Evidence map›Paper›PMID 40615580›Full record

ArticleScientific reports2025

Comparative performance and health economic analysis of prenatal screening for down syndrome in Fujian province, China.

Lin Zheng, Nuolan Yin, Meiying Wang, Hailong Huang, Shuyu Zhang, Linjuan Su, Liangpu Xu

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lin Zheng *Department of Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital, School of Public Health, Fujian Medical University, Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fuzhou, 350001, Fujian, China.
Nuolan Yin *Department of School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian, China.
Meiying WangDepartment of Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital, Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fuzhou, 350001, Fujian, China.
Hailong HuangDepartment of Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital, Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fuzhou, 350001, Fujian, China.
Shuyu ZhangDepartment of Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital, Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fuzhou, 350001, Fujian, China. zhangshuyu0710@163.com.
Linjuan SuDepartment of Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital, Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fuzhou, 350001, Fujian, China. candy_2005518@126.com.
Liangpu XuDepartment of Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital, Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fuzhou, 350001, Fujian, China. xiliangpu@fjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To compare the performance of first-trimester serum screening (FTSS) and non-invasive prenatal testing (NIPT) in detecting fetal chromosomal aneuploidies trisomy 21 (T21) and trisomy 18 (T18), and to evaluate the cost-effectiveness and clinical feasibility of four screening strategies for the prevention of Down syndrome (DS) from a health economics perspective. This retrospective study included 33,559 pregnant women who underwent DS screening at Fujian Maternity and Child Health Hospital between February 25, 2022, and December 29, 2023. Participants were divided into an FTSS group (n = 23,136) and an NIPT group (n = 10,423) based on the type of screening received. All subjects were followed for eight months postpartum to collect pregnancy outcome data. Screening performance was compared between the two groups based on prenatal diagnostic results. Subsequently, four screening strategies were constructed and assessed using cost-effectiveness analysis and NIPT price sensitivity analysis to determine their health-economic value. A comparison of screening performance between the two groups showed that NIPT had significantly higher specificity (99.96%) and positive predictive value (55.56%) than FTSS (specificity: 92.93%, positive predictive value: 0.91%). Meanwhile, the false-positive rate of NIPT (0.04%) was notably lower than that of FTSS (7.07%). Cost-effectiveness analysis of the four strategies indicated that strategy 2 was the most cost-effective, with a cost-effectiveness ratio (CER) of 1,252,400 CNY and an incremental cost-effectiveness ratio (ICER) of -3,626,700 CNY, achieving improved screening outcomes while reducing costs. Sensitivity analysis further demonstrated that even when the screening scale expanded under strategy 4 or when NIPT prices fluctuated between 700 and 2,100 CNY, strategy 2 maintained the lowest CER. NIPT is superior to FTSS in terms of screening efficacy. Based on a health economic evaluation, among the four strategies, Strategy 2 (using NIPT as conditional screening) offers the best cost-effectiveness while improving detection efficacy, making it the optimal strategy recommended in this study.

Indexed as

Down SyndromeNoninvasive Prenatal TestingPrenatal DiagnosisAdultChinaCost-Benefit AnalysisFemaleHumansPregnancyPregnancy Trimester, FirstRetrospective StudiesSensitivity and SpecificityTrisomy 18 SyndromeCost-effectiveness analysisDown syndromeFirst-trimester serological screeningNoninvasive prenatal testPrenatal screening

Identifiers

PMID40615580
PMCPMC12227617

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.