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Comparative Modeling of Lung Cancer Prevention and Control Policies

肺癌预防和控制政策的比较模型

基本信息

批准号：
9145172
负责人：
THEODORE R HOLFORD
金额：
$ 168.03万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-16 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9145172
关键词：
Accounting Adopted Advisory Committees Age Biological Markers Cancer Burden Cancer Control Cancer Etiology Cancer Intervention and Surveillance Modeling Network Cancer Model Cause of Death Cessation of life Colon Carcinoma Cost Effectiveness Analysis Country Decision Aid Dose Education Effectiveness Eligibility Determination Ethnic Origin Future Gender Geographic Locations Goals Health Heterogeneity Incidence Income Individual Insurance Coverage Investigation Joints Lung Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of prostate Measures Mexico Modeling Multiple Birth Offspring Other Genetics Outcome Policies Population Prevalence Preventive service Protocols documentation Race Recommendation Research Personnel Risk Screening for cancer Smoker Smoking Smoking Behavior Smoking History Subgroup Surveys Thailand Tobacco Translating United States Variant Work X-Ray Computed Tomography base cancer health disparity cancer risk cohort comparative cost effectiveness health disparity high risk lung cancer prevention lung cancer screening malignant breast neoplasm models and simulation mortality network models population health prevent public policy on tobacco response screening smoking prevalence tobacco control tobacco screening trend working group

项目摘要

DESCRIPTION (provided by applicant): Lung cancer is the top cancer killer and smoking remains the leading preventable cause of death in the US. Furthermore, major disparities in smoking and lung cancer exist by education, income, and race/ethnicity. While tobacco control policies are the most effective strategies to prevent lung cancers, lung cancer computed tomography (CT) screening has also been shown to reduce lung cancer risk among heavy current and former smokers. The Cancer Intervention and Surveillance Modeling Network (CISNET) lung group develops and applies population models for lung cancer, quantifying the impact of tobacco control and CT screening on lung cancer and all-cause mortality. To date, this work has focused on the country as a whole and has yet to account for tobacco and lung cancer disparities by subgroup and region. This proposed work will extend existing CISNET lung models to investigate the synergistic impacts of tobacco control policies and lung cancer screening in the US and in middle-income nations, focusing on disparities in both smoking behavior and lung cancer risk. The smoking and lung cancer models will incorporate other factors that reflect different smoking risks such as race/ethnicity, education, income, and geographic location. This will allow for analyses of the effects of tobacco control policies on US smoking prevalence in relevant high-risk groups, and estimation of the impact of policies on health disparities in smoking and lung cancer outcomes. Models will also be adapted for two middle-income nations, Mexico and Thailand, to evaluate the impact of tobacco control policies on smoking and lung cancer rates in middle-income countries with different demographic and smoking behavior profiles than the US. Although lung cancer screening is now supported by the USPSTF and other major national organizations, there is still considerable debate over the potential benefits and harms of screening at the population level. This project will extend CISNET lung cancer screening models to assess the consequences of heterogeneity in lung cancer screening implementation, insurance coverage, and compliance with recommended protocols on population health. Models will also be used to investigate the implications of using alternative individual risk prediction measures as eligibility criteria for screening, and to perfom cost-effectiveness analyses of current and alternative screening recommendations, with the goal of identifying optimal strategies at the population level.