Health Inequities in Manitoba: Is the Socioeconomic Gap

Health Inequities in Manitoba: Is the Socioeconomic Gap in Health Widening or Narrowing Over Time?
Urban over time:
• In T1, 19.8% of the people aged 16 and older diagnosed with MS are accounted for in the 20.2% of
the population in U1. In T4, 19.0% of the people diagnosed with MS are accounted for in the 19.8% of
the population in U1.
• The Lorenz curves for urban residents for both T1 and T4 confirm that there were no significant
disparities in distribution of MS prevalence across urban neighbourhood income quintiles for these
periods.
• There were no statistically significant changes in Gini coefficients over time for MS in urban
populations (p=0.92, NS).
Rural compared to urban in most recent time period:
• Although the Gini coefficient (0.054) for the most recent time period among rural residents indicated
a trend to slightly higher MS prevalence in higher neighbourhood income quintiles, this Gini
coefficient was not statistically significantly different (p=0.12, NS) from that for urban residents for
the same period, confirming no significant differences between these groups in disparities.
Disparity measures over time by rural and urban:
• The disparity rate ratios (DRRs) (i.e., the ratio of MS prevalence in the lowest compared to the highest
neighbourhood income group) are similar across time for both rural and urban, showing very little
difference both within and between groups. The one exception is in T4, the most recent time, where
the DRR indicates greater gap between R1 and R5 in rural (0.61) compared to the urban area where
there is little if any gap between U1 and U5 (0.96, close to 1). 5
• For the first three time periods, the disparity rate differences (DRDs) that measure rate differences are
similar in urban and rural areas. However, in the most recent time period T4, rural gaps between R1
and R5 appear to be widening; whereas urban gaps between U1 and U5 appear to be narrowing to
the point where there is a statistically significantly greater rate difference in rural compared to urban
in the most recent time period T4 (–110.89 vs. –10.73).
What is this telling us?
• There was not a strong relationship observed between neighbourhood income levels and rates
of diagnosed MS in Manitoba. There was a weak trend towards higher MS prevalence in higher
neighbourhood income quintiles (and low MS prevalence in the lowest neighbourhood income
quintile) in rural areas, but this relationship did not persist in urban residents. These observations
suggest that factors other than neighbourhood income and access to care are more important
in determining MS prevalence. While the cause(s) of MS are still not well understood, and may be
“multi–factorial” including a combination of genetic and/or environmental factors (Poppe, Wolfson,
& Zhu, 2008; Marie, Yu, Blanchard, Leung, & Elliot, 2010), neighbourhood income and socioeconomic
status do not appear to play a prominent role for MS in Manitoba.
Where to from here?
• The observed high prevalence of diagnosed MS in the NF group may merit further investigation: if
many of these individualized are institutionalized due to their MS, it would be important to describe
their neighbourhood income and socioeconomic status to ensure no disparities are being masked
by lack of information on this group.
5 In the case of certain indicators where fewer events or percentages occur in the lowest compared to the highest quintile (such
as in the case of Pap tests), DRRs are below 1 and DRDs are negative—the directions of differences are somewhat non–intuitive.
The “lower” the ratio below 1 (for example, 0.7 rather than 0.8), the greater the disparity (i.e., 30% compared to 20% difference). As
well, the more negative the difference (for example, –20 compared to –10), the greater the disparity. So what appears as a positive
outcome (“lower” disparity) of lower DRRs or lower DRDs on the y–axis is actually a greater disparity.
Manitoba Centre for Health Policy 107