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The Burden of Cancer in Nova Scotia: Evaluating Loss in Expectation of Life

This presentation discusses the concept of expectation of life and examines its impact on the general population and cancer patients in Nova Scotia. It evaluates the loss in expectation of life for cancer patients and presents results and interpretations at the individual and population levels. Computational and data resources used in the analysis are highlighted. The presentation concludes with considerations and potential uses of expectation of life estimates in cancer management and planning.

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The Burden of Cancer in Nova Scotia: Evaluating Loss in Expectation of Life

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  1. The Burden of Cancer in Nova Scotiaan evaluation of loss in expectation of lifeRon Dewar Registry and AnalyticsPresented to the joint NAACCR / IACR meeting, June 2019

  2. 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

  3. Expectation of Life: All Cause Mortality and Proportion Surviving, Women in Nova Scotia, 2015 Proportion alive (survival curve) Mortality rate (all causes)

  4. 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

  5. Data Sources (1) • 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 diagnosisCurrent: diagnosed 2008 – 2016, alive 31 Dec 2017 or deceased in 2014 or later above covariates plus CS stage group Available Cancer Data

  6. Data Sources (2)cases used in analysis

  7. 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…)

  8. Expectation of Life, Women in Nova Scotia, 2015diagnosed with colon cancer at age 55showing Loss in Expectation of Life (LEL) General population Cancer population LEL %

  9. Expectation of Life, Women in Nova Scotia, 2015diagnosed with colon cancer at age 65showing Loss in Expectation of Life (LEL) General population Cancer population

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

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

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

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

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

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

  16. * based on current patient experience

  17. 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

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

  19. 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?

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