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Principal Investigator: Karen M. Kuntz
Institution: University of Minnesota School of Public Health
Grant Number: 2U01CA088204-05
Awarded under CA-05-018
Originally funded under CA-99-013 (view abstract)
In the first CISNET project we developed a population-based Monte Carlo simulation model
that evaluates national trends in the incidence and mortality of colorectal cancer (CRC).
The HSPH-CISNET Model incorporates age-, sex-, and race-specific trends in CRC risk factors,
screening, and treatment, as well as the effects of risk factors and screening on the underlying
natural history of colorectal disease and the effectiveness of treatment for patients with
diagnosed CRC. The Model currently simulates the US population aged 25 years and older from
1970 to 2020. We have used the Model to examine the relative contribution of changes in risk
factors, screening, and treatment to the overall population trends in CRC incidence and mortality.
The purpose of this project is to use the HSPH-CISNET Model to evaluate the population impact
of existing cancer control strategies, as well as the impact of strategies on the horizon.
Of particular importance is to incorporate the associations between risk factors and screening
trends, and to determine the potential impact of disparities in CRC risk and mortality at
a population level. Other aims will focus on evaluating the population-level impact of specific
risk factor trends, screening modalities, and treatment advancements. To validate our modeling
assumptions, we will use our model structure to simulate the population of Norway, a country
that does not have a CRC screening program, in an attempt to re-create their incidence and
mortality statistics. We will continue to collaborate with other modeling groups, as well
as with the new CRC Coordinating Center and the NCI to focus on questions that apply across
all modeling groups. In addition, we propose a concrete plan for making our model more accessible
to outside collaborators. This plan involves posting the model structure on a public web
site, sharing code with established outside investigators, and providing the necessary model
support.
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Awarded under CA-99-013
Abstract:
The purpose of this project is to further develop a population-based policy model that is specific
for the prevention, detection, and treatment of colorectal cancer in the United States. The
model will be a dynamic Monte Carlo simulation of the US population that will include all
of the modifiable and non-modifiable risk factors that have been found in epidemiological
studies to be associated with the incidence of colorectal cancer: smoking history, body mass
index, physical activity, red meat consumption, fruit and vegetable consumption, aspirin
use, multivitamin use (as a proxy for folate intake), alcohol consumption, postmenopausal
hormone use, and family history of colorectal cancer, as well as demographic variables (age,
gender, race). Risk equations will be derived primarily from Nurses' Health Study and the
Health Professionals Follow-up Study; risk factor distributions over time in the US population
will be obtained primarily from the NHANES I, II, and III. The model will track the underlying
progression and location of adenomatous polyps and undiagnosed cancer, thus enabling a screening
test to detect and remove an adenomatous polyp, or to possibly detect a cancer at an earlier
stage. Once a cancer is detected and staged (either by a screening test or by symptoms),
all relevant colorectal cancer treatment strategies will be incorporated, allowing for the
evaluation of current or hypothetical interventions. The model will be used to analyze the
potential contributions to the observed cancer trends and to predict the potential impact
on national trends of risk factor interventions, screening, and colorectal cancer treatment.
Throughout this project, the research team will collaborate with the National Cancer Institute
to incorporate national data sources and to focus research questions. The research team will
also be involved with other modeling groups for purposes of calibration, validation, and
the comparison of model results.
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