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Title: Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification
Authors: Coyle, K
Quan, AML
Wilson, LA
Hawken, S
Bota, AB
Coyle, D
Murray, JC
Wilson, K
Keywords: cost-effectiveness;economic evaluation;metabolomics;preterm birth;small for gestational age
Issue Date: 21-Nov-2020
Publisher: Elsevier
Citation: Coyle, K. et al. (2021) 'Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification', American Journal of Obstetrics and Gynecology MFM, 3, 100279, pp. 1 - 10. doi: 10.1016/j.ajogmf.2020.100279.
Abstract: Background: Preterm birth complications are the leading cause of death among children under 5 years of age, and this imposes a heavy burden on healthcare and social systems, particularly in low- and middle-income countries where reliable estimates of gestational age may be difficult to obtain. Metabolic analyte data can aid in accurately estimating gestational age. However, important costs are associated with this approach, which are related to the collection and analysis of newborn samples, and its cost-effectiveness has yet to be determined. Objective: This study aimed to evaluate the cost-effectiveness of an internationally validated gestational age estimation algorithm based on neonatal blood spot metabolite data in combination with clinical and demographic variables (birthweight, sex, and multiple birth status) compared with a basic algorithm that uses only clinical and demographic variables in classifying infants as preterm or term (using a 37-week dichotomous preterm or term classification) and determining gestational age. Study Design: The cost per correctly classified preterm infant and per correctly classified small-for-gestational-age infant for the metabolic algorithm vs the basic algorithm were estimated with data from an implementation study in Bangladesh. Results: Over 1 year, the metabolic algorithm correctly classified an average of 8.7 (95% confidence interval, 1.3–14.7) additional preterm infants and 145.3 (95% confidence interval, 128.0–164.7) additional small-for-gestational-age infants per 1323 infants screened compared with the basic algorithm using only clinical and demographic variables. The incremental annual cost of adopting the metabolic algorithm was $100,031 (95% confidence interval, $86,354–$115,725). If setup costs were included, the cost was $120,496 (95% confidence interval, $106,322–$136,656). Compared with the basic algorithm, the incremental cost per preterm infant correctly classified by the metabolic algorithm is $11,542 ($13,903 with setup), and the incremental cost per small-for-gestational-age infant is $688 ($829 with setup). Conclusion: This research quantifies the cost per detection of preterm or small-for-gestational-age infant in the implementation of a newborn screening program to aid in improved classification of preterm and, in particular, small-for-gestational-age infants in low- and middle-income countries.
Description: Translational Obstetrics.
Other Identifiers: ORCiD: Kathryn Coyle
ORCiD: Doug Coyle
ORCiD: Kumanan Wilson
Appears in Collections:Dept of Health Sciences Research Papers
Institute of Environment, Health and Societies

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