Department of Health Sciences, Institute of Environment, Health and Societies, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom.
Dalla School of Public Health, University of Toronto, Toronto, Ontario Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Am J Obstet Gynecol MFM. 2021 Jan;3(1):100279. doi: 10.1016/j.ajogmf.2020.100279. Epub 2020 Nov 21.
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.
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.
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.
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).
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.
早产并发症是 5 岁以下儿童死亡的主要原因，这给医疗保健和社会系统带来了沉重负担，尤其是在中低收入国家，这些国家可能难以获得可靠的胎龄估计。代谢分析物数据有助于准确估计胎龄。然而，这种方法存在重要成本，涉及新生儿样本的采集和分析，其成本效益尚未确定。
本研究旨在评估一种基于新生儿血斑代谢物数据并结合临床和人口统计学变量（出生体重、性别和多胎生育状况）的国际验证胎龄估计算法的成本效益，该算法与仅使用临床和人口统计学变量的基本算法相比，用于将婴儿分类为早产或足月（使用 37 周二分类早产或足月分类）和确定胎龄。
使用孟加拉国实施研究的数据，估计代谢算法与基本算法相比，每正确分类 1 例早产婴儿和每正确分类 1 例小于胎龄儿的成本。
在 1 年期间，与仅使用临床和人口统计学变量的基本算法相比，代谢算法平均每筛查 1323 例婴儿可正确分类 8.7（95%置信区间，1.3-14.7）例额外的早产婴儿和 145.3（95%置信区间，128.0-164.7）例额外的小于胎龄儿。采用代谢算法的年增量成本为 100031 美元（95%置信区间，86354 美元至 115725 美元）。如果包括设置成本，则成本为 120496 美元（95%置信区间，106322 美元至 136656 美元）。与基本算法相比，代谢算法每正确分类 1 例早产婴儿的增量成本为 11542 美元（设置成本为 13903 美元），每正确分类 1 例小于胎龄儿的增量成本为 688 美元（设置成本为 829 美元）。
本研究量化了在实施新生儿筛查计划时，每检测到 1 例早产或小于胎龄儿的成本，以帮助改善中低收入国家早产和特别是小于胎龄儿的分类。
