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Holford T.R.,0 College Street | McKay L.A.,0 College Street | Clarke L.,Cornerstone Systems Northwest Inc. | Racine B.,Cornerstone Systems Northwest Inc. | And 4 more authors.
American Journal of Preventive Medicine | Year: 2014

Background Characterizing the smoking patterns for different birth cohorts is essential for evaluating the impact of tobacco control interventions and predicting smoking-related mortality, but the process of estimating birth cohort smoking histories has received limited attention. Purpose Smoking history summaries were estimated beginning with the 1890 birth cohort in order to provide fundamental parameters that can be used in studies of cigarette smoking intervention strategies. Methods U.S. National Health Interview Surveys conducted from 1965 to 2009 were used to obtain cross-sectional information on current smoking behavior. Surveys that provided additional detail on history for smokers including age at initiation and cessation and smoking intensity were used to construct smoking histories for participants up to the date of survey. After incorporating survival differences by smoking status, age-period-cohort models with constrained natural splines were used to estimate the prevalence of current, former, and never smokers in cohorts beginning in 1890. This approach was then used to obtain yearly estimates of initiation, cessation, and smoking intensity for the age-specific distribution for each birth cohort. These rates were projected forward through 2050 based on recent trends. Results This summary of smoking history shows clear trends by gender, cohort, and age over time. If current patterns persist, a slow decline in smoking prevalence is projected from 2010 through 2040. Conclusions A novel method of generating smoking histories has been applied to develop smoking histories that can be used in micro-simulation models, and has been incorporated in the National Cancer Institute's Smoking History Generator. These aggregate estimates developed by age, gender, and cohort will provide a complete source of smoking data over time. © 2014 American Journal of Preventive Medicine.


Clark L.,Cornerstone Systems Northwest Inc.
Risk Analysis | Year: 2012

Publication of the Surgeon General's Report in 1964 marshaled evidence of the harm to public health caused by cigarette smoking, including lung cancer mortality, and provided an impetus for introducing control programs. The purpose of this article is to develop estimates of their effect on basic smoking exposure input parameters related to introduction of the report. Fundamental inputs used to generate exposure to cigarettes are initiation and cessation rates for men and women, as well as the distribution of the number of cigarettes smoked per day. These fundamental quantities are presented for three scenarios: actual tobacco control in the United States; no tobacco control in which the experience before 1955 was assumed to continue; and complete tobacco control in which all smoking ceased following publication of the report. These results were derived using data from National Health Interview Surveys, and they provide basic input parameters for the Smoking History Generator used by each of the lung cancer models developed by the Cancer Intervention and Surveillance Modeling Network. © 2012 Society for Risk Analysis.


PubMed | University of Michigan, Fred Hutchinson Cancer Research Center, Stanford University, Brock University and 6 more.
Type: Comparative Study | Journal: PloS one | Year: 2014

The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking.We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. Efficient (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 consensus efficient (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient.Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.


Moolgavkar S.H.,Fred Hutchinson Cancer Research Center | Holford T.R.,Yale University | Levy D.T.,University of Baltimore | Levy D.T.,Pacific Institute for Research and Evaluation | And 23 more authors.
Journal of the National Cancer Institute | Year: 2012

Background Considerable effort has been expended on tobacco control strategies in the United States since the mid-1950s. However, we have little quantitative information on how changes in smoking behaviors have impacted lung cancer mortality. We quantified the cumulative impact of changes in smoking behaviors that started in the mid-1950s on lung cancer mortality in the United States over the period 1975-2000. Methods A consortium of six groups of investigators used common inputs consisting of simulated cohort-wise smoking histories for the birth cohorts of 1890 through 1970 and independent models to estimate the number of US lung cancer deaths averted during 1975-2000 as a result of changes in smoking behavior that began in the mid-1950s. We also estimated the number of deaths that could have been averted had tobacco control been completely effective in eliminating smoking after the Surgeon General's first report on Smoking and Health in 1964.Results Approximately 795851 US lung cancer deaths were averted during the period 1975-2000: 552574 among men and 243 277 among women. In the year 2000 alone, approximately 70218 lung cancer deaths were averted: 44135 among men and 26083 among women. However, these numbers are estimated to represent approximately 32% of lung cancer deaths that could have potentially been averted during the period 1975-2000, 38% of the lung cancer deaths that could have been averted in 1991-2000, and 44% of lung cancer deaths that could have been averted in 2000. Conclusion s Our Results reflect the cumulative impact of changes in smoking behavior since the 1950s. Despite a large impact of changing smoking behaviors on lung cancer deaths, lung cancer remains a major public health problem. Continued efforts at tobacco control are critical to further reduce the burden of this disease. © 2012 The Author(s).


Mcmahon P.M.,Massachusetts General Hospital | Hazelton W.D.,Fred Hutchinson Cancer Research Center | Kimmel M.,Rice University | Clarke L.D.,Cornerstone Systems Northwest Inc.
Risk Analysis | Year: 2012

Sophisticated modeling techniques can be powerful tools to help us understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Readers of journal articles are, however, rarely supplied with modeling details. Six modeling groups collaborated as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) to investigate the contribution of U.S. tobacco-control efforts toward reducing lung cancer deaths over the period 1975-2000. The six models included in this monograph were developed independently and use distinct, complementary approaches toward modeling the natural history of lung cancer. The models used the same data for inputs, and agreed on the design of the analysis and the outcome measures. This article highlights aspects of the models that are most relevant to similarities of or differences between the results. Structured comparisons can increase the transparency of these complex models. © 2011 Society for Risk Analysis.


