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Prediction of special education placement from birth certificate data
H Andrews1, D Goldberg, N Wellen
1New York State Psychiatric Institute, NY 10032, USA.
Insights
Birth certificate data can identify children at high risk for special education placement. Factors like poverty, low prenatal care, and male gender are key predictors for learning disability, emotional disorder, and mental retardation.
Area of Science:
- Pediatrics
- Public Health
- Special Education
Background:
- Accurate identification of children at high risk for special education placement is crucial for early intervention.
- Existing methods may not fully utilize readily available birth information.
Purpose of the Study:
- To develop and validate a predictive model for identifying children at high risk for special education placement using birth record data.
- To analyze specific risk factors associated with placement in learning disability, emotional disorder, and mental retardation categories.
Main Methods:
- A large dataset of New York City births (1976-1986) was linked with public school enrollment data (1992).
- Survival analysis models were employed using parental, pregnancy, and child-related risk factors from birth certificates.
- Risk factors included socioeconomic indicators, maternal health, and infant characteristics.
Main Results:
- Significant predictors of special education placement included poverty indicators (Medicaid payment), unmarried maternal status, large family size, low parental education, and limited prenatal care.
- Infant factors such as male gender, low birthweight, and low Apgar scores were also strong predictors.
- Male gender emerged as the most potent risk factor across all disability categories.
Conclusions:
- The developed models demonstrate substantial predictive power for identifying children requiring special education services.
- This methodology enables early identification of at-risk children for targeted screening and intervention programs.
- Utilizing birth certificate data offers a cost-effective approach to proactive child development support.
Abstract:
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.(ABSTRACT TRUNCATED AT 250 WORDS)