Analysis (BIA) is an integral element of a comprehensive Health Technology Assessment. Prior systematic reviews showed significant methodological dissimilarities in BIAs published from 2002 to 2015 (1,2). Aimed to improve the generalisability and transferability of outcomes, a guidance on methods was updated in 2014 (3). The objective of this study was to measure the adherence to Principles of Good Practice of BIAs published after the release of the updated guidelines.

Fifteen features representative of methodological appropriateness were identified from the Principles of Good Practice. A systematic review of the extant literature was conducted to identify BIAs published from January 2015 to December 2016. The adherence of each BIA to the Principles of Good Practice was defined by the number of representative characteristics taken into consideration as a percent of the total.

The full study protocol is available online: http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016049287

A sample of thirty-nine published BIAs were included in the analysis. The mean adherence of BIAs to the Principles of Good Practice was 69 percent (10.4 representative features out of 15). The highest adherence was 87 percent, while the lowest was 33 percent. The distribution of the scores was highly concentrated around the mean value, with thirty-four BIAs (87 percent of total sample) showing a level of adherence ≥ 60 percent. Only two BIAs reported an adherence < 50 percent (5 percent of total sample). Six representative features showed a level of adherence < 50 percent: off-label use (0 percent); uncertainty (26 percent); validation (33 percent); choice of computing framework (44 percent); eligible population (44 percent) and relevant features of healthcare system (49 percent).

Compared to the Principles of Good Practice, the BIAs included in the systematic review were overcomplicated and deterministic, ignoring the impact of possible scenarios relevant to budget holders. The research advocates a wider use of scenario planning as a tool to link uncertainty to the economic assessment of new interventions.