Download Health Inequalities Research Programme

CancerTrends
A study funded by the Health Research Council and the Ministry of Health
Trends in cancer survival by
ethnic and socioeconomic group,
New Zealand, 1991-2004
Soeberg M, Blakely T, Sarfati D, Tobias M, Costilla R, Carter K, Atkinson J
A study published by the University of Otago and Ministry of Health, 2012
Key concepts, data, methods and results
1
Index
Structure of this presentation
• Current knowledge and gaps in knowledge
• Measuring cancer survival
• Data and methods
• Results and interpretation
2
Current knowledge, and gaps in knowledge
3
Current New Zealand evidence
Cancer survival is improving over time
But little is know about the magnitude of these changes over time,
including for each ethnic and socioeconomic group.
Five-year relative survival for all cancers
combined, by ethnic group and calendar period
(Ministry of Health, 2010)
Five-year relative survival for all cancers
combined, by deprivation group and calendar
period (Ministry of Health, 2010)
1
1
0.8
0.8
Five-year 0.6
relative
survival 0.4
Five-year 0.6
relative
survival 0.4
0.2
0.2
0
0
1998-1999 2000-2001 2002-2003 2004-2005 2006-2007
Māori
non-Māori
1998-1999 2000-2001 2002-2003 2004-2005 2006-2007
Least deprived
Most deprived
4
Current New Zealand evidence
Ethnic and socioeconomic inequalities in cancer survival exist
But little is know about whether these inequalities
are narrowing or widening over time.
Five-year RSR by ethnic group
(Jeffreys et al., 2005)
Five-year relative survival by deprivation
group (Jeffreys et al., 2009)
1
0.9
0.8
0.7
Five-year 0.6
relative 0.5
survival 0.4
0.3
0.2
0.1
0
1
0.9
0.8
0.7
Five-year 0.6
relative 0.5
survival 0.4
0.3
0.2
0.1
0
Breast cancer
Colorectal cancer
non-Māori
Māori
Lung cancer
Breast cancer
Least depried
Colorectal cancer
Lung cancer
Most deprived
5
Study objectives
• To present cancer survival trends for 21 adult cancer sites
in New Zealand from 1991-2004 with follow-up to 2006 for:
– Ethnic groups (Māori and non-Māori separately)
– Income groups (low income and high income patients separately)
• And to assess gaps in survival between:
– Māori and non-Māori averaged over time, and for any change in
time
– Income groups averaged over time, and for any change in time.
6
Study objectives
Changes over time in cancer survival by ethnic and socioeconomic group
This study measured changes over time in cancer
survival for each ethnic and socioeconomic group.
Example using female breast cancer
Example using female breast cancer
1
1
0.9
0.9
Net survival
0.8
five-years
following a
cancer
0.7
diagnosis
Net cancer
0.8
survival fiveyears after a
cancer
0.7
diagnosis
0.6
0.6
0.5
0.5
1991
1996
Most advantaged group
2001
1991
1996
2001
Least advantaged group
7
Study objectives
Cancer survival inequalities, averaged over time
This study measures the gap between ethnic and
socioeconomic groups, averaged over time.
This study also measured ethnic and socioeconomic
cancer survival inequalities, averaged over time.
Example using female breast cancer
1
0.9
Net survival 0.8
five-years
following a
cancer
0.7
diagnosis
0.6
0.5
1991
1996
2001
Most advantaged group
Least advantaged group
8
Study objectives
Changes over time in cancer survival inequalities
This study also measured changes over time in ethnic
and socioeconomic cancer survival inequalities.
Example using female breast cancer
1
0.9
Net survival 0.8
five-years
following a
cancer
0.7
diagnosis
0.6
0.5
1991
1996
2001
Most advantaged group
Least advantaged group
9
Measuring trends in cancer survival
10
Measuring cancer survival
Time-to-event studies
In this study, we were interested in the time from cancer
diagnosis to the event (in this case death).
Time
Cancer diagnosis
Death
11
Measuring cancer survival
Time-to-event studies, where death from a specific cancer is of interest
Some studies in NZ have looked at the time from a cancer diagnosis to
death from the diagnosed cancer (cause-specific survival).
Time
Breast cancer
diagnosis
Death from breast
cancer where deaths
from all other causes are
censored
but the quality of cause of death data in New Zealand is poor.
