The Use of Gene Tests to Detect Hereditary Predisposition to Chronic Disease: Is Cost-Effectiveness Analysis Relevant?

Pharmacogenetic testing for adverse drug reaction prevention: systematic review of economic evaluations and the appraisal of quality matters for clinical practice and implementation

Background

Genetic testing has potential roles in identifying whether an individual would have risk of adverse drug reactions (ADRs) from a particular medicine. Robust cost-effectiveness results on genetic testing would be useful for clinical practice and policy decision-making on allocating resources effectively. This study aimed to update a systematic review on economic evaluations of pharmacogenetic testing to prevent ADRs and critically appraise the quality of reporting and sources of evidence for model input parameters.

Methods

We searched studies through Medline via PubMed, Scopus and CRD’s NHS Economic Evaluation up to October 2019. Studies investigating polymorphism-based pharmacogenetic testing, which guided drug therapies to prevent ADRs, using economic evaluation methods were included. Two reviewers independently performed data extraction and assessed the quality of reporting using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines and the quality of data sources using the hierarchy of evidence developed by Cooper et al.

Results

Fifty-nine economic evaluations of pharmacogenetic testing to avoid drug-induced ADRs were found between 2002 and 2018. Cost-utility and cost-effectiveness analyses were the most common methods of economic evaluation of pharmacogenetic testing. Most studies complied with the CHEERS checklist, except for single study-based economic evaluations which did not report uncertainty analysis (78%). There was a lack of high-quality evidence not only for estimating the clinical effectiveness of pharmacogenetic testing, but also baseline clinical data. About 14% of the studies obtained clinical effectiveness data of testing from a meta-analysis of case-control studies with direct comparison, which was not listed in the hierarchy of evidence used.

Conclusions

Our review suggested that future single study-based economic evaluations of pharmacogenetic testing should report uncertainty analysis, as this could significantly affect the robustness of economic evaluation results. A specific ranking system for the quality of evidence is needed for the economic evaluation of pharmacogenetic testing of ADRs. Differences in parameters, methods and outcomes across studies, as well as population-level and system-level differences, may lead to the difficulty of comparing cost-effectiveness results across countries.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07025-8.

Data on Utility in Cost-Utility Analyses of Genetic Screen-and-Treat Strategies for Breast and Ovarian Cancer

Simple Summary

The prevention of hereditary breast and ovarian cancer involves genetic counselling and several highly preference-sensitive alternatives (i.e., risk-reducing surgeries). In health economics models, data on health preferences applied (i.e., utility values) are heterogeneous. In this methodological analysis, we compared the application of utility values among cost–utility models of targeted genetic testing for the prevention of breast and ovarian cancer. While varying utilities on risk-reducing surgeries and cancer states did not impact the cost–utility ratio, utility losses/gains due to a positive/negative test may strongly affect the cost–utility ratio and should be considered mandatory in future models. Because women’s health preferences may have changed as a result of improved oncologic care and genetic counselling, studies for ascertaining women’s health preferences should be updated.

Abstract

Genetic screen-and-treat strategies for the risk-reduction of breast cancer (BC) and ovarian cancer (OC) are often evaluated by cost–utility analyses (CUAs). This analysis compares data on health preferences (i.e., utility values) in CUAs of targeted genetic testing for BC and OC. Based on utilities applied in fourteen CUAs, data on utility including related assumptions were extracted for the health states: (i) genetic test, (ii) risk-reducing surgeries, (iii) BC/OC and (iv) post cancer. In addition, information about the sources of utility and the impact on the cost-effectiveness was extracted. Utility for CUAs relied on heterogeneous data and assumptions for all health states. The utility values ranged from 0.68 to 0.97 for risk-reducing surgeries, 0.6 to 0.85 for BC and 0.5 to 0.82 for OC. In two out of nine studies, considering the impact of the test result strongly affected the cost–effectiveness ratio. While in general utilities seem not to affect the cost–utility ratio, in future modeling studies the impact of a positive/negative test on utility should be considered mandatory. Women’s health preferences, which may have changed as a result of improved oncologic care and genetic counselling, should be re-evaluated.

