when individual-level determinants and community-level economic status were controlled for. Racial differences in the rates of delivery of low-birth-weight infants remained substantial, however, with African American mothers being about twice as likely to deliver a low-birth-weight infant than white mothers, even after individual- and community-level factors were taken into account (Roberts, 1997, Table 2).

Nested data structures are common meaning that individuals operate within multiple realities such as the household, the neighborhood, the city or town, and the state. Another classic example of nested date derives from the educational world with students, within classrooms, within schools, within neighborhoods, etc. Following the educational example, we can assume that the students within a certain class share common characteristics such as the teacher and the physical surroundings and therefore they are not independent. In other words, students within a specific class are more alike than a random sample of students drawn from the larger population. Given that many statistical techniques require that observations are independent, nested data poses challenges. Until recently nested data would have to be aggregated or disaggregated prior to analyses so that only one level of the data was being assessed (e.g., students, or classrooms or schools but not students, classrooms, and schools). Now, however, nested data can be analyzed using hierarchical linear models or multilevel models. Multilevel models permit the simultaneous assessment of the association between nested data and an outcome of interest.

O’Campo and colleagues (1997) were among the first scientists to use multilevel models to investigate the effects of maternal characteristics and neighborhood conditions on the risk of low birth weight in Baltimore, Maryland, using data recovered from 1985 to 1989 in a multilevel framework. Controlling for individual-level characteristics, which included maternal age, education, prenatal care use, and health insurance coverage, the authors found that women living in census tracts with per-capita incomes of less than $8,000 had a significantly higher risk of delivering an infant of low birth weight than women who lived in higher-income census tracts. They also found a number of significant interactions between neighbor-hood-level variables and individual-level risk factors for low birth weight. The protective effects of prenatal care, for example, were less strong in neighborhoods with high levels of unemployment, and the elevated risk of low birth weight among women with low levels of schooling was stronger in tracts with higher crime rates. They did not investigate whether these effects varied by race or whether the contextual and individual-level variables explained racial differences in low birth weight.

Pearl and colleagues (2001) conducted a multilevel analysis of the impact of socioeconomic status (SES) on birthweight. SES was measured at the individual level as maternal education, Medi-Cal coverage during preg-