A total of 1.74 million households were registered for MSP and had valid postal codes for linkage with area-based income strata. This cohort accounts for 95% of the total population in the province. Of these households, 1.36 million were registered for the Fair PharmaCare program. Cross-tabulations of the household-level and area-based income measures are shown in Table , where NR indicates the percentage of households in each area-based decile who were not registered for the Fair PharmaCare program at the time of data collection. This table confirms that rates of participation with the income-based program are lower in higher income neighbourhoods. This concentration of low incomes for the household-level income variable is because the registration for income-based drug coverage involves a degree of self-selection bias. To adjust for this, our tables below present a "best case" scenario wherein all non-registered households are assigned a hypothetical household-level income variable that is identical to their area-level income.
Entire BC population, 2003. Agreement between household-level validated income deciles and area-based income deciles
Table shows the level of discrepancy between the household-level and area-based income measures. The area-based measures classify 15.6% of senior households and 14.9% of non-senior households as being within the same income-decile as is determined by tax-reported household income. Approximately a third of non-senior households and two fifths of senior households are classified by area-based measures to be within one decile of the classification based on household-level incomes. In the "best-case" scenario, just over half of non-seniors and approximately 43% of seniors are within one decile of their household-level income.
Percentage of discrepancy by decile between area-based and household-level income measures
Statistical evidence of the disagreement between income measures can be found in Table . The Spearman's correlations between the actual household-level income and area-based measures are always less than 0.40, suggesting little agreement. The kappa coefficient of non-random, complete agreement never exceeds 0.31 indicating very little complete agreement between area-based and actual household-level deciles even under the assumption of perfect correlation between area-based and household-level measures for all non-registrants. Again, when examining the weighted kappa coefficients, incorporating partial agreement, we see that they never exceed 0.5, even in the best-case scenario.
Spearman's correlation, Kappa and weighted Kappa coefficients for the association between the area-based income measures and the household income measure
To examine whether these discrepancies result in any meaningful differences in an applied research context, we start by examining the distribution of total prescription drug expenditures by income deciles stratified by senior and non-senior households, first using household-level CRA validated income and then using aggregate neighbourhood level income (Table ). Table indicates that total prescription drug expenditures appear more equally distributed when we rank households by neighbourhood income than by household-level income, suggesting that neighbourhood level income masks variation in the underlying household-level income variable.
Total drug costs by income decile
In Table we estimate the effect of household income on total prescription drug expenditures by using both household-level income and neighbourhood level income in separate regressions. The dummy variable for the highest income decile was not included in the regression; thus, the coefficients can be interpreted as the difference in total prescription drug costs between each income decile and the highest income decile. The regression results also reflect the pattern noted in Table . While the signs never differ, the household-level variables pick up a substantially larger coefficient than the corresponding neighbourhood-level variable. This again suggests that the neighbourhood-level variables are smoothing the distribution of total prescription drug expenditures across income deciles. While the coefficients on income deciles differ substantially between the two models, it is interesting to note that the coefficients on presence of seniors and household size do not. Both coefficients are in the same direction and are of the same magnitude indicating that the difference in income variable does not have a large effect on other coefficients in the model. The model based on household-level income also reports a higher adjusted R[2
] statistic than that using the area-based measure, indicating that the goodness of fit is higher in the regression using household-level variables. We also find that the inclusion of covariates in the model does not attenuate the bias between the variables substantially (Table ).
Results for the regression of dummy variables indicating income decile against total drug costs