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Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification
Kathryn Coyle1, Amanda My Linh Quan2, Lindsay A Wilson3
1Department of Health Sciences, Institute of Environment, Health and Societies, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom.
American Journal of Obstetrics & Gynecology MFM
|January 16, 2021
Summary
Estimating gestational age using metabolic analyte data accurately identifies more preterm and small-for-gestational-age infants. This approach offers a cost-effective solution for improving newborn screening in low-resource settings.
Area of Science:
- Neonatal Medicine
- Biochemistry
- Health Economics
Background:
- Preterm birth complications are a leading cause of under-5 mortality, particularly in low- and middle-income countries (LMICs).
- Accurate gestational age estimation is crucial but challenging in LMICs due to limited reliable data.
- Metabolic analyte data offers potential for improved gestational age estimation, but cost-effectiveness requires evaluation.
Purpose of the Study:
- To assess the cost-effectiveness of a metabolic analyte-based gestational age estimation algorithm.
- To compare this algorithm against a basic clinical and demographic model for classifying preterm and small-for-gestational-age infants.
- To determine the cost per correctly classified infant in a newborn screening program.
Main Methods:
- An internationally validated algorithm using neonatal blood spot metabolites, birthweight, sex, and multiple birth status was evaluated.
- This was compared to a basic algorithm using only clinical and demographic variables.
- Cost-effectiveness was analyzed using data from an implementation study in Bangladesh.
Main Results:
- The metabolic algorithm correctly classified significantly more preterm (8.7 additional infants/1323) and small-for-gestational-age (145.3 additional infants/1323) infants.
- The incremental annual cost for the metabolic algorithm was $100,031 (excluding setup) or $120,496 (including setup).
- The incremental cost per correctly classified preterm infant was $11,542 ($13,903 with setup), and per small-for-gestational-age infant was $688 ($829 with setup).
Conclusions:
- The metabolic algorithm significantly improves the classification of preterm and small-for-gestational-age infants.
- This study quantifies the cost per detection, supporting its implementation in newborn screening programs in LMICs.
- The findings highlight the value of metabolic data for enhancing neonatal health outcomes in resource-limited settings.
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