Transcript Ethnic Mortality Trends in Aotearoa New Zealand: 1980-1999

Tracking Disparity:
Trends in ethnic and socio-economic inequalities
in mortality, 1981 - 2004
Professor Tony Blakely, ASBHM 2008
Martin Tobias, June Atkinson, Li-Chia Yeh, Ken Huang.
1
Overview
•
•
•
•
Some background on NZCMS
Part I: Ethnic results
Part II: Socio-economic results
Part III: Contribution of socioeconomic position to ethnic
inequalities
• Part IV: Contribution of “behaviour” to ethnic and socioeconomic inequalities trends in mortality:
–
–
–
–
Behaviour of society, institutions and governments - structural
Behaviour of health services
Behaviour of individuals – tobacco (diet, PA)
Discriminatory behaviour – racism
There will be audience participation!
Keen to have your questions/challenges during, and comments at end (eg,
other behavioural data from NZ, Australian comparisons)
2
www.wnmeds.
ac.nz/nzcmsinfo.html
3
Main sources for this presentation
1. Blakely T, Tobias M, Atkinson J, Yeh L-C, Huang K. Tracking Disparity:
Trends in ethnic and socioeconomic inequalities in mortality, 1981-2004.
Wellington: Ministry of Health, 2007.
2. Blakely T, Tobias M, Robson B, Ajwani S, Bonne M, Woodward A.
Widening ethnic mortality disparities in New Zealand 1981-99. Soc Sci
Med 2005;61(10):2233-2251.
3. Blakely T, Fawcett J, Hunt D, Wilson N. What is the contribution of
smoking and socioeconomic position to ethnic inequalities in mortality in
New Zealand? The Lancet 2006;368(9529):44-52.
4. Blakely T, Tobias M, Atkinson J. Inequalities in mortality during and
after restructuring of the New Zealand economy: repeated cohort studies,
2008:BMJ.39455.596181.25. (Appearing in hardcopy 16 Feb.)
4
NZCMS: method in one slide
1991 census cohort
(0-74 yr olds)
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Anonymous and probabilistic
record linkage
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Deaths
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5
Method to calculate mortality rates
• Calculate rates directly off linked NZCMS data, using
census ethnicity and income and standard cohort methods
• Ages 1-74, age-standardised to WHO world population
• Each of five ‘periods’ (ie, 1981-84, … 2001-04) are three
years in duration – not full five-year intercensal
• What has NZCMS added to New Zealand information?
– Now have correct trends in mortality by ethnicity
– Now have one of world’s largest cohort studies of smoking –
active and passive
– Rates by many socio-economic factors, with ability for
multivariable analyses
6
Part I: Ethnic
inequalities in mortality
7
All-cause mortality rates by ethnicity, 1-74 yrs
M a le s
P e rc e nta g e d ec lin e 1 9 8 1 -8 4 to 2 00 1 -04
M ä o ri
P a c ific
A s ia n
E u ro p e a n/O th e r
25%
14%
58%
42%
F e m a le s
22%
10%
50%
35%
8
Absolute and relative measures of inequality
Rate ratio = 2.37
Rate difference =
403 per 100,000
9
Māori compared to nMnPnA - standardised rates
differences and ratios (SRDs and SRRs)
3
Ethnic disparities
700
2.5
600
2
500
1.5
400
1
300
0.5
200
0
1981-84
1986-89
1991-94
1996-99
SRR
SRD per 100,000
800
2001-04
SRD, Males
SRD, Females
SRR, Males
SRR, Females
10
Life expectancy in years
85
80
75
70
65
60
55
50
1950
1960
1970
1980
1990
2000
Non-Mäori (SNZ) Male
Non-Mäori (SNZ) Female
Mäori (SNZ) Male
Mäori (SNZ) Female
Mäori (NZMCS) Male
Mäori (NZMCS) Female
11
Cardiovascular disease, 1-74 year
olds combined
• By what percentge has CVD mortality rates declined
from 1981-84 to 2001-04: a) for European; b) for
Māori?
