Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
The Comparative Health Outcomes, Policy and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA.
Genet Med. 2022 Oct;24(10):2014-2027. doi: 10.1016/j.gim.2022.06.004. Epub 2022 Jul 14.
Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES.
We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening.
Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis.
Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.
方法学上的挑战限制了基因组测序(GS)和外显子组测序(ES)的经济评估。我们的目标是为基于模型的诊断 GS/ES 成本效益分析(CEA)制定概念框架。
我们对经济分析进行了范围审查,以与专家一起制定和迭代一套用于产前检测、儿科早期诊断、儿科迟发性疾病诊断、癌症基因检测、新生儿筛查和一般人群筛查的 GS/ES 的概念性 CEA 框架。
反思符合纳入标准的 57 项研究,我们建议针对每个临床情况考虑以下因素。对于产前检测,对 ES 策略和产后护理的成本进行比较分析,以及遗传诊断和妊娠结局。对于儿科的早期诊断,对快速周转的 GS/ES 进行质量调整生命年(QALY)和超过 20 年的成本建模。对于遗传性癌症综合征检测,对个体和一级/二级/三级亲属的累积成本和 QALY 进行建模。对于肿瘤分析,不仅限于治疗的接受或反应,还包括 QALY 和下游结果的成本。对于筛查,对终身成本和 QALY 进行建模,并考虑低外显率和 GS/ES 重新分析的后果。
我们的框架可以指导基于模型的 CEA 的设计,并最终为 GS/ES 的经济价值提供有力的证据。