BUDGET IMPACT ANALYSES- INCONSISTENCY AND BIAS IN CHOICE OF REPORTING MEASURES

Author(s)

Potluri R1, Ranjan S2, Raj M2, Kumar A2, Bhandari H2
1SmartAnalyst Inc., New York, NY, USA, 2SmartAnalyst India Pvt. Ltd., Gurgaon, India

OBJECTIVES: Budget impact evaluation is a widely used tool in pharmacoeconomic analysis. In the absence of guidelines, different measures are used to report budget impacts. Some report only the absolute impact while others report as a percentage/fraction impact; where the latter, there is variability on the choice of base budget. The objective of this study was to evaluate whether there is consistency in reporting measures between analyses carried out by drug/technology sponsors and those carried out by academicians/independent evaluators, and examine likely bias. METHODS: Budget impact studies presented in ISPOR’s recent meetings were analyzed, and were grouped into whether these were carried out by drug/technology sponsors (Group-1) or independent evaluators (Group-2). These were then bucketed into one of the following based on the key reporting measure used: 1) absolute difference, 2) per patient utilizing the drug/technology, 3) per disease-prevalent plan population, and 4) per overall plan population. RESULTS: Of the 255 studies analyzed, a third in both Group-1 (n=193) and Group-2 (n=62) showed an incremental budget impact, while the remaining showed savings. When reporting an increased budget, as many as 60% of Group-1 chose the largest base (per plan population) versus only 22% of Group-2. In contrast, 15% were reported only in absolute terms by Group-1 versus 61% of Group-2. When reporting savings however, there was little difference between the groups – 51% and 46% respectively reported absolute savings. CONCLUSIONS: The choice of reporting measures was inconsistent, and appeared to be biased by the nature of the evaluators. Within Group-1, the measures were chosen to amplify (in case of savings) or understate (in case of incremental budget) the magnitude of the impact. There is good basis to put in place guidelines to standardize reporting measures, to both remove evaluator bias, and to allow decision-makers to more easily compare different evaluations.

Conference/Value in Health Info

2018-05, ISPOR 2018, Baltimore, MD, USA

Value in Health, Vol. 21, S1 (May 2018)

Code

PHP139

Topic

Health Service Delivery & Process of Care

Topic Subcategory

Health Care Research

Disease

Multiple Diseases

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