Background: This paper assesses the agreement between household-level income data and an area-based income measure, and whether or not discrepancies create meaningful differences when applied in regression equations estimating total household prescription drug expenditures.
Methods: Using administrative data files for the population of BC, Canada, we calculate income deciles from both area-based census data and Canada Revenue Agency validated household-level data. These deciles are then compared for misclassification. Spearman's correlation, kappa coefficients and weighted kappa coefficients are all calculated. We then assess the validity of using the area-based income measure as a proxy for household income in regression equations explaining socio-economic inequalities in total prescription drug expenditures.
Results: The variability between household-level income and area-based income is large. Only 37% of households are classified by area-based measures to be within one decile of the classification based on household-level incomes. Statistical evidence of the disagreement between income measures also indicates substantial misclassification, with Spearman's correlations, kappa coefficients and weighted kappa coefficients all indicating little agreement. The regression results show that the size of the coefficients changes considerably when area-based measures are used instead of household-level measures, and that use of area-based measures smooths out important variation across the income distribution.
Conclusion: These results suggest that, in some contexts, the choice of area-based versus household-level income can drive conclusions in an important way. Access to reliable household-level income/socio-economic data such as the tax-validated data used in this study would unambiguously improve health research and therefore the evidence on which health and social policy would ideally rest.