The value of genomic sequencing in complex pediatric neurological disorders: a discrete choice experiment

Public Preferences for Genetic and Genomic Risk-Informed Chronic Disease Screening and Early Detection: A Systematic Review of Discrete Choice Experiments

PURPOSE

Genetic and genomic testing can provide valuable information on individuals' risk of chronic diseases, presenting an opportunity for risk-tailored disease screening to improve early detection and health outcomes. The acceptability, uptake and effectiveness of such programmes is dependent on public preferences for the programme features. This study aims to conduct a systematic review of discrete choice experiments assessing preferences for genetic/genomic risk-tailored chronic disease screening.

METHODS

PubMed, Embase, EconLit and Cochrane Library were searched in October 2023 for discrete choice experiment studies assessing preferences for genetic or genomic risk-tailored chronic disease screening. Eligible studies were double screened, extracted and synthesised through descriptive statistics and content analysis of themes. Bias was assessed using an existing quality checklist.

RESULTS

Twelve studies were included. Most studies focused on cancer screening (n = 10) and explored preferences for testing of rare, high-risk variants (n = 10), largely within a targeted population (e.g. subgroups with family history of disease). Two studies explored preferences for the use of polygenic risk scores (PRS) at a population level. Twenty-six programme attributes were identified, with most significantly impacting preferences. Survival, test accuracy and screening impact were most frequently reported as most important. Depending on the clinical context and programme attributes and levels, estimated uptake of hypothetical programmes varied from no participation to almost full participation (97%).

CONCLUSION

The uptake of potential programmes would strongly depend on specific programme features and the disease context. In particular, careful communication of potential survival benefits and likely genetic/genomic test accuracy might encourage uptake of genetic and genomic risk-tailored disease screening programmes. As the majority of the literature focused on high-risk variants and cancer screening, further research is required to understand preferences specific to PRS testing at a population level and targeted genomic testing for different disease contexts.

Determining priority indicators of utility for genomic testing in rare disease: A Delphi study

PURPOSE

Determining the value of genomic tests in rare disease necessitates a broader conceptualization of genomic utility beyond diagnostic yield. Despite widespread discussion, consensus toward which aspects of value to consider is lacking. This study aimed to use expert opinion to identify and refine priority indicators of utility in rare disease genomic testing.

METHODS

We used 2 survey rounds following Delphi methodology to obtain consensus on indicators of utility among experts involved in policy, clinical, research, and consumer advocacy leadership in Australia. We analyzed quantitative and qualitative data to identify, define, and determine priority indicators.

RESULTS

Twenty-five experts completed round 1 and 18 completed both rounds. Twenty indicators reached consensus as a priority in value assessment, including those relating to prognostic information, timeliness of results, practical and health care outcomes, clinical accreditation, and diagnostic yield. Whereas indicators pertaining to discovery research, disutility, and factors secondary to primary reason for testing were considered less of a priority and were removed.

CONCLUSION

This study obtained expert consensus on different utility indicators that are considered a priority in determining the value of genomic testing in rare disease in Australia. Indicators may inform a standardized approach to evidence generation and assessment to guide future research, decision making, and implementation efforts.

The value of genomic testing in severe childhood speech disorders

With increasing gene discoveries for severe speech disorders, genomic testing can alter the diagnostic and clinical paradigms, enabling better life outcomes for children and their families. However, evidence on the value of the outcomes generated is lacking, impeding optimal translation into health care. This study aims to estimate the value and uptake of genomic testing for severe childhood speech disorders. A discrete choice experiment was undertaken to elicit preferences for genomic testing from the perspective of the Australian public (n = 951) and parents of children experiencing severe speech disorder (n = 56). Choice attributes associated with genomic testing were identified through focus groups. A Bayesian D-efficient design was used to develop choice scenarios and choice data were analyzed using a panel error component mixed logit model and a latent class model. Statistically significant preferences were identified across all seven attributes. The mean monetary value of the benefits of genomic testing relative to standard diagnostic care in Australia was estimated at AU$7489 (US$5021) and AU$4452 (US$2985) from the perspectives of the Australian public and families with lived experience of severe speech disorders, with a corresponding test uptake of 94.2% and 99.6%. To ensure fair prioritization of genomics, decision-makers need to consider the wide range of risks and benefits associated with genomic information.