Using Cost-Effectiveness Analysis to Quantify the Value of Genomic-Based Diagnostic Tests: Recommendations for Practice and Research

AIMS

New sequencing technologies allow increased opportunities to use genomic-based diagnostic tests (genomic tests) in routine clinical practice, which will impact healthcare budgets and patients' outcomes. This article aims to generate a list of recommendations on how the principles and methods of cost-effectiveness analysis (CEA) can be used to quantify the costs and benefits of genomic tests.

METHODS

A systematic literature search identified publications describing the use of CEA to evaluate genomic tests. Data were extracted as key concepts to produce a thematic list of previously described challenges and solutions to using CEA to evaluate genomic tests. Defining features of evaluating genomic tests were categorized into a list of key recommendations for applying methods in practice and for research needs.

RESULTS

Features producing challenges in the implementation of CEA to evaluate genomic tests were as follows: the ability of the tests to diagnose multiple disorders; potential consequences for future generations suggesting an infinite time horizon; and the potential need to consider nonhealth benefits.

CONCLUSIONS

CEA was identified as an appropriate evaluative framework for genomic tests, although standard methods may need modification and important method research questions remain. Key recommendations suggest a need for research to reflect: sharing genomic information across generations; genomic tests for multiple disorders; and health and nonhealth benefits.

Predictive genetic testing for complex diseases: a public health perspective

From a public health perspective, systematic, evidence-based technology assessments and economic evaluations are needed to guide the incorporation of genomics into clinical and public health practice. However, scientific evidence on the effectiveness of predictive genetic tests is difficult to obtain. This review first highlights the similarities and differences between traditional screening tests and predictive genetic testing for complex diseases and goes on to describe frameworks for the evaluation of genetic testing that have been developed in recent years providing some evidence that currently genetic tests are not used in an appropriate way. Nevertheless, evidence-based recommendations are already available for some genomic applications that can reduce morbidity and mortality and many more are expected to emerge over the next decade. The time is now ripe for the introduction of a range of genetic tests into healthcare practice, but this will require the development of specific health policies, proper public health evaluations, organizational changes within the healthcare systems, capacity building among the healthcare workforce and the education of the public.

Knowledge, attitudes and behavior of physicians regarding predictive genetic tests for breast and colorectal cancer

BACKGROUND

Genetic testing for cancer susceptibility is an emerging technology in medicine. This study assessed the knowledge, attitudes and professional behavior of Italian physicians regarding the use of predictive genetic tests for breast and colorectal cancer, including the BRCA1/2 and APC tests.

METHODS

A cross-sectional survey of a random sample of Italian physicians was performed in 2010 through a self-administered questionnaire.

RESULTS

A response rate of 69.6% (1079 questionnaires) was achieved. A significant lack of knowledge was detected, particularly for APC testing. Less than half of the physicians agreed on the importance of efficacy and cost-effectiveness evidence in the selection of predictive genetic tests to be offered to the patients. Multiple logistic regression analyses showed that education had a positive influence on knowledge, attitudes and, to a lesser extent, professional use. The factor most strongly related to the physicians' use of genetic testing was patients requests for breast (odds ratio=12.65; 95% confidence interval 7.77-20.59) or colorectal cancer tests (odds ratio=7.02; 95% confidence interval 3.61-13.64). A high level of interest for specific training was reported by almost all physicians surveyed.

CONCLUSIONS

Targeted educational programs are needed to improve the expertise of physicians, and, ultimately, to enhance the appropriate use of genetic tests in clinical practice.

Genetic screening for the predisposition to venous thromboembolism: a cost-utility analysis of clinical practice in the Italian health care system

OBJECTIVES

In the Italian health care system, genetic tests for factor V Leiden and factor II are routinely prescribed to assess the predisposition to venous thromboembolism (VTE) of women who request oral contraception. With specific reference to two subpopulations of women already at risk (i.e., familial history or previous event of VTE), the study aimed to assess whether current screening practices in Italy are cost-effective.

METHODS

Two decisional models accrued costs and quality-adjusted life-years (QALY) annually from the perspective of the National Health Service. The two models were derived from a decision analysis exercise concerning testing practices and consequent prescribing behavior for oral contraception conducted with 250 Italian gynecologists. Health care costs were compiled on the basis of 10-year hospital discharge records and the activities of a thrombosis center. Whenever possible, input data were based on the Italian context; otherwise, the data were taken from the international literature.

