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：
8967752
负责人：
THEODORE R HOLFORD
金额：
$ 175.18万
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/8967752
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 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 Protocols documentation Race Recommendation Research Personnel Risk Screening for cancer Services 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 health relevance 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.

描述（应用程序提供）：肺癌是癌症的最大杀手，吸烟仍然是美国可预防的死亡原因。此外，受教育，收入和种族/种族存在的吸烟和肺癌的主要分布。尽管烟草控制政策是预防肺癌的最有效策略，但肺癌计算机断层扫描（CT）筛查也已被证明可以降低浓度吸烟者和形式吸烟者中的肺癌风险。癌症干预和监视建模网络（CISNET）肺部开发并应用人群模型进行肺癌，量化了烟草控制和CT筛查对肺癌和全因死亡率的影响。迄今为止，这项工作一直集中在整个国家，尚未考虑亚组和地区的烟草和肺癌分布。这项提出的工作将扩展现有的CISNET肺模型，以研究美国和中等收入国家的烟草控制政策和肺癌筛查的协同影响，重点是吸烟行为和肺癌风险的差异。吸烟和肺癌模型将结合其他反映不同吸烟风险的因素，例如种族/种族，教育，收入和地理位置。这将允许分析烟草控制政策对相关高风险群体中美国吸烟率的影响，并估计政策对健康和肺癌结果中健康差异的影响。模型还将适用于墨西哥和泰国的两个中等收入国家，以评估烟草控制政策对与美国不同人口统计学和吸烟行为概况的中等收入国家中吸烟和肺癌发生率的影响。尽管现在由USPSTF和其他主要国家组织的肺癌筛查提供了支持，但关于人口水平的筛查的潜在益处和危害仍然存在很大的争论。该项目将扩展Cisnet肺癌筛查模型，以评估肺癌筛查实施，保险范围以及遵守建议人口健康方案的异质性的后果。模型还将用于研究使用替代个人风险预测措施作为筛查的资格标准的含义，以及对当前和替代性筛查建议的培训成本效益分析，目的是确定人口水平的最佳策略。