Improving the immunization and health status of children in the Women, Infants, and Children (WIC) Program

J Health Care Poor Underserved. 2004 Feb;15(1):127-40. doi: 10.1353/hpu.2004.0013.


Maintaining enrollment in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and continued exposure of these children to immunization-promoting and nutritional benefits within the program is essential to improve the health status of this vulnerable population. Logistic regression was used to determine characteristics of 2 groups of children: those who dropped out of the program despite being eligible and those who remained in the program but were underimmunized. Of over 20,000 children 19-35 months old, 49% had participated in WIC but only 50% were still enrolled. Factors most strongly associated with dropping out of the program were older age of child; white, black, or American Indian race; living in an urban or suburban area; higher socioeconomic status but still eligible for the program; having only 1 child at home; and having mothers who were unmarried or less than 30 years old (p<0.05). Among current participants, factors most strongly associated with under-vaccination included younger age of the child; black or Asian race; moving from another state since birth; mother with less than a high-school education; and having 2 or more children under 18 years old living in the household (p<0.05). Routinely collected child/family information can be used to target outreach and immunization-promoting interventions toward children most likely to drop out of the program or to be underimmunized.

MeSH terms

  • Aid to Families with Dependent Children / statistics & numerical data*
  • Child Health Services / economics
  • Child Health Services / statistics & numerical data*
  • Child Welfare
  • Child, Preschool
  • Health Care Surveys
  • Humans
  • Immunization Programs / economics
  • Immunization Programs / statistics & numerical data*
  • Infant
  • Logistic Models
  • Minority Groups / statistics & numerical data
  • Patient Dropouts / statistics & numerical data*
  • Poverty / ethnology
  • Poverty / statistics & numerical data
  • Risk Factors
  • Socioeconomic Factors
  • United States