Eliciting parental preferences and values for the return of additional findings from genomic sequencing

Health economic evidence is needed to inform the design of high-value and cost-effective processes for returning genomic results from analyses for additional findings (AF). This study reports the results of a discrete-choice experiment designed to elicit preferences for the process of returning AF results from the perspective of parents of children with rare conditions and to estimate the value placed on AF analysis. Overall, 94 parents recruited within the Australian Genomics and Melbourne Genomics programmes participated in the survey, providing preferences in a total of 1128 choice scenarios. Statistically significant preferences were identified for the opportunity to change the choices made about AF; receiving positive AF in person from a genetic counsellor; timely access to a medical specialist and high-quality online resources; receiving automatic updates through a secure online portal if new information becomes available; and lower costs. For AF uptake rates ranging between 50-95%, the mean per person value from AF analysis was estimated at AU$450-$1700 (US$300-$1140). The findings enable the design of a value-maximising process of analysis for AF in rare-disease genomic sequencing.

Genetic Testing of Movements Disorders: A Review of Clinical Utility

Currently, pathogenic variants in more than 500 different genes are known to cause various movement disorders. The increasing accessibility and reducing cost of genetic testing has resulted in increasing clinical use of genetic testing for the diagnosis of movement disorders. However, the optimal use case(s) for genetic testing at a patient level remain ill-defined. Here, we review the utility of genetic testing in patients with movement disorders and also highlight current challenges and limitations that need to be considered when making decisions about genetic testing in clinical practice.

Highlights

The utility of genetic testing extends across multiple clinical and non-clinical domains. Here we review different aspects of the utility of genetic testing for movement disorders and the numerous associated challenges and limitations. These factors should be weighed on a case-by-case basis when requesting genetic tests in clinical practice.

QALYs and rare diseases: exploring the responsiveness of SF-6D, EQ-5D-5L and AQoL-8D following genomic testing for childhood and adult-onset rare genetic conditions in Australia

BACKGROUND

Genomic testing transforms the diagnosis and management of rare conditions. However, uncertainty exists on how to best measure genomic outcomes for informing healthcare priorities. Using the HTA-preferred method should be the starting point to improve the evidence-base. This study explores the responsiveness of SF-6D, EQ-5D-5L and AQoL-8D following genomic testing across childhood and adult-onset genetic conditions.

METHOD

Self-reported patient-reported outcomes (PRO) were obtained from: primary caregivers of children with suspected neurodevelopmental disorders (NDs) or genetic kidney diseases (GKDs) (carers' own PRO), adults with suspected GKDs using SF-12v2; adults with suspected complex neurological disorders (CNDs) using EQ-5D-5L; and adults with dilated cardiomyopathy (DCM) using AQol-8D. Responsiveness was assessed using the standardised response mean effect-size based on diagnostic (having a confirmed genomic diagnosis), personal (usefulness of genomic information to individuals or families), and clinical (clinical usefulness of genomic information) utility anchors.

RESULTS

In total, 254 people completed PRO measures before genomic testing and after receiving results. For diagnostic utility, a nearly moderate positive effect size was identified by the AQoL-8D in adult DCM patients. Declines in physical health domains masked any improvements in mental or psychosocial domains in parents of children affected by NDs and adult CNDs and DCM patients with confirmed diagnosis. However, the magnitude of the changes was small and we did not find statistically significant evidence of these changes. No other responsiveness evidence related to diagnostic, clinical, and personal utility of genomic testing was identified.

CONCLUSION

Generic PRO measures may lack responsiveness to the diagnostic, clinical and personal outcomes of genomics, but further research is needed to establish their measurement properties and relevant evaluative space in the context of rare conditions. Expected declines in the physical health of people experiencing rare conditions may further challenge the conventional application of quality of life assessments.

