A novel method for predicting the budget impact of innovative medicines:validation study for oncolytics

Background High budget impact (BI) estimates of new drugs have led to decision-making challenges potentially resulting in restrictions in patient access. However, current BI predictions are rather inaccurate and short term. We therefore developed a new approach for BI prediction. Here, we describe the validation of our BI prediction approach using oncology drugs as a case study. Methods We used Dutch population-level data to estimate BI where BI is defined as list price multiplied by volume. We included drugs in the antineoplastic agents ATC category which the European Medicines Agency (EMA) considered a New Active Substance and received EMA marketing authorization (MA) between 2000 and 2017. A mixed-effects model was used for prediction and included tumor site, orphan, first in class or conditional approval designation as covariates. Data from 2000 to 2012 were the training set. BI was predicted monthly from 0 to 45 months after MA. Cross-validation was performed using a rolling forecasting origin with e|Ln(observed BI/predicted BI)| as outcome. Results The training set and validation set included 25 and 44 products, respectively. Mean error, composed of all validation outcomes, was 2.94 (median 1.57). Errors are higher with less available data and at more future predictions. Highest errors occur without any prior data. From 10 months onward, error remains constant. Conclusions The validation shows that the method can relatively accurately predict BI. For payers or policymakers, this approach can yield a valuable addition to current BI predictions due to its ease of use, independence of indications and ability to update predictions to the most recent data

Cost-utility and cost-effectiveness analysis of a clinical medication review focused on personal goals in older persons with polypharmacy compared to usual care: Economic evaluation of the DREAMeR study

AIMS: The ageing society may lead to increasing healthcare expenditure. A clinical medication review (CMR) could potentially reduce costs. The aim of this study is to perform a cost-utility and cost-effectiveness analysis from a societal perspective of a patient-centred CMR. METHODS: A trial-based cost-utility and cost-effectiveness analysis was performed as part of the DREAMeR study, a pragmatic controlled trial that randomised patients aged ≥70 years using at least seven drugs to either CMR or usual care. Over six months, healthcare consumption and drug use were collected to estimate costs, and effects were collected in terms of quality-adjusted life years (QALYs) measured with EQ-5D-5 L and EQ-VAS and as reduced health-related complaints with impact on patients' daily lives. RESULTS: The total mean costs per patient (n = 588) over six months were €4,189 ± 6,596 for the control group (n = 294) and €4,008 ± 6,678 for the intervention group (n = 294), including estimated intervention costs of €199 ± 67, which resulted in a mean incremental total cost savings of €181 for the intervention group compared to the control group. Compared to the control group, for the intervention group, the mean incremental QALYs over six months were: -0.00217 measured with EQ-5D and 0.003 measured with EQ-VAS. The incremental effect of reduced health-related complaints with impact was -0.34. There was a likelihood of >90% that the intervention was cost-saving. CONCLUSIONS: The benefits of a patient-centred CMR were inconsistent with no benefits on HR-QoL measured with EQ-5D-5 L and small benefits on HR-QoL measured with EQ-VAS and health-related complaints with impact on patients' daily lives. Additionally, a CMR could potentially be cost saving from a societal perspective

Noacs replace VKA as preferred oral anticoagulant among new patients

Background: In 2012, around 400,000 patients in the Netherlands were treated with vitamin K antagonists (VKA) for thromboembolic diseases. Since 2011, non-VKA oral anticoagulants (NOACs) have been available. NOACs do not require frequent INR monitoring and cause less bleeding, which benefits patients, but also imposes a risk of reduced therapy adherence. Objectives: The objective of this study is to describe uptake of and patient compliance with NOACs in The Netherlands between July 2011 and October 2016. Methods: We analysed prescription data for 247.927 NOAC and/or VKA patients across 560 pharmacies. All patients who received at least one prescription of either VKA or NOACs between 1 July 2011 and 30 September 2016 were included in the study. Our database contained (not exhaustive) the following information about the prescriptions: dispensed medication and quantity, dispensing date, prescribed dosage and prescriber type, patient age and gender. We used these data to describe patient profiles, uptake of NOACs among new naïve patients and switch of patients between VKA and NOACs. We developed an algorithm to classify patients as new naïve starters, switcher or repeat patients. We calculated therapy compliance as the percentage of days covered (PDC). To obtain reliable results, in our PDC calculations we included only patients with a time period of at least 12 months between their first and last prescription. Results: During the studied period the share of NOACs in oral anticoagulants has grown to 57% of prescriptions to new patients. More than 70% of new NOAC users were new naïve patients and around 26% switched from VKA. The overall share of NOACs among starters is largest in the group of patients of 50-80 years. Calculated percentages of days covered (PDC) for NOAC patients show that 87% of all users were compliant. Conclusions: NOACs have overtaken VKA as the major treatment prescribed to patients starting on oral anticoagulants, and the number of starters on VKA is at present decreasing. We expect that almost all oral anticoagulants prescribed to new patients will be NOACs. NOAC users are in general compliant with therapy. This may provide additional confidence to physicians in prescribing NOACs instead of VKAs

