ArticleBMC pregnancy and childbirth2026
Self-assessment risk scoring system for early screening of preterm birth in low resource settings.
Article in BMC pregnancy and childbirth, 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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Abstract
introductionPreterm birth is a leading cause of neonatal mortality and morbidity globally. According to the World Health Organization (WHO), 15 million babies were born prematurely in 2010. Indonesia has the fifth-highest number of preterm births in the world, with 675,700 occurring every year. Early screening for preterm birth is crucial, but many health centers in low-resource settings lack the equipment required to do so. This study aims to develop a scoring system that can predict preterm birth based on socio-demographics, nutrition, lifestyle, previous pregnancy history, and maternal condition. The goal is to create a simple, fast, and inexpensive method that can be easily accessed for early screening in low-resource settings.
methodsThis case-control study was conducted between June 2021 and June 2022 at Cipto Mangunkusumo General Hospital, Jakarta. Subjects were categorized into preterm and control groups. Clinical data were obtained through history taking and medical records and analyzed using IBM SPSS 23. Significant variables in bivariate analysis underwent multivariable analysis through logistic regression. The receiver operating characteristics (ROC) indicate how accurately the risk-scoring system performs. The validity of the scoring formula was analyzed using the Hosmer-Lemeshow test. The performance of the score was assessed using the area under the curve parameter.
resultsMultivariate analysis showed that socioeconomic level, antenatal visits, history of preterm, hypertension, and weight gain during pregnancy were independent predictors of preterm birth, with an area under the curve of 0.844 (95% CI 0.802-0.885) and p < 0.001, indicating a good predictive ability. Hosmer-Lemeshow test showed a well-fitted model (p = 0.26). Based on the scoring system in this study, the cut-off ≥ 4 had a sensitivity and specificity of 81.3% and 82.2%, respectively, in predicting preterm birth.
conclusionSocioeconomic level, history of preterm delivery, antenatal visits, weight gain during pregnancy, and history of hypertension were independent predictors associated with preterm birth.
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