• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>PLoS Computational Biology >Deploying digital health data to optimize influenza surveillance at national and local scales
【2h】

Deploying digital health data to optimize influenza surveillance at national and local scales

机译：部署数字健康数据以优化国家和地方范围的流感监测

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.
机译：流感活动的监视对于及早发现流行病和大流行以及制定疾病控制策略至关重要。通过哨兵医生自愿网络进行病例报告是一种被动监测在全世界范围内监测流感样疾病（ILI）率的常用方法。尽管无处不在，但对前哨监视数据的观察，收集和空间聚集及其对流行病学理解的后续影响的过程却鲜有关注。我们利用数据库中代表医疗索赔的诊断代码的高度特异性，该数据库每年代表来自12万多名美国医疗保健提供者的25亿次访问。在2002-2009年和2009年大流行的流感季节中，我们模拟了前哨监视系统的局限性，例如覆盖率低和空间分辨率较粗糙，并进行了贝叶斯推理以探究生态推理的鲁棒性和疾病负担的空间预测。我们的模型表明，除了当地人群的相互作用，特定州的卫生政策以及抽样工作外，许多社会环境因素也可能是美国前哨ILI监测中的空间格局所致。此外，我们发现与空间聚集相关的偏见在疾病风险更加异构的地区之间更加突出，并且设计有固定报告位置且跨季节固定的哨兵系统可以提供可靠的推断和预测。随着世界范围内与健康相关的大数据的可用性不断提高，我们的研究结果提出了优化数字数据流的机制，以补充发达环境中的传统监视并增加发展中国家的监视机会。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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