Determining the utility of diagnostic genomics: a conceptual framework

BACKGROUND

Diagnostic efficacy is now well established for diagnostic genomic testing in rare disease. Assessment of overall utility is emerging as a key next step, however ambiguity in the conceptualisation and measurement of utility has impeded its assessment in a comprehensive manner. We propose a conceptual framework to approach determining the broader utility of diagnostic genomics encompassing patients, families, clinicians, health services and health systems to assist future evidence generation and funding decisions. BODY: Building upon previous work, our framework posits that utility of diagnostic genomics consists of three dimensions: the domain or type and extent of utility (what), the relationship and perspective of utility (who), and the time horizon of utility (when). Across the description, assessment, and summation of these three proposed dimensions of utility, one could potentially triangulate a singular point of utility axes of type, relationship, and time. Collectively, the multiple different points of individual utility might be inferred to relate to a concept of aggregate utility.

CONCLUSION

This ontological framework requires retrospective and prospective application to enable refinement and validation. Moving forward our framework, and others which have preceded it, promote a better characterisation and description of genomic utility to inform decision-making and optimise the benefits of genomic diagnostic testing.

Defining Need Amid Exponential Change: Conceptual Challenges in Workforce Planning for Clinical Genetic Services

Rapid growth in the volume of referrals to clinical genetics services in many countries during the past 15 years makes workforce planning a critical policy tool in ensuring that the capacity of the clinical genetics workforce is large enough to meet current and future needs. This article explores the distinctive challenges of workforce planning in clinical genetics and provides recommendations for addressing these challenges using a needs-based planning approach. Specifically, at least 3 features complicate efforts to estimate the need for clinical genetic services: the difficulty in linking many clinical genetic services to concrete health outcomes; the rapidly changing nature of genetic medicine, which creates intrinsic uncertainty about the appropriate level of service; and the heightened relevance of patient preferences in this context. Our recommendations call for needs-based planning studies to include an explicit definition of necessary care, to be flexible in considering nonhealth benefits, to err on the side of including services currently funded by health systems even when evidence about outcomes is limited, and to use scenario analysis and expert input to explore the impact of uncertainty about patients' preferences and future technologies on estimates of workforce requirements.

Australian Genomics: Outcomes of a 5-year national program to accelerate the integration of genomics in healthcare

Australian Genomics is a national collaborative partnership of more than 100 organizations piloting a whole-of-system approach to integrating genomics into healthcare, based on federation principles. In the first five years of operation, Australian Genomics has evaluated the outcomes of genomic testing in more than 5,200 individuals across 19 rare disease and cancer flagship studies. Comprehensive analyses of the health economic, policy, ethical, legal, implementation and workforce implications of incorporating genomics in the Australian context have informed evidence-based change in policy and practice, resulting in national government funding and equity of access for a range of genomic tests. Simultaneously, Australian Genomics has built national skills, infrastructure, policy, and data resources to enable effective data sharing to drive discovery research and support improvements in clinical genomic delivery.

The Value of Genomic Testing: A Contingent Valuation Across Six Child- and Adult-Onset Genetic Conditions

OBJECTIVES

The aim of this study was to elicit the willingness-to-pay (WTP) for genomic testing, using contingent valuation, among people with lived experience of genetic conditions in Australia.

METHODS

Parents of children with suspected mitochondrial disorders, epileptic encephalopathy, leukodystrophy, or malformations of cortical development completed a dynamic triple-bounded dichotomous choice (DC) contingent valuation. Adult patients or parents of children with suspected genetic kidney disease or complex neurological and neurodegenerative conditions completed a payment card (PC) contingent valuation. DC data were analyzed using a multilevel interval regression and a multilevel probit model. PC data were analyzed using a Heckman selection model.

RESULTS

In total, 360 individuals participated in the contingent valuation (CV), with 141 (39%) and 219 (61%) completing the DC and PC questions, respectively. The mean WTP for genomic testing was estimated at AU$2830 (95% confidence interval [CI] 2236-3424) based on the DC data and AU$1914 (95% CI 1532-2296) based on the PC data. The mean WTP across the six cohorts ranged from AU$1879 (genetic kidney disease) to AU$4554 (leukodystrophy).

CONCLUSIONS

Genomic testing is highly valued by people experiencing rare genetic conditions. Our findings can inform cost-benefit analyses and the prioritization of genomics into mainstream clinical care. While our WTP estimates for adult-onset genetic conditions aligned with estimates derived from discrete choice experiments (DCEs), for childhood-onset conditions our estimates were significantly lower. Research is urgently required to directly compare, and critically evaluate, the performance of CV and DCE methods.

