tracking disparity: trends in ethnic and socio-economic inequalities in mortality, 1981 - 2004
DESCRIPTION
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. Overview. Some background on NZCMS Part I: Ethnic results Part II: Socio-economic results - PowerPoint PPT PresentationTRANSCRIPT
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Tracking Disparity: Trends in ethnic and socio-economic inequalities
in mortality, 1981 - 2004
Professor Tony Blakely, ASBHM 2008Martin Tobias, June Atkinson, Li-Chia Yeh, Ken Huang.
2
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 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
There will be audience participation!
Keen to have your questions/challenges during, and comments at end (eg, other behavioural data from NZ, Australian comparisons)
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www.wnmeds.ac.nz/nzcms-info.html
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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.)
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NZCMS: method in one slide
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1991 census cohort (0-74 yr olds)
DeathsAnonymous and probabilistic record linkage
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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
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Part I: Ethnic inequalities in mortality
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All-cause mortality rates by ethnicity, 1-74 yrs
Percentage decline 1981-84 to 2001-04 Mäori Pacific Asian European/Other Males 25% 14% 58% 42% Females 22% 10% 50% 35%
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Absolute and relative measures of inequality
Rate ratio = 2.37
Rate difference = 403 per 100,000
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Ethnic disparities
Māori compared to nMnPnA - standardised rates differences and ratios (SRDs and SRRs)
200
300
400
500
600
700
800
1981-84 1986-89 1991-94 1996-99 2001-04
SR
D p
er 1
00,0
00
0
0.5
1
1.5
2
2.5
3
SR
R
SRD, Males SRD, Females
SRR, Males SRR, Females
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50
55
60
65
70
75
80
85
1950 1960 1970 1980 1990 2000
Lif
e e
xp
ec
tan
cy
in y
ea
rs
Non-Mäori (SNZ) Male Non-Mäori (SNZ) Female
Mäori (SNZ) Male Mäori (SNZ) Female
Mäori (NZMCS) Male Mäori (NZMCS) Female
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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
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CVD mortality rates by ethnicity, 1-74 yrs
15
IHD mortality rates by ethnicity, 1-74 yrs
17
All-cancer mortality rates by ethnicity, 1-74 yrs
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Lung cancer mortality rates by ethnicity
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Colorectal cancer mortality rates by ethnicity
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Lung cancer, males
0
20
40
60
80
100
1980-84 1985-89 1990-95 1996-99
Prostate cancer
0
5
10
15
20
25
1980-84 1985-89 1990-95 1996-99
Colorectal cancer, males
0
5
10
15
20
25
1980-84 1985-89 1990-95 1996-99
Lung cancer, females
0
15
30
45
60
75
1980-84 1985-89 1990-95 1996-99
Breast cancer
0
10
20
30
40
50
1980-84 1985-89 1990-95 1996-99
Colorectal cancer, females
0
4
8
12
16
20
1980-84 1985-89 1990-95 1996-99
Colorectal cancer mortality rates:
Decades of Disparity I
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Breast cancer rates by ethnicity
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Suicide rates by ethnicity, 1-74 yrs
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Cause of death contributions to absolute inequality
24
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Part II:Socio-economic inequalities in mortality
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Ethnicity, socio-economic position and health
SES
Ethnicity
Mortality
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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 age-standardised mortality rates
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Low-medium income cut-point (1996 real dollars)
Medium-high income cut-point (1996 real dollars)
$15,000
$30,000
$45,000
$60,000
$75,000
1981 1986 1991 1996
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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
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Are inequalities increasing (81-84 to 96-99)?
