Multilevel studies of neighborhood effects on health frequently aggregate individual-level data to create contextual measures. For example, percent of residents living in poverty and median household income are both aggregations of Census data on individual-level household income. Because household income is sensitive and complex, it is likely to be reported with error.
To assess the impact of such error on effect estimates for neighborhood contextual factors, we conducted simulation studies to relate neighborhood measures derived from Census data to individual body mass index, varying the extent of nondifferential misclassification/measurement error in the underlying Census data. We then explored the impact on the magnitude of bias owing to the form of variables chosen for neighborhood measure and outcome, modeling technique used, size and number of neighborhoods, and categorization of neighborhoods.
For neighborhood contextual variables expressed as percentages (eg, percent of residents living in poverty), nondifferential misclassification in the underlying individual-level Census data always biases the parameter estimate for the neighborhood variable away from the null. However, estimates of differences between quantiles of neighborhoods using such contextual variables are unbiased. Aggregation of the same underlying individual-level Census income data into a continuous variable, such as median household income, also introduces bias into the regression parameter. Such bias is non-negligible if the sampled groups are small.
Decisions regarding the construction and analysis of neighborhood contextual measures substantially alter the impact on study validity of measurement error in the data used to construct the contextual measure.