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）：癌症筛查测试利用中差异的证据在国家预防癌症预防和控制的全部努力中起着核心作用。行为风险因素监视系统（BRFSS）和国家健康访谈调查（NHIS）是两个关键资源。识别有较低筛查利用风险的人群中的群体通常始于对一组潜在的协变量和筛选测试的利用率之间关联的单变量分析，然后进行多变量分析，最常见于逻辑回归，以确定最终的变量集合，以确定获得统计学意义的最终变量集。然后将这些具有统计学意义的变量转化为需要有针对性干预措施以减少观察到的差异的人群群体。这些干预措施的目的是提供改善对筛查测试的访问和利用所需的资源。 这项拟议的研究旨在研究分类分析的潜力，以告知识别高危人群以筛查计划和服务的过程。分类分析使用数据集的进行性分割（即递归分区），以识别因变量（例如癌症筛查）对率较高而较低率的组。这些组中的每一个都由特定特征组合定义。这两年的二级分析R21提案的具体目的是：目标1.）将分类方法应用于2006和2008 BRFSS以及2005年和2008年NHIS，以识别基于多变量分类的较低筛查利用率的人群中的群体。筛查域将用于乳房，宫颈，结直肠癌和前列腺癌。 目标2.）检查跨分类方法获得的结果，以便更好地了解如何最好地利用使用分类方法来识别低癌症筛查的人群中的群体的潜力。 目标3.）使用分类方法来研究BRFSS州选项模块的内容是否可以补充来自核心BRFS的信息，以识别可能具有较低利用率和复杂服务需求的组。 通过CTA方法告知的高风险组的多变量分类可以增强受众对需求评估，障碍/促进者识别的重要干预前任务的靶向，并编写改善这些组筛查状态所需的资源。 公共卫生相关性： 国家级调查用于确定需要额外资源和服务的人群中的群体，以改善其对癌症筛查测试的利用。重要的是要继续开发识别这些群体的方法，以便那些有低使用风险的人可以针对筛查计划和服务。这项研究将研究“分类分析”方法如何改善对癌症筛查测试（乳腺癌，结直肠，宫颈，前列腺）的使用量较低的人群中的识别。