1 key concepts, data, methods and results index trends in cancer survival by ethnic and...

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Key concepts, data, methods and results

Index

Trends in cancer survival by ethnic and socioeconomic group,

New Zealand, 1991-2004Soeberg 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

CancerTrends

A study funded by the Health Research Council and the Ministry of Health

Structure of this presentation

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• Current knowledge and gaps in knowledge

• Measuring cancer survival

• Data and methods

• Results and interpretation

Current knowledge, and gaps in knowledge

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Current New Zealand evidence

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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.

Current New Zealand evidence

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Ethnic and socioeconomic inequalities in cancer survival exist

But little is know about whether these inequalities

are narrowing or widening over time.

Study objectives

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• 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.

Study objectives

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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.

Study objectives

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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.

Study objectives

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Changes over time in cancer survival inequalities

This study also measured changes over time in ethnic and socioeconomic cancer survival inequalities.

Measuring trends in cancer survival

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Measuring cancer survival

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Time-to-event studies

In this study, we were interested in the time from cancer diagnosis to the event (in this case death).

Cancer diagnosis Death

Time


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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).

Breast cancer diagnosis

Death from breast cancer where deaths

from all other causes are censored

Time

but the quality of cause of death data in New Zealand is poor.


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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.

Breast cancer diagnosis

Death from any cause taking into account all other causes of death

Time


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Relative survival

The relative survival ratio is commonly used in population-based cancer survival studies.

RSR of 0.80 = 0.75 (observed survival) / 0.92 (expected survival)


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Key disadvantage of relative survival

Non-comparability bias is introduced in relative survival analyses where the mortality rates in the cancer and non-

cancer populations are not comparable.

Mortality rates in the Māori cancer population

Mortality rates in the total non-cancer

population


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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%


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• 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



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• 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

Other disadvantages of relative survival


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Survival and mortality scales

Relative survival can also be presented on an excess mortality rate scale (mirror image of relative survival).

Relative survival scale Equivalent annual excess mortality rate scale


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• 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.

Modelling excess cancer mortality rates

Measuring differences in cancer survival

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• 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)

Reasons to measure differences in cancer survival


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• Absolute and relative differences

• On the relative survival ratio (RSR) scale

• On the excess mortality rate (EMR) scale

Ways to measure differences in cancer survival


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A framework for absolute and relative differences in cancer survival

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)

Cancer survival inequalities can be assessed using absolute or relative measures calculated on the RSR or EMR scales.


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Different conclusions from the same data

Scale Cancer site Absolute measure

Relative measure

Five-year relative survival scale

RSRD RSRR

Breast -0.05 0.94

Colorectal -0.10 0.80

Lung -0.05 0.50

Annual excess mortality rate scale

EMRD EMRR

Breast 0.01 1.29

Colorectal 0.04 1.32

Lung 0.14 1.30

In this study, we have mostly measured the RSRDs and the EMRRs.

Data and methods

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Data and methods

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• 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 1991-2004

Observed and expected survival data and analyses

Data and methods

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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 2006

* 1991, 1996 and 2001 were Census years

Observed survival data

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• 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.

Linked Census, cancer and mortality records

Observed survival data

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• 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

Total number of patients included in analyses

Expected survival data

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• 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

Minimising the impact of non-comparability bias

Expected survival data

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Example of data from life tables

Probability of a person aged x surviving to age x + 1

Statistical analyses

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• 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

Relative survival and excess mortality analyses

Statistical analyses

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• 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

Relative survival and excess mortality analyses

Results

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Trends in cancer survival

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Cancer excess mortality rates reduced by 26% per decade

Equivalent to a 3% reduction per annum in excess mortality rates

Trends in cancer survival

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• 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.

Possible explanations

Ethnic inequalities in cancer survival

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Māori had 29% greater excess mortality compared to non-Māori

Māori had 29% greater excess mortality compared to non-Maori

Income inequalities in cancer survival

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Low income had 12% greater excess mortality compared to high income

Low income patients had 12% greater excess mortality compared high income patients

Inequalities in cancer survival

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• 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)


Trends in ethnic inequalities in cancer survival

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% changes per decade in absolute and relative differences

Measure Scale

Absolute Relative

Relative survival RSRDPossible 18% decrease to a possible 41% increase per decade

RSRR20-24% decrease per decade

Excess mortality rate EMRD25% decrease per decade, with a possible 13% to 35% decrease

EMRR4% increase per decade with a possible 6% decrease to 14% increase

There was little change over time in ethnic inequalities when looking at the change in the EMRR.

but a narrowing of ethnic inequalities over time when looking at the EMRD and RSRR.

Trends in income inequalities in cancer survival

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% changes per decade in absolute and relative differences

Measure Scale

Absolute Relative

Relative survival RSRDPossible 14% decrease to a possible 40% increase per decade

RSRR20-23% decrease per decade

Excess mortality rate EMRD24% decrease per decade, with a possible 17% to 30% decrease

EMRR9% increase per decade with a possible 1% to 17% increase

There was a 9% widening over time in income inequalities over time when looking at the per decade change in the EMRR.

but a narrowing of income inequalities over time when looking at the EMRD and RSRR.

Trends in cancer survival inequalities

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• 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


Conclusions

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• 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

Acknowledgements

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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.

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