Evaluating genetic and genomic tests for heritable conditions in Australia: lessons learnt from health technology assessments

The Medical Services Advisory Committee (MSAC) is an independent non-statutory committee established by the Australian government to provide recommendations on public reimbursement of technologies and services, other than pharmaceuticals. MSAC has established approaches for undertaking health technology assessment (HTA) of investigative services and codependent technologies. In 2016, MSAC published its clinical utility card (CUC) Proforma, an additional tool to guide assessments of genetic testing for heritable conditions. We undertook a review and narrative synthesis of information extracted from all MSAC assessments of genetic testing for heritable conditions completed since 2016, regardless of the HTA approach taken. Ten assessments met our inclusion criteria, covering a range of testing methods (from gene panels to whole-exome sequencing) and purposes (including molecular diagnosis, genetic risk assessment, identification of congenital anomaly syndromes, and carrier screening). This analysis identified a range of methodological and policy challenges such as how to incorporate patient and societal preferences for the health and non-health outcomes of genomic testing, how best to capture the concept of co-production of utility, and how to engage clinicians as referrers for genomics tests whilst at the same time ensuring equity of access to a geographically dispersed population. A further challenge related to how qualitative assessments of patient and community needs influenced the evidence thresholds against which decisions were made. These concepts should be considered for incorporation within the value assessment frameworks used by HTA agencies around the world.

Patient and public preferences for being recontacted with updated genomic results: a mixed methods study

Variants of uncertain significance (VUS) are frequently reclassified but recontacting patients with updated results poses significant resource challenges. We aimed to characterize public and patient preferences for being recontacted with updated results. A discrete choice experiment (DCE) was administered to representative samples of the Canadian public and cancer patients. DCE attributes were uncertainty, cost, recontact modality, choice of results, and actionability. DCE data were analyzed using a mixed logit model and by calculating willingness to pay (WTP) for types of recontact. Qualitative interviews exploring recontact preferences were analyzed thematically. DCE response rate was 60% (n = 1003, 50% cancer patient participants). 31 participants were interviewed (11 cancer patients). Interviews revealed that participants expected to be recontacted. Quantitatively, preferences for how to be recontacted varied based on certainty of results. For certain results, WTP was highest for being recontacted by a doctor with updates ($1075, 95% CI: $845, $1305) and for contacting a doctor to request updates ($1038, 95% CI: $820, $1256). For VUS results, WTP was highest for an online database ($1735, 95% CI: $1224, $2247) and for contacting a doctor ($1705, 95% CI: $1102, $2307). Qualitative data revealed that preferences for provider-mediated recontact were influenced by trust in healthcare providers. Preferences for a database were influenced by lack of trust in providers and desire for control. Patients and public participants support an online database (e.g. patient portal) to recontact for VUS, improving feasibility, and provider-mediated recontact for certain results, consistent with usual care.

Genomic sequencing for the diagnosis of childhood mitochondrial disorders: a health economic evaluation

The diagnostic and clinical benefits of genomic sequencing are being increasingly demonstrated across multiple rare genetic conditions. Despite the expanding clinical literature, there is a significant paucity of health economics evidence to inform the prioritization and implementation of genomic sequencing. This study aims to evaluate whether genomic sequencing for pediatric-onset mitochondrial disorders (MDs) is cost-effective and cost-beneficial relative to conventional care from an Australian healthcare system perspective. Two independent and complementary health economic modeling approaches were used. Approach 1 used a decision tree to model the costs and outcomes associated with genomic sequencing and conventional care. Approach 2 used a discrete-event simulation to incorporate heterogeneity in the condition and clinical practice. Deterministic and probabilistic sensitivity analyses were performed. Genomic sequencing was less costly and more effective compared with conventional care, saving AU$1997 (Approach 1) to AU$8823 (Approach 2) per child tested, while leading to an additional 11 (Approach 1) to 14 (Approach 2) definitive diagnoses per 100 children tested. The mean monetary value of the incremental benefits of genomic sequencing was estimated at AU$5890 (95% CI: AU$5730-$6046). Implementation of genomic sequencing for MDs in Australia could translate to an annual cost-saving of up to AU$0.7 million. Genomic sequencing is cost-saving relative to traditional investigative approaches, while enabling more diagnoses to be made in a timely manner, offering substantial personal benefits to children and their families. Our findings support the prioritization of genomic sequencing for children with MDs.

