...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Annals of epidemiology >Spatiotemporal analysis and mapping of oral cancer risk in changhua county (taiwan): an application of generalized bayesian maximum entropy method.
【24h】

Spatiotemporal analysis and mapping of oral cancer risk in changhua county (taiwan): an application of generalized bayesian maximum entropy method.

机译：彰化县（台湾）口腔癌风险的时空分析和绘图：广义贝叶斯最大熵方法的应用。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

PURPOSE: Incidence rate of oral cancer in Changhua County is the highest among the 23 counties of Taiwan during 2001. However, in health data analysis, crude or adjusted incidence rates of a rare event (e.g., cancer) for small populations often exhibit high variances and are, thus, less reliable. METHODS: We proposed a generalized Bayesian Maximum Entropy (GBME) analysis of spatiotemporal disease mapping under conditions of considerable data uncertainty. GBME was used to study the oral cancer population incidence in Changhua County (Taiwan). Methodologically, GBME is based on an epistematics principles framework and generates spatiotemporal estimates of oral cancer incidence rates. In a way, it accounts for the multi-sourced uncertainty of rates, including small population effects, and the composite space-time dependence of rare events in terms of an extended Poisson-based semivariogram. RESULTS: The results showed that GBME analysis alleviates the noises of oral cancer data from population size effect. Comparing to the raw incidence data, the maps of GBME-estimated results can identify high risk oral cancer regions in Changhua County, where the prevalence of betel quid chewing and cigarette smoking is relatively higher than the rest of the areas. CONCLUSIONS: GBME method is a valuable tool for spatiotemporal disease mapping under conditions of uncertainty.
机译：目的：彰化县的口腔癌发病率在2001年期间是台湾23个县中最高的。但是，在健康数据分析中，少数人群罕见事件（例如癌症）的粗略或调整发病率通常表现出高差异因此可靠性较差。方法：我们提出了时空疾病映射的广义贝叶斯最大熵（GBME）分析在相当大的数据不确定性条件下。 GBME用于研究彰化县（台湾）的口腔癌人群发病率。在方法论上，GBME基于认识论原理框架，并生成口腔癌发病率的时空估计。从某种意义上说，它以扩展的基于泊松的半变异函数来解释利率的多源不确定性，包括小的人口影响，以及罕见事件的复合时空依赖性。结果：结果表明GBME分析减轻了人口规模效应对口腔癌数据的影响。与原始发病率数据相比，GBME估计结果的地图可以识别出彰化县的高风险口腔癌地区，那里的槟榔咀嚼和吸烟率相对高于其余地区。结论：GBME方法是在不确定条件下进行时空疾病作图的有价值的工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号