Estimating the Burden of Disease Examining the impact of changing risk factors on colorectal cancer incidence and mortality

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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.
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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

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Age-standardized incidence and mortality
Cases
Deaths
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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)

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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)

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NHS HPFS Data

• Multivariate logistic regression of NHS/HPFS data
  provide information about the relationship
  between risk factors and diagnosed (but not
  underlying) CRC

Aggregate CRC risk function
Diagnosis free
Detected CRC
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Stage-Specific Risk Functions

• Goal decompose the aggregate function into
  stage-specific risk functions

Aggregate CRC risk function
Disease free
Undetected CRC
Adenoma
Risk function2 f(age, activity, etc.)
Risk function1 f(age, aspirin use, etc.)
Detected CRC
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Our Approach

• 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

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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
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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
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CISNET Model
Risk factor trends
CRC Model
CRC incidence mortality
Screening behavior
Diffusion of new treatments
Calendar Time
1970
1975
1980
1985
1990
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Age-standardized incidence
US Population
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Age-standardized incidence
US population
Model population
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Age-standardized incidence
Flat trends since 1970
85
74
Risk factor trends
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Age-standardized incidence
74
71
Healthy weight in 1970
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Age-standardized incidence
74
63
No smoking in 1970
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Age-standardized incidence
74
56
All MV users in 1970
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Age-standardized incidence
185
Worst case
74
Best case
27
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Age-standardized mortality
US Population
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Age-standardized mortality
US population
Model population
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Age-standardized mortality
Flat trends since 1970
39
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Risk factor trends
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Age-standardized mortality
79
Worst case
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Best case
14
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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