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

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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 Disease Examining the impact of changing risk factors on colorectal cancer incidence and mortality Karen M. Kuntz, ScD – PowerPoint PPT presentation

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


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
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

Aggregate CRC risk function
Diagnosis free
Detected CRC
7
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
8
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

9
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
10
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
11
(No Transcript)
12
CISNET Model
Risk factor trends
CRC Model
CRC incidence mortality
Screening behavior
Diffusion of new treatments
Calendar Time
1970
1975
1980
1985
1990
13
Age-standardized incidence
US Population
14
Age-standardized incidence
US population
Model population
15
Age-standardized incidence
Flat trends since 1970
85
74
Risk factor trends
16
Age-standardized incidence
74
71
Healthy weight in 1970
17
Age-standardized incidence
74
63
No smoking in 1970
18
Age-standardized incidence
74
56
All MV users in 1970
19
Age-standardized incidence
185
Worst case
74
Best case
27
20
Age-standardized mortality
US Population
21
Age-standardized mortality
US population
Model population
22
Age-standardized mortality
Flat trends since 1970
39
34
Risk factor trends
23
Age-standardized mortality
79
Worst case
34
Best case
14
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
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