Work

Integrating socioeconomic determinants into preterm birth risk prediction: evidence from Northwest China

Year: 2026

Type: article

Abstract: BACKGROUND: Preterm birth is a leading cause of neonatal mortality worldwide. Socioeconomic status (SES) is associated with preterm birth risk, yet existing studies in low-resource settings have rarely examined whether incorporating SES into clinical models can enhance prediction performance. This study aimed to develop and validate a SES-integrated risk prediction model for preterm birth in Northwest China. METHODS: A retrospective cohort of 2,781 deliveries from county-level hospitals (January–November 2024) was analyzed. Logistic regression was used to construct a baseline model (clinical variables) and an extended model incorporating SES indicators (education, occupation, living environment). Predictive performance was evaluated using the area under the ROC curve (AUC) and calibration plots. RESULTS: Including SES and healthcare accessibility variables markedly improved model discrimination (AUC increased from 0.688 to 0.854) and showed good calibration, indicating strong agreement between predicted and observed outcomes. CONCLUSION: Socioeconomic factors significantly enhance the accuracy and calibration of preterm birth prediction models. Integrating SES indicators into perinatal assessment can improve risk identification and inform equitable maternal health policies.

Source: BMC Pregnancy and Childbirth

DOI: 10.1186/s12884-026-08939-z

Authors: Xiaoning Wang, Qin Yuan, Yongmei Yang

Institutions: Communication University of China, Capital Medical University, Xuan Wu Hospital of the Capital Medical University

Language: English

Topic: Maternal and Neonatal Healthcare

Subfield: General Health Professions

Field: Health Professions

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $3,290