Graduation Year

2016

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Nursing

Major Professor

Maureen Groer, Ph.D.

Committee Member

Melissa Shelton, Ph.D.

Committee Member

Denise Maguire, Ph.D.

Committee Member

Terri Ashmeade, M.D.

Keywords

Donor Human Milk, Mother’s Own Milk, Multilevel Modeling, Premature Infants, Score for Neonatal Acute Physiology

Abstract

Very low birth weight and extremely low birth weight neonates have tremendous risk of mortality. This is a grave concern; however, survival alone is not the goal of neonatal intensive care. Survival, along with a reduction or elimination of life long morbidity is the aim of neonatal intensive care.

Human milk is known as the best nutrition for babies and a growing body of evidence supports that human milk is critical in helping these fragile neonates mitigate the overwhelming risks they face. Therefore, the purpose of this study was to examine the relationship between neonatal severity of illness and human milk, specifically mothers own milk (MOM), donor human milk (DHM), and total human milk (THM) intake in very low birth weight (VLBW) and extremely low birth weight (ELBW) infants over the first six weeks of life. Although there is a growing body of evidence that supports the use of human milk in this fragile neonatal population, information is lacking about the relationship between human milk and neonatal illness severity.

The current study was a secondary data analysis from a National Institutes of Health (NIH) funded R21 study in a level three NICU in Florida. Multilevel modeling was used for data analysis to examine relationships between maternal dyad characteristics and severity of illness, operationalized by the Score for Neonatal Acute Physiology-II (SNAP-II), at 12 hours of life and at the end of each week of life for six weeks.

Growth models (linear, quadratic, piecewise) were examined to determine the best model fit for the data, then predictor variables were added and model fit was tested. Birth weight was added to final models as a control as it is seen as a proxy for severity of illness in the literature. Model six demonstrated a significant inverse relationship between MOM(mL) (γMOM(mL)) = -.000079, p < .05) and SNAP-II scores (Deviance = 287.862, Δχ2(df) = 31.38(1), p < .001, AIC = 303.862, BIC = 336.930). Model 11 demonstrated a significant inverse relationship between THM(mL) (γTHM(mL) = -.000127, p < .001) and SNAP-II scores (Deviance = 279.280, Δχ2(df) = 30.859(1), p < .001, AIC = 295.280, BIC = 328.347). No relationships were noted between severity of illness and DHM(mL), MOM(%), DHM(%), or THM(%). Therefore the relationships noted between MOM(mL) and THM(mL) and neonatal severity of illness should be interpreted with caution.

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