ArticleBMC pediatrics2026
Dried blood spot quality in newborn inherited metabolic disease screening in Beijing, 2020-2024: a retrospective analysis of incidence, causes, and improvement strategies.
Article in BMC pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
backgroundThis study focuses on assessing the quality of dried blood spots (DBS) for newborn inherited metabolic disease screening in Beijing from 2020 to 2024, identify the causes and risk factors of unqualified DBS, and provide evidence-based quality improvement strategies for clinical practice and public health management.
methodsWe retrospectively analyzed all DBS samples collected from all obstetric institutions in Beijing. Data were extracted from the Beijing Newborn Disease Screening Management Information System. Unqualified DBS rates were analyzed by year, season, hospital type, and annual screening volume using chi-square tests, chi-square trend tests, and Bonferroni correction.
resultsA total of 714,369 DBS samples were included in the study, of which 1,735 (0.24%) were unqualified. The recall rate for unqualified samples was 98.96%. The unqualified rate declined significantly from 0.33% in 2021 to 0.17% in 2024 (P < 0.001). The leading causes were incorrect/incomplete information filling (30.95%), unclear QR codes (18.44%), and blood seepage rings from repeated dipping (15.33%). The unqualified rate was highest in winter (0.31%, P < 0.001). Non-maternal and child health specialty hospitals had a higher rate than maternal and child health specialty hospitals (0.27% vs. 0.20%, P < 0.001). Institutions with ≤ 1,500 annual screening cases had a markedly higher rate than those with > 1,500 cases (0.40% vs. 0.16%, P < 0.001).
conclusionsDBS quality in Beijing is at an advanced international level, with a low unqualified rate and high recall rate. However, information-related errors and technical collection problems remain prominent. Targeted strategies include optimizing the information management system, implementing hierarchical training, strengthening three-level quality control, and establishing seasonal early warning mechanisms, particularly for non-maternal and child health hospitals and low-volume institutions.
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