12
Measuring cancer survival
Time-to-event studies, where deaths from any cause are of interest
An alterative method is relative survival where deaths
from any cause are the event of interest, but where all
other causes of death are accounted for.
Time
Breast cancer
diagnosis
Death from any
cause taking into
account all other
causes of death
13
Measuring cancer survival
Relative survival
The relative survival ratio is commonly used in populationbased cancer survival studies.
Relative survival ratio = observed survival rate
/ expected survival rate
1
0.9
0.8
0.7
0.6
Survival
0.5
scale
0.4
0.3
0.2
0.1
0
Observed
survival
Expected
survival
Relative
survival
RSR of 0.80 = 0.75 (observed survival) / 0.92 (expected survival)
14
Measuring cancer survival
Key disadvantage of relative survival
Non-comparability bias is introduced in relative survival
analyses where the mortality rates in the cancer and noncancer populations are not comparable.
Mortality rates in the
Māori cancer
population
Mortality rates in the
total non-cancer
population
15
Measuring cancer survival
Key disadvantage of relative survival
Using simulated data, it was possible to consider the impact of
non-comparability bias for the research questions in this study.
Five-year RSR for
breast cancer
Using total
population life
tables
Using social
group-specific life
tables
Difference
Most advantaged
group
0.76
0.75
-1%
Least advantaged
group
0.66
0.70
+6%
16
Measuring cancer survival
Key disadvantage of relative survival
• Non-comparability bias leads to:
•
Modest to moderate under-estimation of relative survival for Māori and
the most deprived groups
•
Slight over-estimation of relative survival for non-Māori and the least
deprived groups
•
Over-estimation of ethnic and socioeconomic inequalities in cancer
survival, at each calendar period
•
Little impact on trends in ethnic and socioeconomic cancer survival
inequalities
17
Measuring cancer survival
Other disadvantages of relative survival
• Sparseness of data
• Relative survival is bound by the values of 0 and 1
• Does not allow for simulatenous consideration of multiple factors
associated with cancer survival, e.g. age, stage at diagnosis,
follow-up time since cancer diagnosis
18
Measuring cancer survival
Survival and mortality scales
Relative survival can also be presented on an excess
mortality rate scale (mirror image of relative survival).
Relative survival scale
Example using female breast cancer
Equivalent annual excess
mortality rate scale
Example using female breast cancer
1
0.1
0.9
0.08
Net survival
five-years 0.8
following a
cancer
0.7
diagnosis
Equivalent
annual 0.06
excess
mortality 0.04
rate
0.6
0.02
0.5
1991
1996
Most advantaged group
Least advantaged group
2001
0
1991
1996
2001
Most advantaged group
Least advantaged group
19
Measuring cancer survival
Modelling excess cancer mortality rates
• Regression methods have been developed to model cancer
excess mortality
• Scale is bound between 0 and positive infinity
• Allows for the various factors associated with trends and
inequalities in cancer survival to be accounted for, e.g.
•
•
•
•
•
•
•
age
sex
ethnicity
socioeconomic position
calendar period
follow-up time since cancer diagnosis
interaction terms.
20
Measuring differences in cancer survival
Reasons to measure differences in cancer survival
• Cancer survival varies by calendar period
• Cancer survival varies by ethnic and socioeconomic group
• Cancer survival varies by combinations of calendar period and
ethnic and socioeconomic group
•
(allowing for investigation of trends in ethnic and socioeconomic inequalities
in cancer survival)
21
Measuring differences in cancer survival
Ways to measure differences in cancer survival
• Absolute and relative differences
• On the relative survival ratio (RSR) scale
• On the excess mortality rate (EMR) scale
22
Measuring cancer survival
A framework for absolute and relative differences in cancer survival
Cancer survival inequalities can be assessed using absolute or
relative measures calculated on the RSR or EMR scales.
Measure
Scale
Absolute
Relative
Relative survival
Relative survival ratio
difference (RSRD)
Ratio of relative survival
ratios (RSRR)
Excess mortality rate
Excess mortality rate
differences (EMRD)
Excess mortality rate ratio
(EMRR)
23
Measuring differences in cancer survival
Different conclusions from the same data
In this study, we have mostly measured
the RSRDs and the EMRRs.