Rate difference = 380 per 100,000
Rate difference = 379 per 100,00
Rate ratio = 1.44
Rate ratio = 1.72
Answer: Absolutely not, relatively yes
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Income disparities
Low compared to high income - slope and relative indices of inequality (SIIs and RIIs)
0
100
200
300
400
500
600
700
800
1981-84 1986-89 1991-94 1996-99 2001-04
SII
per
100
,000
0
0.5
1
1.5
2
2.5
3
3.5
RII
Income SII, males Income SII, females
Income RII, males Income RII, females
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Cardiovascular disease, 1-74 year olds
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CVD mortality, 1-74 years: relative and absolute measures of inequality
Sex Age Cohort SRR
Low:High Relative index of inequality (RII)
SRD Low:High
Slope index of inequality (SII)
Males 1-74 yrs 1981-84 1.50 1.9 (1.6 - 2.1) 101 150 (118 - 182) 1986-89 1.45 1.8 (1.5 - 2.1) 81 125 (76 - 174) 1991-94 1.54 2.1 (1.8 - 2.5) 78 129 (99 - 159) 1996-99 1.81 2.8 (2.3 - 3.4) 82 134 (114 - 154) 2001-04 1.84 2.9 (2.4 - 3.5) 66 103 (80 - 126) P (Trend) 0.04 0.03 0.04 0.11 Females 1-74 yrs 1981-84 1.43 1.8 (1.4 - 2.3) 42 68 (53 - 83) 1986-89 1.48 1.7 (1.3 - 2.1) 40 53 (51 - 54) 1991-94 1.69 2.3 (1.7 - 3.0) 41 65 (51 - 79) 1996-99 1.76 2.6 (2.0 - 3.5) 34 56 (43 - 69) 2001-04 1.66 2.8 (2.1 - 3.7) 26 46 (44 - 49) P (Trend) 0.08 0.03 0.02 0.07
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All cancer, 1-74 year olds
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Lung cancer, 1-74 year olds
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Cause of death contributions to absolute inequality
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Part III: Socio-economic mediation of ethnic
inequalities in mortality
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Audience question: how much of the gap in mortality rates is due to differences in socio-economic position?
0
250
500
750
1000
1250
1500
1750
2000
non-Māorinon-Pacific
Māori a. 10% b. 25% c. 50% d. 75% e. 90%
Dea
th r
ate
pe
r 1
00,0
00
Total death rate Gap attributable to SES Gap NOT attributable to SES
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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
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All-cause mortality by ethnicity (Māori [black], European [orange]) by income
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All-cause RR (Māori cf European), adjusting for socio-economic factors, PLM and NZDep
Age Sex Model 1981-84 1986-89 1991-94 1996-99 2001-04
25-59 years Males A: Adjusted for age and region 2.42 2.21 2.60 2.65 2.57
B: Model A plus socio-economic factors *
C: Model B plus position in labour market
D: Model C plus NZDep
% reduction excess rate ratio A to C
% reduction excess rate ratio A to D
Females A: Adjusted for age and region 2.41 2.38 2.82 2.80 2.59
B: Model A plus socio-economic factors *
C: Model B plus position in labour market
D: Model C plus NZDep
% reduction excess rate ratio A to C
% reduction excess rate ratio A to D
60-74 years Males A: Adjusted for age and region 1.61 1.61 2.05 2.11 2.14
B: Model A plus socio-economic factors *
D: Model B plus NZDep
% reduction excess rate ratio A to B
% reduction excess rate ratio A to D
Females A: Adjusted for age and region 2.15 2.24 2.37 2.55 2.54
B: Model A plus socio-economic factors *
D: Model B plus NZDep
% reduction excess rate ratio A to B
% reduction excess rate ratio A to D
43
All-cause RR (Māori cf European), adjusting for socio-economic factors, PLM and NZDep
Age Sex Model 1981-84 1986-89 1991-94 1996-99 2001-04
25-59 years Males A: Adjusted for age and region 2.42 2.21 2.60 2.65 2.57
B: Model A plus socio-economic factors * 2.04 1.89 2.03 2.09 2.00
C: Model B plus position in labour market 1.97 1.79 1.81 1.91 1.88
D: Model C plus NZDep . . 1.67 1.78 1.81
% reduction excess rate ratio A to C
% reduction excess rate ratio A to D
Females A: Adjusted for age and region 2.41 2.38 2.82 2.80 2.59
B: Model A plus socio-economic factors * 2.06 1.98 2.26 2.28 2.02
C: Model B plus position in labour market 2.04 1.98 2.19 2.23 2.00
D: Model C plus NZDep . . 2.01 2.03 1.83
% reduction excess rate ratio A to C
% reduction excess rate ratio A to D
60-74 years Males A: Adjusted for age and region 1.61 1.61 2.05 2.11 2.14
B: Model A plus socio-economic factors * 1.43 1.36 1.69 1.79 1.73
D: Model B plus NZDep . . 1.59 1.68 1.63
% reduction excess rate ratio A to B
% reduction excess rate ratio A to D
Females A: Adjusted for age and region 2.15 2.24 2.37 2.55 2.54
B: Model A plus socio-economic factors * 1.92 1.94 2.02 2.21 2.11
D: Model B plus NZDep . . 1.88 2.06 1.97
% reduction excess rate ratio A to B
% reduction excess rate ratio A to D
