Human Milk Bank at the National Institute of Women, Children and Adolescents Health Fernandes Figueira (IFF) of the Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil.
National Institute of Infectious Diseases (FIOCRUZ), Rio de Janeiro, RJ, Brazil.
Int Breastfeed J. 2021 Jan 4;16(1):2. doi: 10.1186/s13006-020-00349-x.
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.
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.
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.
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.
多个层面的决定因素可能会影响母乳喂养行为。除了已知的历史、社会经济、文化和个体因素外，其他因素也对母乳喂养构成了重大挑战。预测现有模式并确定可改变的因素对于尽早获得最佳结果非常重要，尤其是在最脆弱的人群中。本研究的目的是构建一个基于树的分析模型，以确定在高危婴儿转诊中心，能够预测出院时以及 3 个月和 6 个月龄时母乳喂养模式的变量。
这是一项前瞻性、纵向研究，纳入了 1003 名婴儿，并在一家高危公立医院进行，研究分为三个阶段：住院时、出院后首次就诊时以及婴儿生命的头 6 个月每月进行电话访谈。自变量被分为四组：与新生儿、母亲、卫生服务和母乳喂养相关的因素。结果按照世界卫生组织（WHO）设定的类别进行母乳喂养分类。对于本研究，我们在两个阶段进行了出院时以及 3 个月和 6 个月时的探索性分析，具体如下：（i）根据三个时期的母乳喂养指标，对基线特征进行分层，确定频率；（ii）决策树分析。
出院时的纯母乳喂养（EBF）率为 65.2%，3 个月时为 51%，6 个月时为 20.6%。在出院时和第 6 个月时，住院时间是喂养行为的最重要预测因素，在第 3 个月时也很重要。除了母亲和孩子的特征（多胎、母亲年龄和产次）外，社会背景、工作、住院期间的喂养方式以及医院对母乳喂养的实践和政策也影响了母乳喂养率。
决策树（一种机器学习技术）的组合算法为了解巴西高风险人群中母乳喂养中断的风险预测因素提供了更好的认识。根据新生儿病房的住院时间和持续喂养时间，决策树可以为针对这一高危人群的建议提供依据。
