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. 2013 Jan;29(1):92-100.
doi: 10.1017/S0266462312000724. Epub 2012 Dec 10.

Relationship between financial impact and coverage of drugs in Australia

Josephine Mauskopf  1 , Costel Chirila, Catherine Masaquel, Kristina S Boye, Lee Bowman, Julie Birt, David Grainger
Affiliations
Free PMC article

Relationship between financial impact and coverage of drugs in Australia

Josephine Mauskopf et al. Int J Technol Assess Health Care. 2013 Jan.
Free PMC article

Abstract

Objectives: The aim of this study was to estimate the relationship between the financial impact of a new drug and the recommendation for reimbursement by the Australian Pharmaceutical Benefits Advisory Committee (PBAC).

Methods: Data in the PBAC summary database were abstracted for decisions made between July 2005 and November 2009. Financial impact-the upper bound of the values presented in the PBAC summary database-was categorized as ≤A$0, >A$0 up to A$10 million, A$10 million up to A$30 million, and >A$30 million per year. Descriptive, logistic, survival, and recursive partitioning decision analyses were used to estimate the relationship between the financial impact of a new drug indication and the recommendation for reimbursement. Multivariable analyses controlled for other clinical and economic variables, including cost per quality-adjusted life-year gained.

Results: Financial impact was a significant predictor of the recommendation for reimbursement. In the logistic analysis, the odds ratios of reimbursement for drug submissions with financial impacts ≥A$10 million to ≥A$30 million or >A$0 to <A$10 million compared with ≤A$0 were 0.12 (95 percent confidence interval [CI]: 0.03-0.51) and 0.16 (95 percent CI: 0.04-0.60), respectively. In the recursive partition decision analysis, the first split of the data was for submissions with a positive financial impact compared with those with a zero or negative financial impact.

Conclusions: In Australia, financial impact on the drug budget is an important determinant of whether a new drug is recommended for reimbursement when cost-effectiveness estimates and other clinical and economic variables are controlled.

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Figures

Figure 1.
Figure 1.
Multivariable logistic regression results (n = 204): odds ratios with 95% CI plots. CI, confidence interval; QALY, quality-adjusted life-year; RCT, randomized controlled trial.
Figure 2.
Figure 2.
Multivariable logistic regression results for discrete time-to-event data (n = 238): odds ratios with 95% CI plots. CI, confidence interval; QALY, quality-adjusted life-year; RCT, randomized controlled trial.
Figure 3.
Figure 3.
Recursive partition decision tree (n = 204).
See this image and copyright information in PMC

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