RESULTS

Current testing practices on women with a familial history of VTE generate an incremental cost-effectiveness ratio of €72,412/QALY, which is well above the acceptable threshold of cost-effectiveness of €40,000 to €50,000/QALY. In the case of women with a previous event of VTE, the most frequently used testing strategy is cost-ineffective and leads to an overall loss of QALY.

CONCLUSIONS

This study represents the first attempt to conduct a cost-utility analysis of genetic screening practices for the predisposition to VTE in the Italian setting. The results indicate that there is an urgent need to better monitor the indications for which tests for factor V Leiden and factor II are prescribed.

A health services research agenda for cellular, molecular and genomic technologies in cancer care

BACKGROUND

In recent decades, extensive resources have been invested to develop cellular, molecular and genomic technologies with clinical applications that span the continuum of cancer care.

METHODS

In December 2006, the National Cancer Institute sponsored the first workshop to uniquely examine the state of health services research on cancer-related cellular, molecular and genomic technologies and identify challenges and priorities for expanding the evidence base on their effectiveness in routine care.

RESULTS

This article summarizes the workshop outcomes, which included development of a comprehensive research agenda that incorporates health and safety endpoints, utilization patterns, patient and provider preferences, quality of care and access, disparities, economics and decision modeling, trends in cancer outcomes, and health-related quality of life among target populations.

CONCLUSIONS

Ultimately, the successful adoption of useful technologies will depend on understanding and influencing the patient, provider, health care system and societal factors that contribute to their uptake and effectiveness in 'real-world' settings.

Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis

Genetic testing in health care can provide information to help with disease prediction, diagnosis, prognosis, and treatment. Assessing the clinical utility of genetic testing requires a process to value and weight different outcomes. This article discusses the relative merits of different economic measures and methods to inform recommendations relative to genetic testing for risk of disease, including cost-effectiveness analysis and cost-benefit analysis. Cost-effectiveness analyses refer to analyses that calculate the incremental cost per unit of health outcomes, such as deaths prevented or life-years saved because of some intervention. Cost-effectiveness analyses that use preference-based measures of health state utility such as quality-adjusted life-years to define outcomes are referred to as cost-utility analyses. Cost-effectiveness analyses presume that health policy decision makers seek to maximize health subject to resource constraints. Cost-benefit analyses can incorporate monetary estimates of willingness-to-pay for genetic testing, including the perceived value of information independent of health outcomes. These estimates can be derived from contingent valuation or discrete choice experiments. Because important outcomes of genetic testing do not fit easily within traditional measures of health, cost-effectiveness analyses do not necessarily capture the full range of outcomes of genetic testing that are important to decision makers and consumers. We recommend that health policy decision makers consider the value to consumers of information and other nonhealth attributes of genetic testing strategies.

Economic evaluation of the familial cancer programme in Western Australia: predictive genetic testing for familial adenomatous polyposis and hereditary non-polyposis colorectal carcinoma

AIM

To evaluate costs and outcomes of genetic testing for familial colorectal cancer through services provided by Genetic Services of Western Australia (GSWA).

METHODS

Costs and outcomes of predictive DNA-based testing for inherited colorectal cancers (CRC) were assessed, specifically for familial adenomatous polyposis (FAP) and hereditary non-polyposis CRC (HNPCC) using a decision-analysis model. Costs were assigned according to standards of care in Western Australia (WA). Cancer risks and the efficacy of surveillance on long-term outcomes were derived from the published literature.

RESULTS

The cost-effectiveness of genetic testing was compared in first-degree relatives of known mutation carriers who have a 50% risk of carrying the mutated gene (intervention group) to individuals with the same risk but who do not undergo a genetic test (control subjects). Compared with control subjects undergoing the same high-level surveillance and surgery, the FAP and HNPCC intervention groups provided total savings of 13,390 US dollars and 14,783-15,460 per person (males-females), respectively. HPNCC mutation carriers also gained 1 CRC-free year. Compared to control subjects having only population surveillance, individuals in the FAP intervention group delayed the onset of CRC by 40 years for a net cost of 9,042 US dollars. Individuals in the HNPCC intervention group delayed the onset of CRC by 8 years at a net cost of 12,141 US dollars for males and 12,596 US dollars for females.

CONCLUSIONS

Genetic testing for familial CRC in WA allows targeted surveillance for mutation carriers, which ensures the efficient use of resources and reduces cancer-related morbidity, if clinical recommendations for intervention are adopted.