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Do health professionals value genomic testing? A discrete choice experiment in inherited cardiovascular disease
James Buchanan1,2, Edward Blair3,4, Kate L Thomson5,6
1Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK. james.buchanan@dph.ox.ac.uk.
Abstract:
Next generation sequencing (NGS) approaches are moving from research into clinical practice. However, the optimal NGS approach in well-defined adult-onset familial diseases, such as inherited cardiovascular disease, remains unclear. We aimed to determine which attributes encouraged or discouraged the uptake of genomic tests in this context, and whether this differed by test type. We conducted a web-based discrete choice experiment in health professionals in the UK who order NGS tests for inherited cardiovascular disease. Respondents completed 12 hypothetical choice tasks in which they selected a preferred test from four alternatives: whole genome sequencing, whole exome sequencing, panel testing and genetic testing not indicated. Tests were specified in terms of five attributes: diagnostic yield, detection rate for variants of unknown significance, cost, quantity of counselling received and disclosure of secondary findings. Mixed logit regression analysis was used to analyse the choice data. We found that uptake of NGS increases if tests identify more pathogenic mutations, identify fewer variants of unknown significance, or cost less. Respondents were willing to pay £117 for every 1% increase in diagnostic yield. Considerable heterogeneity was observed around preferences for several test attributes. Overall, panel testing had the highest predicted uptake rate. Our results indicate that NGS tests are valued by health professionals for well-defined adult-onset familial diseases, however, these professionals have strong preferences for panel testing rather than whole genome sequencing and whole exome sequencing. This finding suggests that different uptake rates should be explicitly modelled when designing and evaluating future genomic testing services.
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