American journal of public health · 1997
Neighborhood risk factors for low birthweight in Baltimore: a multilevel analysis.
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O'Campo P, Xue X, Wang M C, Caughy M
Аннотация
OBJECTIVES: Past research on low birthweight has focused on individual-level risk factors. We sought to assess the contribution of macrolevel social factors by using census tract-level data on social stratification, community empowerment, and environmental stressors.
METHODS: Census tract-level information on social risk was linked to birth certificate records from Baltimore, Md, for the period 1985 through 1989. Individual level factors included maternal education, maternal age, medical assistance health insurance (Medicaid), and trimester of prenatal care initiation. Methods of multilevel modeling using two-stage regression analyses were employed.
RESULTS: Macrolevel factors had both direct associations and interactions with low birthweight. All individual risk factors showed interaction with macrolevel variables; that is, individual-level risk factors for low birthweight behaved differently depending upon the characteristics of the neighborhood of residence. For example, women living in high-risk neighborhoods benefited less from prenatal care than did women living in lower-risk neighborhoods.
CONCLUSIONS: Multilevel modeling is an important tool that allows simultaneous study of macro- and individual-level risk factors. Multilevel analyses should play a larger role in the formulation of public health policies.
METHODS: Census tract-level information on social risk was linked to birth certificate records from Baltimore, Md, for the period 1985 through 1989. Individual level factors included maternal education, maternal age, medical assistance health insurance (Medicaid), and trimester of prenatal care initiation. Methods of multilevel modeling using two-stage regression analyses were employed.
RESULTS: Macrolevel factors had both direct associations and interactions with low birthweight. All individual risk factors showed interaction with macrolevel variables; that is, individual-level risk factors for low birthweight behaved differently depending upon the characteristics of the neighborhood of residence. For example, women living in high-risk neighborhoods benefited less from prenatal care than did women living in lower-risk neighborhoods.
CONCLUSIONS: Multilevel modeling is an important tool that allows simultaneous study of macro- and individual-level risk factors. Multilevel analyses should play a larger role in the formulation of public health policies.