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

BACKGROUND: High budget impact (BI) estimates of new drugs limit access to patients due to concerns regarding affordability and displacement effects. The accuracy and methodological quality of BI analyses are often low, potentially mis-informing reimbursement decision making. Using hepatitis C as a case study, we aim to quantify the accuracy of the BI predictions used in Dutch reimbursement decision-making and to characterize the influence of market-dynamics on actual BI. METHODS: We selected hepatitis C direct-acting antivirals (DAAs) that were introduced in the Netherlands between January 2014 and March 2018. Dutch National Health Care Institute (ZIN) BI estimates were derived from the reimbursement dossiers. Actual Dutch BI data were provided by FarmInform. BI prediction accuracy was assessed by comparing the ZIN BI estimates with the actual BI data. RESULTS: Actual BI, from 1 Jan 2014 to 1 March 2018, was €248 million whilst the BI estimates ranged from €388-€510 million. The latter figure represents the estimated BI for the reimbursement scenario that was adopted, implying a €275 million overestimation. Absent incorporation of timing of regulatory decisions and inadequate correction for the introduction of new products were main drivers of BI overestimation, as well as uncertainty regarding the patient population size and the impact of the final reimbursement decision. DISCUSSION: BI in reimbursement dossiers largely overestimated actual BI of hepatitis C DAAs. When BI analysis is performed according to existing guidelines, the resulting more accurate BI estimates may lead to better informed reimbursement decisions

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

BACKGROUND: High budget impact (BI) estimates of new drugs limit access to patients due to concerns regarding affordability and displacement effects. The accuracy and methodological quality of BI analyses are often low, potentially mis-informing reimbursement decision making. Using hepatitis C as a case study, we aim to quantify the accuracy of the BI predictions used in Dutch reimbursement decision-making and to characterize the influence of market-dynamics on actual BI. METHODS: We selected hepatitis C direct-acting antivirals (DAAs) that were introduced in the Netherlands between January 2014 and March 2018. Dutch National Health Care Institute (ZIN) BI estimates were derived from the reimbursement dossiers. Actual Dutch BI data were provided by FarmInform. BI prediction accuracy was assessed by comparing the ZIN BI estimates with the actual BI data. RESULTS: Actual BI, from 1 Jan 2014 to 1 March 2018, was €248 million whilst the BI estimates ranged from €388-€510 million. The latter figure represents the estimated BI for the reimbursement scenario that was adopted, implying a €275 million overestimation. Absent incorporation of timing of regulatory decisions and inadequate correction for the introduction of new products were main drivers of BI overestimation, as well as uncertainty regarding the patient population size and the impact of the final reimbursement decision. DISCUSSION: BI in reimbursement dossiers largely overestimated actual BI of hepatitis C DAAs. When BI analysis is performed according to existing guidelines, the resulting more accurate BI estimates may lead to better informed reimbursement decisions

The need for precision medicine clinical trials in childhood asthma: rationale and design of the PUFFIN trial

A 'one-size fits all'-approach does not fit all pediatric asthma patients. Current evidence suggests that in children with persistent asthma, ADRB2 genotype-guided treatment can improve treatment outcomes, yet this evidence is mainly derived from observational and genotype-stratified studies. Implementation of precision medicine-guided asthma treatment in clinical practice will only occur if randomized clinical trials can show that this approach will improve patient outcomes and is cost effective. In this paper, we will discuss why precision medicine trials are currently needed to improve childhood asthma management and present the rationale and design of the PUFFIN trial, that has been set up to address this nee

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

Disparities in model-based cost-effectiveness analyses of tuberculosis diagnosis : A systematic review

BACKGROUND: Structural approach disparities were minimally addressed in past systematic reviews of model-based cost-effectiveness analyses addressing Tuberculosis management strategies. This review aimed to identify the structural approach disparities in model-based cost-effectiveness analysis studies addressing Tuberculosis diagnosis and describe potential hazards caused by those disparities. METHODS: A systematic search to identify studies published before October 2015 was performed in five electronic databases. After removal of duplication, studies' titles and abstracts were screened based on predetermined criteria. The full texts of potentially relevant studies were subsequently screened and excluded when they did not address active pulmonary Tuberculosis diagnosis. Quality of the studies was assessed using the "Philips' checklist." Various data regarding general information, cost-effectiveness results, and disease modeling were extracted using standardized data extraction forms. Data pertaining to models' structural approaches were compared and analyzed qualitatively for their applicability in various study settings, as well as their potential influence on main outcomes and cost-effectiveness conclusion. RESULTS: A total of 27 studies were included in the review. Most studies utilized a static model, which could underestimate the cost-effectiveness of the diagnostic tools strategies, due to the omission of indirect diagnosis effects, i.e. transmission reduction. A few structural assumption disparities were found in the dynamic models. Extensive disparities were found in the static models, consisting of varying structural assumptions regarding treatment outcomes, clinical diagnosis and empirical treatment, inpatient discharge decision, and re-diagnosis of false negative patients. CONCLUSION: In cost-effectiveness analysis studies addressing active pulmonary Tuberculosis diagnosis, models showed numerous disparities in their structural approaches. Several structural approaches could be inapplicable in certain settings. Furthermore, they could contribute to under- or overestimation of the cost-effectiveness of the diagnosis tools or strategies. They could thus lead to ambiguities and difficulties when interpreting a study result. A set of recommendations is proposed to manage issues related to these structural disparities

