The influence of neighborhood environment on the incidence of childhood asthma: a multilevel approach

Some ecological analyses suggest an influence of neighborhood environment on asthma outcomes. However, no previous study has applied a multilevel approach to assess an ecological effect of neighborhood environment on the incidence of childhood asthma accounting for individual risk factors. This study assessed the influence of neighborhood and individual-level factors on the incidence of childhood asthma among all children born in Rochester, Minnesota, between 1976 and 1979. We identified asthmatics among all children born in Rochester, between 1976 and 1983. We applied a multilevel survival model with the frailty term to assess the effects of neighborhood characteristics, such as mean family income per census tract (n=16) from the 1980 census report and the status of whether a census tract faces intersections with major highways or railroads, on asthma incidence. The relative risks (RR) of neighborhood socioeconomic status (SES), the status of whether census tracts face intersections with highways or railroads and the variance of random effect of census tracts were calculated adjusting individual-level covariates for asthma, including gender, birth weight, motherʹs age at birth and parental educational level at birth. We found that the RR of developing asthma among children living in census tracts facing intersections with highways or railroads was 1.6 (95% CI: 1.1–2.2) compared to those who lived in census tracts not facing intersections, adjusting individual- and neighborhood-level covariates. The variance of the frailty term attributable to census tracts was small (0.0085) and was modified (from 0.004 to 0.0085, 112% change) by adding neighborhood covariates. The overall effects of individual-level factors on asthma incidence were independent of neighborhood environment. The influence of neighborhood environment on childhood asthma in a non-inner-city setting, like Rochester, Minnesota, was small to modest. Incorporating pertinent neighborhood-level covariates into multilevel models needs to be considered in assessing the random effect of clusters.