Effects of disability on the diagnosis of breast cancer

残疾对乳腺癌诊断的影响

基本信息

批准号：
7275388
负责人：
Mabel E Caban
金额：
$ 12.82万
依托单位：
UNIVERSITY OF TEXAS MED BR GALVESTON
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-09-30 至 2010-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7275388
关键词：
Address Age Algorithms Area Arts Breast Cancer Detection Breast Cancer Treatment Caring Characteristics Clinical Research Communication Communities Computer Assisted Condition Data Databases Diagnosis Diagnostic Equipment Diagnostic Neoplasm Staging Disadvantaged Disease Doctor of Medicine Education Effectiveness Elderly Ethnic Origin Expenditure Female Future Gerontology Goals Grant Health Health Insurance Health Services Research Health Status Healthcare Hispanics Incidence Income Institution Insurance Coverage Interview Invasive Investigation Lead Life Lifting Link Malignant Neoplasms Mammography Medical Medical Surveillance Medicare Medicine Methodology Methods Minority Groups Modeling Nature Orthopedics Outcome Outcomes and Health Services Research Patients Performance Physical activity Physicians Population Positioning Attribute Principal Investigator Process Public Speaking Purpose Race Rate Registries Rehabilitation therapy Research Research Design Research Personnel Sampling Science Screening for cancer Screening procedure Socioeconomic Status Stage at Diagnosis Staging Standards of Weights and Measures Subgroup Surveys Time Transportation Tumor stage United States Walking Woman Women&apos s Group Women&apos s Health Work base beneficiary breast cancer diagnosis cancer care cancer diagnosis career college data management design disability functional disability health related quality of life improved malignant breast neoplasm novel older women oncology payment professor programs size skills socioeconomics statistics tumor

项目摘要

DESCRIPTION (provided by applicant): Candidate: Mabel Caban, M.D., is an assistant professor in the Department of Orthopedics and Rehabilitation who will serve as principal investigator of this project. As a research fellow at Baylor College of Medicine in 1999, she initiated studies on disparities in the breast cancer treatment of female patients with disabilities. Dr. Caban seeks to study disability using Medicare beneficiary claims. Career Goals: (1) Develop skills as an independent investigator on health services and outcome research; (2) Develop expertise using SEER Medicare linked database to examine the effect of disability on the diagnosis of breast cancer in older women; (3) Develop public speaking skills and improve scientific communication; and (4) Submit an R01 grant to continue the study of disability and health outcomes. Research Plan: The purpose of the study is to determine the association between disability and tumor size/stage of breast cancer diagnosis. The specific aims of the research are to: (1) Define and validate an algorithm using Medicare claims that identifies women with disabilities (functional limitations). (2) Using the disability algorithm from Aim 1, determine the effect of disability on: (a) The use of screening mammography using SEER Medicare linked database. (b) Tumor size and stage at diagnosis of breast cancer using the SEER Medicare linked database. (c) Tumor size and stage at diagnosis for different subpopulations defined by age at diagnosis, race/ethnicity, and socio-economic status using the SEER Medicare linked database. (3) Build two multivariable models (to predict size and stage) to determine the effect of disability on tumor size and stage adjusting for other relevant confounders using the SEER Medicare database. I will apply the algorithm from Aim 1. Method: The disability algorithm will be developed using Medicare Current Beneficiary Survey. The algorithm will then be applied using SEER Medicare linked database to study all breast cancer cases in women aged 69 and older who are in the SEER registries from 1995 through 1999 to determine the effect of disability on use of mammography and on tumor stage/size. First, the analysis will be unadjusted, then stratified by subgroups (age, race, stage), and then adjusted by relevant confounders in a multivariate model.

描述（由申请人提供）：候选人：Mabel Caban，M.D。是骨科和康复系的助理教授，将担任该项目的首席研究员。作为1999年贝勒医学院的研究员，她开始研究残疾女性乳腺癌治疗的差异。 Caban博士寻求使用Medicare受益人主张来研究残疾。职业目标：（1）发展技能作为卫生服务和结果研究的独立研究者；（2）使用Seer Medicare链接数据库发展专业知识，以检查残疾对老年妇女乳腺癌诊断的影响；（3）发展公开演讲技巧并改善科学沟通；（4）提交R01赠款继续研究残疾和健康结果。研究计划：研究的目的是确定残疾与肿瘤大小/乳腺癌诊断阶段之间的关联。研究的具体目的是：（1）使用Medicare主张来定义并验证算法，该算法确定残疾妇女（功能限制）。（2）使用AIM 1的残疾算法，确定残疾对：（a）使用Seer Medicare链接数据库使用筛查乳房X线摄影。（b）使用SEER Medicare链接数据库诊断乳腺癌时的肿瘤大小和阶段。（c）使用SEER Medicare链接数据库诊断，种族/种族和社会经济状况定义的不同亚群的肿瘤大小和阶段。（3）构建两个多变量模型（以预测尺寸和阶段），以确定残疾对肿瘤大小的影响，并使用SEER Medicare数据库对其他相关混杂因素进行调整。我将应用AIM 1的算法。方法：将使用Medicare当前的受益人调查来开发残疾算法。然后，该算法将使用SEER Medicare链接数据库应用，以研究从1995年至1999年在SEER注册处的69岁及以上女性的所有乳腺癌病例，以确定残疾对使用乳房X线摄影和肿瘤阶段/大小的影响。首先，分析将不受调整，然后由子组（年龄，种族，阶段）进行分层，然后在多元模型中由相关混杂因素进行调整。