– 64% and 65% for male and female Europeans
– 40% and 45% for male and female Māori
12
CVD mortality rates by ethnicity, 1-74 yrs
13
IHD mortality rates by ethnicity, 1-74 yrs
15
All-cancer mortality rates by ethnicity, 1-74 yrs
17
Lung cancer mortality rates by ethnicity
18
Colorectal cancer mortality rates by ethnicity
19
Colorectal cancer
mortality rates:
Decades of Disparity I
Prostate cancer
Colorectal cancer, males
25
25
20
20
15
15
10
10
5
5
0
0
1996-99
1980-84
1985-89
1990-95
1996-99
1980-84
Breast cancer
1996-99
20
40
16
30
12
20
8
10
4
0
0
1985-89
1990-95
1990-95
1996-99
Colorectal cancer, females
50
1980-84
1985-89
1996-99
1980-84
1985-89
1990-95
1996-99
20
Breast cancer rates by ethnicity
21
Suicide rates by ethnicity, 1-74 yrs
22
Cause of death contributions to absolute inequality
23
24
Part II:Socio-economic
inequalities in mortality
25
Ethnicity, socio-economic position
and health
Ethnicity
SES
Mortality
26
Method
• Use NZCMS data: 81-84, 86-89, 91-94, 96-99, 01-04
• Treated equivalised (Jensen) household income as
main socio-economic factor:
– same fixed $ groups
(ie, real 1996 dollars)
• Calculate agestandardised
mortality rates
27
$75,000
$60,000
$45,000
Medium-high income cut-point
(1996 real dollars)
$30,000
Low-medium income cut-point
(1996 real dollars)
$15,000
1981
1986
1991
1996
28
All-cause mortality rates by income
•
•
•
Mostly parallel tracking in absolute terms
30% and 41% decreases for low and high income males, respectively
27% and 37% decreases for low and high income females, respectively
29
Are inequalities increasing (81-84 to 96-99)?
Rate difference =
380 per 100,000
Rate ratio = 1.44
Rate difference =
379 per 100,00
Rate ratio = 1.72
Answer: Absolutely not, relatively yes
30
800
Low compared to high income - slope and relative indices
of inequality (SIIs and RIIs)
3.5
Income disparities
700
3
2.5
500
2
RII
SII per 100,000
600
400
1.5
300
1
200
0.5
100
0
0
1981-84
1986-89
1991-94
1996-99
2001-04
Income SII, males
Income SII, females
Income RII, males
Income RII, females
31
32
Cardiovascular disease, 1-74 year olds
33
CVD mortality, 1-74 years: relative and
absolute measures of inequality
Sex
Age
M a le s
1 -7 4 yrs
F e m a le s 1 -7 4 yrs
SRR
L o w :H ig h
1 9 8 1 -8 4
1 .5 0
1 9 8 6 -8 9
1 .4 5
1 9 9 1 -9 4
1 .5 4
1 9 9 6 -9 9
1 .8 1
2 0 0 1 -0 4
1 .8 4
P (T re n d )
0 .0 4
C o h o rt
1 9 8 1 -8 4
1 9 8 6 -8 9
1 9 9 1 -9 4
1 9 9 6 -9 9
2 0 0 1 -0 4
P (T re n d )
1 .4 3
1 .4 8
1 .6 9
1 .7 6
1 .6 6
0 .0 8
R e la tive in d e x o f
in e q u a lity (R II)
1 .9 (1 .6 - 2 .1 )
1 .8 (1 .5 - 2 .1 )
2 .1 (1 .8 - 2 .5 )
2 .8 (2 .3 - 3 .4 )
2 .9 (2 .4 - 3 .5 )
0 .0 3
1 .8
1 .7
2 .3
2 .6
2 .8
(1 .4 - 2 .3 )
(1 .3 - 2 .1 )
(1 .7 - 3 .0 )
(2 .0 - 3 .5 )
(2 .1 - 3 .7 )
0 .0 3
SRD
L o w :H ig h
101
81
78
82
66
0 .0 4
42
40
41
34
26
0 .0 2
S lo p e in de x o f
in e q u a lity (S II)
1 5 0 (1 1 8 - 1 8 2 )
1 2 5 (7 6 - 1 7 4 )
1 2 9 (9 9 - 1 5 9 )
1 3 4 (1 1 4 - 1 5 4 )
1 0 3 (8 0 - 1 2 6 )
0 .1 1
68
53
65
56
46
(5 3 - 8 3 )
(5 1 - 5 4 )
(5 1 - 7 9 )
(4 3 - 6 9 )
(4 4 - 4 9 )
0 .0 7
34
All cancer, 1-74 year olds
35
Lung cancer, 1-74 year olds
36
Cause of death contributions to absolute inequality
37
Part III: Socio-economic
mediation of ethnic
inequalities in mortality
38
Audience question: how much of the gap in mortality
rates is due to differences in socio-economic position?