44
All-cause RR (Māori cf European), adjusting for socio-economic factors, PLM and NZDep
Age Sex Model 1981-84 1986-89 1991-94 1996-99 2001-04
25-59 years Males A: Adjusted for age and region 2.42 2.21 2.60 2.65 2.57
B: Model A plus socio-economic factors * 2.04 1.89 2.03 2.09 2.00
C: Model B plus position in labour market 1.97 1.79 1.81 1.91 1.88
D: Model C plus NZDep . . 1.67 1.78 1.81
% reduction excess rate ratio A to C 31% 35% 49% 45% 44%
% reduction excess rate ratio A to D - - 58% 53% 48%
Females A: Adjusted for age and region 2.41 2.38 2.82 2.80 2.59
B: Model A plus socio-economic factors * 2.06 1.98 2.26 2.28 2.02
C: Model B plus position in labour market 2.04 1.98 2.19 2.23 2.00
D: Model C plus NZDep . . 2.01 2.03 1.83
% reduction excess rate ratio A to C 26% 29% 35% 32% 37%
% reduction excess rate ratio A to D - - 44% 43% 48%
60-74 years Males A: Adjusted for age and region 1.61 1.61 2.05 2.11 2.14
B: Model A plus socio-economic factors * 1.43 1.36 1.69 1.79 1.73
D: Model B plus NZDep . . 1.59 1.68 1.63
% reduction excess rate ratio A to B 29% 41% 34% 28% 36%
% reduction excess rate ratio A to D - - 44% 39% 44%
Females A: Adjusted for age and region 2.15 2.24 2.37 2.55 2.54
B: Model A plus socio-economic factors * 1.92 1.94 2.02 2.21 2.11
D: Model B plus NZDep . . 1.88 2.06 1.97
% reduction excess rate ratio A to B 21% 24% 26% 22% 28%
% reduction excess rate ratio A to D - - 36% 32% 37%
45
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 socio-economic 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.
46
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
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48
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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
50
Social determinants of health
Hui Taumata 1984:
‘shock absorbers in the economy’
51
Unemployment rates by ethnicity (Social Report, MSD; Source: Statistics New Zealand,
Household Labour Force Survey)
52
50
55
60
65
70
75
80
85
1950 1960 1970 1980 1990 2000
Lif
e e
xp
ec
tan
cy
in y
ea
rs
Non-Mäori (SNZ) Male Non-Mäori (SNZ) Female
Mäori (SNZ) Male Mäori (SNZ) Female
Mäori (NZMCS) Male Mäori (NZMCS) Female
53
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 socio-economic factors and PLM
54
RR Māori cf European, decomposed by contribution from socio-economic factors and PLM
a) 25-59 yrs
1.0
1.5
2.0
2.5
3.0
1981
-84
1986
-89
1991
-94
1996
-99
2001
-04
1981
-84
1986
-89
1991
-94
1996
-99
2001
-04
Males Females
Rat
e ra
tio
fo
r M
aori
co
mp
ared
to
Eu
rop
ean
Component or RR attributable to socio-econmic factors
Component of RR attributable to position in labour market (PLM)
Component of RR NOT attributable to measured socio-economicfactors and PLM
b) 60-74 yrs
1.0
1.5
2.0
2.5
1981
-84
1986
-89
1991
-94
1996
-99
2001
-04
1981
-84
1986
-89
1991
-94
1996
-99
2001
-04
Males Females
Rat
e ra
tio
fo
r M
aori
co
mp
ared
to
Eu
rop
ean
Component or RR attributable to socio-econmic factors
Component of RR NOT attributable to measured socio-economic factorsand PLM
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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?
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Structural reform of 1980s and 1990s – impact on trends in socio-economic 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.
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11.5
22.5
33.5
44.5
1980
1985
1990
1995
Denmark
Finland
Norw ay
New Zealand(age andethnicitystandardised)
New Zealand(agestandardised)
Males, All Causes
1
1.5
2
2.5
3
3.5
4
4.5
1980 1985 1990 1995
Males, CVD
1
1.5
2
2.5
3
3.5
4
4.5
1980 1985 1990 1995
Males, Other Causes
1
1.5
2
2.5
3
3.5
4
4.5
1980 1985 1990 1995
Females, All Causes
1
2
3
1980 1985 1990 1995
Females, CVD
1
2
3
4
5
6
7
8
1980 1985 1990 1995
Females, Other Causes
1
2
3
1980 1985 1990 1995
RII by Education Level, by Time Period, Country and Cause of Death – 30-59 years
58
Possible explanations, I: Structural reform of 1980s and 1990s?
• Hypothesis: Inequalities in health may have Hypothesis: Inequalities in health may have increased in New Zealand as a result of structural increased in New Zealand as a result of structural reforms in 1980s and 1990s.reforms in 1980s and 1990s.
• Test: Compare trends in New Zealand with trends Test: Compare trends in New Zealand with trends in other countries without such rapid changes.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
59
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
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Trends in amenable mortality, 1981-84 to 2001-04
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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.
