2015 - December

Identification of Predictors of Postpartum Depression Risk in New Jersey

By Kathy Mahoney


Postpartum depression is the most common complication of childbirth affecting between ten and fifteen percent of mothers and thus their infants and families.  This study evaluated specific predictors that can be linked to an increased risk for postpartum depression for women giving birth in the state of New Jersey in 2010.   Descriptive statistics were used to portray the population as a whole. Evaluation of selected variables was conducted using logistic regression to determine if risk could be forecast based on race and ethnicity.   Analysis of the data was done using the public health constructs of time, place and person.  This organizes the findings into usable concepts for provider use in risk identification, screening and proactive patient education and referral. Findings were significant for each indicator for the state as a whole.  Counties with lower household incomes had the highest percentages of women at risk.  Adolescents and mothers over the age of forty presented a significantly higher risk. Racial minorities had higher percentages of risk. Delivering the infant by cesarean section created a higher risk, with particular significance for African American women.  For all women, medical risk factors increased the risk for postpartum depression, as did the birth of an infant at less than term (less than 37 weeks gestation).   A NICU stay created a higher risk, which was significant for the White and African American population.  Breastfeeding appears to be protective and formula feeding mothers comprised the highest risk group. This study potentially creates a model that can be used to proactively identify and educate women in the prenatal period.  Providers can utilize the model to assist families to cope with and recognize risks and symptoms.


  • Prof. Barbara A. Caldwell, Ph.D.
    Rutgers School of Nursing
  • Prof. Jeffrey R. Backstrand, Ph.D.
    Rutgers School of Public Affairs & Administration; School of Public Health
  • Prof. Peijia Zha, Ph.D.
    Rutgers School of Nursing
  • Pamela Kelley, Ph.D.
    Kelley Analytics LLC

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