BMC Pregnancy and Childbirth (Feb 2020)

Cost-effectiveness analysis of chromosomal microarray as a primary test for prenatal diagnosis in Hong Kong

  • Claudia Ching Yan Chung,
  • Kelvin Yuen Kwong Chan,
  • Pui Wah Hui,
  • Patrick Kwok Cheung Au,
  • Wai Keung Tam,
  • Samuel Kai Man Li,
  • Gordon Ka Chun Leung,
  • Jasmine Lee Fong Fung,
  • Marcus Chun Yin Chan,
  • Ho Ming Luk,
  • Annisa Shui Lam Mak,
  • Kwok Yin Leung,
  • Mary Hoi Yin Tang,
  • Brian Hon Yin Chung,
  • Anita Sik Yau Kan

DOI
https://doi.org/10.1186/s12884-020-2772-y
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 15

Abstract

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Abstract Background Chromosomal microarray (CMA) has been shown to be cost-effective over karyotyping in invasive prenatal diagnosis for pregnancies with fetal ultrasound anomalies. Yet, information regarding preceding and subsequent tests must be considered as a whole before the true cost-effectiveness can emerge. Currently in Hong Kong, karyotyping is offered free as the standard prenatal test while genome-wide array comparative genome hybridization (aCGH), a form of CMA, is self-financed. A new algorithm was proposed to use aCGH following quantitative fluorescent polymerase chain reaction (QF-PCR) as primary test instead of karyotyping. This study aims to evaluate the cost-effectiveness of the proposed algorithm versus the current algorithm for prenatal diagnosis in Hong Kong. Methods Between November 2014 and February 2016, 129 pregnant women who required invasive prenatal diagnosis at two public hospitals in Hong Kong were prospectively recruited. The proposed algorithm was performed for all participants in this demonstration study. For the cost-effectiveness analysis, cost and outcome (diagnostic rate) data were compared with that of a hypothetical scenario representing the current algorithm. Further analysis was performed to incorporate women’s willingness-to-pay for the aCGH test. Impact of government subsidies on the aCGH test was explored as a sensitivity analysis. Results The proposed algorithm dominated the current algorithm for prenatal diagnosis. Both algorithms were equally effective but the proposed algorithm was significantly cheaper (p ≤ 0.05). Taking into account women’s willingness-to-pay for an aCGH test, the proposed algorithm was more effective and less costly than the current algorithm. When the government subsidy reaches 100%, the maximum number of diagnoses could be made. Conclusion By switching to the proposed algorithm, cost saving can be achieved whilst maximizing the diagnostic rate for invasive prenatal diagnosis. It is recommended to implement aCGH as a primary test following QF-PCR to replace the majority of karyotyping for prenatal diagnosis in Hong Kong.

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