Presentation is loading. Please wait.

Presentation is loading. Please wait.

CPS - June 2004 1 Does the income-mortality gradient vary across urban areas in Canada? Philippe Finès Russell Wilkins HAMG, Statistics Canada Canadian.

Similar presentations


Presentation on theme: "CPS - June 2004 1 Does the income-mortality gradient vary across urban areas in Canada? Philippe Finès Russell Wilkins HAMG, Statistics Canada Canadian."— Presentation transcript:

1 CPS - June Does the income-mortality gradient vary across urban areas in Canada? Philippe Finès Russell Wilkins HAMG, Statistics Canada Canadian Population Society, June 2004

2 CPS - June Outline of the study Introduction: There is still a positive relation between income and life expectancy: the richer you are the longer you live. Objectives: determine if the income-mortality gradient of mortality indicators was present in all urban areas across Canada examine the gap of mortality indicators between extreme (richest and poorest) income quintiles Data used (within each geographic unit) number of deaths (death records) number of persons (census data) per income quintile, sex, cause of death, age group  for deaths occurring from 1996 through 1998.

3 CPS - June Methodology 1) Definition of geographic units CMAs of > persons RegionNoNamePopulation in 1996 Atlantic 205Halifax333 K Québec Québec City Montréal 672 K K Ontario Ottawa-Gatineau Toronto Hamilton St.Catharines-Niagara Kitchener London K K 625 K 373 K 383 K 398 K West Winnipeg Calgary Edmonton Vancouver Victoria 668 K 821 K 862 K K 304 K Totals Total of 14 Canadian CMAs of > persons K Canada total K

4 CPS - June Methodology 2) Definition of income quintiles We defined the quintiles based on either census tracts (CT) or enumeration areas (EA) N.B.: EAs are components of CTs, which are components of CMAs (and larger CAs) Quintiles based on census tractsQuintiles based on enumeration areas The low-income cut off point [LICO] for income depends on the size of the family and the size of the community. In any CMA of at least 500 K, the values were: Family size Cut off point ($/year) ………. ………. 7 or more In each CT in a geographic unit, we count the number of persons living below LICO, we convert this number to the percentage of persons living below LICO In each EA in a geographic unit, we compute the Income per person equivalent [IPPE]: measure based on the total income in the EA divided by the number of persons (adjusted for the size of families) in the EA In each geographic unit, we sort the CTs by ascending percentage of persons living below LICO In each geographic unit, we sort the EAs by descending IPPE The first 20% of persons make the 1 st quintile, the next 20% make the 2 nd quintile, and so on until the 5 th quintile  Q1=Richest quintile, Q5=Poorest quintile.

5 CPS - June Methodology 3) Comparisons performed We compared life expectancy 1. across the geographic units  to examine trends by region and size 2. across quintiles  to assess the gradient 3. by the gap between extreme (richest and poorest) quintiles  to summarize the gradient 4. using the two different ways to define quintiles  to test our conjecture: Since with EAs, we are using smaller parts when building the quintiles, the mortality-income gradient should be steeper with EAs than with CTs (that is, the gap should be larger)

6 CPS - June 2004 Results 1) Comparison of LE among geographic units (1) CMAs > 300K, both sexes, all quintiles

7 CPS - June 2004 Results 1) Comparison of LE among geographic units (2) Both sexes, all quintiles

8 CPS - June Results 2) Gradient of LE (Richest)(Poorest)

9 CPS - June Results 3) Gap of LE between extreme quintiles „ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ† ‚ ‚ CT ‚ EA ‚ ‚ ‚ T ‚ M ‚ F ‚ T ‚ M ‚ F ‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒ‰ ‚All 14 CMAs of > 300Ks‚ 3.07‚ 4.46‚ 1.79‚ 3.91‚ 5.36‚ 2.60‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒ‰ ‚Halifax ‚ 3.86‚ 4.86‚ 2.97‚ 4.93‚ 5.40‚ 4.41‚ ‚Québec City ‚ 3.37‚ 4.87‚ 2.46‚ 4.33‚ 5.31‚ 3.50‚ ‚Montréal ‚ 2.97‚ 4.30‚ 1.83‚ 4.07‚ 5.52‚ 2.81‚ ‚Ottawa-Gatineau ‚ 3.50‚ 4.92‚ 2.33‚ 4.47‚ 6.12‚ 3.03‚ ‚Toronto ‚ 2.03‚ 3.10‚ 1.04‚ 2.71‚ 3.97‚ 1.64‚ ‚Hamilton ‚ 4.12‚ 5.76‚ 2.45‚ 4.84‚ 6.60‚ 3.17‚ ‚St.Catharines-Niagara ‚ 3.12‚ 3.12‚ 3.44‚ 3.41‚ 4.25‚ 2.94‚ ‚Kitchener ‚ 2.23‚ 3.58‚ 0.92‚ 2.34‚ 4.21‚ 0.59‚ ‚London ‚ 2.29‚ 4.29‚ 0.43‚ 3.88‚ 5.71‚ 2.32‚ ‚Winnipeg ‚ 5.51‚ 7.35‚ 3.75‚ 6.24‚ 8.76‚ 3.76‚ ‚Calgary ‚ 2.29‚ 3.14‚ 1.47‚ 3.22‚ 4.63‚ 1.81‚ ‚Edmonton ‚ 3.16‚ 4.11‚ 2.41‚ 4.98‚ 6.40‚ 3.50‚ ‚Vancouver ‚ 3.53‚ 5.63‚ 1.03‚ 3.93‚ 5.19‚ 2.57‚ ‚Victoria ‚ 4.99‚ 6.91‚ 3.36‚ 5.29‚ 7.45‚ 3.33‚ Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒŒ

