Australian public perspectives on genomic newborn screening: which conditions should be included?

BACKGROUND: Implementing genomic sequencing into newborn screening programs allows for significant expansion in the number and scope of conditions detected. We sought to explore public preferences and perspectives on which conditions to include in genomic newborn screening (gNBS). METHODS: We recruited English-speaking members of the Australian public over 18 years of age, using social media, and invited them to participate in online focus groups. RESULTS: Seventy-five members of the public aged 23-72 participated in one of fifteen focus groups. Participants agreed that if prioritisation of conditions was necessary, childhood-onset conditions were more important to include than later-onset conditions. Despite the purpose of the focus groups being to elicit public preferences, participants wanted to defer to others, such as health professionals or those with a lived experience of each condition, to make decisions about which conditions to include. Many participants saw benefit in including conditions with no available treatment. Participants agreed that gNBS should be fully publicly funded. CONCLUSION: How many and which conditions are included in a gNBS program will be a complex decision requiring detailed assessment of benefits and costs alongside public and professional engagement. Our study provides support for implementing gNBS for treatable childhood-onset conditions.

Prospective cohort study of genomic newborn screening: BabyScreen+ pilot study protocol.

INTRODUCTION: Newborn bloodspot screening (NBS) is a highly successful public health programme that uses biochemical and other assays to screen for severe but treatable childhood-onset conditions. Introducing genomic sequencing into NBS programmes increases the range of detectable conditions but raises practical and ethical issues. Evidence from prospectively ascertained cohorts is required to guide policy and future implementation. This study aims to develop, implement and evaluate a genomic NBS (gNBS) pilot programme. METHODS AND ANALYSIS: The BabyScreen+ study will pilot gNBS in three phases. In the preimplementation phase, study materials, including education resources, decision support and data collection tools, will be designed. Focus groups and key informant interviews will also be undertaken to inform delivery of the study and future gNBS programmes. During the implementation phase, we will prospectively recruit birth parents in Victoria, Australia, to screen 1000 newborns for over 600 severe, treatable, childhood-onset conditions. Clinically accredited whole genome sequencing will be performed following standard NBS using the same sample. High chance results will be returned by genetic healthcare professionals, with follow-on genetic and other confirmatory testing and referral to specialist services as required. The postimplementation phase will evaluate the feasibility of gNBS as the primary aim, and assess ethical, implementation, psychosocial and health economic factors to inform future service delivery. ETHICS AND DISSEMINATION: This project received ethics approval from the Royal Children's Hospital Melbourne Research Ethics Committee: HREC/91500/RCHM-2023, HREC/90929/RCHM-2022 and HREC/91392/RCHM-2022. Findings will be disseminated to policy-makers, and through peer-reviewed journals and conferences.

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.

Australian Public Perspectives on Genomic Newborn Screening: Risks, Benefits, and Preferences for Implementation.

Recent dramatic reductions in the timeframe in which genomic sequencing can deliver results means its application in time-sensitive screening programs such as newborn screening (NBS) is becoming a reality. As genomic NBS (gNBS) programs are developed around the world, there is an increasing need to address the ethical and social issues that such initiatives raise. This study therefore aimed to explore the Australian public's perspectives and values regarding key gNBS characteristics and preferences for service delivery. We recruited English-speaking members of the Australian public over 18 years of age via social media; 75 people aged 23-72 participated in 1 of 15 focus groups. Participants were generally supportive of introducing genomic sequencing into newborn screening, with several stating that the adoption of such revolutionary and beneficial technology was a moral obligation. Participants consistently highlighted receiving an early diagnosis as the leading benefit, which was frequently linked to the potential for early treatment and intervention, or access to other forms of assistance, such as peer support. Informing parents about the test during pregnancy was considered important. This study provides insights into the Australian public's views and preferences to inform the delivery of a gNBS program in the Australian context.

The cost of proband and trio exome and genome analysis in rare disease: A micro-costing study.

