Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Lyme Disease Research Center, Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
PLoS One. 2024 Nov 13;19(11):e0312277. doi: 10.1371/journal.pone.0312277. eCollection 2024.
The geographic footprint of Lyme disease is expanding in the United States, which calls for novel methods to identify emerging endemic areas. The ubiquity of internet use coupled with the dominance of Google's search engine makes Google user search data a compelling data source for epidemiological research.
We evaluated the potential of Google Health Trends to track spatiotemporal patterns in Lyme disease and identify the leading edge of disease risk in the United States.
We analyzed internet search rates for Lyme disease-related queries at the designated market area (DMA) level (n = 206) for the 2011-2019 and 2020-2021 (COVID-19 pandemic) periods. We used maps and other exploratory methods to characterize changes in search behavior. To assess statistical correlation between searches and Lyme disease cases reported to Centers for Disease Control and Prevention (CDC) between 2011 and 2019, we performed a longitudinal ecological analysis with modified Poisson generalized estimating equation regression models.
Mapping DMA-level changes in "Lyme disease" search rates revealed an expanding area of higher rates occurring along the edges of the northeastern focus of Lyme disease. Bivariate maps comparing search rates and CDC-reported incidence rates also showed a stronger than expected signal from Google Health Trends in some high-risk adjacent states such as Michigan, North Carolina, and Ohio, which may be further indication of a geographic leading edge of Lyme disease that is not fully apparent from routine surveillance. Searches for "Lyme disease" were a significant predictor of CDC-reported disease incidence. Each 100-unit increase in the search rate was significantly associated with a 10% increase in incidence rates (RR = 1.10, 95% CI: 1.07, 1.12) after adjusting for environmental covariates of Lyme disease identified in the literature.
Google Health Trends data may help track the expansion of Lyme disease and inform the public and health care providers about emerging risks in their areas.
莱姆病在美国的地理分布范围正在扩大,这需要新的方法来确定新的流行地区。互联网的普及以及谷歌搜索引擎的主导地位,使得谷歌用户搜索数据成为流行病学研究的一个极具吸引力的数据源。
我们评估了谷歌健康趋势(Google Health Trends)在跟踪莱姆病的时空模式以及识别美国疾病风险前沿方面的潜力。
我们分析了 2011 年至 2019 年和 2020 年至 2021 年(COVID-19 大流行期间)指定市场区域(DMA)级别与莱姆病相关查询的互联网搜索率(n=206)。我们使用地图和其他探索性方法来描述搜索行为的变化。为了评估搜索与 2011 年至 2019 年向疾病预防控制中心(CDC)报告的莱姆病病例之间的统计相关性,我们使用修改后的泊松广义估计方程回归模型进行了纵向生态分析。
绘制 DMA 级别“莱姆病”搜索率的变化图,揭示了在莱姆病东北部焦点的边缘,更高搜索率的区域不断扩大。比较搜索率和 CDC 报告发病率的双变量地图还显示,谷歌健康趋势在密歇根州、北卡罗来纳州和俄亥俄州等一些高风险相邻州的信号强于预期,这可能进一步表明莱姆病的地理前沿尚未完全从常规监测中显现出来。搜索“莱姆病”是 CDC 报告疾病发病率的一个重要预测因素。在调整文献中确定的莱姆病环境协变量后,搜索率每增加 100 个单位,与发病率增加 10%相关(RR=1.10,95%CI:1.07,1.12)。
谷歌健康趋势数据可能有助于跟踪莱姆病的扩展,并告知公众和医疗保健提供者其所在地区新出现的风险。