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谷歌流感趋势空间变异性与急诊科流感相关就诊情况的验证

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits.

作者信息

Klembczyk Joseph Jeffrey, Jalalpour Mehdi, Levin Scott, Washington Raynard E, Pines Jesse M, Rothman Richard E, Dugas Andrea Freyer

机构信息

Johns Hopkins University, School of Medicine, Hampstead, NC, United States.

出版信息

J Med Internet Res. 2016 Jun 28;18(6):e175. doi: 10.2196/jmir.5585.

DOI:10.2196/jmir.5585
PMID:27354313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4942685/
Abstract

BACKGROUND

Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations.

OBJECTIVE

The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness.

METHODS

Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression.

RESULTS

Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status.

CONCLUSIONS

GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness.

摘要

背景

流感是一个致命且代价高昂的公共卫生问题。其季节性模式的变化导致急诊科(ED)患者数量出现危险的激增。谷歌流感趋势(GFT)能够比美国疾病控制与预防中心(CDC)的传统方法提供更快的流感监测信息,有可能改善公共卫生防范措施。已发现GFT与报告的流感情况相关性良好,并能改进流感预测模型。然而,以往的验证研究主要集中在孤立的临床地点。

目的

本研究的目的是通过将GFT与美国19个城市在七个流感季节中与流感相关的急诊科就诊情况进行关联,来衡量GFT监测的有效性,并探讨哪些城市特征会导致GFT有效性更好或更差。

方法

利用医疗保健成本与利用项目数据,我们收集了2005 - 2011年美国19个不同城市中所有诊断为(国际疾病分类第9版)与流感相关就诊代码患者的每周急诊科就诊次数。我们测量了每个城市每周GFT搜索量与与流感相关的急诊科就诊次数之间的相关性(即GFT急诊科监测有效性)。我们使用单变量线性回归评估了15个公开可用的城市指标(11个社会人口统计学指标、2个医疗保健利用指标和2个气候指标)与GFT监测有效性之间的关系。

结果

城市层面的GFT与与流感相关的急诊科就诊次数之间的相关性中位数为0.84,19个城市的范围在0.67至0.93之间。观察到时间上的变异性,相关性中位数从2009年的0.78到2005年的0.94不等。与GFT监测改善显著相关(P<0.10)的城市指标包括女性人口比例较高、医疗保险覆盖比例较高、人均急诊科就诊次数较多以及社会经济地位较低。

结论

GFT在城市层面与急诊科流感相关就诊次数密切相关,但地理位置和时间上无法解释的变异限制了其作为独立监测工具的效用。GFT可能最有用的是作为与其他更全面监测技术结合使用的早期信号。与GFT监测改善相关的城市指标为GFT有效性的变异性提供了一些见解。例如,社会经济地位较低的人群可能更倾向于最初通过互联网寻求健康问题的答案,从而导致GFT有效性提高。GFT有潜力为急诊科医护人员提供有关患者护理的有价值信息,并为急诊科应对激增情况的管理人员提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/02428a5924a4/jmir_v18i6e175_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/fcf01fa5c455/jmir_v18i6e175_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/c6fc85ba05d7/jmir_v18i6e175_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/f3e07885ad5e/jmir_v18i6e175_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/43ac54a27c37/jmir_v18i6e175_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/02428a5924a4/jmir_v18i6e175_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/fcf01fa5c455/jmir_v18i6e175_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/c6fc85ba05d7/jmir_v18i6e175_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/f3e07885ad5e/jmir_v18i6e175_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/43ac54a27c37/jmir_v18i6e175_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfed/4942685/02428a5924a4/jmir_v18i6e175_fig5.jpg

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