Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation
Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation
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DOI：
10.2196/13403
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发表时间：
2019-10-01
影响因子：
8.5
通讯作者：
Santillana, Mauricio
中科院分区：
文献类型：
--
作者：
Baltrusaitis, Kristin;Vespignani, Alessandro;Santillana, Mauricio
背景资料：疾病控制和预防中心（CDC）通过门诊流感样疾病监测网络（ILINet）使用患者访问卫生保健提供者的信息来跟踪流感样疾病（ILI）。由于参与这一系统是自愿的，因此卫生保健报告的组成、覆盖范围和一致性因州而异，导致不同地区对ILI活动的衡量标准不同。这些指标在多大程度上反映了流感活动的实际差异或收集和汇总数据所用方法的系统差异尚不清楚。目的：我们研究的目的是定性和定量地比较美国4个监测数据源-CDC ILINet、Flu Near You（FNY）、athenahealth、和HealthTweets.org-以确定这些通常用作流感建模工作输入的数据源是否显示出与CDC ILINet数据中观察到的地理模式相似的地理模式。我们还比较了FNY参与者在不同地理区域因ILI症状寻求医疗保健的年度百分比。方法：我们比较了国家和地区2018-2019年ILI活动基线，使用前几年的非流感周计算，每个监测数据源。我们还比较了2015-2016年、2016-2017年和2017-2018年3个流感季节各地理区域的ILI活动指标。还使用相对平均差异和时间序列热图评估了每个数据源内每周ILI活动的地理差异。通过将因ILI症状寻求医疗护理的FNY参与者人数除以流感季节内ILI报告总数，计算每个流感季节的国家和地区年龄调整的医疗护理百分比。Pearson相关性被用来评估每个监测datasource.Results的医疗保健寻求百分比和基线之间的关联：我们观察到一致的差异，疾病预防控制中心ILINet和athenahealth数据的地理区域的ILI活动。FNY的ILI活动在不同地理区域之间几乎没有变化，而HealthTweets.org的ILI活动差异与地理区域内的推文总数有关。FNY参与者的百分比谁寻求医疗保健ILI症状略有不同的地理区域，这些百分比与疾病预防控制中心ILINet和athenahealth basels.Conclusions：我们的研究结果表明，ILI活动的差异，跨地理区域的报告由一个给定的监测系统可能无法准确地反映真正的差异，在ILI的患病率。相反，这些差异可能反映了每个系统特有的系统性收集和聚集偏倚，并且在流感季节之间保持一致。这些发现在流感季节的实时分析和无偏预测模型的定义中可能相关。
Background: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear.Objective: The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources-CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org-to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet's data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas.Methods: We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care-seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care-seeking percentages and baselines for each surveillance data source.Results: We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines.Conclusions: Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.