Measuring the health benefits of genome and exome sequencing: a systematic review of economic evaluations

TL;DR

This paper reviews economic studies on genome and exome sequencing to assess their health benefits and cost-effectiveness in clinical settings.

Contribution

The study systematically evaluates methodological approaches and findings of economic evaluations for genome and exome sequencing in health care.

Findings

Genome/exome sequencing may be cost-effective for diagnosing rare diseases and guiding cancer treatment under favorable conditions.

Methodological rigor is higher in treatment guidance studies compared to pediatric diagnostic evaluations.

Standardized methods and real-world data are needed to improve economic evaluations of sequencing technologies.

Abstract

Genome sequencing (GS) and exome sequencing (ES) technologies have gained increasing attention in health economics for evaluating their clinical and public health introduction, but their complexity challenges traditional methods. This systematic review aimed to investigate and discuss full economic evaluations (EEs) of GS and ES in relation to health outcomes, with a focus on methodological issues.

A systematic search of several databases was carried out (PROSPERO CRD42023430992). Quality was evaluated using the Quality of Health Economic Studies instrument. Key methodological features were investigated, and a narrative synthesis of the findings was performed after grouping studies by testing scope.

Overall, 12 recently published cost-utility analyses (CUAs) were included, assessing the use of GS/ES for guiding targeted therapy in oncology (N = 4) or major depressive disorder (N = 1), and diagnosing rare genetic diseases (N = 7). The findings suggested that GS/ES may be cost-effective for diagnosing rare diseases and may also be cost-effective for treatment guidance under favorable conditions. Methodological rigor tended to be higher in treatment guidance studies, whereas EEs in pediatric diagnostics faced greater challenges. Utility values were largely derived from a common survey using validated multi-attribute utility instruments, and studied on proxy conditions. Variability in perspectives, target populations, and costs limited comparability. To strengthen future EEs, standardized methodologies and long-term, real-world data on clinical and non-clinical benefits are needed.

Traditional CUA approaches are essential to guide the implementation of new technologies, but they should be accommodated or complemented by alternative methods, innovative and comprehensive frameworks that capture the broader value of GS/ES and support their integration into clinical and public health practice.

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