癌症流行病学-猫眼课题宝

癌症流行病学

批准号：

10438633

负责人：

CELESTE Leigh PEARCE

金额：

$3.94万

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：

1997

资助国家：

美国

项目状态：

已结题

起止时间：

1997-06-01 至 2023-05-31

来源：

https://reporter.nih.gov/project-details/10438633

关键词：

Address Advanced Malignant Neoplasm Affect Big Data Methods Biostatistical Methods Biotechnology Black Populations Cancer Burden Cancer Center Support Grant Cancer Control Cancer Etiology Catchment Area Characteristics Clinic Colorectal Cancer Complement Complex Data Early Diagnosis Early treatment Effectiveness Environmental Risk Factor Epigenetic Process Estrogens Funding Genetic Genetic Heterogeneity Genetic Risk Genomics Goals Head Cancer Image Incidence Individual Industry Internal Medicine International Intervention Knowledge Life Style Malignant Neoplasms Malignant neoplasm of esophagus Malignant neoplasm of lung Malignant neoplasm of ovary Medical Menopause Methodology Minority Groups Mission Molecular Neck Cancer Oncology Online Systems Paper Population Population Sciences Prevention Prevention Research Prevention program Prevention strategy Primary Prevention Prognostic Factor Prostate Cancer therapy Proteomics Publications Publishing Quality of life Race Research Research Personnel Research Support Resource Sharing Risk Risk Factors Risk Reduction Schools Science Scientist Secondary Prevention Shapes Societal Factors Strategic vision Techniques Technology Testing Tobacco use Translating University of Michigan Comprehensive Cancer Center Work analytical method anticancer research base cancer epidemiology cancer health disparity cancer prevention cancer risk colorectal cancer screening cost effective early onset colorectal cancer epidemiology study health disparity imaging approach improved improved outcome individualized prevention innovation interest lung cancer prevention lung cancer screening mathematical methods member metabolomics modifiable lifestyle factors mortality novel novel chemoprevention personalized screening population health prevent programs protective factors racial disparity risk prediction tertiary prevention tool

中文摘要

翻译

项目摘要/摘要(癌症流行病学和预防) 癌症流行病学和预防(CEP)计划是一组跨学科的人口健康和进行研究以阐明癌症的环境、生活方式和遗传原因的医学科学家，以及制定和测试预防和降低癌症风险的战略和干预措施。CEP计划成员还将重点放在开发统计和数学方法以推进癌症研究。该计划是由来自17个不同部门和3个学院的29名成员组成，年度直接研究总额 580万美元的支持-其中470万美元(81%)来自NCI。会员工作效率很高，有533名会员项目期间的出版物，其中17.4%是方案内出版物，35.3%是方案间出版物程序化的。调查人员参与方案内和方案间的互动，并积极协作与密歇根大学综合癌症中心其他项目的研究人员合作。CEP 该方案有三个主要的具体目标：1)阐明常见和高死亡率的风险和保护因素癌症，重点关注生活方式、环境和分子暴露；2)发现和实施初级、二级和三级预防战略，包括环境和个人暴露以及最先进的基因组、蛋白质组、代谢组和成像生物技术；3)开发和运作新的生物统计和分析方法，用于大数据和个性化预防和筛查。

英文摘要

PROJECT SUMMARY/ABSTRACT (CANCER EPIDEMIOLOGY AND PREVENTION) The Cancer Epidemiology and Prevention (CEP) Program is a group of interdisciplinary population health and medical scientists that conduct research to elucidate environmental, lifestyle and genetic causes of cancer, and develop and test strategies and interventions to prevent and reduce the risk of cancer. CEP program members also focus on developing statistical and mathematical approaches to advance cancer research. The program is comprised of 29 members from 17 different departments and three schools with total annual direct research support of $5.8 million - of which $4.7 million (81%) is from the NCI. Members are highly productive, with 533 publications during the project period, of which 17.4% are intra-programmatic and 35.3% are inter- programmatic. Investigators are involved in intra- and inter-programmatic interactions and actively collaborate with researchers in the other University of Michigan Comprehensive Cancer Center programs. The CEP program has three main specific aims: 1) Elucidate risk and protective factors for common and high-mortality cancers, with a focus on lifestyle, environmental and molecular exposures; 2) Discover and implement primary, secondary and tertiary prevention strategies incorporating environmental and personal exposures and state-of- the-art genomic, proteomic, metabolomic, and imaging biotechnologies; and 3) Develop and operationalize novel biostatistical and analytic methods for big data and personalized prevention and screening.

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