Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

Abstract The Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting...

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Autores principales:	Christopher H. Arehart, Michael Z. David, Vanja Dukic
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac71f77eff1e46e79590225b15d94306
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spelling	oai:doaj.org-article:ac71f77eff1e46e79590225b15d943062021-12-02T13:35:12ZTracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State10.1038/s41598-019-56385-z2045-2322https://doaj.org/article/ac71f77eff1e46e79590225b15d943062019-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-56385-zhttps://doaj.org/toc/2045-2322Abstract The Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004–2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2 = 0.2369, p < 0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.Christopher H. ArehartMichael Z. DavidVanja DukicNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christopher H. Arehart Michael Z. David Vanja Dukic Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
description	Abstract The Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004–2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2 = 0.2369, p < 0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.
format	article
author	Christopher H. Arehart Michael Z. David Vanja Dukic
author_facet	Christopher H. Arehart Michael Z. David Vanja Dukic
author_sort	Christopher H. Arehart
title	Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
title_short	Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
title_full	Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
title_fullStr	Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
title_full_unstemmed	Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
title_sort	tracking u.s. pertussis incidence: correlation of public health surveillance and google search data varies by state
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/ac71f77eff1e46e79590225b15d94306
work_keys_str_mv	AT christopherharehart trackinguspertussisincidencecorrelationofpublichealthsurveillanceandgooglesearchdatavariesbystate AT michaelzdavid trackinguspertussisincidencecorrelationofpublichealthsurveillanceandgooglesearchdatavariesbystate AT vanjadukic trackinguspertussisincidencecorrelationofpublichealthsurveillanceandgooglesearchdatavariesbystate
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Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

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