A Systematic Review of Discrete Choice Experiments and Conjoint Analysis on Genetic Testing

BACKGROUND

Although genetic testing has the potential to offer promising medical benefits, concerns regarding its potential negative impacts may influence its acceptance. Users and providers need to weigh the benefits, costs and potential harms before deciding whether to take up or recommend genetic testing. Attribute-based stated-preference methods, such as discrete choice experiment (DCE) or conjoint analysis, can help to quantify how individuals value different features of genetic testing.

OBJECTIVES

The aim of this paper was to conduct a systematic review of DCE and conjoint analysis studies on genetic testing, including genomic tests.

METHODS

A systematic search was conducted in seven databases: Web of Science, CINAHL Plus with Full Text (EBSCO), PsycINFO, PubMed, Embase, The Cochrane Library and SCOPUS. The search was conducted in February 2021 and was limited to English peer-reviewed articles published until the search date. The search keywords included relevant keywords such as 'genetic testing', 'genomic testing', 'pharmacogenetic testing', 'discrete choice experiment' and 'conjoint analysis'. Narrative synthesis of the studies was conducted on survey population, testing type, recruitment and data collection, survey development, questionnaire content, survey validity, analysis, outcomes and other design features.

RESULTS

Of the 292 articles retrieved, 38 full-text articles were included in this review. Nearly two-thirds of the studies were published since 2015 and all were conducted in high-income countries. Survey samples included patients, parents, general population and healthcare providers. The articles assessed preferences for pharmacogenetic testing (28.9%), predictive testing and diagnostic testing (18.4%), while only one (2.6%) study investigated preferences for carrier testing. The most common sampling method was convenience sampling (57.9%) and the majority recruited participants via web-enabled surveys (60.5%). Review of literature (84.6%), discussions with healthcare professionals (71.8%) and cognitive interviews (53.8%) were commonly used for attribute identification. A survey validity test was included in only one-quarter of the studies (28.2%). Cost attributes were the most studied attribute type (76.9%), followed by risk attributes (61.5%). Among those that reported relative attribute importance, attributes related to benefits were the most commonly reported attributes with the highest relative attribute importance. Preference heterogeneity was investigated in most studies by modelling, such as via mixed logit analysis (82.1%) and/or by using interaction effects with respondent characteristics (74.4%). Willingness to pay was the most commonly estimated outcome and was presented in about two-thirds (n = 25; 64.1%) of the studies.

CONCLUSION

With the continuous advancement in genetic technology resulting in expanding options for genetic testing and improvements in delivery methods, the application of genetic testing in clinical care is expected to rise. DCEs and conjoint analysis remain robust and useful methods to elicit preferences of potential stakeholders. This review serves as a summary for future researchers when designing similar studies.

Preferences and values for rapid genomic testing in critically ill infants and children: a discrete choice experiment

Healthcare systems are increasingly considering widespread implementation of rapid genomic testing of critically ill children, but evidence on the value of the benefits generated is lacking. This information is key for an optimal implementation into healthcare systems. A discrete choice experiment survey was designed to elicit preferences and values for rapid genomic testing in critically ill children. The survey was administered to members of the Australian public and families with lived experience of rapid genomic testing. A Bayesian D-efficient explicit partial profiles design was used, and data were analysed using a panel error component mixed logit model. Preference heterogeneity was explored using a latent class model and fractional logistic regressions. The public (n = 522) and families with lived experiences (n = 25) demonstrated strong preferences for higher diagnostic yield and clinical utility, faster result turnaround times, and lower cost. Society on average would be willing to pay an additional AU$9510 (US$6657) for rapid (2 weeks results turnaround time) and AU$11,000 (US$7700) for ultra-rapid genomic testing (2 days turnaround time) relative to standard diagnostic care. Corresponding estimates among those with lived experiences were AU$10,225 (US$7158) and AU$11,500 (US$8050), respectively. Our work provides further evidence that rapid genomic testing for critically ill children with rare conditions generates substantial utility. The findings can be used to inform cost-benefit analyses as part of broader healthcare system implementation.