RESUMO
The world is now facing the most severe health, social, and economic event of the last hundred years, which has made the need to acquire statistical thinking to interpret the information disseminated on a daily basis by the media clear to society. This article proposes a discussion on the role that statistics education might play in supporting the acquisition of such knowledge, contributing to the development of critical citizens, aware of their social responsibility. In this context, we present examples of curves and other charts to demonstrate how to use the several levels defined by experts in reading and interpreting the charts. At a more advanced level, these examples will allow discussion on the impacts of this epidemic on the most vulnerable population in Brazil. The charts presented reveal a great regional inequality, suggesting that mortality due to the virus is distinguished by region and micro-region when considering access to hospital beds. The examples signpost ways for educators to be able to develop projects or research based on the discussion on the reality of the pandemic, the necessary public policies, and how political coordination grounded on science and on a humanitarian vision would have mitigated the Brazilian tragedy.
RESUMO
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.
