Economic Perspectives on Personalized Health Care and Prevention

Tools for the Economic Evaluation of Precision Medicine: A Scoping Review of Frameworks for Valuing Heterogeneity-Informed Decisions

BACKGROUND AND OBJECTIVE

Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine.

METHODS

We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine.

RESULTS

Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider.

CONCLUSIONS

Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

Implementing Interventions with Varying Marginal Cost-Effectiveness: An Application in Precision Medicine

Purpose. A range of barriers may constrain the effective implementation of strategies to deliver precision medicine. If the marginal costs and consequences of precision medicine vary at different levels of implementation, then such variation will have an impact on relative cost-effectiveness. This study aimed to illustrate the importance and quantify the impact of varying marginal costs and benefits on the value of implementation for a case study in precision medicine. Methods. An existing method to calculate the value of implementation was adapted to allow marginal costs and consequences of introducing precision medicine into practice to vary across differing levels of implementation. This illustrative analysis used a case study based on a published decision-analytic model-based cost-effectiveness analysis of a 70-gene recurrence score (MammaPrint) for breast cancer. The impact of allowing for varying costs and benefits for the value of the precision medicine and of implementation strategies was illustrated graphically and numerically in both static and dynamic forms. Results. The increasing returns to scale exhibited by introducing this specific example of precision medicine mean that a minimum level of implementation (51%) is required for using the 70-gene recurrence score to be cost-effective at a defined threshold of €20,000 per quality-adjusted life year. The observed variation in net monetary benefit implies that the value of implementation strategies was dependent on the initial and ending levels of implementation in addition to the magnitude of the increase in patients receiving the 70-gene recurrence score. In dynamic models, incremental losses caused by low implementation accrue over time unless implementation is improved. Conclusions. Poor implementation of approaches to deliver precision medicine, identified to be cost-effective using decision-analytic model-based cost-effectiveness analysis, can have a significant economic impact on health systems. Developing and evaluating the economic impact of strategies to improve the implementation of precision medicine will potentially realize the more cost-effective use of health care budgets.

PARC report: health outcomes and value of personalized medicine interventions: impact on patient care

The incorporation of personalized medicine interventions into routine healthcare constitutes an opportunity to improve patients' quality of life, as it empowers implementation of innovative, individualized clinical interventions that maximize efficacy and/or minimize the risk of adverse drug reactions. In order to ensure equal access to genomic testing for all patients, the costs associated with these interventions must be reimbursed by payers and insurance bodies. As such, it is of utmost importance to thoroughly evaluate these interventions both in terms of their clinical effectiveness and their economic cost. This article discusses the impact of personalized medicine interventions in terms of both health outcomes and value, which directly impacts on their pricing and reimbursement by the various national healthcare systems.

Application of Economic Evaluation to Assess Feasibility for Reimbursement of Genomic Testing as Part of Personalized Medicine Interventions

Background: The incorporation of genomic testing into clinical practice constitutes an opportunity to improve patients' lives, as it makes possible the implementation of innovative, individualized clinical interventions that maximize efficacy and/or minimize the risk of adverse drug reactions. In order to ensure equal access to genomic testing for all patients, the costs associated with these tests should be reimbursed by their respective national healthcare systems. Given that funding for the public health sector is decreasing in real terms, it is of paramount importance that the emerging interventions are thoroughly evaluated both in terms of their clinical effectiveness and their full economic cost. Objective: The aim of this study was to identify those genome-guided interventions that could be adopted and reimbursed by national healthcare systems. Further, we recorded the underlying factors determining the broad adoption of genome-guided interventions in clinical practice, in order to identify potential reimbursement criteria. Methods: We performed a systematic review of published (PubMed-listed) scientific articles on the economic evaluation of those individualized clinical interventions that include genomic tests. Information on genomic tests reimbursed by the US Medicare program was also included. Subsequently, we correlated the regulatory guidance given for the interventions collated in our systematic review with the corresponding economic evaluation results and policies of the Medicare program. Regulatory guidance information was collected from the PharmGKB online knowledgebase and the Clinical Pharmacogenetics Implementation Consortium (CPIC). Results: Most of the included studies constitute cost-utility analyses, in which the outcome of the interventions has been measured in quality-adjusted life years (QALYs) whereas an estimate of the total cost has been based upon direct medical cost data. Favorable economic evaluation results, as well as concrete evidence demonstrating the clinical utility of pre-emptive genotyping, are considered as prerequisites for the broad adoption and reimbursement of the costs incurred during genomic testing. Indicatively, pre-emptive HLA-B*5701 and TPMT testing before administration of abacavir and azathioprine, respectively, is reimbursed by Medicare based on both economic and efficacy evidence. Likewise, the medical necessary screening for MMR and BRCA1/2 genes are reimbursed for high-risk populations. Conclusions: Our findings further underline the need for further cost-utility analyses within different national healthcare systems, in order to promote the reimbursement of the cost of innovative genome-guided therapeutic interventions.

Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review

BACKGROUND AND OBJECTIVE

Precision (stratified or personalised) medicine is underpinned by the premise that it is feasible to identify known heterogeneity using a specific test or algorithm in patient populations and to use this information to guide patient care to improve health and well-being. This study aimed to understand if, and how, previous economic evaluations of precision medicine had taken account of the impact of capacity constraints.

METHODS

A meta-review was conducted of published systematic reviews of economic evaluations of precision medicine (test-treat interventions) and individual studies included in these reviews. Due to the volume of studies identified, a sample of papers published from 2007 to 2015 was collated. A narrative analysis identified whether potential capacity constraints were discussed qualitatively in the studies and, if relevant, which quantitative methods were used to account for capacity constraints.

RESULTS

A total of 45 systematic reviews of economic evaluations of precision medicine were identified, from which 222 studies focusing on test-treat interventions, published between 2007 and 2015, were extracted. Of these studies, 33 (15%) qualitatively discussed the potential impact of capacity constraints, including budget constraints; quality of tests and the testing process; ease of use of tests in clinical practice; and decision uncertainty. Quantitative methods (nine studies) to account for capacity constraints included static methods such as capturing inefficiencies in trials or models and sensitivity analysis around model parameters; and dynamic methods, which allow the impact of capacity constraints on cost effectiveness to change over time.

CONCLUSIONS

Understanding the cost effectiveness of precision medicine is necessary, but not sufficient, evidence for its successful implementation. There are currently few examples of evaluations that have quantified the impact of capacity constraints, which suggests an area of focus for future research.

Cost-effectiveness analyses of genetic and genomic diagnostic tests

Developments in next-generation sequencing technologies have driven the clinical application of diagnostic tests that interrogate the whole genome, which offer the chance to diagnose rare inherited diseases or inform the targeting of therapies. New genomic diagnostic tests compete with traditional approaches to diagnosis, including the genetic testing of single genes and other clinical strategies, for finite health-care budgets. In this context, decision analytic model-based cost-effectiveness analysis is a useful method to help evaluate the costs versus consequences of introducing new health-care interventions. This Perspective presents key methodological, technical, practical and organizational challenges that must be considered by decision-makers responsible for the allocation of health-care resources to obtain robust and timely information about the relative cost-effectiveness of the increasing numbers of emerging genomic tests.

"What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine

BACKGROUND

The growth of "big data" and the emphasis on patient-centered health care have led to the increasing use of two key technologies: personalized medicine and digital medicine. For these technologies to move into mainstream health care and be reimbursed by insurers, it will be essential to have evidence that their benefits provide reasonable value relative to their costs. These technologies, however, have complex characteristics that present challenges to the assessment of their economic value. Previous studies have identified the challenges for personalized medicine and thus this work informs the more nascent topic of digital medicine.

OBJECTIVES

To examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison.

METHODS

We focused specifically on digital biomarker technologies and multigene tests. We identified similarities in these technologies that can present challenges to economic evaluation: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects.

RESULTS

Using a structured review, we found that there are few economic evaluations of digital biomarker technologies, with limited results.

CONCLUSIONS

We conclude that more evidence on the effectiveness of digital medicine will be needed but that the experiences with personalized medicine can inform what data will be needed and how such analyses can be conducted. Our study points out the critical need for typologies and terminology for digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value.

Is individualized medicine more cost-effective? A systematic review

BACKGROUND

Individualized medicine (IM) is a rapidly evolving field that is associated with both visions of more effective care at lower costs and fears of highly priced, low-value interventions. It is unclear which view is supported by the current evidence.

OBJECTIVE

Our objective was to systematically review the health economic evidence related to IM and to derive general statements on its cost-effectiveness.

DATA SOURCES

A literature search of MEDLINE database for English- and German-language studies was conducted.

STUDY APPRAISAL AND SYNTHESIS METHOD

Cost-effectiveness and cost-utility studies for technologies meeting the MEDLINE medical subject headings (MeSH) definition of IM (genetically targeted interventions) were reviewed. This was followed by a standardized extraction of general study characteristics and cost-effectiveness results.

RESULTS

Most of the 84 studies included in the synthesis were from the USA (n = 43, 51 %), cost-utility studies (n = 66, 79 %), and published since 2005 (n = 60, 71 %). The results ranged from dominant to dominated. The median value (cost-utility studies) was calculated to be rounded $US22,000 per quality-adjusted life year (QALY) gained (adjusted to $US, year 2008 values), which is equal to the rounded median cost-effectiveness in the peer-reviewed English-language literature according to a recent review. Many studies reported more than one strategy of IM with highly varying cost-effectiveness ratios. Generally, results differed according to test type, and tests for disease prognosis or screening appeared to be more favorable than tests to stratify patients by response or by risk of adverse effects. However, these results were not significant.

LIMITATIONS

Different definitions of IM could have been used. Quality assessment of the studies was restricted to analyzing transparency.

CONCLUSIONS

IM neither seems to display superior cost-effectiveness than other types of medical interventions nor to be economically inferior. Instead, rather than 'whether' healthcare was individualized, the question of 'how' it was individualized was of economic relevance.