62
0
5
10
15
20
CABG Angioplasty
MäoriPacificOther
CABG and PTCA rates per 100,000 (1990 -1999) Females
Source: Tukuitonga & Bindman, 2002
63
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1997/98 1998/99 1999/00 2000/01 2001/02 2002/03
Sta
nd
ard
ise
d D
isc
ha
rge
Ra
tio
All surgery Coronary artery bypass graft operations
Source: Ministry of Health. Health and Independence Report: Director-General's annual report on the state of public health. Wellington: Ministry of Health, 2003.
Māori:non-Māori Standardised Discharge Ratios
64
Relative survival from cancerEthnic specific life tables
1.00
0.64
0.55
0.500.47
0.45
1.00
0.69
0.620.58 0.58 0.57
1.00
0.77
0.710.68
0.65 0.64
0.00
0.20
0.40
0.60
0.80
1.00
0 1 2 3 4 5
Time since diagnosis (years)
Re
lati
ve
su
rviv
al
(eth
nic
-sp
ec
ific
lif
e t
ab
les
)
Māori Pacific Non-Māori, non-Pacific
65
Ratio of Mäori to non-Mäori non-Pacific 5-year relative
cancer survival, before and after adjusting for stage
0
0.2
0.4
0.6
0.8
1
Breast
Cervix
Colon/
rectu
m
Kidne
y/ur
eter
/ure
thra
Melano
ma
Ovary
Stom
ach
Thyr
oid g
land
Uteru
sRa
tio
of
Ma
ori
to
no
n-M
ao
ri n
on
-Pa
cif
ic r
ela
tiv
e s
urv
iva
ls
Relative survivaladjusted for age
Relative survivaladjusted for ageand 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)
66
Possible explanations (I)
DiagnosisDetection Treatment Death
Age Co-morbidities
Access to primary care
Breast: effect of screening
67
Possible explanations (II)
DiagnosisDetection Treatment Death
Age Co-morbidities
Access through care
Co-morbidities may limit treatment choices
68
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
69
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.
70
Rate ratios of 45-74 year old mortality for nil cf. post-school education, before and after adjusting for smoking
1
1.1
1.2
1.3
1.4
1.5
Females1981-84
Males1981-84
Females1996-99
Males1996-99
RR Age &Ethnicityadjusted
Plus adjustedfor smoking
3%
16%
11%
21%
Reduction in ‘excess RR’ (ie RR-1) due to adjusting for smoking
71
Possible explanations, III: Smoking?
• Hypothesis: The contribution of tobacco to socio-Hypothesis: The contribution of tobacco to socio-economic inequalities in mortality may have increased economic inequalities in mortality may have increased over time?over time?
• Test: Compare rate ratios for mortality by education Test: Compare rate ratios for mortality by education over time. 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.)
72
Tobacco consumption by ethnicity
1981 1996
Māori 51.9% 40.5%
Pacific 31.6% 28.0%
Non-Mäori non-Pacific 30.9% 21.5%
Source: Borman, Wilson, Mailing. NZ Med J 1999: 112:460-3.
73
Audience question: how much of the ethnic gap in mortality rates is due to differences in smoking?
0
250
500
750
1000
1250
1500
1750
2000
non-Māorinon-Pacific
Māori a. 10% b. 25% c. 50% d. 75% e. 90%
Dea
th r
ate
per
100,
000
Total death rate Gap attributable to smoking Gap NOT attributable to smoking
74
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
275400
125
0
250
500
750
1000
1250
1500
1750
2000
non-Māori non-Pacific (nMnP)
Māori Gap in death ratesbetween Māori and
nMnP
Dea
th r
ate
per
100,
000 Amount NOT
attributableto smoking
Amountattributableto smoking
75
Trends in %Fat intake
32
34
36
38
40
42
44
Mäori Males Non-MäoriMales
Mäori Females Non-MäoriFemales
%FatLINZ: 1989
NNS: 1997
32
34
36
38
40
42
44
Males LINZ-89
Males NNS-97
FemalesLINZ-89
FemalesNNS-97
%Fat Mäori
Non-Mäori
76
Trends in BMI
22
24
26
28
30
Mäori Males Non-MäoriMales
MäoriFemales
Non-MäoriFemales
BMI
LINZ: 1989
NNS: 1997
22
24
26
28
30
Males LINZ-89
Males NNS-97
FemalesLINZ-89
FemalesNNS-97
BMI
Mäori
Non-Mäori
77
SES
Access tohealth care
Healthoutcom es
The Impacts of Racism on Health
Wider Determinants
Jones et al, 2001
78
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.
79
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)