Using Big Data to Estimate the Effects of Complex Cost-Sharing Rules on Colorectal Cancer Screening and Patient Health

使用大数据估计复杂的费用分摊规则对结直肠癌筛查和患者健康的影响

• 批准号: 9373907
• 负责人: Christopher Whaley
• 金额: $ 20.85万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-07 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9373907
• 关键词: Adult; Affect; Age; Big Data; Cancer Detection; Cancer Etiology; Caring; Colonoscopy; Colorectal Cancer; Complex; Cost Sharing; Data; Data Analyses; Data Sources; Databases; Deductibles; Diagnostic; Diagnostic Services; Early Diagnosis; Econometric Models; Environment; Face; Future; Growth; Guidelines; Health; Health Planning; Heterogeneity; Individual; Insurance; Insurance Benefits; Insurance Carriers; Knowledge; Link; Machine Learning; Malignant Neoplasms; Medical; Methodology; Modeling; Paper; Patients; Policies; Policy Maker; Population; Population Heterogeneity; Preventive; Reporting; Research; Research Design; Research Personnel; Screening for cancer; Services; Site; Source; Surgical complication; Survival Rate; Techniques; Testing; Time; Uncertainty; United States; Variant; Vulnerable Populations; base; colon cancer patients; colorectal cancer screening; compliance behavior; data mining; design; experience; improved; learning strategy; member; mortality; novel; patient population; prevent; response; screening; treatment effect

Colorectal cancer is the third-leading cause of cancer-related mortality and approximately 5% of individuals will develop colorectal cancer during their lifetimes. However, colorectal cancer is highly preventable if detected early. Colonoscopies are the most common form of colorectal cancer screenings and all adults between the ages of 50 and 74 are recommended to receive a colonoscopy at least once every 10 years. This study will examine how two recent policies, coverage mandates and high-deductible health plans, that change consumer cost-sharing for colonoscopies have changed patient adherence to these guidelines among the commercially insured population. This study will also examine how changes in patient utilization of colonoscopies have led to changes in colorectal cancer detection and mortality. These policies have created a rapidly changing cost- sharing environment for patients and so fully understanding the patient health effects of these policies is will help inform policy makers on the patient health effects of these changes. Recent years have also seen a rapid increase in data, computing power, and analytical methodologies. This study will apply recent advances in data mining techniques to one of the largest sources of data available to researchers. The machine learning approaches used in this study will two sources of bias that are potentially present in traditional approaches—multiple hypothesis testing and selective reporting of results. The machine learning model approaches will also be used to estimate heterogeneity in treatment effects, which will help inform policy makers of how to tailor cost-sharing policies for colonoscopy services.

结直肠癌是癌症相关死亡率的第三大原因，大约5%的个体将 在他们的一生中患上结肠直肠癌。然而，如果发现结肠直肠癌， 早了结肠镜检查是结直肠癌筛查的最常见形式， 建议50岁至74岁的人至少每10年接受一次结肠镜检查。本研究将 研究最近的两项政策，覆盖任务和高免赔额的健康计划， 结肠镜检查的费用分摊已经改变了患者对这些指南的依从性， 受保人口。本研究还将研究患者使用结肠镜检查的变化如何导致 结直肠癌检测和死亡率的变化。这些政策造成了迅速变化的成本- 为患者提供共享环境，并充分了解这些政策对患者健康的影响， 帮助决策者了解这些变化对患者健康的影响。 近年来，数据、计算能力和分析方法也迅速增加。这 这项研究将把数据挖掘技术的最新进展应用于最大的数据来源之一， 研究人员本研究中使用的机器学习方法将潜在的两个偏差来源 存在于传统方法中-多重假设检验和选择性报告结果。机器 学习模型方法也将用于估计治疗效果的异质性，这将有助于 告知决策者如何为结肠镜检查服务制定费用分摊政策。