Risk Factors for Late- Preterm Infants in One Public Hospital at Banyumas District Indonesia - Haryatiningsih Purwandari; Eni Rahmawati; Aprilia Kartikasari (Journal of Bionursing, 2020)
Background. Late preterm infants are near-term infants, but it is a critical development period. Evidence supported that late preterm infants’ birth impact on the short-and long-term outcomes. Investigating the risk factors associated with late preterm infants in Indonesia is important since Indonesia has a high number of preterm infants. However, a limited study investigating risk factors for late-preterm infants in Indonesia.     Methods.A cross-sectional study was used in this study. A convenience sample of 46 dyads of mothers and preterm infants (33 late preterm infants aged equal and more than 34 weeks gestation, and 13 preterm infants aged < 34 weeks gestation) retrieved from level 1 and 2 neonatal carein one public hospital at Banyumas district, Indonesia. Self-reported questionnaire and medical record were utilized to collecting the data. Univariate, Chi-Square, Logistic regression, Hosmer and Lemeshow test, and Area under Curve with Receiver Operating Curve method (AUC ROC) were used in this study.Results. Working mother had risk 16.2 times for developing late preterm infant (LPI) compared to housewife (OR= 16.2; 95% CI: 2.315-123.444). Mother’s age < 31 years old, and multipara mother were found as the protective factors for delivering LPI (OR= .148; 95%CI: .013-1.632; OR=.059; 95%CI: .004-.927), however, the chance as the protective factors of LPI seems very low. The model prediction was y= 2.900-1.913 (mothers’ age) -2.833 (parity) +2.828 (mothers’occupation). Application the model was discussed. Hosmer and Lemeshow test demonstrated that the model had good calibration (p=.869).The AUC ROC was .809 indicated the model had good discriminant.Conclusion. The LPIs’ birth can be predicted by the mothers’ age, parity, and occupation. The mothers’ occupation was identified as the dominant factor in the model prediction. The model for predicting LPIs’ delivery had good quality and can be used for predicting the LPIs’ birth in the clinical practice.