2000
Death rate per 100,000
1750
1500
1250
1000
750
500
250
0
non-Māori
non-Pacific
Māori
Total death rate
a. 10%
b. 25%
Gap attributable to SES
c. 50%
d. 75%
e. 90%
Gap NOT attributable to SES
39
Answering the question “What proportion (on
average) of the Māori:European mortality inequality
was mediated by socioeconomic position?”
1. Examine mortality rate trends cross-classified by
ethnicity and income
2. Use regression analyses to adjust ethnic gaps in
mortality for multiple socio-economic factors,
labour force status and NZDep
40
All-cause mortality by ethnicity (Māori
[black], European [orange]) by income
41
All-cause RR (Māori cf European), adjusting for
socio-economic factors, PLM and NZDep
Age
Sex
M odel
2 5 -5 9 ye a rs
M a le s
A : A d jus te d fo r ag e an d re g ion
1 9 8 1 -84 1 9 8 6 -89 1 9 9 1 -94 1 9 9 6 -99 2 0 0 1 -04
2 .4 2
2 .2 1
2 .6 0
2 .6 5
2 .5 7
2 .4 1
2 .3 8
2 .8 2
2 .8 0
2 .5 9
1 .6 1
1 .6 1
2 .0 5
2 .1 1
2 .1 4
2 .1 5
2 .2 4
2 .3 7
2 .5 5
2 .5 4
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
C : M o d e l B p lus p os itio n in la bo u r m a rk et
D : M o d e l C p lu s N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to C
% re d u c tio n e xc e ss ra te ra tio A to D
F e m ale s A : A d jus te d fo r ag e an d re g ion
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
C : M o d e l B p lus p os itio n in la bo u r m a rk et
D : M o d e l C p lu s N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to C
% re d u c tio n e xc e ss ra te ra tio A to D
6 0 -7 4 ye a rs
M a le s
A : A d jus te d fo r ag e an d re g ion
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
D : M o d e l B p lus N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to B
% re d u c tio n e xc e ss ra te ra tio A to D
F e m ale s A : A d jus te d fo r ag e an d re g ion
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
D : M o d e l B p lus N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to B
% re d u c tio n e xc e ss ra te ra tio A to D
42
All-cause RR (Māori cf European), adjusting for
socio-economic factors, PLM and NZDep
Age
Sex
M odel
2 5 -5 9 ye a rs
M a le s
A : A d jus te d fo r ag e an d re g ion
2 .4 2
2 .2 1
2 .6 0
2 .6 5
2 .5 7
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
2 .0 4
1 .8 9
2 .0 3
2 .0 9
2 .0 0
C : M o d e l B p lus p os itio n in la bo u r m a rk et
1 .9 7
1 .7 9
1 .8 1
1 .9 1
1 .8 8
.
.
1 .6 7
1 .7 8
1 .8 1
2 .4 1
2 .3 8
2 .8 2
2 .8 0
2 .5 9
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
2 .0 6
1 .9 8
2 .2 6
2 .2 8
2 .0 2
C : M o d e l B p lus p os itio n in la bo u r m a rk et
2 .0 4
1 .9 8
2 .1 9
2 .2 3
2 .0 0
.
.
2 .0 1
2 .0 3
1 .8 3
A : A d jus te d fo r ag e an d re g ion
1 .6 1
1 .6 1
2 .0 5
2 .1 1
2 .1 4
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
1 .4 3
1 .3 6
1 .6 9
1 .7 9
1 .7 3
.
.
1 .5 9
1 .6 8
1 .6 3
2 .1 5
2 .2 4
2 .3 7
2 .5 5
2 .5 4
1 .9 2
1 .9 4
2 .0 2
2 .2 1
2 .1 1
.
.
