Development of Small Area Interactive Risk Maps for Cancer Control Efforts

为癌症控制工作开发小区域交互式风险地图

• 批准号: 10645147
• 负责人: Jacob Oleson
• 金额: $ 30.99万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10645147
• 关键词: Age; Area; Atlas of Cancer Mortality in the United States; Bayesian Modeling; Cancer Burden; Cancer Center; Cancer Control; Cessation of life; Code; Collaborations; Communication; Communities; Computer software; County; Data; Development; Educational Materials; Ensure; Future; Geographic Locations; Geography; Health; Incidence; Individual; Intervention; Iowa; Kentucky; Lead; Malignant Neoplasms; Maps; Methodology; Methods; Modeling; Mortality Map; National Cancer Institute; Needs Assessment; New Mexico; Online Systems; Outcome; Pattern; Population; Prevention; Privacy; Probability; Public Health; Publishing; Registries; Research; Research Personnel; Resource Allocation; Risk; Risk Adjustment; Risk Estimate; Risk Factors; Rural; Rural Population; SEER Program; Sample Size; Site; Socioeconomic Factors; Statistical Methods; Statistical Models; Surveys; Time; Update; Visualization; Work; Writing; age group; cancer risk; data visualization; interest; lifestyle factors; mortality; neoplasm registry; novel; open source; rural area; surveillance data; tool; trend; trustworthiness; user-friendly; web site

PROJECT SUMMARY This study will use a Bayesian hierarchical modeling statistical framework to obtain rates of new cancer cases and cancer deaths at the ZIP-code level, which will allow cancer control researchers and practitioners to focus their efforts on populations at greatest risk. The Bayesian hierarchical model will produce small area estimates by borrowing strength from neighboring ZIP-codes as well as over time. Typically, the ZIP-code levels would need to be combined to much larger regions to obtain sample sizes big enough to either de-identify individuals or stabilize the variance, or both. We employ a zero-inflated model since many ZIP-codes will have small and especially zero counts in a given year and age group. Given that the challenges of, and needs for, calculating small area cancer estimates is greatest in rural areas, we will focus on the largely rural states of Iowa, Kentucky, and New Mexico. We will then construct user friendly maps and other interactive graphics that can be tailored and included on cancer control/public health websites and develop communication and educational materials to promote the use of these maps for cancer control purposes. The result is an interactive visualization approach that displays age-adjusted cancer rates, risk of cancer relative to the state average, and levels of confidence, all at the small area ZIP-code level even for rural areas with small and zero counts. The probabilities and cancer risk estimates will be disseminated through a widely available platform that is easy to use and understand.

项目摘要 这项研究将使用贝叶斯分层模型统计框架，以获得新的 癌症病例和癌症死亡在邮政编码水平，这将使癌症控制 研究人员和从业人员将工作重点放在风险最大的人群上。贝叶斯 层次模型将通过从邻近区域借用强度来产生小面积估计值 随着时间的推移，ZIP代码也在不断增加。通常，需要将ZIP代码级别组合起来， 更大的区域，以获得足够大的样本量，以去识别个体， 稳定方差，或两者兼有。我们采用零膨胀模型，因为许多ZIP码将具有 在特定的年份和年龄组中数量少，尤其是零。鉴于面临的挑战， 和需要，计算小面积癌症估计是最大的农村地区，我们将重点 主要分布在爱荷华州、肯塔基州和新墨西哥州等农村地区。然后我们将构造用户 友好的地图和其他交互式图形，可以定制和包括在癌症 控制/公共卫生网站，编制宣传和教育材料， 将这些地图用于癌症控制目的。其结果是一个交互式的可视化 这种方法显示了年龄调整后的癌症发病率，相对于州平均水平的癌症风险， 和信心水平，所有在小地区邮政编码的水平，即使是农村地区的小， 零计数。概率和癌症风险估计将通过一个广泛的 易于使用和理解的可用平台。