Accuracy of budget impact estimations and impact on patient access: a hepatitis C case study

Budget Impact Analysis of Risperidone Use and Adverse Event Monitoring in Autism Spectrum Disorder in Brazil: Assessment of Theoretical Versus Real Data

BACKGROUND AND AIMS

Risperidone is used in autism spectrum disorder (ASD) to manage aggressive behavior. Budget impact analysis (BIA) assists managers in promoting more sustainable health systems; however, it is unclear whether BIAs underestimate or overestimate the estimates derived from real-world data. This study aimed to compare the estimated BIA values of risperidone use and the monitoring of adverse events in ASD using theoretical and real data.

METHODS

Analyses were conducted based on the clinical protocol and the Brazilian therapeutic guidelines for ASD. The perspective adopted was that of the Unified Health System (SUS), considering a time horizon of 5 years. Three possible scenarios were considered based on the maximum daily dose of risperidone. Expenses related to the acquisition of risperidone and the monitoring of adverse events were taken into account using health databases in Brazil. For the calculation based on theoretical data, the prevalence of ASD was estimated using information from the scientific literature and the Brazilian demographic census. The model calculated from real data was obtained by analyzing the linear trend of the number of users assisted in the SUS from 2017 to 2021.

RESULTS

The population estimated by the theoretical model compared with the model calculated from the real data was higher. Likewise, the 5-year budgetary impact of the theoretical model versus the model calculated from the real data was higher, with statistical significance in all scenarios (p < 0.001). In the real data model, the most economically advantageous scenarios were Scenario 1 for children (International dollars [I$] 7,630,040.73) and Scenario 3 for adults (I$60,329,288.17). Estimated expenditures for monitoring adverse events ranged from 17 to 74% in children and from 50% to 63% in adults.

CONCLUSIONS

The data revealed significant differences in population and cost estimation between theoretical data and real-world data. The expenses associated with monitoring adverse events represented a substantial expenditure estimate for the SUS.

Adoption of new therapies in the treatment of Hepatitis: a verification of the accuracy of budget impact analysis to guide investment decisions

OBJECTIVES

While there are good Budget Impact Analysis (BIA) guidelines, studies still register potential bias. To do this, we compared the results between theoretical and real-world evidence (RWE) expenditures for medicines for Hepatitis C: boceprevir (BOC) and telaprevir (TVR). While both are not currently recommended in treatment guidelines following recent developments, this is an emblematic case because for 4 years these medicines consumed considerable resources.

METHODS

Theoretical results and RWE expenditures were compared regarding the incorporation of BOC and TVR in 2013-2014 into the Brazilian Public Health System. Theoretical values were extracted from Commission for Technology Incorporation Report and RWE expenditures were extracted from the administrative data records using deterministic-probabilistic linkage.

RESULTS

The estimated number of patients treated (BOC+TVR) was 13,012 versus 7,641 (real). The estimated purchase price for BOC was US$6.20 versus US$11.07 (real) and for TVR was US$42.21 versus US$84.09 (average/real). The estimated budget impact was US$285.16 million versus US$128.58 million (real).

CONCLUSION

This study demonstrates appreciable divergence (US$156.58 million) between the theoretical budget impact and RWE expenditures due to underestimated purchase prices and overestimated populations. The greater the degree of accuracy the more reliable and usable BIAs become for decision-making.

Assessing the Accuracy of Sales Forecasts Submitted by Pharmaceutical Companies Applying for Reimbursement in Austria

Objectives: Reimbursement decisions on new medicines require an assessment of their value. In Austria, when applying for reimbursement of new medicines, pharmaceutical companies are also obliged to submit forecasts of future sales. We systematically examined the accuracy of these pharmaceutical sales forecasts and hence the usefulness of these forecasts for reimbursement evaluations. Methods: We retrospectively analyzed reimbursement applications of 102 new drugs submitted between 2005 and 2014, which were accepted for reimbursement outside of hospitals, and for which actual reimbursed sales were available for at least 3 years. The main outcome variable was the accuracy ratio, defined as the ratio of forecasted sales submitted by pharmaceutical companies when applying for reimbursement to actual sales from reimbursement data. Results: The median accuracy ratio [95% confidence interval] was 1.33 [1.03; 1.74, range 0.15–37.5], corresponding to a median overestimation of actual sales by 33%. Forecasts of actual sales for 55.9% of all examined products either overestimated actual sales by more than 100% or underestimated them by more than 50%. The accuracy of sales forecasts did not show systematic change over the analyzed decade nor was it discernibly influenced by reimbursement status (restricted or unrestricted), the degree of therapeutic benefit, or the therapeutic area of the pharmaceutical product. Sales forecasts of drugs with a higher degree of innovation and those within a dynamic market tended to be slightly more accurate. Conclusions: The majority of sales forecasts provided by applicants for reimbursement evaluations in Austria were highly inaccurate and were on average too optimistic. This is in line with published results for other jurisdictions and highlights the need for caution when using such forecasts for reimbursement procedures.