1 .8 8
2 .0 6
1 .9 7
D : M o d e l C p lu s N Z D e p
1 9 8 1 -84 1 9 8 6 -89 1 9 9 1 -94 1 9 9 6 -99 2 0 0 1 -04
% re d u c tio n e xc e ss ra te ra tio A to C
% re d u c tio n e xc e ss ra te ra tio A to D
F e m ale s A : A d jus te d fo r ag e an d re g ion
D : M o d e l C p lu s N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to C
% re d u c tio n e xc e ss ra te ra tio A to D
6 0 -7 4 ye a rs
M a le s
D : M o d e l B p lus N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to B
% re d u c tio n e xc e ss ra te ra tio A to D
F e m ale s A : A d jus te d fo r ag e an d re g ion
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
D : M o d e l B p lus N Z D e p
% re d u c tio n e xc e ss ra te ra tio A to B
% re d u c tio n e xc e ss ra te ra tio A to D
43
All-cause RR (Māori cf European), adjusting for
socio-economic factors, PLM and NZDep
Age
Sex
M odel
2 5 -5 9 ye a rs
M a le s
A : A d jus te d fo r ag e an d re g ion
2 .4 2
2 .2 1
2 .6 0
2 .6 5
2 .5 7
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
2 .0 4
1 .8 9
2 .0 3
2 .0 9
2 .0 0
C : M o d e l B p lus p os itio n in la bo u r m a rk et
1 .9 7
1 .7 9
1 .8 1
1 .9 1
1 .8 8
.
.
1 .6 7
1 .7 8
1 .8 1
% re d u c tio n e xc e ss ra te ra tio A to C
31%
35%
49%
45%
44%
% re d u c tio n e xc e ss ra te ra tio A to D
-
-
58%
53%
48%
2 .4 1
2 .3 8
2 .8 2
2 .8 0
2 .5 9
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
2 .0 6
1 .9 8
2 .2 6
2 .2 8
2 .0 2
C : M o d e l B p lus p os itio n in la bo u r m a rk et
2 .0 4
1 .9 8
2 .1 9
2 .2 3
2 .0 0
.
.
2 .0 1
2 .0 3
1 .8 3
% re d u c tio n e xc e ss ra te ra tio A to C
26%
29%
35%
32%
37%
% re d u c tio n e xc e ss ra te ra tio A to D
-
-
44%
43%
48%
A : A d jus te d fo r ag e an d re g ion
1 .6 1
1 .6 1
2 .0 5
2 .1 1
2 .1 4
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
1 .4 3
1 .3 6
1 .6 9
1 .7 9
1 .7 3
.
.
1 .5 9
1 .6 8
1 .6 3
% re d u c tio n e xc e ss ra te ra tio A to B
29%
41%
34%
28%
36%
% re d u c tio n e xc e ss ra te ra tio A to D
-
-
44%
39%
44%
2 .1 5
2 .2 4
2 .3 7
2 .5 5
2 .5 4
1 .9 2
1 .9 4
2 .0 2
2 .2 1
2 .1 1
.
.
1 .8 8
2 .0 6
1 .9 7
% re d u c tio n e xc e ss ra te ra tio A to B
21%
24%
26%
22%
28%
% re d u c tio n e xc e ss ra te ra tio A to D
-
-
36%
32%
3 744
%
D : M o d e l C p lu s N Z D e p
F e m ale s A : A d jus te d fo r ag e an d re g ion
D : M o d e l C p lu s N Z D e p
6 0 -7 4 ye a rs
M a le s
D : M o d e l B p lus N Z D e p
F e m ale s A : A d jus te d fo r ag e an d re g ion
B : M o d e l A p lus so c io -ec o n om ic fac to rs *
D : M o d e l B p lus N Z D e p
1 9 8 1 -84 1 9 8 6 -89 1 9 9 1 -94 1 9 9 6 -99 2 0 0 1 -04
What proportion (on average) of the
Māori:European mortality inequality was
mediated by socioeconomic position?
• At least half for working age adults, and about one third for
older adults.
• Small area deprivation contributed an extra 10%, over and
above personal socio-economic factors.
• For 25-59 year olds position in the labour market contributed
10% to 15%.
• We have probably underestimated the contribution of socioeconomic position in total (i.e. due to measurement error), BUT
without doubt not all of ethnic inequalities in mortality are
explained by socio-economic position.
45
Part IV: Contribution of “behaviour”
to ethnic and socio-economic
inequalities trends in mortality:
- Behaviour of society, institutions and governments
(structural)
- Behaviour of health services
- Behaviour of individuals – tobacco (diet, PA)
- Discriminatory behaviour – racism
46
47
48
1984 and all that ….