10 CPS - June Verification of the conjecture: If there is a gap with CT-based quintiles, this gap is usually larger (by about one year) when using EA-based quintiles But there are some exceptions: St.Catharines-Niagara (F), Kitchener (F), Vancouver (M), Victoria (F) „ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒ† ‚ ‚ T ‚ M ‚ F ‚ ‚ ‚ CT ‚ EA ‚ CT ‚ EA ‚ CT ‚ EA ‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒ‰ ‚All 14 CMAs of > 300Ks ‚ 3.07‚ 3.91‚ 4.46‚ 5.36‚ 1.79‚ 2.60‚ ‚Halifax ‚ 3.86‚ 4.93‚ 4.86‚ 5.40‚ 2.97‚ 4.41‚ ‚Québec City ‚ 3.37‚ 4.33‚ 4.87‚ 5.31‚ 2.46‚ 3.50‚ ‚Montréal ‚ 2.97‚ 4.07‚ 4.30‚ 5.52‚ 1.83‚ 2.81‚ ‚Ottawa-Gatineau ‚ 3.50‚ 4.47‚ 4.92‚ 6.12‚ 2.33‚ 3.03‚ ‚Toronto ‚ 2.03‚ 2.71‚ 3.10‚ 3.97‚ 1.04‚ 1.64‚ ‚Hamilton ‚ 4.12‚ 4.84‚ 5.76‚ 6.60‚ 2.45‚ 3.17‚ 3.44‚ 2.94 ‚St.Catharines-Niagara ‚ 3.12‚ 3.41‚ 3.12‚ 4.25‚ 3.44‚ 2.94‚ 0.92‚ 0.59 ‚Kitchener ‚ 2.23‚ 2.34‚ 3.58‚ 4.21‚ 0.92‚ 0.59‚ ‚London ‚ 2.29‚ 3.88‚ 4.29‚ 5.71‚ 0.43‚ 2.32‚ ‚Winnipeg ‚ 5.51‚ 6.24‚ 7.35‚ 8.76‚ 3.75‚ 3.76‚ ‚Calgary ‚ 2.29‚ 3.22‚ 3.14‚ 4.63‚ 1.47‚ 1.81‚ ‚Edmonton ‚ 3.16‚ 4.98‚ 4.11‚ 6.40‚ 2.41‚ 3.50‚ 5.63‚ 5.19 ‚Vancouver ‚ 3.53‚ 3.93‚ 5.63‚ 5.19‚ 1.03‚ 2.57‚ 3.36‚ 3.33 ‚Victoria ‚ 4.99‚ 5.29‚ 6.91‚ 7.45‚ 3.36‚ 3.33‚ Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒŒ Results 4) Impact of the definition of quintiles

11 CPS - June Results A closer look at Winnipeg (1)

12 CPS - June Results A closer look at Winnipeg (2) Brookside Notre-Dame Railroad St.Mary’s Fermor Panet Plessis

13 CPS - June Conclusions The income-mortality gradient is generally present in all geographic units The gap for EA-based quintiles is larger (by about one year) than the gap for CT-based quintiles LE varies according to region and CMA/CA size group Winnipeg has the largest gap between extreme quintiles (but this gap has decreased slightly over the years)

14 CPS - June Thank you! Future work: Further examine CT/EA conjecture (for other definitions of quintiles) Analyze other mortality indicators (disability-adjusted LE, probability of survival to age 75, age-standardised mortality rate, rates of potential years of life lost) Include indicators of income inequality (like Gini score) Acknowledgements: Jean-Marie Berthelot, Statistics Canada Nancy Ross, McGill University Contact:

15 CPS - June Annex 1 5) A closer look at Winnipeg (4)


Download ppt "CPS - June 2004 1 Does the income-mortality gradient vary across urban areas in Canada? Philippe Finès Russell Wilkins HAMG, Statistics Canada Canadian."

Similar presentations


Ads by Google