title: A prediction model of childhood immunization rates.
abstract: BACKGROUND
  This research begins by providing background on the status and literature of childhood immunization in the USA. Vaccine-preventable diseases have been on the rise in Europe and the USA in the last few years. Cases of measles and pertussis have all been increasing at alarming rates. The article begins with a discussion of the use of immunization exemptions across the states and a brief history of US immunization policy. A review of the literature confirms that socioeconomic status and other demographic characteristics can be important predictors of childhood vaccine uptake.
  
  
  OBJECTIVE
  Given the seriousness of this public health issue, the primary objective of this research is to analyze the determinants of a child in the USA being fully vaccinated.
  
  
  METHODS
  A range of socioeconomic and demographic characteristics, along with data from the National Immunization Survey, are used to develop an immunization prediction model. Logistic regression is the chosen method in determining whether a preschool-age child in the USA today is likely to be vaccinated based on various demographic and socioeconomic characteristics.
  
  
  RESULTS
  Model results reveal a number of significant socioeconomic and demographic characteristics that contribute to the likelihood of a child being immunized. The overall logistic regression model was highly significant at the 5 % level and model parameters are significant. Significant variables in the model include categories of educational attainment, first born child, race and ethnicity, age of mother, and census region. This model does not definitively reveal that later born children are less likely to get fully vaccinated than first born children but does confirm the significance of geography in immunization outcomes. All levels of education were found to be significant along with all census regions.
  
  
  CONCLUSIONS
  Overall, these models reveal that demographic and socioeconomic characteristics are predictors of childhood immunization and if leveraged appropriately can assist policy makers and public health officials to understand immunization rates and craft policy to improve them.
authors: The Division of Policy and Research on Medicaid and Medicare (PRMM), Institute for Families in Society, University of South Carolina, 1600 Hampton Street, Suite 507, Columbia, SC, 29208, USA, crouchel@mailbox.sc.edu.
authors: Elizabeth
authors: E
authors: Crouch
authors: Lori A
authors: LA
authors: Dickes
mesh_terms: D002648:Child; D002675:Child, Preschool; D003710:Demography; D005260:Female; D006291:Health Policy; D006306:Health Surveys; D006801:Humans; D017589:Immunization Programs / Q000706:statistics & numerical data*; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D011237:Predictive Value of Tests; D012923:Social Class; D014481:United States; D014611:Vaccination / Q000706:statistics & numerical data*