• 1970s and early 1980s:
– subsidies, regulated economy, low unemployment, etc..
• 1984 to 1993:
– deregulation of the financial sector
– reorganising the state sector
– ending of state support for industry
Resulting in:
– flatter tax rates, targeted welfare, regressive consumption tax,
market rentals, privatisation, user charges, widening income
inequalities, etc…
– health reform
49
Social determinants of health
Hui Taumata 1984:
‘shock absorbers in the economy’
50
Unemployment rates by ethnicity
(Social Report, MSD; Source: Statistics New Zealand,
Household Labour Force Survey)
51
Life expectancy in years
85
80
75
70
65
60
55
50
1950
1960
1970
1980
1990
2000
Non-Mäori (SNZ) Male
Non-Mäori (SNZ) Female
Mäori (SNZ) Male
Mäori (SNZ) Female
Mäori (NZMCS) Male
Mäori (NZMCS) Female
52
Empirically answering the question “How
much of the increase in inequality in mortality
between Māori and non-Māori was attributable
to increasing socioeconomic inequality?”
•
•
Complex, but highly policy (& politically) relevant
Lets look at the RRs over time, and see how much of the
increase was due to increasing contributions from socioeconomic factors and PLM
53
Males
Females
1.5
Males
2001-04
1996-99
1991-94
1986-89
1981-84
2001-04
1.0
1996-99
2001-04
1996-99
1991-94
1986-89
1981-84
2001-04
1996-99
1991-94
1986-89
1.0
2.0
1991-94
1.5
2.5
1986-89
2.0
b) 60-74 yrs
1981-84
2.5
1981-84
Rate ratio for Maori compared to European
a) 25-59 yrs
3.0
Rate ratio for Maori compared to European
RR Māori cf European, decomposed by contribution
from socio-economic factors and PLM
Females
Component or RR attributable to socio-econmic factors
Component of RR attributable to position in labour market (PLM)
Component or RR attributable to socio-econmic factors
Component of RR NOT attributable to measured socio-economic
factors and PLM
Component of RR NOT attributable to measured socio-economic f
and PLM
54
How much of the increase in inequality in mortality
between Māori and non-Māori was attributable to
increasing socioeconomic inequality?
• Much of it for 25-59 year old males
• About half of it for 25-59 year old females
• Some of it 60-74 year olds. Other explanations for 60-74
year olds might include:
– Cohort effects?
– Misclassification of socio-economic position?
– Socio-economic position earlier in life course more important?
55
Structural reform of 1980s and
1990s – impact on trends in socioeconomic inequalities in mortality?
• Hypothesis: Inequalities in health may have
increased in New Zealand as a result of structural
reforms in 1980s and 1990s.
• Test: Compare trends in New Zealand with trends
in other countries without such rapid changes.
56
RII by Education Level, by Time Period, Country and Cause of Death – 30-59 years
F e m a le s , C V D
F e m a le s , A ll C a u s e s
8
3
F e m a le s , O t h e r C a u s e s
3
7
6
5
2
2
4
Denm ark
3
2
1
1
1980
1985
1990
1995
1980
F in la n d
1
1985
1990
1995
1980
1985
1990
1995
Norw ay
M a le s , A ll C a u s e s
M a le s , C V D
M a le s , O t h e r C a u s e s
4 .5
4 .5
4 .5
4
4
4
3 .5
3 .5
3 .5
4 .5
New
4
Z e a la n d
(age and
3 .5
e t h n ic it y
3
s t a n d a r d is e d )
2 .5
3
3
3
2 .5
2 .5
2 .5
New
2
1 .5
1
s t a n d a r d is e d )
19 19 19 19
2
2
2
1 .5
1 .5
1 .5
1
1
1
1980
1985
1990
1995
1980
1985
1990
1995
1980
80 85 90 95
57
1985
Z e a la n d
(age
1990
1995
Possible explanations, I: Structural
reform of 1980s and 1990s?
• Hypothesis: Inequalities in health may have
increased in New Zealand as a result of structural
reforms in 1980s and 1990s.
• Test: Compare trends in New Zealand with trends
in other countries without such rapid changes.
• Answer: We do not find more rapidly increasing
inequalities in NZ compared to Nordic countries
• Limitations: Time lags; other factors; detecting
‘period effect’ given lifecourse determinants of
health and cohort effects; etc
58
Possible explanations, II: The role of
health services?
