Predicting risk of early discontinuation of exclusive breastfeeding at a Brazilian referral hospital for high-risk neonates and infants: a decision-tree analysis

Abstract

Background Determinants at several levels may affect breastfeeding practices. Besides the known historical, socio-economic, cultural, and individual factors, other components also pose major challenges to breastfeeding. Predicting existing patterns and identifying modifiable components are important for achieving optimal results as early as possible, especially in the most vulnerable population. The goal of this study was building a tree-based analysis to determine the variables that can predict the pattern of breastfeeding at hospital discharge and at 3 and 6 months of age in a referral center for high-risk infants. Methods This prospective, longitudinal study included 1003 infants and was conducted at a high-risk public hospital in the following three phases: hospital admission, first visit after discharge, and monthly telephone interview until the sixth month of the infant’s life. Independent variables were sorted into four groups: factors related to the newborn infant, mother, health service, and breastfeeding. The outcome was breastfeeding as per the categories established by the World Health Organization (WHO). For this study, we performed an exploratory analysis at hospital discharge and at 3 and at 6 months of age in two stages, as follows: (i) determining the frequencies of baseline characteristics stratified by breastfeeding indicators in the three mentioned periods and (ii) decision-tree analysis. Results The prevalence of exclusive breastfeeding (EBF) was 65.2% at hospital discharge, 51% at 3 months, and 20.6% at 6 months. At hospital discharge and the sixth month, the length of hospital stay was the most important predictor of feeding practices, also relevant at the third month. Besides the mother’s and child’s characteristics (multiple births, maternal age, and parity), the social context, work, feeding practice during hospitalization, and hospital practices and policies on breastfeeding influenced the breastfeeding rates. Conclusions The combination algorithm of decision trees (a machine learning technique) provides a better understanding of the risk predictors of breastfeeding cessation in a setting with a large variability in expositions. Decision trees may provide a basis for recommendations aimed at this high-risk population, within the Brazilian context, in light of the hospital stay at a neonatal unit and period of continuous feeding practice.

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

“…It is commonly used, easy to implement, and specifically useful for classification and prediction at the same time. Recently, the algorithm has been widely applied by researchers and practitioners for different purposes such as telemarketing for banks [36], retail banking [37], medical data [38], inventory systems [39], and customer purchasing behavior prediction [40]. In this study, the implementation of the DT algorithm is applied to the dataset collected from an e-learning management system's data predicting student performance using different assessment and behavior factors.…”

Impact of Using Social Marketing Principles on Promoting Breastfeeding Practices and Physical Growth: an Egyptian Community Based Longitudinal Study

“…However, as a result of interventions, all these defective practices were signi cantly corrected and their percentages lowered to be 28.5 %, 18 % and 12.1 % respectively. The use of a paci er was found to be a predictor of early termination of EBF at discharge (57)(58)(59). This nding re ects the importance of the use of SM approach during counseling and education on BF practices in local communities.…”

The effect of risk at birth on breastfeeding duration and exclusivity: A cohort study at a Brazilian referral center for high-risk neonates and infants

“…Such a result can be attributed to long hospital stay, resulting in greater exposure to conditions that can inhibit EBF, and to the feeding practice during hospital stay, which would indicate the influence of health practices and services [54]. Similarly to previous studies, the use of infant formula during hospitalization resulted in shorter EBF, which provides greater protection and health recovery [55][56][57], while EBF during hospitalization predicts longer EBF duration [54,58]. McCoy & Heggie [55]…”

“…In Brazil, Human Milk Banks collect and distribute certified milk for high-risk NBs, and also offer breastfeeding support, especially regarding the mother-hospitalized-high-risk-infant dyad. All assistance and follow-up are carried out by the team of nurses and pediatricians of the HMB, with greater expertise and ability to clinically manage breastfeeding with this group of high-risk NBs, offering care and defining specific procedures during the prenatal period, all the hospitalization, and the first follow-up visits after hospital discharge [58]. A study comparing the rate of EBF in 4,277 babies with very low birth weight hospitalized in 83 NICUs in Italy reported higher EBF rates at hospital discharge in NICU that had an HMB in the hospital (29.6%) compared to NICU with no HMB (16.0%) [62].…”