Scale
Five-year relative
survival scale
Annual excess
mortality rate
scale
Cancer site
Absolute
measure
Relative
measure
RSRD
RSRR
Breast
-0.05
0.94
Colorectal
-0.10
0.80
Lung
-0.05
0.50
EMRD
EMRR
Breast
0.01
1.29
Colorectal
0.04
1.32
Lung
0.14
1.30
24
Data and methods
25
Data and methods
Observed and expected survival data and analyses
• Cancer population data (linked Census, cancer and mortality
records)
• Non-cancer population data (ethnic- and income-specific life
tables)
• Relative survival analyses for 3 calendar periods
• Excess mortality rate analyses for all patients diagnosed 19912004
26
Data and methods
Linked Census, cancer and mortality data
Cancer cases
1991* – 1996
1996* - 2001
2001* - 2004
1. Dx
2. Dx
Died
3. Dx
Died
4. Dx
1991
Mortality follow up period
* 1991, 1996 and 2001 were Census years
2006
27
Observed survival data
Linked Census, cancer and mortality records
• Approximately 80% of cancer registrations were linked to
Census records, with 95% of those being true links.
• Between 11% and 15% of records were excluded because their
income was missing, but only approximately 1% were excluded
because of missing ethnicity data.
• Between 6% and 9% of records were excluded because they
had zero survival time (mostly their basis of cancer diagnosis
was from death certificate).
• Stage at diagnosis was not included as a variable in analyses
due to large variations in the quality of reporting stage over time.
28
Observed survival data
Total number of patients included in analyses
• A total of 147,344 patients were included in relative survival
analyses by ethnic group for patients diagnosed 1991-2004
• A total of 127,305 patients were included in relative survival
analyes by income group for patients diagnosed 1991-2004
• A total of 125,567 patients were included in excess mortality
analyses for patients diagnosed 1991-2004
29
Expected survival data
Minimising the impact of non-comparability bias
• Life tables are an essential input in relative survival and excess
mortality analyses
• Life tables provide data on the expected survival and the
mortality from all other (non-cancer) causes of death
• Ethnic-, income- and combined ethnic- and income-specific life
tables were constructed for this study for the periods 1991, 1996
and 2001
30
Expected survival data
Example of data from life tables
Probability of a person aged x surviving to age x + 1
Low-income males by ethnic group, 1991 and 2001
Low-income females by ethnic group, 1991 and 2001
1
1
0.95
0.9
0.9
0.85
0.8
Probabiity of
surviving to the 0.75
next year of age
0.7
0.8
Probability of
surviving to the
next year of age
0.7
0.65
0.6
0.6
0.55
0.5
0.5
0
20
40
60
80
100
0
20
Age group
40
60
80
100
Age group
Low-income Māori 1991
Low-income Māori 2001
Low-income Māori 1991
Low-income Māori 2001
Low-income Non-Māori 1991
Low-income Non-Māori 2001
Low-income Non-Māori 1991
Low-income Non-Māori 2001
31
Statistical analyses
Relative survival and excess mortality analyses
•
Estimation of relative survival ratios (RSRs)
– 1-year and 5-year RSRs by ethnic and income group for patients
diagnosed 1991-1996, 1996-2001, 2001-2004
– Ethnic-specific and income-specific life tables used
– RSRDs calculated for ethnic and income group differences at each
calendar period
32
Statistical analyses
Relative survival and excess mortality analyses
• Excess mortality rate (EMR) modelling
– Four EMR models run for each cancer site to estimate a) ethnic
trends in cancer survival and b) income trends in cancer survival
– EMRRs derived from EMR models to assess a) trends in survival,
b) inequalities in survival, and c) trends in survival inequalities
– Pooled EMRRs estimated across cancer sites
– Combined ethnic- and income-specific life tables used
33
Results
34
Trends in cancer survival
Cancer excess mortality rates reduced by 26% per decade
EMRR comparing patients diagnosed in 2001 to patients diagnosed in 1991
EMRR log scale)
Leukaemia
Breast (female)
Thyroid gland
Non-Hodgkin's lymphoma
Ovary
Melanoma
Liver
Hodgkin's lymphoma
Uterus
Kidney
Testis
Colorectum
Cervix
Equivalent to a 3%
reduction per annum in
excess mortality rates
POOLED ESTIMATE
Bladder
Stomach
Lung
Oesophagus
Brain
Head, neck and larynx
Pancreas
0.100
1.000
10.000
35
Trends in cancer survival
Possible explanations
•
Changes in the date of diagnosis and/or
the date of death through
•
improvements in treatment, and/or
•
advances in diagnosis, and/or
•
the introduction of cancer screening.
