Cancer Trends A study funded by the Health

Cancer. Trends 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) 0 non-Māori Least deprived 06 20 20 04 - 20 05 03 20 02 - 20 20 00 - 20 19 98 19 20 06 20 20 04 20 20 02 20 20 00 20 19 98 19 Māori 20 0 07 0. 2 99 0. 2 01 0. 6 Five-year relative survival 0. 4 07 0. 6 Five-year relative survival 0. 4 05 0. 8 03 0. 8 01 1 99 1 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) 1 0. 9 0. 8 0. 7 0. 6 Five-year relative survival 0. 5 0. 4 0. 3 0. 2 0. 1 0 Five-year relative survival by deprivation group (Jeffreys et al. , 2009) 1 0. 9 0. 8 0. 7 0. 6 Five-year relative survival 0. 5 0. 4 0. 3 0. 2 0. 1 0 Breast cancer non-Māori Colorectal Lung cancer Breast cancer Least depried Colorectal 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. 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 0. 8 Net survival five-years following a cancer diagnosis 0. 7 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. 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 scale 0. 5 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 Equivalent annual excess mortality rate scale Example using female breast cancer 1 0. 9 0. 08 0. 8 Net survival five-years following a cancer diagnosis 0. 7 0. 06 Equivalent annual excess mortality rate 0. 04 0. 6 Most advantaged group Least advantaged group 1996 2001 0. 02 1991 0. 5 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 survival ratio difference (RSRD) Ratio of relative survival ratios (RSRR) 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 Relative measure RSRD RSRR Breast -0. 05 0. 94 -0. 10 0. 80 Lung Annual excess mortality rate scale Absolute measure Colorectal Five-year relative survival scale Cancer site -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* - 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. 85 0. 8 Probabiity of surviving to the next year of age 0. 75 Probability of surviving to the next year of age 0. 7 0. 65 0. 6 0. 55 0. 5 0 10 20 30 40 50 60 70 80 90100 Age group 0 10 20 30 40 50 60 70 80 90 100 Age group Low-income Māori 1991 Low-income 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 POOLED ESTIMATE Equivalent to a 3% reduction per annum in excess mortality rates 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 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 Lung Low income patients had 12% greater excess mortality compared high income patients Non-Hodgkin's lymphoma Uterus Prostate Brain Liver Hodgkin's lymphoma Kidney Ovary 0. 500 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 survival RSRD RSRR Possible 18% decrease to a 20 -24% decrease per possible 41% increase 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 survival RSRD RSRR Possible 14% decrease to a 20 -23% decrease per possible 40% increase 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 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