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：
7683830
负责人：
Mabel E Caban
金额：
$ 0.4万
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 2010-04-12
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7683830
关键词：
Address Age Algorithms Area Arts Breast Cancer Detection Breast Cancer Treatment Caring Characteristics Clinical Research Communication Communities Computer Assisted Data Databases Diagnosis Diagnostic Equipment Diagnostic Neoplasm Staging Disadvantaged Economically Deprived Population 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 Investigation Lead Life Lifting Link Malignant Neoplasms Mammography Medical Medical Surveillance Medicare Medicare claim 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 Race Registries Rehabilitation therapy Research Research Design Research Personnel Sampling Screening for cancer Screening procedure Socioeconomic Status Stage at Diagnosis Staging Subgroup Surveys Time Transportation Tumor stage United States Walking Woman Women&apos s Group Women&apos s Health Work advanced disease aged 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 rehabilitation science skills socioeconomics statistics tumor

项目摘要

Candidate. Mabel Caban, MD, 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.

候选人。医学博士Mabel Caban是骨科和康复系的助理教授作为该项目的主要研究员。作为1999年贝勒医学院的研究员，她发起了关于残疾女性乳腺癌治疗的差异的研究。 Caban博士试图学习使用Medicare受益人索赔的残疾。职业目标：（1）发展技能作为独立研究者卫生服务和结果研究；（2）使用Seer Medicare链接数据库来开发专业知识来检查残疾对老年妇女诊断乳腺癌的影响；（3）发展公开演讲技巧并提高科学交流；（4）提交R01赠款继续研究残疾和健康结果。研究计划。该研究的目的是确定残疾与肿瘤大小/阶段之间的关联乳腺癌诊断。研究的具体目的是：（1）使用Medicare主张来定义和验证算法，该算法确定残疾妇女（功能限制）。（2）使用AIM 1的残疾算法，确定残疾的影响：（a）使用Seer Medicare链接数据库筛查乳房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）诊断时由年龄定义在诊断，种族/种族和种族和使用Seer Medicare链接数据库的社会经济状况。（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和使用SEER Medicare数据库对其他相关混杂因素进行调整。我将应用算法目标1。方法。残疾算法将使用Medicare当前的受益人调查开发。该算法将然后使用Seer Medicare链接数据库应用于69岁及以上女性的所有乳腺癌病例从1995年到1999年，在SEER注册处，以确定残疾对使用乳房X线摄影的影响肿瘤阶段/大小。首先，分析将不受调整，然后由子组（年龄，种族，阶段）进行分层，然后调整由多元模型中的相关混杂因素。