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

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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.

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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

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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.

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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

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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

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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)

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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

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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

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ResultsA closer look at Winnipeg (1)

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ResultsA closer look at Winnipeg (2)

Brookside

Notre-Dame

Railroad

St.Mary’s

Fermor

Panet

Plessis

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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)

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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: philippe.fines@statcan.ca

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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 %