Defining Low Milk Supply: A Data-Driven Diagnostic Framework and Risk Factor Analysis for Breastfeeding Women

Area of Science:

• Lactation and Infant Nutrition
• Reproductive Health
• Data Science in Healthcare

Background:

• Current definitions of low milk supply (LMS) rely on subjective perceptions or arbitrary thresholds, leading to potential misclassification.
• Objective evaluation of LMS is crucial for accurate diagnosis and timely intervention.

Purpose of the Study:

• To re-evaluate the definition of LMS using data-driven approaches.
• To identify and investigate maternal risk factors associated with LMS subgroups.

Main Methods:

• Latent profile analysis and receiver operating characteristic (ROC) curve analysis were employed.
• Data included 24-hour milk production (MP), infant intake, infant growth (weight-for-age z-score), and maternal/infant characteristics from 460 postpartum mothers.
• Multinomial logistic regression was used for risk factor assessment.

Main Results:

• Four distinct milk supply classes were identified, with 38.3% classified as LMS.
• New, highly accurate diagnostic thresholds were established for 24-hour MP (708 mL), infant breast milk intake (694 mL), infant weight gain (26 g/day), formula intake (122 mL/day), and formula-to-growth ratio (4 mL/g).
• Significant LMS risk factors included minimal breast growth during pregnancy, advanced maternal age, and gestational diabetes mellitus, with synergistic effects observed for combined factors.

Conclusions:

• A data-driven classification of LMS enhances diagnostic precision.
• Identification of specific risk factors allows for targeted interventions to support lactating mothers experiencing LMS.