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J Pediatr ; 203: 197-203.e2, 2018 12.
Article in English | MEDLINE | ID: mdl-30297289

ABSTRACT

OBJECTIVES: To predict the duration of any breastfeeding using the duration of exclusive breastfeeding in a socioeconomically heterogeneous sample of mothers using receiver operator characteristic (ROC) analysis. STUDY DESIGN: The Mother Baby Health Survey, a birth certificate-linked cross-sectional survey was sent at 4-5 months postpartum to a stratified random sample of socioeconomically and racially diverse women in upstate New York; 797 mothers who initiated exclusive breastfeeding were included in this study. Split-sample validation was employed; eligible subjects were divided into training or test samples at random (80% and 20%, respectively). ROC curves were constructed using the training sample and optimal exclusive breastfeeding duration thresholds were tested using the remaining test sample. Logistic regression using the training sample provided estimates of the predictive ability (sensitivity, specificity, positive predictive value) of thresholds in both unadjusted and adjusted analyses (covariates: age, education, parity, marital status, and race). RESULTS: The ROC analysis in this sample demonstrated that 9 weeks of exclusivity was required for maintenance of breastfeeding at 3 months, and 14.9 weeks of exclusivity was required for maintenance at 20 weeks. Unadjusted and adjusted models yielded similar results; women who exclusively breastfed for at least 9 weeks had 2.2 times the risk (95% CI 1.7-2.8) of maintaining any breastfeeding at 3 months. CONCLUSIONS: These results are similar to our previous results, from a less diverse cohort, and support that these thresholds may be useful in clinical settings for helping mothers achieve breastfeeding duration goals.


Subject(s)
Breast Feeding/methods , Mothers/education , Mothers/psychology , Socioeconomic Factors , Adult , Attitude to Health , Breast Feeding/psychology , Cross-Sectional Studies , Educational Status , Humans , Infant , Infant, Newborn , Logistic Models , Male , New York , Predictive Value of Tests , ROC Curve , Risk Assessment , Time Factors
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