Positive changes in breastfeeding and complementary feeding indicators in Brazil are associated with favorable nurturing care environments

Objectives: To analyze trends in breastfeeding and complementary feeding indicators for infants and young children receiving primary health care (PHC) services in Brazil, considering the contextual aspects of local nurturing care (NC) environments. Study design: Ecological time-series study. Methods: Ten feeding indicators were extracted from 1,055,907 food intake records of children aged <2 years reported by PHC facilities from 2015 to 2019. Local NC environments were assessed with the Brazilian Early Childhood Friendly Municipal Index, calculating overall and stratified scores for the NC domains of adequate nutrition, good health, opportunities for early learning, and security and safety. Prais-Winsten regression was used to calculate annual percent changes (APC) by sex and the contrast in APC between the lower and upper quintiles of NC scores. Positive or negative APC with P-values <0.05 represented increasing or decreasing trends. Results: No significant trends of exclusive and continued breastfeeding, food introduction, or minimum dietary diversity were observed, with 2019 prevalences of 54.5%, 45.2%, 92.5%, and 78.2%, respectively. Increasing trends were observed for mixed milk feeding (2019: 19.2%; APC, +2.42%) and minimum meal frequency (2019: 61.1%; APC, +2.56%), while decreasing trends were observed for sweet beverage consumption (2019: 31.9%; APC,-5.92%) and unhealthy foods (2019: 16.1%; APC,-4.69%). Indicator improvements were significantly stronger in environments more favorable for NC. Conclusions: Although the indicators did not meet global targets for infant feeding practices, the results suggest that the local NC environment encompasses facilitators that may be strategic in the design of early childhood programs and policies to improve nutrition. (c) 2024 The Royal Society for Public Health. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.