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|Title:||Predicting risk of early discontinuation of exclusive breastfeeding at a Brazilian referral hospital for high-risk neonates and infants: a decision-tree analysis|
|Authors:||Silva, Maíra Domingues Bernardes Silva|
Oliveira, Raquel de Vasconcellos Carvalhaes de
Alves, Davi da Silveira Barroso
Melo, Enirtes Caetano Prates
|Affilliation:||Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente. Rio de Janeiro, RJ, Brasil.|
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Estado do Rio de Janeiro. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Rio de Janeiro, RJ, Brasil.
|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.|
Neonatos de alto risco
|Citation:||SILVA, Maíra Domingues Bernardes; OLIVEIRA, Raquel de Vasconcellos Carvalhaes de; ALVES, Davi da Silveira Barroso; MELO, Enirtes Caetano Prates. Predicting risk of early discontinuation of exclusive breastfeeding at a Brazilian referral hospital for high-risk neonates and infants: a decision-tree analysis. International Breastfeeding Journal, v. 16, n.2, p. 1-13, 2021.|
|Description:||We are grateful for our participants’ support. The authors would like to acknowledge developer Vinicius Ramires Leite, who created the web application for cohort. We wish to thank Dr. João Aprígio Guerra de Almeida, coordinator of the Global Network of Human Milk Banks (FIOCRUZ), Brazil, and Dr. Danielle Aparecida da Silva, coordinator of the National reference center of Human Milk Banks (FIOCRUZ) for their support and highly valuable comments. We also acknowledge the colleagues of the Human Milk Bank at IFF/FIOCRUZ for support; and Marlene Assumpção, Alana Kohn, Antonio Azeredo, Rosânea Santos, Flavia Benedicto, Rafaelle Cristine, Pernelle Pastorelli, Silvia Azevedo, Alexia Martins, Taina Gomes, Caroline Lima, Pamela Mourão, Luiza Reis and Camila Chaves for assisting in data collection.|
|Appears in Collections:||IFF - Artigos de Periódicos|
INI - Artigos de Periódicos
ENSP - Artigos de Periódicos
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