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Partners with a Highly Favorable Attitude Toward Breastfeeding Contribute to Promoting Initiation and Length of Breastfeeding for More than 5 Months in a Population-Based Canadian Study.

Gabriel Arantes TiraboschiCaroline FitzpatrickJonathan Y BernardJuliana Cristina Dos Santos MonteiroLaurie-Anne KosakGabrielle Garon-Carrier
Published in: Breastfeeding medicine : the official journal of the Academy of Breastfeeding Medicine (2024)
Introduction: Promoting maternal breastfeeding for at least 6 months is important to improve children physical health during infancy and neurodevelopmental outcomes in childhood. For this guideline to be followed, it is paramount to identify what factors best support the initiation and length of breastfeeding. This study estimates the contribution of various child- and parent-level factors, as well as the sociodemographic context in predicting maternal breastfeeding initiation and duration. Methods: This study draws on data from the Quebec Longitudinal Study of Child Development. Mothers who never breastfed when the infant was 5 months old ( n  = 630, 28.3%) were compared to mothers who breastfed for less than 5 months ( n  = 844, 38.0%) and mothers breastfeeding for more than 5 months ( n  = 749, 33.7%), using multivariable multinomial regression models. Results: Mothers with a partner showing a positive attitude toward breastfeeding were up to 13 times more likely to breastfeed their infant for more than 5 months. The positive attitude of partners toward breastfeeding was the strongest predictor of breastfeeding duration, followed by the maternal educational attainment and timing she returns to work. Most prenatal and perinatal child-level factors and the sociodemographic context predicted breastfeeding duration, but to a lesser extent. Discussion: This finding underscores the role of the partner's attitude in promoting initiation and length of breastfeeding. As such, educational campaigns and health practitioners could target both the mother and their partner in promoting breastfeeding.
Keyphrases
  • preterm infants
  • mental health
  • healthcare
  • public health
  • pregnant women
  • type diabetes
  • young adults
  • machine learning
  • metabolic syndrome
  • body mass index
  • deep learning
  • preterm birth
  • glycemic control