PURPOSE: Rare disease genomic testing is a complex process involving various resources. Accurate resource estimation is required for informed prioritization and reimbursement decisions. This study aims to analyze the costs and cost drivers of clinical genomic testing. METHODS: Based on genomic sequencing workflows we microcosted limited virtual panel analysis on exome sequencing backbone, proband and trio exome, and genome testing for proband and trio analysis in 2023 Australian Dollars ($). Deterministic and probabilistic sensitivity analyses were undertaken. RESULTS: Panel testing costs AUD $2373 ($733-$6166), and exome sequencing costs $2823 ($802-$7206) and $5670 ($2006-$11,539) for proband and trio analysis, respectively. Genome sequencing costs $4840 ($2153-$9890) and $11,589 ($5842-$16,562) for proband and trio analysis. The most expensive cost component of genomic testing was sequencing (36.9%-69.4% of total cost), with labor accounting for 27.1%-63.2% of total cost. CONCLUSION: We provide a comprehensive analysis of rare disease genomic testing costs, for a range of clinical testing types and contexts. This information will accurately inform economic evaluations of rare disease genomic testing and decision making on policy settings that assist with implementation, such as genomic testing reimbursement.

Australian public perspectives on genomic data governance: responsibility, regulation, and logistical considerations.

Genomic sequencing generates huge volumes of data, which may be collected or donated to form large genomic databases. Such information can be stored for future use, either for the data donor themselves or by researchers to help improve our understanding of the genetic basis of disease. Creating datasets of this magnitude and diversity is only possible if patients, their families, and members of the public worldwide share their data. However, there is no consensus on the best technical approach to data sharing that also minimises risks to individuals and exploration of stakeholders' views on aspects of genomic data governance models-the ways genomic data is stored, managed, shared and used-has been minimal. To address this need, we conducted focus groups with 39 members of the Australian public exploring their views and preferences for different aspects of genomic data governance models. We found that consent and control were essential to participants, as they wanted the option to choose who had access to their data and for what purposes. Critically, participants wanted a trustworthy body to enforce regulation of data storage, sharing and usage. While participants recognised the importance of data accessibility, they also expressed a strong desire for data security. Finally, financial responsibility for data storage raised concerns for inequity as well as organisations and individuals using data in ethically contentious ways to generate profit. Our findings highlight some of the trade-offs that need to be considered in the development of genomic data governance systems.

System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual model.

INTRODUCTION: Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment. AREAS COVERED: We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation. EXPERT OPINION: The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.

Quality of life in caregivers of a child with a developmental and epileptic encephalopathy.

AIM: To explore the relationship between social care-related quality of life (SCrQoL) for caregivers of a child with a developmental and epileptic encephalopathy (DEE; such as SCN2A and Dravet syndrome) and health literacy, illness perceptions, and caregiver activation. METHOD: As part of a larger pre-post pilot study of an information linker service, caregivers completed a baseline questionnaire which included demographics and measures to assess SCrQoL, health literacy, illness perceptions, and caregiver activation. We used Spearman's Rho to determine relationships between variables. RESULTS: Seventy-two caregivers completed the questionnaire. Total SCrQoL varied widely, ranging from an 'ideal state' to 'high needs state'. Caregivers most frequently reported high needs regarding doing activities they enjoy and looking after themselves. Total SCrQoL was correlated with cognitive (r[70] = -0.414, p < 0.000) and emotional representations of illness (r[70] = -0.503, p < 0.000), but not coherence (r = -0.075, p = 0.529). Total SCrQoL was not correlated with health literacy (r[70] = 0.125, p = 0.295) or caregiver activation (r[70] = 0.181, p = 0.127). INTERPRETATION: Future research should explore whether interventions that help caregivers cognitively reframe the negative experiences of having a child with a DEE, and support them to partake in activities they enjoy, boost their SCrQoL. WHAT THIS PAPER ADDS: Caregiver social care-related quality of life (SCrQoL) varied widely, from 'ideal state' to 'high needs state'. Most common high needs were doing enjoyable activities and self-care. Caregivers with higher SCrQoL may perceive their child's illness as less threatening. SCrQoL does not appear to be related to caregiver activation in this sample.

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.

Development of a microcosting protocol to determine the economic cost of diagnostic genomic testing for rare diseases in Australia.

