CISNET and BCSC: Working Together To Model The Population

Impact Breast Cancer Screening

A Celebration of the Work of the Breast Cancer Surveillance Consortium

April 27, 2010

Kathleen Cronin

Surveillance Research Program

National Cancer Institute

Cancer Intervention and Surveillance Modeling Network (CISNET)

NCI Sponsored Consortium of Modelers Focused on

• Modeling of the Impact of Cancer Control Interventions on Current and Future Population Trends in Incidence and Mortality

• Optimal Cancer Control Planning

15 funded grantees in Breast, Prostate, Colorectal, and Lung Cancer

Comparative modeling approach • Base Cases - joint modeling exercises with common inputs

• Model Profiler - series of templates for describing models

Breast Cancer Investigators in CISNET

Dana Farber - Marvin Zelen, Sandra Lee

Erasmus University – Dik Habbema, Harry de Koning

Georgetown University – Jeanne Mandelblatt

MD Anderson – Donald Berry

Stanford University – Sylvia Plevritis

University of Rochester – Andrei Yakovlev

University of Wisconsin – Dennis Fryback

NCI – Rocky Feuer, Kathy Cronin

General Formulation of CISNET Models

Risk Factors

Screening Behavior

Diffusion of New Treatments

Cancer Models

Example Outputs:

•Mortality•Incidence•Quality–Adjusted Life Years•Overdiagnosis•Medical costs

Common InputsSimulation or

Analytical Model

Common Outputs: Benefits and Harms of

Interventions

Development and Validation of Breast Cancer Natural History Models

BCSC data played a key role in the development and validation of the central cancer models that represent the natural history of disease

Characteristics of cases conditioned on the time since last screening test

Characteristics of screen detected cases• Stage distribution• ER status

Age dependent sensitivity and specificity of mammography

• False positive rates• Unnecessary biopsies

Modeling the Dissemination and Usage of Mammography in the US Population

BCSC provided data on repeat mammography use and collaborated with CISNET to develop a model to describe the patterns of mammography use in the population

Classified women who ever have a mammogram into categories of screeners

• Annual• Biennial• Irregular

Use longitudinal data on individual women to estimate the time between successive screening exams for each category

Distribution of Screening Categories By Age

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

18-39 40-49 50-59 60-69 70-79 80+

Age Group

% o

f P

op

ula

tio

n

irregular

biennial

annual

Time Between Subsequent Screening Exams For Women age 50-59

0

20

40

60

80

100

0 5 10 15

years

% h

ad

ne

xt

ex

am

Annual

Biennial

Irregular

Modeled Mammography Screening Over Time, Women age 40-79

0

25

50

75

100

1985 1990 1995 2000

Year

Biannual

Annual

Irregular

Never

Biennial

Application: Modeling the Impact of Screening and Adjuvant Treatment On Breast Cancer Mortality

0

10

20

30

40

50

60

70

1975 1980 1985 1990 1995 2000

year

mor

tali

ty r

ate

Observed US Mortality

No Screening or Adjuvant Treatment

Screening onlyTreatment Only

Both Screening and Treatment

Estimated Percent Decline in Mortality Due To Screening and Adjuvant Therapy For

The 7 Models

Due to Screening

Due

to T

reat

men

t

0 5 10 15 20 25 30

0

5

10

15

20

25

30

W

G

D

S

EMR

Berry et al. N Engl J Med 2005:Seven statistical models showed that both screening mammography and treatment have helped reduce the rates of death from breast cancer

Application: Modeling the Harms and Benefits of Different Screening Schedules

Analysis requested by the USPSTF

Two primary measures of benefit (vs. no screening):

• % reduction in breast cancer mortality • Life years gained (per 1000 women)

Exposure to harms:• False positive screens• Number of un-necessary biopsies• Detection of tumors never destined to cause breast

cancer death (“over diagnosis”)

Breast Cancer Screening Strategies

Annual

Biennial

# Strategy0 No Screening1 40-692 40-793 40-844 45-695 50-69 6 50-747 50-798 50-849 55-6910 60-69

0%

10%

20%

30%

40%

50%

60%

0 10000 20000 30000 40000

Average # of mammographies per 1000 women

Per

cen

t mo

rtal

ity d

eclin

e

0%

10%

20%

30%

40%

50%

60%

0 10000 20000 30000 40000

Average # of mammographies per 1000 women

Per

cen

t mo

rtal

ity d

eclin

e

0%

10%

20%

30%

40%

50%

60%

0 10000 20000 30000 40000

Average # of mammographies per 1000 women

Per

cen

t mo

rtal

ity d

eclin

e

D G

S

0%

10%

20%

30%

40%

50%

60%

0 10000 20000 30000 40000

Average # of mammographies per 1000 women

Per

cen

t mo

rtal

ity d

eclin

e M

0%

10%

20%

30%

40%

50%

60%

0 10000 20000 30000 40000

Average # of mammographies per 1000 women

Per

cen

t mo

rtal

ity d

eclin

e E

0%

10%

20%

30%

40%

50%

60%

0 10000 20000 30000 40000

Average # of mammographies per 1000 women

Per

cen

t mo

rtal

ity d

eclin

e W

B60-69

B50-84

A40-84

B60-69

B50-79B40-84

A40-84

B60-69

B50-79

A40-84

B50-84

B60-69

B50-69

A40-84B40-84

B60-69

B50-74

B40-84

A40-84

B50-84

B60-69

B55-69

B50-84

A40-84

B50-74

B40-84

B55-69

B40-84

B55-69B50-84

B55-69 B50-69B50-79 B50-79

B50-69

B50-74B50-79 B50-74

B50-69

B55-69 B50-69B50-74

B40-84

B55-69 B50-74

B50-84

B50-79

B50-69

Efficiency frontier for each model

• Each dot is a strategy (Red dot is annual screening ages 40-79)

• All models reached qualitatively similar results

• Moving from annual to biennial maintains on average 81% of the benefits with reduced harms

Looking Ahead

BCSC continues to be a primary resource for the CISNET consortium on many levels

• Population level data on screening usage and outcomes not available elsewhere

• Provide expertise on use and interpretation of data• Active collaborator on a number of research questions

Next Steps

• Activities to Promote Research Collaborations (APRC) – CISNET/BCSC/EPC

Compare effectiveness of film vs. digital in subgroups of women

• Grand Opportunities (GO) GrantCompare clinical and cost-effectiveness of various screening strategies