The representativeness of a European multi-center network for influenza-like-illness participatory surveillance
The representativeness of a European multi-center network for influenza-like-illness participatory surveillance
复制标题
DOI：
10.1186/1471-2458-14-984
复制
发表时间：
2014-09-20
影响因子：
4.5
通讯作者：
Colizza, Vittoria
中科院分区：
文献类型：
--
作者：
Cantarelli, Pietro;Debin, Marion;Colizza, Vittoria
背景：互联网越来越普遍地用作疾病监测工具。与其他监测系统和使用在线数据收集的研究类似，基于互联网的监测在参与方面会存在偏差，影响结果的普遍性。在这里，我们量化了 Influenzanet 的参与偏差，Influenzanet 是一个持续的欧洲范围内基于互联网的参与性流感样疾病监测系统网络。方法：2011/2012 年，Influenzanet 推出了适用于七个欧洲国家的标准化数据收集通用框架。将 Influenzanet 参与者与参与国家的一般人口进行比较，以评估样本在一组人口、地理、社会经济和健康指标方面的代表性。结果：2011/2012 季节有超过 30,000 名欧洲居民在该系统中注册，并选出了 25,481 名参与者的子集参加本研究。该队列涵盖了所有年龄段（10 岁），包括 10 岁以下和 70 岁以上。就年龄分布而言，流感人群并不代表总人口，未能充分代表最年轻和最年长的年龄层。各国之间的性别失衡有所不同。特定性别的信息寻求行为（在女性中更为突出）和互联网使用（在男性人群中比例更高）之间的平衡可能是这种差异的根源。经过人口指标调整后，每个国家都观察到类似的通勤倾向，并且七个国家中有六个国家使用相同的前三种交通方式。大多数国家的吸烟者和糖尿病患者所占比例不足；连续两个季节（2010/2011 和 2011/2012）65 岁以上个体的哮喘患病率和疫苗接种覆盖率的代表性因国家而异。结论：现有的人口和国家数据集允许量化一般人群中流感样疾病监测的大型队列的参与偏差。 Influenzanet 参与者与普通人群之间存在显着差异。在分析流感流行病学研究时需要考虑量化偏差，并为招募工作应针对的人群提供指示。
Background: The Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness.Methods: In 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators.Results: More than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries.Conclusions: Existing demographic and national datasets allowed the quantification of the participation biases of a large cohort for influenza-like-illness surveillance in the general population. Significant differences were found between Influenzanet participants and the general population. The quantified biases need to be taken into account in the analysis of Influenzanet epidemiological studies and provide indications on populations groups that should be targeted in recruitment efforts.