INTRODUCTION: Genomic testing is a relatively new, disruptive and complex health technology with multiple clinical applications in rare diseases, cancer and infection control. Genomic testing is increasingly being implemented into clinical practice, following regulatory approval, funding and adoption in models of care, particularly in the area of rare disease diagnosis. A significant barrier to the adoption and implementation of genomic testing is funding. What remains unclear is what the cost of genomic testing is, what the underlying drivers of cost are and whether policy differences contribute to cost variance in different jurisdictions, such as the requirement to have staff with a medical license involved in testing. This costing study will be useful in future economic evaluations and health technology assessments to inform optimal levels of reimbursement and to support comprehensive and comparable assessment of healthcare resource utilisation in the delivery of genomic testing globally. METHODS: A framework is presented that focuses on uncovering the process of genomic testing for any given laboratory, evaluating its utilisation and unit costs, and modelling the cost drivers and overall expenses associated with delivering genomic testing. The goal is to aid in refining and implementing policies regarding both the regulation and funding of genomic testing. A process-focused (activity-based) methodology is outlined, which encompasses resources, assesses individual cost components through a combination of bottom-up and top-down microcosting techniques and allows disaggregation of resource type and process step. ETHICS AND DISSEMINATION: The outputs of the study will be reported at relevant regional genetics and health economics conferences, as well as submitted to a peer-reviewed journal focusing on genomics. Human research ethics committee approval is not required for this microcosting study. This study does not involve research on human subjects, and all data used in the analysis are either publicly available.

"Somewhere to turn to with my questions": A pre-post pilot of an information linker service for caregivers who have a child with a Developmental and Epileptic Encephalopathy.

BACKGROUND: Caregivers of a child with a Developmental and Epileptic Encephalopathy (DEE) often report challenges accessing relevant and understandable information regarding their child's condition. We developed GenE Compass, an information linker service where caregivers are invited to submit questions and receive high-quality, personalised reports. We conducted a pilot evaluation to determine the feasibility and acceptability of GenE Compass. METHODS: We invited eligible caregivers to complete a baseline questionnaire (Q1) prior to receiving three months access to submit an unlimited number of questions to GenE Compass. We then invited caregivers to complete a follow-up questionnaire (Q2) and optional interview. Caregivers also had the opportunity to share report-specific feedback at the time of receiving each report. RESULTS: Seventy-two caregivers completed Q1, of which 41 submitted at least one question (range = 1-7). We received a total of 76 questions. The median turnaround time was 12 working days for our information linker (range = 1-28). Thirty-seven caregivers completed Q2, of whom 32 submitted at least one question (87 %). Overall, caregivers were highly satisfied with GenE Compass and their reports, and indicated that they would use it in the future if they had another question. Caregivers' qualitative data from Q1 and interviews highlighted the ongoing need for an information linker service like GenE Compass due to a lack of understandable information and limited resources, and the benefit in reducing burden of constant information searching. CONCLUSION: Our study shows that GenE Compass is feasible with the appropriate allocation of resources and highly acceptable to caregivers who have a child with a DEE.

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.

The Feasibility and Validity of Preference-Based Quality of Life Measures With Informal Carers: A Think-Aloud Study.

OBJECTIVES: A range of preference-based quality of life (QoL) measures have been proposed for use with informal carers. Qualitative evaluation of validity and feasibility of the measures is an important step in understanding whether measures will work as intended. At present, little is known about the performance of different types of preference-based QoL measures with informal carers. The objective of this study was to qualitatively assess the feasibility, content validity (including face validity), and acceptability of 5 QoL measures (the Carer Experience Scale, CarerQoL-7D, ASCOT-C, ICECAP-A, and EQ-5D-5L) with informal carers. METHODS: A total of 24 "think-aloud" interviews were conducted with a cross-section of carers of adults in the United Kingdom. This think-aloud process was followed by semistructured discussion to probe issues of validity and feasibility in more detail. The interview data were transcribed, coded to identify the frequency of errors in completing the QoL measures and thematically analyzed to study the validity, feasibility, and acceptability of the measures. RESULTS: Few errors (3%-7% per item) were identified in completing each of the measures with little distinct pattern. Most participants found the measures to be concise, clear, and relevant. Challenges included relevance, context, time period, missing items, multiple questions, and response options. Informal carers generally expressed a preference for using a care-related QoL measure. CONCLUSIONS: Existing preference-based QoL measures have encouraging validity and feasibility within a mixed sample of informal carers, with minor challenges raised. These challenges ought to be considered, alongside the decision context, when administering QoL measures in this context.

