1. 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

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. 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)

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) 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 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

6. 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

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, 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

9. 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

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. 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 Women 32.3
* based on current patient experience

14. Population Expectation
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 survived Colon cancer patients age 65 at diagnosis, Stage III, Nova Scotia, 2015
* based on current patient experience

16. LEL if all were Stage I
Lung
28,500
Melanoma
300
* 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)
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

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?