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Estimating the Burden of DiseaseExamining 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.
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
Age-standardized incidence and mortality
Cases
Deaths
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000
Year
CR
C E
ven
ts p
er
10
0,0
00
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)
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)
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 freeDetected
CRC
Aggregate CRC risk function
Stage-Specific Risk Functions
Goal: decompose the aggregate function into stage-specific risk functions
Disease freeUndetected
CRC
Risk function1
f(age, aspirin use, etc.)
Aggregate CRC risk function
Detected CRC
Adenoma
Risk function2
f(age, activity, etc.)
• 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
Example: 50 yo white woman
BMI = 25 kg/m2
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%
Example: 50 yo white woman BMI = 35 kg/m2
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%
CISNET Model
CRCModel
Risk factortrends
Screeningbehavior
Diffusion ofnew treatments
Calendar Time
1970 1975 1980 1985 1990
CRCincidence &
mortality
Age-standardized incidence
US Population
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
100,0
00
Age-standardized incidence
US population
Model population
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
10
0,0
00
Age-standardized incidence
Flat trends since 1970
8574
Risk factor trends
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
10
0,0
00
Age-standardized incidence
Healthy weight in 1970
7174
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
10
0,0
00
Age-standardized incidence
No smoking in 19706374
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
10
0,0
00
Age-standardized incidence
All MV users in 197056
74
0
20
40
60
80
100
120
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
10
0,0
00
Age-standardized incidence
Worst case
Best case27
185
74
0
20
40
60
80
100
120
140
160
180
200
1975 1980 1985 1990 1995 2000 2005
Year
CR
C C
ase
s p
er
10
0,0
00
US Population
Age-standardized mortality
0
10
20
30
40
50
60
1975 1980 1985 1990 1995 2000 2005
Year
CR
C D
eath
s p
er
10
0,0
00
US population
Model population
Age-standardized mortality
0
10
20
30
40
50
60
1975 1980 1985 1990 1995 2000 2005
Year
CR
C D
eath
s p
er
10
0,0
00
Age-standardized mortality
Risk factor trends34
Flat trends since 1970
39
0
10
20
30
40
50
60
1975 1980 1985 1990 1995 2000 2005
Year
CR
C D
eath
s p
er
10
0,0
00
Age-standardized mortality
Worst case
Best case14
79
34
0
20
40
60
80
1975 1980 1985 1990 1995 2000 2005
Year
CR
C D
eath
s p
er
10
0,0
00
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