Eliciting Societal Preferences for Non-health Outcomes: A Person Trade-Off Study in the Context of Genomics.

PURPOSE: Economic evaluations of health technologies traditionally aim to maximize population health outcomes measured by using quality-adjusted life-years (QALYs). Non-health outcomes, however, may have high social value, and their exclusion has the potential to bias decisions regarding allocation of health care resources. This research positions Australian participants as societal decision-makers to explore their willingness to trade-off health gains in adults for non-health benefits in families with a child affected by a rare disease. METHODS: To estimate the social value of the different health care interventions, a person trade-off (PTO) method was used. PTOs present participants with groups of beneficiaries that vary in terms of the number of individuals who will benefit, the individuals' characteristics, their expected benefits, or a combination, and ask which group should be prioritized. Each trade-off presented health gains from the treatment of moderate physical and mental health conditions described by the 3-level version of the EuroQol 5-Dimension (EQ-5D-3L) health states. The health gains in these groups were traded-off against non-health gains in families accessing diagnostic genomic testing, and equivalence values were calculated, using median and ratio of means methods, based on the ratio of the group sizes at the point of equivalence. Participants were recruited through Prolific and were stratified according to age, sex, and education. The impact of participant characteristics on equivalence values was assessed using Kruskal-Wallis H tests and ordinary least-squares log-linear regressions. FINDINGS: Participants (N = 434) positioned as societal decision-makers were generally willing to trade-off adult health gains with the familial non-health benefits of genomic testing, showing a preference for valuing both types of outcomes within public health policy. The aggregation of preferences generated 2 weightings for genomic testing against each health treatment, an unadjusted value and a reweighted value to match target demographic characteristics. Converted into QALY value per test, it was found that participants valued the non-health benefits of genomic testing between 0.730 and 0.756 QALY. A minority of participants always prioritized diagnostic genomic testing over the physical (6.0%) or mental (4.6%) health treatments, with a larger minority always prioritizing the physical (15.4%) or mental (14.8%) health treatments. IMPLICATIONS: The findings indicate that participants perceived the non-health parental benefits in children experiencing rare disease to have comparable value to health gains in adults experiencing the moderate physical or mental health conditions described using EQ-5D-3L. These findings suggest that the benefits of genomic tests would be underestimated if only health benefits are included in economic evaluations.

Genomic testing for suspected monogenic kidney disease in children and adults: A health economic evaluation.

PURPOSE: To assess the relative cost-effectiveness of genomic testing compared with standard non-genomic diagnostic investigations in patients with suspected monogenic kidney disease from an Australian health care system perspective. METHODS: Diagnostic and clinical information was used from a national cohort of 349 participants. Simulation modelling captured diagnostic, health, and economic outcomes during a time horizon from clinical presentation until 3 months post-test results based on the outcome of cost per additional diagnosis and lifetime horizon based on cost per quality-adjusted life-year (QALY) gained. RESULTS: Genomic testing was Australian dollars (AU$) 1600 more costly per patient and led to an additional 27 diagnoses out of a 100 individuals tested, resulting in an incremental cost-effectiveness ratio of AU$5991 per additional diagnosis. Using a lifetime horizon, genomic testing resulted in an additional cost of AU$438 and 0.04 QALYs gained per individual compared with standard diagnostic investigations, corresponding to an incremental cost-effectiveness ratio of AU$10,823 per QALY gained. Sub-group analyses identified that the results were largely driven by the cost-effectiveness in glomerular diseases. CONCLUSION: Based on established or expected thresholds of cost-effectiveness, our evidence suggests that genomic testing is very likely to be cost saving for individuals with suspected glomerular diseases, whereas no evidence of cost-effectiveness was found for non-glomerular diseases.