• A deprivation record is only available for patients who have a valid postal code (recorded within the EMR and submitted to the central CPCSSN repository).
  • This table provides one measure of vulnerability indicator – the Pampalon index (link to Pampalon publication).
  • This is a neighborhood-level measure that uses the postal code to map to a dissemination area (DA), which is what the census data is linked to.
  • The Pampalon indicator provides a material index and a social index. Each index is a quintile. A patient who lives in a certain postal code has a probability of being in a certain quintile.
  • Since the mapping is not one-to-one between DAs and postal codes, it requires us to use probabilistic sampling and to consider the possibility of a single postal code mapping onto different SES levels (whether it be income quintiles or deprivation indices).
  • Within this table, you will see the probability distribution for each index (material and social) for each patient. There are several ways you can work with this:
    • 1) Weighted average: For each patient take the weighted average. Essentially the probabilities within each quintile of the deprivation index will generate a number within the range of 1 to 5. This will transform the ordinal variable (1,2,3,4,5) onto a somewhat continuous scale (e.g., patient with 1.36 deprivation index).
    • 2) Random assignment: Group patients according to their probability distribution and do a random assignment of patients to a quintile, by group. You will need to do the random assignment multiple times to get measures of variability. By creating multiple copies of the same data file through probabilistic sampling; and via multiple imputation methods (or its bootstrap analogues) should, on average, provide an estimate that closely resembles the underlying population-level quantity. Sampling variability will be high with small samples, and thus it can lead to estimation with greater variability. In contrast, sampling variability will be less with bigger samples.

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Data Tables

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