36
Ethnic inequalities in cancer survival
Māori had 29% greater excess mortality compared to non-Māori
EMRR comparing Māori to Non-Māori for patients diagnosed 1991-2004
EMRR (log scale)
Oesophagus
Testis
Cervix
Uterus
Kidney
Melanoma
Prostate
Head, neck and larynx
Breast (female)
Colorectum
POOLED ESTIMATE
Liver
Māori had 29% greater excess
mortality compared to non-Maori
Non-Hodgkin's lymphoma
Lung
Stomach
Leukaemia
Hodgkin's lymphoma
Pancreas
Ovary
Bladder
Brain
Thyroid gland
0.500
5.000
37
Income inequalities in cancer survival
Low income had 12% greater excess mortality compared to high income
EMRR comparing low and high income groups for patients diagnosed 1991-2004
EMRR (log scale)
Thyroid gland
Testis
Head, neck and larynx
Breast (female)
Pancreas
Cervix
Melanoma
Bladder
Stomach
Colorectum
POOLED ESTIMATE
Leukaemia
Oesophagus
Low income patients had 12%
greater excess mortality compared
high income patients
Lung
Non-Hodgkin's lymphoma
Uterus
Prostate
Brain
Liver
Hodgkin's lymphoma
Kidney
Ovary
0.500
5.000
38
Inequalities in cancer survival
Possible explanations
•
Differences between ethnic and socioeconomic
groups in:
•
stage at diagnosis (not adjusted for in this study)
•
quality and timing of treatment
•
patient factors, such as co-morbidities
•
(and possibly tumour biology)
39
Trends in ethnic inequalities in cancer survival
% changes per decade in absolute and relative differences
There was little change over time in ethnic inequalities when
looking at the change in the EMRR.
Measure
Scale
Absolute
Relative
Relative survival
RSRD
Possible 18% decrease to
a possible 41% increase
per decade
RSRR
20-24% decrease per
decade
Excess mortality rate
EMRD
25% decrease per decade,
with a possible 13% to 35%
decrease
EMRR
4% increase per decade
with a possible 6%
decrease to 14% increase
but a narrowing of ethnic inequalities over time
when looking at the EMRD and RSRR.
40
Trends in income inequalities in cancer survival
% changes per decade in absolute and relative differences
There was a 9% widening over time in income inequalities over
time when looking at the per decade change in the EMRR.
Measure
Scale
Absolute
Relative
Relative survival
RSRD
Possible 14% decrease to
a possible 40% increase
per decade
RSRR
20-23% decrease per
decade
Excess mortality rate
EMRD
24% decrease per decade,
with a possible 17% to 30%
decrease
EMRR
9% increase per decade
with a possible 1% to 17%
increase
but a narrowing of income inequalities over time
when looking at the EMRD and RSRR.
41
Trends in cancer survival inequalities
Possible explanations
•
Different rates by ethnic and socioeconomic group
over time in the receipt of cancer detection, diagnosis and
treatment services (the ‘inverse equity’ hypothesis)
•
Differences over time in the recording of ethnicity
•
Use of absolute and relative measures on the RSR and EMR
scales
•
Changes in the income gap distribution between Māori and
non-Māori driving changes in ethnic inequalities in cancer
survival
42
Conclusions
• Cancer survival is improving over time for all cancer sites, with
variation by cancer site in the magnitude of those improvements
• Ethnic and, to a lesser extent, socioeconomic inequalities in
cancer survival were reported for the majority of cancer sites
• There was evidence of a relative increase per decade in excess
mortality comparing low- to high-income groups
43
Acknowledgements
This work was supported by the Health Research Council of New
Zealand and the Ministry of Health.
Access to the data used in this study was provided by and sourced
from Statistics New Zealand under conditions designed to give
effect to the security and confidentiality provisions of the Statistics
Act 1975. The results presented in this study are the work of the
authors, not Statistics New Zealand.
44