While we have so far discussed the multilevel structure in terms of individuals at level-1 and places at level-2, we argue that a similar framework of people within places can be established using routinely available aggregate data (e.g., census and mortality data). As is well-known, analyses of aggregated data confounds the micro scale of people and the macro scale of places. Although regrettable, this situation is usually tolerated owing to the other obvious attractions of these data sets (e.g., large, extensive coverage of places at multiple levels). A multilevel approach offers a solution to this problem (Subramanian, Duncan et al., 2001).
Table 1 provides hypothetical data of deaths for two social groups in a format that is typical for spatially aggregated data.
Thus, in Area 1, 9 out of 50 in the low social class category died in a particular year; in Area 2, 5 out of 95 in the high social class category died, and so on. In this table, individuals are grouped as ‘types’ (low and high social class) and are represented as ‘cells’ of a table that contain counts of death for each social group in every area. Importantly, by using the compact, aggregated form of Table 1, data agencies can preserve individual confidentiality.
Five points needs to be made about this table.
Consequently, routinely available aggregated data can readily be adapted to a multilevel data structure with table cells at level-1 (representing the population groups) nested within places at level-2. The counts within each cell give the number of people with the outcome of interest (e.g., number of deaths) together with the ‘denominator’ (the total population). The proportion so formed becomes the response variable and the cell characteristics, meanwhile, are the individual predictor variables. Such a structure now lends itself to all the analytical capabilities that were discussed earlier (Subramanian, Duncan et al., 2001).