• Hypothesis: Differential access, utilisation and quality
of health services explains trends in health inequalities?
• Test: Determine parallel trends in access, utilisation and
quality of health services. Problem - no data.
• Test: Determine trends in causes of death amenable to
treatment. Problem - amenable diseases change over
time, and nothing is absolute
• Speculate: We can draw on theory and other
information
59
Trends in amenable mortality, 1981-84 to
2001-04
60
Inverse care law, and inverse equity law
• It is well known that receipt of health care is often not best
matched with need
• Social position is likely to predict access to, and quality and
receipt of, health services independent of ‘health need’
• Therefore, health services are likely to contribute to
inequalities in health for diseases amenable to treatment:
– CVD in the last 30 years
– Cancer - less dramatically, but increasingly so
• The same argument can be extended to primary prevention,
health promotion, screening, etc...
• But health services are also a tool to address inequalities, not
just an inevitable cause of health inequalities
Hart JT. The inverse care law. Lancet 1971;1:405-12.
Victora C, Vaughan J, Barros F, Silva A, Tomasi E. Explaining trends in inequities: evidence from Brazilian
child health studies. Lancet 2000;356:1093-1098.
61
CABG and PTCA rates per 100,000 (1990 -1999)
Females
20
15
Mäori
Pacific
Other
10
5
0
CABG
Angioplasty
Source: Tukuitonga & Bindman, 2002
62
Māori:non-Māori Standardised Discharge Ratios
Standardised Discharge Ratio
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1997/98
All surgery
1998/99
1999/00
2000/01
2001/02
2002/03
Coronary artery bypass graft operations
Source: Ministry of Health. Health and Independence Report: Director-General's annual 63
report on the state of public health. Wellington: Ministry of Health, 2003.
Relative survival from cancer
Ethnic specific life tables
1.00
1.00
Relative survival (ethnic-specific life tables)
1.00
0.77
0.80
0.71
0.68
0.69
0.64
0.60
0.65
0.64
0.58
0.57
0.62
0.58
0.55
0.50
0.40
0.47
0.45
0.20
0.00
0
1
2
3
4
5
Time since diagnosis (years)
Māori
Pacific
Non-Māori, non-Pacific
64
1
Relative survival
adjusted for age
0.8
0.6
Relative survival
adjusted for age
and stage
0.4
0.2
s
Ut
er
u
an
d
gl
Th
yr
oi
d
m
ac
h
St
o
O
va
ry
M
el
an
om
a
Ce
rv
i
x
Co
lo
n/
re
Ki
ct
dn
um
ey
/u
re
te
r/u
re
th
ra
0
Br
ea
st
Ratio of Maori to non-Maori non-Pacific relative survivals
Ratio of Mäori to non-Mäori non-Pacific 5-year relative
cancer survival, before and after adjusting for stage
Source: Jeffreys M, Stevanovic V, Tobias M, Lewis C, Ellison-Loschmann L, Pearce N, Blakely T. Ethnic inequalities
in cancer survival in New Zealand: linkage study. American Journal of Public Health 2005;95(5):834-7.
(Restricted to cancers with more than 100 Mäori cases and greater than 65% with stated stage)
65
Possible explanations (I)
Age Co-morbidities
Detection
Diagnosis Treatment
Death
Access to primary care
Breast: effect of screening
66
Possible explanations (II)
Age Co-morbidities
Detection
Diagnosis Treatment
Death
Access through care
Co-morbidities may limit treatment choices
67
Possible explanations, II: The role of
health services?
• Hypothesis: Differential access, utilisation and quality
of health services explains trends in health inequalities?
• Answer/concluding remarks:
– traditionally, health services not thought to major contributor
to health inequalities
– improvements in treatments in recent decades and the inverse
care law surely mean that health services are making an
increasing contribution
– but, health services policy is also a tool to address inequalities
- not just an inevitable cause of health inequalities
68
Possible explanations, III: Smoking?
• Hypothesis: The contribution of tobacco smoking to
socio-economic inequalities in mortality may have
increased over time?
• Test: Compare rate ratios for mortality by education
over time, before an after adjusting for smoking.
Blakely T, Wilson N. The contribution of smoking to inequalities in mortality by education varies over time
and by sex: two national cohort studies, 1981-84 and 1996-99. Int. J. Epidemiol. 2005;34(5):1054-1062.
