PREDICTION OF SPECIAL-EDUCATION PLACEMENT FROM BIRTH CERTIFICATE DATA

The overall goal of this research effort was to develop procedures for accurately identifying children at high risk for special education placement, based on information available at the time of birth. A file containing information on all births in New York City between 1976 and 1986 was matched against the 1992 BIOFILE, which contains information on all children enrolled in the New York City public school system in 1992. A matched file containing birth and school information on 471,165 children resulted from this process. Three sets of risk factors were derived from birth certificate data: parental, pregnancy-related, and child-related. Using these risk factors as independent variables, a survival analysis model was developed predicting special education placement for each of three major disability categories: learning disability, emotional disorder, and mental retardation. A model combining all disability categories was also developed. The significant predictors of special education placement were Medicaid payment for birth (a poverty indicator), unmarried status of mother, large family size, low parental education, a mother born in the United States, a low level of prenatal care, male gender, low birthweight, and a low Apgar score. Male gender was the strongest risk factor in all models. Examination of selected survival curves indicated that the predictive power of the models is substantial. The methodology described in this article can be used to identify at-risk children for whom screening and other early interventions, including preschool programs, may be appropriate. In addition, these methods can be adapted to conduct long-term tracking of at-risk children, to conduct cost-effective evaluation of early interventions, and to contribute to the development of long-term enrollment projection models.