Studies that combine individual-level and aggregate data are common in epidemiologic research. Such studies are often subject to ecological fallacy which arises from confounding of the individual-level relationship due to heterogeneity of exposure variables and covariates within groups. One approach to address this concern is to use multilevel modeling. The advantage of using multilevel modeling is that it takes the hierarchical structure of the data into account by specifying random effects at each level of analysis, and thus results in a more conservative inference for the aggregate effect. In this study, we combined data from two databases for analysis. Data from the National Occupational Mortality Surveillance System (NOMS) containing individual-level information from death certificates was linked by occupation to the Occupational Information Network (O*NET) which contains job characteristics at the occupational level. We examined the adjusted association between job characteristics and select causes of death. A recently available generalized linear mixed models procedure, PROC GLIMMIX, was used to fit the multilevel logistic regression model to our data. Results are compared to those obtained from logistic regression modeling that ignores the hierarchical structure of the data. Results demonstrate the potential of drawing incorrect conclusions when multilevel modeling is not used. Problems encountered from use of PROC GLIMMIX with large data sets will be discussed.
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