Upload
jan-tolhurst
View
214
Download
0
Tags:
Embed Size (px)
Citation preview
CPS - June 2004 1
Does the income-mortality gradient vary across urban areas in Canada?
Philippe FinèsRussell Wilkins
HAMG, Statistics CanadaCanadian Population Society, June 2004
CPS - June 2004 2
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.
CPS - June 2004 3
Methodology1) Definition of geographic units
CMAs of > 300 000 persons
Region No Name Population in 1996
Atlantic 205 Halifax 333 K
Québec421
462
Québec City
Montréal
672 K
3 326 K
Ontario
505
535
537
539
541
555
Ottawa-Gatineau
Toronto
Hamilton
St.Catharines-Niagara
Kitchener
London
1 011 K
4 264 K
625 K
373 K
383 K
398 K
West
602
825
835
933
935
Winnipeg
Calgary
Edmonton
Vancouver
Victoria
668 K
821 K
862 K
1 830 K
304 K
TotalsTotal of 14 Canadian CMAs of > 300 000 persons 15 870 K
Canada total 28 843 K
CPS - June 2004 4
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 tracts Quintiles 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)
1 16 874
2 21 092
………. ……….
7 or more 42 978
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 1st quintile, the next 20% make the 2nd quintile, and so on until the 5th quintile Q1=Richest quintile, Q5=Poorest quintile.
CPS - June 2004 5
Methodology3) Comparisons performedWe compared life expectancy1. 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)
CPS - June 2004
77.0
77.5
78.0
78.5
79.0
79.5
80.0
Total of CMAs > 300K
Canada total
AtlanticQuébecOntarioWest
Results1) Comparison of LE among geographic units (1)
CMAs > 300K, both sexes, all quintiles
CPS - June 2004
75.0
75.5
76.0
76.5
77.0
77.5
78.0
78.5
79.0
79.5
80.0
Atlantic Québec Ontario West
CMAs> 300K CMAs and CAs 100 - 300K CMAs and CAs 10 - 100K Rural and small towns
Total of CMAs > 300K
Canada total
Results1) Comparison of LE among geographic units (2)
Both sexes, all quintiles
CPS - June 2004 8
Results2) Gradient of LE
Life expectancy at birth, men, selected CMAs in Canada, quintiles based on EAs
71
73.5
76
78.5
81
1 2 3 4 5
Quintile
Lif
e e
xp
ecta
ncy a
t b
irth
(years
)
Canada total Halifax Montréal Ottawa-Gatineau Toronto
Winnipeg Edmonton Vancouver CMAs of >300K
(Richest) (Poorest)
CPS - June 2004 9
Results3) 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
CPS - June 2004 10
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‚‚St.Catharines-Niagara ‚ 3.12‚ 3.41‚ 3.12‚ 4.25‚ 3.44‚ 2.943.44‚ 2.94‚‚Kitchener ‚ 2.23‚ 2.34‚ 3.58‚ 4.21‚ 0.92‚ 0.590.92‚ 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‚‚Vancouver ‚ 3.53‚ 3.93‚ 5.63‚ 5.195.63‚ 5.19‚ 1.03‚ 2.57‚‚Victoria ‚ 4.99‚ 5.29‚ 6.91‚ 7.45‚ 3.36‚ 3.333.36‚ 3.33‚Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒ‹ƒƒƒƒƒŒ
Results4) Impact of the definition of quintiles
CPS - June 2004 11
ResultsA closer look at Winnipeg (1)
CPS - June 2004 12
ResultsA closer look at Winnipeg (2)
Brookside
Notre-Dame
Railroad
St.Mary’s
Fermor
Panet
Plessis
CPS - June 2004 13
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)
CPS - June 2004 14
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: [email protected]
CPS - June 2004 15
Annex 15) A closer look at Winnipeg (4)
Census tracts of Winnipeg,% below LICO vs aboriginal identity
0
10
20
30
40
50
60
70
80
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%
% Aboriginal identity
% b
elo
w L
ico
Q1: 2.97%
Q4: 7.81 %
Q3: 5.68 %
Q2: 5.19 %
Q5: 21.19 %