{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T11:07:33Z","timestamp":1791284853315,"version":"4.1.0"},"reference-count":71,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"David P. Nolan Charitable Fund"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>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\u2019s search engine makes Google user search data a compelling data source for epidemiological research.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Objective<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Materials and methods<\/jats:title>\n<jats:p>We analyzed internet search rates for Lyme disease-related queries at the designated market area (DMA) level (n = 206) for the 2011\u20132019 and 2020\u20132021 (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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Results<\/jats:title>\n<jats:p>Mapping DMA-level changes in \u201cLyme disease\u201d 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 \u201cLyme disease\u201d 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0312277","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T18:21:06Z","timestamp":1731522066000},"page":"e0312277","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Identifying the geographic leading edge of Lyme disease in the United States with internet searches: A spatiotemporal analysis of Google Health Trends data"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2875-8812","authenticated-orcid":true,"given":"Cara","family":"Wychgram","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John N.","family":"Aucott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alison W.","family":"Rebman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank C.","family":"Curriero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"issue":"22","key":"pone.0312277.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15585\/mmwr.ss6622a1","article-title":"Surveillance for Lyme disease\u2014United States, 2008\u20132015","volume":"66","author":"AM Schwartz","year":"2017","journal-title":"MMWR Surveill Summ"},{"issue":"2","key":"pone.0312277.ref002","doi-asserted-by":"crossref","first-page":"499","DOI":"10.3201\/eid2702.202728","article-title":"Use of commercial claims data for evaluating trends in Lyme disease diagnoses, United States, 2010\u20132018","volume":"27","author":"AM Schwartz","year":"2021","journal-title":"Emerg Infect Dis"},{"key":"pone.0312277.ref003","volume-title":"The impacts of climate change on human health in the United States: a scientific assessment","author":"CB Beard","year":"2016"},{"issue":"2","key":"pone.0312277.ref004","doi-asserted-by":"crossref","first-page":"616","DOI":"10.3201\/eid2702.202731","article-title":"Estimating the frequency of Lyme disease diagnoses, United States, 2010\u20132018","volume":"27","author":"KJ Kugeler","year":"2021","journal-title":"Emerg Infect Dis"},{"issue":"10","key":"pone.0312277.ref005","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.3201\/eid2710.210903","article-title":"Effects of COVID-19 pandemic on reported Lyme disease, United States, 2020","volume":"27","author":"DW McCormick","year":"2021","journal-title":"Emerg Infect Dis"},{"issue":"2","key":"pone.0312277.ref006","doi-asserted-by":"crossref","first-page":"e0116767","DOI":"10.1371\/journal.pone.0116767","article-title":"Health care costs, utilization and patterns of care following Lyme disease","volume":"10","author":"ER Adrion","year":"2015","journal-title":"PLOS ONE"},{"issue":"9","key":"pone.0312277.ref007","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.3201\/eid2109.150344","article-title":"Enhancing Lyme disease surveillance by using administrative claims data, Tennessee, USA","volume":"21","author":"JL Clayton","year":"2015","journal-title":"Emerg Infect Dis"},{"issue":"9","key":"pone.0312277.ref008","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.3201\/eid2109.150417","article-title":"Incidence of clinician-diagnosed Lyme disease, United States, 2005\u20132010","volume":"21","author":"CA Nelson","year":"2015","journal-title":"Emerg Infect Dis"},{"issue":"10","key":"pone.0312277.ref009","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1089\/vbz.2015.1790","article-title":"Using nation-wide health insurance claims data to augment Lyme disease surveillance","volume":"15","author":"YJ Tseng","year":"2015","journal-title":"Vector Borne Zoonotic Dis"},{"issue":"2","key":"pone.0312277.ref010","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ttbdis.2018.10.010","article-title":"Epidemiology of Lyme disease in Pennsylvania 2006\u20132014 using electronic health records","volume":"10","author":"KA Moon","year":"2019","journal-title":"Ticks Tick Borne Dis"},{"issue":"1","key":"pone.0312277.ref011","first-page":"e1157","article-title":"Infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the internet","volume":"11","author":"Eysenbach G. 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