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Initiation and Duration of Breastfeeding among WIC Participants: Impact of State Policies

Monday, June 23, 2014
Argue Plaza

Author(s): W. David Bradford

Discussant:

The Special Supplemental Nutrition Program for Women, Infants, and Children (commonly referred to as “WIC”) is designed to promote the health of low-income pregnant, post-partum and breastfeeding moms, and their children, who are at nutritional risk (USDA, 2013).  Since 1989, a central program goal has been to encourage and support breastfeeding among participants because of the clear benefits for maternal and child health outcomes. Child health benefits include better dental health, decreases in bacterial and viral infections, fewer gastrointestinal problems, and lower rates of obesity in childhood (Salone et. al, 2013).  Health outcomes for mothers include better mental health, lower risk for cardiovascular disease and breast cancer, and obesity prevention (Moreno, 2011; Mehta et. al, 2011). 

Despite these benefits, many women choose not to initiate breastfeeding.  Among those who breastfeed, many cease within thirty days from birth (Ahluwalia et. al, 2005).  Past research had found a negative association between WIC participation and breastfeeding (Ma & Magnus, 2012). However, little of the extant literature on the subject adequately controls for selection into WIC.  Our study focuses on the relationship between WIC participation and breastfeeding practices using data from 30 states in the Pregnancy Risk Assessment Monitoring System (PRAMS) from 2002 through 2011, and controlling for self-selection of women into the program. Unlike previous research we study both the initiation and the duration of breastfeeding, which are both important factors in achieving health benefits. To address the self-selection bias, we use instrumental variables (our instrument set includes the WIC coverage rate in each state / year) to predict respondent’s program take-up.  Our models also explore the significant variation in breastfeeding initiation and duration across states observed in the data, and identify which state policies are most strongly predictive of this variation.