Confounding associated with social stratification or other selection processes has been called structural confounding. In the presence of structural confounding, certain covariate strata will contain only subjects who could never be exposed, a violation of the positivity or experimental treatment effect assumption. Thus, structural confounding can prohibit the exchangeability necessary for meaningful causal contrasts across levels of exposure. The authors explored the presence and magnitude of structural confounding by estimating the independent effects of neighborhood deprivation and neighborhood racial composition (segregation) on rates of preterm birth in Wake and Durham counties, North Carolina (1999-2001). Tabular analyses and random-intercept fixed-slope multilevel logistic models portrayed different structural realities in these counties. The multilevel modeling results suggested some nonsignificant effect of residence in tracts with high levels of socioeconomic deprivation or racial residential segregation on adjusted odds of preterm birth for white and black women living in these counties, and the confidence limit ratios indicated fairly consistent levels of precision around the estimates. The results of the tabular analysis, however, suggested that many of these regression modeling findings were off-support and based on no actual data. The implications for statistical and public health inference, in the presence of no data, are considered.