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Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification
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. Metab...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805344/ https://www.ncbi.nlm.nih.gov/pubmed/33451597 http://dx.doi.org/10.1016/j.ajogmf.2020.100279 |
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author | Coyle, Kathryn Quan, Amanda My Linh Wilson, Lindsay A. Hawken, Steven Bota, A. Brianne Coyle, Doug Murray, Jeffrey C. Wilson, Kumanan |
author_facet | Coyle, Kathryn Quan, Amanda My Linh Wilson, Lindsay A. Hawken, Steven Bota, A. Brianne Coyle, Doug Murray, Jeffrey C. Wilson, Kumanan |
author_sort | Coyle, Kathryn |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7805344 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-78053442021-01-22 Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification Coyle, Kathryn Quan, Amanda My Linh Wilson, Lindsay A. Hawken, Steven Bota, A. Brianne Coyle, Doug Murray, Jeffrey C. Wilson, Kumanan Am J Obstet Gynecol MFM Original Research 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. Elsevier 2021-01 /pmc/articles/PMC7805344/ /pubmed/33451597 http://dx.doi.org/10.1016/j.ajogmf.2020.100279 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Original Research Coyle, Kathryn Quan, Amanda My Linh Wilson, Lindsay A. Hawken, Steven Bota, A. Brianne Coyle, Doug Murray, Jeffrey C. Wilson, Kumanan Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
title | Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
title_full | Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
title_fullStr | Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
title_full_unstemmed | Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
title_short | Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
title_sort | cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805344/ https://www.ncbi.nlm.nih.gov/pubmed/33451597 http://dx.doi.org/10.1016/j.ajogmf.2020.100279 |
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