Disparities in model-based cost-effectiveness analyses of tuberculosis diagnosis : A systematic review

BACKGROUND: Structural approach disparities were minimally addressed in past systematic reviews of model-based cost-effectiveness analyses addressing Tuberculosis management strategies. This review aimed to identify the structural approach disparities in model-based cost-effectiveness analysis studies addressing Tuberculosis diagnosis and describe potential hazards caused by those disparities. METHODS: A systematic search to identify studies published before October 2015 was performed in five electronic databases. After removal of duplication, studies' titles and abstracts were screened based on predetermined criteria. The full texts of potentially relevant studies were subsequently screened and excluded when they did not address active pulmonary Tuberculosis diagnosis. Quality of the studies was assessed using the "Philips' checklist." Various data regarding general information, cost-effectiveness results, and disease modeling were extracted using standardized data extraction forms. Data pertaining to models' structural approaches were compared and analyzed qualitatively for their applicability in various study settings, as well as their potential influence on main outcomes and cost-effectiveness conclusion. RESULTS: A total of 27 studies were included in the review. Most studies utilized a static model, which could underestimate the cost-effectiveness of the diagnostic tools strategies, due to the omission of indirect diagnosis effects, i.e. transmission reduction. A few structural assumption disparities were found in the dynamic models. Extensive disparities were found in the static models, consisting of varying structural assumptions regarding treatment outcomes, clinical diagnosis and empirical treatment, inpatient discharge decision, and re-diagnosis of false negative patients. CONCLUSION: In cost-effectiveness analysis studies addressing active pulmonary Tuberculosis diagnosis, models showed numerous disparities in their structural approaches. Several structural approaches could be inapplicable in certain settings. Furthermore, they could contribute to under- or overestimation of the cost-effectiveness of the diagnosis tools or strategies. They could thus lead to ambiguities and difficulties when interpreting a study result. A set of recommendations is proposed to manage issues related to these structural disparities

Managing Uncertainties Due to Limited Evidence in Economic Evaluations of Novel Anti-Tuberculosis Regimens: A Systematic Review

Background: Limited evidence for the implementation of new health technologies in low- and middle-income countries (LMICs) may lead to uncertainties in economic evaluations and cause the evaluations to produce inaccurate information for decision making. We performed a systematic review of economic evaluations on implementing new short-course regimens (SCR) for drug-sensitive and drug-resistant tuberculosis (TB), to explore how uncertainties due to the limited evidence in the studies were dealt with and to identify useful information for decision making from these studies. Methods: We searched in electronic databases PubMed, EMBASE, NHSEED, and CEA registry for economic evaluations addressing the implementation of new anti-TB SCRs in LMICs published until September 2018. We included studies addressing both the cost and outcomes of implementing a new regimen for drug-sensitive and drug-resistant TB with a shorter treatment duration than the currently used regimens. The quality of the included studies was assessed using The Consensus Health Economic Criteria checklist. We extracted information from the included studies on uncertainties and how they were managed. The management of uncertainties was compared with approaches used in early health technology assessments (HTAs), including sensitivity analyses and pragmatic scenario analyses. We extracted information that could be useful for decision making such as cost-effectiveness conclusions, and barriers to implementing the intervention. Results: Four of the 322 studies found in the search met the eligibility criteria. Three studies were model-based studies that investigated the cost effectiveness of a new first-line SCR. One study was an empirical study investigating the cost effectiveness of new regimens for drug-resistant TB. The model-based studies addressed uncertainties due to limited evidence through various sensitivity analyses as in early HTAs. They performed a deterministic sensitivity analysis and found the main drivers of the cost-effectiveness outcomes, that is, the rate of treatment default and treatment delivery costs. Additionally, two of the model-based studies performed a pragmatic scenario analysis and found a potential barrier to implementing the new first-line SCR, that is, a weak health system with a low TB care utilization rate. The empirical study only performed a few scenario analyses with different regimen prices and volumes of TB care utilization. Therefore, the study could only provide information on the main cost drivers. Conclusion: Using an approach similar to that used in early HTAs, where uncertainties due to the limited evidence are rigorously explored upfront, the economic evaluations could inform not only the decision to implement the intervention but also how to manage risks and implementation barriers