Jeon J.,Fred Hutchinson Cancer Research Center | Meza R.,University of Michigan | Krapcho M.,Management Information Services Inc. | Clarke L.D.,Cornerstone Systems Northwest Inc. | And 3 more authors.
Risk Analysis | Year: 2012

The smoking history generator (SHG) developed by the National Cancer Institute simulates individual life/smoking histories that serve as inputs for the Cancer Intervention and Surveillance Modeling Network (CISNET) lung cancer models. In this chapter, we review the SHG inputs, describe its outputs, and outline the methodology behind it. As an example, we use the SHG to simulate individual life histories for individuals born between 1890 and 1984 for each of the CISNET smoking scenarios and use those simulated histories to compute the corresponding smoking prevalence over the period 1975-2000. © 2012 Society for Risk Analysis.


Sullivan S.D.,Fred Hutchinson Cancer Research Center | Sullivan S.D.,University of Washington | Ramsey S.D.,Fred Hutchinson Cancer Research Center | Blough D.K.,University of Washington | And 4 more authors.
Value in Health | Year: 2011

Objectives: We examined health care use in conjunction with primary prophylaxis use of colony stimulating factors (CSF) during patients' initial course of chemotherapy. Methods: This retrospective cohort study identified adults aged 25 years and older with a diagnosis of breast, colorectal, or nonsmall cell lung cancer between 2002 and 2005 from the Western Washington Surveillance Epidemiology and End Results Puget Sound registry. We linked these records to health insurance claims from four payers representing 75% of those insured in the state. Claims records were used to determine chemotherapy regimen type, CSF use, febrile neutropenia occurrences, and supportive care. Chemotherapy regimens were categorized as conferring high, intermediate, or low risk of myelosuppression according to the National Comprehensive Cancer Network guidelines. CSF use was described as primary prophylaxis, other, or none. Antibiotics and antifungal and antiviral agents per National Comprehensive Cancer Network guidelines for supportive care for cancer infection were categorized using Healthcare Common Procedure Coding System and National Drug Code assignments. Results: Use of CSF as primary prophylaxis is not significantly associated with a reduction in antibiotic use or inpatient or outpatient visits. Primary prophylactic CSF use was associated with less use of antiviral drugs. Conclusions: CSF use is not associated with a reduction in health care use, with the exception of antiviral drug use. Given the expense associated with CSF use, pragmatic trials and additional research are needed to further assess the affects of CSF on health care use. Copyright © 2011, International Society for Pharmacoeconomics and Outcomes Research (ISPOR).


Peng B.,University of Houston | Chen H.-S.,U.S. National Institutes of Health | Mechanic L.E.,U.S. National Institutes of Health | Racine B.,Cornerstone Systems Northwest Inc. | And 3 more authors.
Genetic Epidemiology | Year: 2015

Computer simulations have played an indispensable role in the development and applications of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the genetic simulation resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators. © 2014 WILEY PERIODICALS, INC.


Peng B.,University of Houston | Chen H.-S.,U.S. National Institutes of Health | Mechanic L.E.,Epidemiology and Genomics Research Program | Racine B.,Cornerstone Systems Northwest Inc. | And 4 more authors.
Bioinformatics | Year: 2013

Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features. © 2013 The Author.


Kurian A.W.,Stanford University | Munoz D.F.,Stanford University | Rust P.,Cornerstone Systems Northwest Inc. | Schackmann E.A.,Stanford University | And 4 more authors.
Journal of Clinical Oncology | Year: 2012

Purpose: Women with BRCA1 or BRCA2 (BRCA1/2) mutations must choose between prophylactic surgeries and screening to manage their high risks of breast and ovarian cancer, comparing options in terms of cancer incidence, survival, and quality of life. A clinical decision tool could guide these complex choices. Methods: We built a Monte Carlo model for BRCA1/2 mutation carriers, simulating breast screening with annual mammography plus magnetic resonance imaging (MRI) from ages 25 to 69 years and prophylactic mastectomy (PM) and/or prophylactic oophorectomy (PO) at various ages. Modeled outcomes were cancer incidence, tumor features that shape treatment recommendations, overall survival, and cause-specific mortality. We adapted the model into an online tool to support shared decision making. Results: We compared strategies on cancer incidence and survival to age 70 years; for example, PO plus PM at age 25 years optimizes both outcomes (incidence, 4% to 11%; survival, 80% to 83%), whereas PO at age 40 years plus MRI screening offers less effective prevention, yet similar survival (incidence, 36% to 57%; survival, 74% to 80%). To characterize patients' treatment and survivorship experiences, we reported the tumor features and treatments associated with risk-reducing interventions; for example, in most BRCA2 mutation carriers (81%), MRI screening diagnoses stage I, hormone receptor-positive breast cancers, which may not require chemotherapy. Conclusion: Cancer risk-reducing options for BRCA1/2 mutation carriers vary in their impact on cancer incidence, recommended treatments, quality of life, and survival. To guide decisions informed by multiple health outcomes, we provide an online tool for joint use by patients with their physicians (http://brcatool.stanford.edu). © 2012 by American Society of Clinical Oncology.

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