69
Rate ratios of 45-74 year old mortality for nil cf. postschool education, before and after adjusting for smoking
Reduction in ‘excess RR’ (ie RR-1) due to adjusting for smoking
1.5
3%
RR
1.4
11%
16%
21%
Age &
Ethnicity
adjusted
1.3
Plus adjusted
for smoking
1.2
1.1
1
Females
1981-84
Males
1981-84
Females
1996-99
Males
1996-99
70
Possible explanations, III: Smoking?
• Hypothesis: The contribution of tobacco to socioeconomic inequalities in mortality may have increased
over time?
• Test: Compare rate ratios for mortality by education
over time.
• Answer: Yes, contribution of smoking does increase
over time
• Limitations: Accuracy of smoking data; contribution of
passive smoking. (It seems highly likely that we
underestimate the contribution of smoking, and possibly its
increasing contribution over time.)
71
Tobacco consumption by ethnicity
1981
1996
M ā o ri
5 1 .9 %
4 0 .5 %
P a c ific
3 1 .6 %
2 8 .0 %
N o n -M ä o ri n o n -P a c ific
3 0 .9 %
2 1 .5 %
S ource: B o rm an, W ilson, M ailing. N Z M ed J 1999: 112:460-3.
72
Audience question: how much of the ethnic gap in
mortality rates is due to differences in smoking?
2000
Death rate per 100,000
1750
1500
1250
1000
750
500
250
0
non-Māori
non-Pacific
Māori
Total death rate
a. 10%
b. 25%
Gap attributable to smoking
c. 50%
d. 75%
e. 90%
Gap NOT attributable to smoking
73
Contribution of smoking to mortality within ethnic
groups, and to the gap in mortality rates between ethnic
groups: highly summarised, 45-74 yrs, 1996-99
2000
Death rate per 100,000
1750
1500
Amount NOT
attributable
to smoking
1250
1000
125
750
Amount
attributable
to smoking
500
250
275
400
0
non-Māori nonPacific (nMnP)
Māori
Gap in death rates
between Māori and
nMnP
74
Trends in %Fat intake
44
LINZ: 1989
%Fat
NNS: 1997
44
%Fat
42
42
40
40
38
38
36
36
34
34
32
32
Mäori Males
Non-Mäori
Males
Mäori Females
Non-Mäori
Females
Mäori
Non-Mäori
Males
LINZ-89
Males
NNS-97
Females
LINZ-89
Females
NNS-97
75
Trends in BMI
LINZ: 1989
30
NNS: 1997
BMI
Non-Mäori
BMI
28
28
26
26
24
24
22
22
Mäori Males Non-Mäori
Males
Mäori
Females
Non-Mäori
Females
Mäori
30
Males
LINZ-89
Males
NNS-97
Females
LINZ-89
Females
NNS-97
76
Wider Determinants
T h e Im pa c ts o f R a c is m o n H e a lth
In s
t i t u t io n a l i z e d
SES
A c c e s s to
h e a lth c a re
Di
He
a
h a lth
v io
rs
ss
be
re
ia l
nt t
re
n
ffe tm e
a
tre
St
H e a lth
o u tc o m e s
Jones et al, 2001
77
Racism – one NZ research example,
Harris et al, Lancet 2006
• New Zealand Health Survey
• Self-rated health, reduction due to adjusting for racial
discrimination in last 12 months, ascertained with five
questions:
– verbal attacks, physical attacks, and unfair treatment by a health
professional, at work, or when buying or renting housing.
78
Conclusions
• Social inequalities in mortality in New Zealand have widened:
– In relative terms for socio-economic inequalities
– In both relative and absolute terms for ethnic inequalities
– But, inequalities may have peaked in last decade – good news!
• Determining drivers of trends over time challenging
• ‘Behaviour’ at many levels matters:
– Structural reforms probably important driver of widening inequalities
– Health services matter – at any one point in time, more so for ethnic
inequalities, and probably increasingly over time
– Tobacco matters – but not as big a driver of ethnic inequalities as most
people thought due to so many other factors behind ethnic inequalities
– Diet/obesity probably matters, role of PA uncertain
– Racism probably matters – at many levels
– Role of neighbourhoods unclear and complex
• Changing disease profile over time important (falling CVD, increasing
cancer; obesity epidemic)
79