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Bayesian cost-effectiveness analysis of Whole genome sequencing versus Whole exome sequencing in a pediatric population with suspected genetic disorders

Author

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  • Mario Cesare Nurchis

    (Università Cattolica del Sacro Cuore
    Fondazione Policlinico Universitario A. Gemelli IRCCS)

  • Francesca Clementina Radio

    (Ospedale Pediatrico Bambino Gesù IRCCS)

  • Luca Salmasi

    (Università Cattolica del Sacro Cuore)

  • Aurora Heidar Alizadeh

    (Università Cattolica del Sacro Cuore)

  • Gian Marco Raspolini

    (Università Cattolica del Sacro Cuore)

  • Gerardo Altamura

    (Università Cattolica del Sacro Cuore)

  • Marco Tartaglia

    (Ospedale Pediatrico Bambino Gesù IRCCS)

  • Bruno Dallapiccola

    (Ospedale Pediatrico Bambino Gesù IRCCS)

  • Gianfranco Damiani

    (Fondazione Policlinico Universitario A. Gemelli IRCCS
    Università Cattolica del Sacro Cuore)

Abstract

Genetic diseases are medical conditions caused by sequence or structural changes in an individual’s genome. Whole exome sequencing (WES) and whole genome sequencing (WGS) are increasingly used for diagnosing suspected genetic conditions in children to reduce the diagnostic delay and accelerating the implementation of appropriate treatments. While more information is becoming available on clinical efficacy and economic sustainability of WES, the broad implementation of WGS is still hindered by higher complexity and economic issues. The aim of this study is to estimate the cost-effectiveness of WGS versus WES and standard testing for pediatric patients with suspected genetic disorders. A Bayesian decision tree model was set up. Model parameters were retrieved both from hospital administrative datasets and scientific literature. The analysis considered a lifetime time frame and adopted the perspective of the Italian National Health Service (NHS). Bayesian inference was performed using the Markov Chain Monte Carlo simulation method. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI). The present analysis showed that implementing first-line WGS would be a cost-effective strategy, against the majority of the other tested alternatives at a threshold of €30,000–50,000, for diagnosing outpatient pediatric patients with suspected genetic disorders. According to the sensitivity analyses, the findings were robust to most assumption and parameter uncertainty. Lessons learnt from this modeling study reinforces the adoption of first-line WGS, as a cost-effective strategy, depending on actual difficulties for the NHS to properly allocate limited resources.

Suggested Citation

  • Mario Cesare Nurchis & Francesca Clementina Radio & Luca Salmasi & Aurora Heidar Alizadeh & Gian Marco Raspolini & Gerardo Altamura & Marco Tartaglia & Bruno Dallapiccola & Gianfranco Damiani, 2024. "Bayesian cost-effectiveness analysis of Whole genome sequencing versus Whole exome sequencing in a pediatric population with suspected genetic disorders," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(6), pages 999-1011, August.
  • Handle: RePEc:spr:eujhec:v:25:y:2024:i:6:d:10.1007_s10198-023-01644-0
    DOI: 10.1007/s10198-023-01644-0
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    1. Nurchis, Mario Cesare & Riccardi, Maria Teresa & Radio, Francesca Clementina & Chillemi, Giovanni & Bertini, Enrico Silvio & Tartaglia, Marco & Cicchetti, Americo & Dallapiccola, Bruno & Damiani, Gian, 2022. "Incremental net benefit of whole genome sequencing for newborns and children with suspected genetic disorders: Systematic review and meta-analysis of cost-effectiveness evidence," Health Policy, Elsevier, vol. 126(4), pages 337-345.
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      More about this item

      Keywords

      Pediatric population; Genomic sequencing; Exome sequencing; Bayesian cost-effectiveness analysis; Public Health;
      All these keywords.

      JEL classification:

      • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
      • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
      • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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