1. Estimating the Burden of Disease Examining the impact of changing risk factors on colorectal cancer incidence and mortality Karen M. Kuntz, ScD Cancer Risk Prediction Models: A Workshop on Development, Evaluation, and Application National Cancer Institute May 20-21, 2004 ** Results presented are preliminary.

2. Decision-Analytic Models Analytical structures that represent key elements of a disease Goal: evaluate policies in terms of costs and health benefits (not estimation) Cohort models vs. population-based model Risk functions often incorporated

3. Age-standardized incidence and mortality Cases Deaths 0 20 40 60 80 100 120 197519801985199019952000 Year CRC Events per 100,000

4. CRC Risk Factors Body mass index (BMI) Smoking Folate intake (multivitamin use) Physical activity Red meat consumption Fruit and vegetable consumption Aspirin use Hormone replacement therapy (HRT)

5. Individual Risk Functions Pr(CRC | BMI, smoking, MV use, etc.) –Annual risk –10-year probability Estimate from cohort studies –Nurses’ Health Study (NHS) –Health Professionals’ Follow-up Study (HPFS)

6. NHS & HPFS Data Multivariate logistic regression of NHS/HPFS data provide information about the relationship between risk factors and diagnosed (but not underlying) CRC Diagnosis free Detected CRC Aggregate CRC risk function

7. Stage-Specific Risk Functions Goal: decompose the aggregate function into stage-specific risk functions Disease free Undetected CRC Risk function 1 f(age, aspirin use, etc.) Aggregate CRC risk function Detected CRC Adenoma Risk function 2 f(age, activity, etc.)

8. Establish “observed relationship” between risk factor and diagnosed CRC Simulate incidence of CRC in hypothetical cohort that is matched to study cohort Use regression analysis to examine simulated relationship between risk factor and diagnosed CRC Calibrate ORs of simulated data analysis to those of cohort analysis Our Approach

9. Example: 50 yo white woman BMI = 25 kg/m 2 Non-smoker MV user 5 met-hr/wk 2 sv/wk red meat 5 sv/dy fruit/veg No aspirin use No HRT use Lifetime CRC risk: 4.8%

10. Example: 50 yo white woman BMI = 35 kg/m 2 Smoker No MV use 5 met-hr/wk 2 sv/wk red meat 5 sv/dy fruit/veg No aspirin use No HRT use Lifetime CRC risk: 9.7%

12. CISNET Model CRC Model Risk factor trends Screening behavior Diffusion of new treatments Calendar Time 19701975198019851990 CRC incidence & mortality

13. Age-standardized incidence US Population 0 20 40 60 80 100 120 1975198019851990199520002005 Year CRC Cases per 100,000

14. Age-standardized incidence US population Model population 0 20 40 60 80 100 120 1975198019851990199520002005 Year CRC Cases per 100,000

15. Age-standardized incidence Flat trends since 1970 85 74 Risk factor trends 0 20 40 60 80 100 120 1975198019851990199520002005 Year CRC Cases per 100,000

16. Age-standardized incidence Healthy weight in 1970 71 74 0 20 40 60 80 100 120 1975198019851990199520002005 Year CRC Cases per 100,000

17. Age-standardized incidence No smoking in 1970 63 74 0 20 40 60 80 100 120 1975198019851990199520002005 Year CRC Cases per 100,000

18. Age-standardized incidence All MV users in 1970 56 74 0 20 40 60 80 100 120 1975198019851990199520002005 Year CRC Cases per 100,000

19. Age-standardized incidence Worst case Best case 27 185 74 0 20 40 60 80 100 120 140 160 180 200 1975198019851990199520002005 Year CRC Cases per 100,000

20. US Population Age-standardized mortality 0 10 20 30 40 50 60 1975198019851990199520002005 Year CRC Deaths per 100,000

21. US population Model population Age-standardized mortality 0 10 20 30 40 50 60 1975198019851990199520002005 Year CRC Deaths per 100,000

22. Age-standardized mortality Risk factor trends 34 Flat trends since 1970 39 0 10 20 30 40 50 60 1975198019851990199520002005 Year CRC Deaths per 100,000

23. Age-standardized mortality Worst case Best case 14 79 34 0 20 40 60 80 1975198019851990199520002005 Year CRC Deaths per 100,000

24. Concluding Remarks Trends in risk factors over the past 35 years account for a 13% decrease in both CRC incidence and mortality compared to “flat trends” Population-based simulation models provide an important tool for evaluating the impact of changing risk factors