{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T02:05:56Z","timestamp":1767924356553,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T00:00:00Z","timestamp":1627948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>The US Centers for Disease Control and Prevention alerted of a suspected outbreak of lung illness associated with using E-cigarette products in September 2019. At the time that the CDC published its alert little was known about the causes of the outbreak or who was at risk for it. Here we provide insights into the outbreak through analysis of passive reporting and participatory surveillance. We collected data about vaping habits and associated adverse reactions from four data sources pertaining to people in the USA: A participatory surveillance platform (YouVape), Reddit, Google Trends, and Bing. Data were analyzed to identify vaping behaviors and reported adverse events. These were correlated among sources and with prior reports. Data was obtained from 720 YouVape users, 4331 Reddit users, and over 1 million Bing users. Large geographic variation was observed across vaping products. Significant correlation was found among the data sources in reported adverse reactions. Models of participatory surveillance data found specific product and adverse reaction associations. Specifically, cannabidiol was found to be associated with fever, while tetrahydrocannabinol was found to be correlated with diarrhea. Our results demonstrate that utilization of different, complementary, online data sources provide a holistic view of vaping associated lung injury while augmenting traditional data sources.<\/jats:p>","DOI":"10.3390\/ijerph18158203","type":"journal-article","created":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T08:16:39Z","timestamp":1627978599000},"page":"8203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data"],"prefix":"10.3390","volume":"18","author":[{"given":"Yulin","family":"Hswen","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA 94158, USA"},{"name":"Bakar Computational Health Sciences Institute, University of California at San Francisco, San Francisco, CA 94143, USA"},{"name":"Innovation Program, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2380-4584","authenticated-orcid":false,"given":"Elad","family":"Yom-Tov","sequence":"additional","affiliation":[{"name":"Microsoft Research Israel, 3 Alan Turing Str., Herzeliya 4672415, Israel"},{"name":"Faculty of Industrial Engineering and Management, Technion, Haifa 3200000, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,3]]},"reference":[{"key":"ref_1","unstructured":"(2019, September 06). 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