Budget impact analysis of medicines: estimated values versus real-world evidence and the implications

Objectives: Budget Impact Analyses (BIA) of medicines helps managers in promoting health systems' sustainability when assessing the role and value of new medicines. However, it is not clear whether BIAs typically underestimate or overestimate the impact on real-world budgets. This retroactive analysis seeks to compare estimated values obtained by a BIA and Real-World Evidence (RWE) to guide discussions.Methods: The estimated values were obtained through a BIA concerning the incorporation of adalimumab for the treatment of Rheumatoid Arthritis into the Brazilian Unified Health System (SUS) carried out retroactively and per international guidelines. RWE data was extracted from SUS computerized systems. We subsequently compared the number of treatments, costs, and Incremental Budget Impact (IBI).Results - The total number of treatments was underestimated by 10% (6,243) and the total expenditure was overestimated by 463% (US$ 4.7 billion). In five years, the total difference between the estimated values and real IBI reached US$ 1.1 billion. A current expenditure of US$ 1.0 was observed for every US$ 5.60 of estimated expenditure.Conclusion - The higher estimates from the BIA might cause decision makers to be more cautious with the introduction of a new medicine to reduce the opportunity costs for other interventions.

Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions

Randomized controlled trials (RCTs) are preferred by payers for health technology assessments and coverage decisions. However, the inclusion of a highly selective patient population and the rigorously controlled conditions in RCTs may not be reflective of real-world clinical practice. Real-world evidence (RWE) obtained from an analysis of real-world data (RWD) from observational studies can bridge gaps in evidence not addressed by RCTs and is thus valuable to public and private payers for decision-making. Through a broad literature search to obtain insights into payers' experience, we found that payers have concerns about real-world studies with respect to data quality, poor internal validity, potential bias, and lack of meaningful endpoints. However, they valued RWE to fill evidence gaps not addressed by RCTs, such as high-quality, real-world, long-term effectiveness and safety data; head-to-head drug comparisons; cost analyses for tiering formulary placement; medication use and adherence patterns; identification of relevant responder and non-responder patient subpopulations; and patient-reported outcomes (PROs). RWE can be used to assess clinically meaningful endpoints and gauge the impact of interventions on the quality of healthcare. Here, we review how payers use or can use RWD on the comparative effectiveness and safety of treatments, PROs, medication adherence and persistence, prescribing patterns, healthcare resource utilization, and patient characteristics and/or biomarkers associated with treatment response when making health technology assessments and payer coverage decisions across therapeutic areas.

Affordability of oncology drugs: accuracy of budget impact estimations

Background: In many countries, Budget Impact (BI) informs reimbursement decisions. Evidence has shown that decision-makers have restricted access based on high BI estimates but studies show that BI estimates are often inaccurate. Objective: To assess the accuracy of BI estimations used for informing access decisions on oncology drugs in the Netherlands. Study Design: Oncology products for which European Medicines Agency Marketing Authorisation was granted between 1-1-2000 and 1-10-2017 were selected. Observed BI data were provided by FarmInform. BI estimates were extracted from the reimbursement dossiers of the Dutch Healthcare Institute. Products without an estimated BI in the reimbursement dossier were excluded. Accuracy is defined as the ratio observed BI/estimated BI. Setting: General community, the Netherlands. Results: Ten products were included in the base case analysis. Mean accuracy was 0.64 and observed BI deviated by more than 40% and 100% from the estimated BI for 4 and 5 products, respectively. For all products together, €141 million BI was estimated and €82 million BI was observed, a €59 million difference. Conclusions: The findings indicate that BI estimates for oncology drugs in the Netherlands are inaccurate. The role and use of BI in reimbursement decisions for these potentially life-saving drugs should therefore be considered carefully, as well as BI estimation methodology.