Health Inequities in Manitoba: Is the Socioeconomic Gap

Chapter 1: Introduction and Methods
8 University of Manitoba
to avoid breeches in confidentiality). This type of information is helpful in giving a realistic look at the
effect of the population burden of illness on the region’s healthcare system—actual numbers of the
population who will require healthcare services for their illness or condition.
Despite the fact that many of the rates and prevalence graphs in this report are based on several years
of data, most graphs are presented as annualized rates/prevalence, that is, the average value for one year
(based on an average over all the years of data used). Exceptions are indicated when they occur.
In our exploration of using data from the time trend analyses to complete the Lorenz curve analyses,
much discussion ensued. Normally, Lorenz curve analyses (see description below) use unadjusted event
counts; but to have a fair comparison between neighbourhood income quintile groupings, there was a
strong desire to age– and sex–adjust the event counts prior to graphing the income inequality. Thus, we
developed a mathematical approach to produce adjusted Lorenz curves and Gini coefficients, which is
available upon request from the authors.
Statistical testing indicates how much confidence to put in the results. If a difference is “statistically
significant,” then this difference is large enough that we are confident it is not just due to chance. In
other words, if some rate is considered “statistically different” than the Manitoba average, we would say
that this difference (either higher or lower than the average) is not due to random fluctuation simply
expected by chance; but rather, this is most likely (we are 95% ‘sure’) that it is a real difference. The
notation “p