The Burden of Cancer in Nova Scotia an evaluation of loss in expectation of life Ron Dewar Registry and Analytics Presented to the joint NAACCR / IACR meeting, June 2019

Overview and Objectives Introduce concept of expectation of life Expectation of life for general population Expectation of life for cancer patient population Loss in Expectation of Life for cancer patients Results and interpretation Individual patient level Population level Computational resources

Proportion alive (survival curve) Expectation of Life: All Cause Mortality and Proportion Surviving, Women in Nova Scotia, 2015 Proportion alive (survival curve) Expectation of life At age Years 83 85 7.4 Mortality rate (all causes)

Calculation of expectation of life, cancer patients Extrapolate patient all causes (observed) survival Evaluation by T. Andersson (2012) suggests: Model, then extrapolate relative survival stable for many sites after 7 – 10 years Calculate observed survival using expected survival estimate, since RS = OS/expected, then OS = RS * expected Numerical integration of extrapolated OS curve

Data Sources (1) Available Cancer Data Statistics Canada (Demographic Projections unit) Complete life tables for Nova Scotia 1981 – 2010, with projections 2011 to 2068 (medium model) Nova Scotia Cancer Registry: Exclusions: subsequent primaries of same site (IARC 2004 rules) zero survival, method of diagnosis is DCO or Autopsy invasive only (except Bladder) Trends: diagnosed 1981 – 2016 followed to end of 2017 age in years (18 – 99) sex survival time in days year of diagnosis Current: diagnosed 2008 – 2016, alive 31 Dec 2017 or deceased in 2014 or later above covariates plus CS stage group Available Cancer Data

Data Sources (2) cases used in analysis Cancer Type Trends Current Lung and Bronchus 25,200 4,400 Breast 20,100 5,700 Prostate 19,800 5,500 Colon excluding Rectum 15,300 3,500 Bladder (incl. in situ) 8,000 900 Rectum 7,300 1,700 Non-Hodgkin Lymphoma 6,000 1,600 Melanomas of the skin 5,900 2,200 Kidney and Renal Pelvis 5,200 Leukemia 3,900 1,000 Total 116,700 28,200

Loss in Expectation of Life Difference between population expectation and expectation for cancer patients Compute at the individual or population level Express as loss in expectation of life (LEL) proportion of future life years lost Possible presentations impact on individual (covariates: age, sex, stage, cancer type, …) population burden of cancer (eg, total years lost) change over time as measure of progress impact of covariate distribution (what if? Scenarios…)

Expectation of Life, Women in Nova Scotia, 2015 diagnosed with colon cancer at age 55 showing Loss in Expectation of Life (LEL) General population Cancer population Expectation of life Years General 32 LEL % Stage I 28 4 12 Stage IV 3.7 28 88

Expectation of Life, Women in Nova Scotia, 2015 diagnosed with colon cancer at age 65 showing Loss in Expectation of Life (LEL) General population Expectation of life Years LEL % General 23 Stage I 20 3 12 Stage IV 2.8 88 Cancer population

Selected Trends* in Expectation of Life in Nova Scotia, 1981 - 2015 Population expectation Cancer Patients *Trends age standardised to 2015 age distributions

Change in Proportion of Life Years Lost, 2015 vs 1981, Male cancer patients in Nova Scotia Greater Change Less Change

Change in Proportion of Life Years Lost, 2015 vs 1981, Female cancer patients in Nova Scotia Greater Change Less Change

Expected Future Life Years Men 29.5 Women 32.3 Loss in Expectation of Life (LEL) * Leukemia patients aged 55 at diagnosis Expected Future Life Years Men 29.5 Women 32.3 * based on current patient experience

Population Expectation Expectation of Life* by stage, Colon cancer patients in Nova Scotia, 2015 Population Expectation * based on current patient experience

Loss in Expectation* of Life (%) by time already survived Colon cancer patients age 65 at diagnosis, Stage III, Nova Scotia, 2015 * based on current patient experience

LEL if all were Stage I Lung 28,500 Melanoma 300 * based on current patient experience

Conclusions Expectation of life can be in your analytic toolbox computational resources (Stata, SAS) data resources: population-based registry relevant population life tables An adjunct to survival estimates patient – physician interaction managers and planners of the cancer system Possibilities for ‘interesting’ counterfactual scenarios Caveats and considerations: need a wider conversation around uses, interpretation availability of projected life tables (ideal, but not entirely necessary) sensitivity to modeling choices should be evaluated and reported stage-specific trends subject to same caveats as survival trends sufficient follow-up to allow for stable relative survival

Computational Resources Software (user-written: SAS macros or Stata .ado files) Stata modules: stpm2 and post-estimation prediction SAS macros: work-alike macros built on Stata model flexible parametric relative survival modeling Stata stpm2, option bhazard SAS %stpm2, option bhazard estimate life expectation Stata predict, option lifelost SAS %predict, option lifelost

Thanks to: Nova Scotia Health Authority, Cancer Care Program Dr. Paul Lambert, Dr. Therese Andersson (authors of Stata routines) Patrice Dion, Demographic Projections Unit, Statistics Canada Questions?