Classification Tree Analysis to Enhance Targeting for Cancer Screening Programs

分类树分析可增强癌症筛查计划的针对性

• 批准号: 7787730
• 负责人: WILLIAM RAKOWSKI
• 金额: $ 17.59万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7787730
• 关键词: Address; Adoption; Behavioral Risk Factor Surveillance System; Breast; Cancer Control; Caring; Cervical; Characteristics; Classification; Colorectal; Complex; Data Set; Diffusion; Group Identifications; Hand; Improve Access; Intervention; Life; Logic; Logistic Regressions; Low income; Malignant neoplasm of prostate; Marshal; Methods; Minority Groups; Modeling; Morbidity - disease rate; National Breast and Cervical Cancer Early Detection Program; National Health Interview Survey; Needs Assessment; Odds Ratio; Persons; Play; Population; Population Group; Populations at Risk; Process; Prostate; Relative (related person); Research; Research Project Grants; Resources; Risk; Risk Factors; Role; Screening for cancer; Screening procedure; Services; Source; Specific qualifier value; Surveys; Targeted Research; Techniques; Testing; Translating; Trees; Woman; base; cancer prevention; evidence base; improved; member; mortality; programs; public health relevance

DESCRIPTION (provided by applicant): Evidence of disparities in the utilization of cancer screening tests plays a central role in the nation's total set of efforts for cancer prevention and control. The Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) are two key resources. Identifying groups in the population who are at risk of lower screening utilization typically starts with univariate analyses of associations between a set of potential covariates and utilization rates of a screening test, followed by multivariable analysis, most often using logistic regression, to determine the final set of variables that achieve statistical significance. These statistically significant variables are then translated into population groups who need targeted interventions to reduce the observed disparities. Those interventions have the objective of providing resources necessary for improving access to, and utilization of, screening tests. This proposed research is intended to examine the potential for Classification Analysis to inform the process of identifying at-risk groups for screening programs and services. Classification Analysis uses progressive segmentation of a dataset (i.e., recursive partitioning), to identify groups with higher versus lower rates on a dependent variable (e.g., cancer screening). Each of these groups is defined by a specific combination of characteristics. The Specific Aims of this two-year, secondary analysis R21 proposal are: Aim 1.) To apply classification methods to the 2006 and 2008 BRFSS, and to the 2005 and 2008 NHIS, in order to identify groups in the population at risk of lower screening utilization based on multivariable classification. Screening domains will be for breast, cervical, colorectal, and prostate cancer. Aim 2.) To examine the results obtained across classification methods, in order to better understand how best to capitalize on the potential of using classification methods to identify groups in the population at-risk of low cancer screening. Aim 3.) To use classification methods to investigate whether content from BRFSS state-optional modules can supplement information from the core BRFSS to identify groups likely to have low utilization and complex service needs. Multivariable classification of at-risk groups, informed by CTA methods, could enhance the targeting of audiences for the important pre-intervention tasks of needs assessment, barrier/facilitator identification, and marshalling the resources needed to improve the screening status of those groups. PUBLIC HEALTH RELEVANCE: National-level surveys are used to identify groups in the population who need extra resources and services in order to improve their utilization of cancer screening tests. It is important to continue to develop ways of identifying these groups, so that those at risk of low use can be targeted for screening programs and services. This research will examine how "classification analysis" methods can improve the identification of groups in the population who have low utilization of cancer screening tests (breast, colorectal, cervical, prostate).

描述(由申请人提供)：癌症筛查测试使用差异的证据在全国癌症预防和控制的整体努力中发挥着核心作用。行为危险因素监测系统(BRFSS)和国家健康访谈调查(NHIS)是两个关键资源。确定人群中面临筛查利用率较低风险的群体通常首先对一组潜在协变量与筛查测试的利用率之间的关联进行单变量分析，然后进行多变量分析，最常见的是使用Logistic回归，以确定达到统计意义的最终变量集。然后，这些具有统计意义的变量被转化为需要有针对性的干预措施以减少观察到的差异的人口群体。这些干预措施的目的是提供必要的资源，以改善获得和利用筛查测试的机会。这项拟议的研究旨在检查分类分析的潜力，以便为筛查计划和服务识别高危群体的过程提供信息。分类分析使用数据集的渐进式分割(即递归分割)，以识别因变量(例如癌症筛查)的比率较高与较低的组。这些群体中的每一个都由特定的特征组合来定义。这项为期两年的次级分析R21提案的具体目标是：目标1。)将分类方法应用于2006年和2008年的BRFSS以及2005年和2008年的NHIS，以便根据多变量分类确定人群中筛查利用率较低的风险群体。筛查领域将是乳腺癌、宫颈癌、结直肠癌和前列腺癌。目标2。)检查通过分类方法获得的结果，以便更好地了解如何最好地利用分类方法的潜力来确定低癌症筛查风险人群中的群体。目标3。)使用分类方法调查BRFSS状态可选模块的内容是否可以补充核心BRFSS的信息，以确定可能具有低利用率和复杂服务需求的群体。通过CTA方法对高危群体进行多变量分类，可以加强针对受众的目标，以开展重要的干预前任务，如需求评估、障碍/促进者的确定，以及调集改善这些群体筛查状况所需的资源。 公共卫生相关性： 国家一级的调查被用来确定人口中需要额外资源和服务的群体，以便改善他们对癌症筛查测试的利用。重要的是继续开发识别这些群体的方法，以便那些处于低使用率风险的人能够成为筛查计划和服务的目标。这项研究将研究“分类分析”方法如何改进对癌症筛查测试(乳腺癌、结直肠癌、子宫颈癌、前列腺癌)使用率低的人群的识别。