Internet-based surveillance of Influenza-like-illness in the UK during the 2009 H1N1 influenza pandemic
Internet-based surveillance of Influenza-like-illness in the UK during the 2009 H1N1 influenza pandemic
复制标题
DOI：
10.1186/1471-2458-10-650
复制
发表时间：
2010-10-27
影响因子：
4.5
通讯作者：
Edmunds, W. John
中科院分区：
文献类型：
--
作者：
Tilston, Natasha L.;Eames, Ken T. D.;Edmunds, W. John
背景：监测流感样疾病（ILI）的基于互联网的监测系统比传统的（以医生为基础的）报告系统具有优势，因为它们可能监测范围更广的病例（即包括那些不寻求治疗的病例）。然而，要求参与者接入互联网并积极参与，这就对数据的代表性提出了质疑。在过去的几年里，这样的制度已经在一些欧洲国家实施，并在2009年7月扩展到英国。在这里，我们提出了调查结果，目的是评估数据的可靠性，并评估纠正可能偏差的方法。方法：在英国甲型h1n1流感大流行第一波高峰前后启动基于互联网的ILI监测。我们将记录的ILI发病率与医生记录的发病率和对流行过程中真实病例数的估计进行了比较。我们还比较了总体攻击率。探讨了不同ILI定义和替代分母假设对发病率估计的影响。结果：基于互联网的系统测量的粗略发病率似乎受到只参加一次调查的个人的影响，他们看起来更有可能生病。这扭曲了总体发病率趋势。将注意力集中在报告不止一次的个体上，得出的ILI发病率时间序列与病例估计的趋势相当接近，相关性为0.713 （p值：0.0001,95% CI: 0.435, 0.867）。事实上，与医生记录的发病率相比，基于互联网的系统似乎能更好地估计英国两波流感大流行的相对高度。然而，总体攻击率高于其他估计，约为16%，而基于模型的估计为6%。结论：如果采用适当的加权方法来纠正差异反应，基于互联网的ILI监测可以捕捉病例数的趋势。然而，发病率的总体水平很难衡量。基于互联网的系统可能是现有ILI监测系统的有用辅助，因为它们可以捕获不一定接触卫生保健的病例。然而，在它们被用来准确评估社区发病率的绝对水平之前，还需要进一步的研究。
Background: Internet-based surveillance systems to monitor influenza-like illness (ILI) have advantages over traditional (physician-based) reporting systems, as they can potentially monitor a wider range of cases (i.e. including those that do not seek care). However, the requirement for participants to have internet access and to actively participate calls into question the representativeness of the data. Such systems have been in place in a number of European countries over the last few years, and in July 2009 this was extended to the UK. Here we present results of this survey with the aim of assessing the reliability of the data, and to evaluate methods to correct for possible biases.Methods: Internet-based monitoring of ILI was launched near the peak of the first wave of the UK H1N1v influenza pandemic. We compared the recorded ILI incidence with physician-recorded incidence and an estimate of the true number of cases over the course of the epidemic. We also compared overall attack rates. The effect of using different ILI definitions and alternative denominator assumptions on incidence estimates was explored.Results: The crude incidence measured by the internet-based system appears to be influenced by individuals who participated only once in the survey and who appeared more likely to be ill. This distorted the overall incidence trend. Concentrating on individuals who reported more than once results in a time series of ILI incidence that matches the trend of case estimates reasonably closely, with a correlation of 0.713 (P-value: 0.0001, 95% CI: 0.435, 0.867). Indeed, the internet-based system appears to give a better estimate of the relative height of the two waves of the UK pandemic than the physician-recorded incidence. The overall attack rate is, however, higher than other estimates, at about 16% when compared with a model-based estimate of 6%.Conclusion: Internet-based monitoring of ILI can capture the trends in case numbers if appropriate weighting is used to correct for differential response. The overall level of incidence is, however, difficult to measure. Internet-based systems may be a useful adjunct to existing ILI surveillance systems as they capture cases that do not necessarily contact health care. However, further research is required before they can be used to accurately assess the absolute level of incidence in the community.