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Google Flu Trends: correlation with emergency department influenza rates and crowding metrics.
Clin Infect Dis. 2012 Feb 15; 54(4):463-9.CI

Abstract

BACKGROUND

Google Flu Trends (GFT) is a novel Internet-based influenza surveillance system that uses search engine query data to estimate influenza activity and is available in near real time. This study assesses the temporal correlation of city GFT data to cases of influenza and standard crowding indices from an inner-city emergency department (ED).

METHODS

This study was performed during a 21-month period (from January 2009 through October 2010) at an urban academic hospital with physically and administratively separate adult and pediatric EDs. We collected weekly data from GFT for Baltimore, Maryland; ED Centers for Disease Control and Prevention-reported standardized influenzalike illness (ILI) data; laboratory-confirmed influenza data; and ED crowding indices (patient volume, number of patients who left without being seen, waiting room time, and length of stay for admitted and discharged patients). Pediatric and adult data were analyzed separately using cross-correlation with GFT.

RESULTS

GFT correlated with both number of positive influenza test results (adult ED, r = 0.876; pediatric ED, r = 0.718) and number of ED patients presenting with ILI (adult ED, r = 0.885; pediatric ED, r = 0.652). Pediatric but not adult crowding measures, such as total ED volume (r = 0.649) and leaving without being seen (r = 0.641), also had good correlation with GFT. Adult crowding measures for low-acuity patients, such as waiting room time (r = 0.421) and length of stay for discharged patients (r = 0.548), had moderate correlation with GFT.

CONCLUSIONS

City-level GFT shows strong correlation with influenza cases and ED ILI visits, validating its use as an ED surveillance tool. GFT correlated with several pediatric ED crowding measures and those for low-acuity adult patients.

Authors+Show Affiliations

Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA. adugas1@jhmi.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Validation Study

Language

eng

PubMed ID

22230244

Citation

Dugas, Andrea Freyer, et al. "Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics." Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America, vol. 54, no. 4, 2012, pp. 463-9.
Dugas AF, Hsieh YH, Levin SR, et al. Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clin Infect Dis. 2012;54(4):463-9.
Dugas, A. F., Hsieh, Y. H., Levin, S. R., Pines, J. M., Mareiniss, D. P., Mohareb, A., Gaydos, C. A., Perl, T. M., & Rothman, R. E. (2012). Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America, 54(4), 463-9. https://doi.org/10.1093/cid/cir883
Dugas AF, et al. Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics. Clin Infect Dis. 2012 Feb 15;54(4):463-9. PubMed PMID: 22230244.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. AU - Dugas,Andrea Freyer, AU - Hsieh,Yu-Hsiang, AU - Levin,Scott R, AU - Pines,Jesse M, AU - Mareiniss,Darren P, AU - Mohareb,Amir, AU - Gaydos,Charlotte A, AU - Perl,Trish M, AU - Rothman,Richard E, Y1 - 2012/01/08/ PY - 2012/1/11/entrez PY - 2012/1/11/pubmed PY - 2012/5/24/medline SP - 463 EP - 9 JF - Clinical infectious diseases : an official publication of the Infectious Diseases Society of America JO - Clin. Infect. Dis. VL - 54 IS - 4 N2 - BACKGROUND: Google Flu Trends (GFT) is a novel Internet-based influenza surveillance system that uses search engine query data to estimate influenza activity and is available in near real time. This study assesses the temporal correlation of city GFT data to cases of influenza and standard crowding indices from an inner-city emergency department (ED). METHODS: This study was performed during a 21-month period (from January 2009 through October 2010) at an urban academic hospital with physically and administratively separate adult and pediatric EDs. We collected weekly data from GFT for Baltimore, Maryland; ED Centers for Disease Control and Prevention-reported standardized influenzalike illness (ILI) data; laboratory-confirmed influenza data; and ED crowding indices (patient volume, number of patients who left without being seen, waiting room time, and length of stay for admitted and discharged patients). Pediatric and adult data were analyzed separately using cross-correlation with GFT. RESULTS: GFT correlated with both number of positive influenza test results (adult ED, r = 0.876; pediatric ED, r = 0.718) and number of ED patients presenting with ILI (adult ED, r = 0.885; pediatric ED, r = 0.652). Pediatric but not adult crowding measures, such as total ED volume (r = 0.649) and leaving without being seen (r = 0.641), also had good correlation with GFT. Adult crowding measures for low-acuity patients, such as waiting room time (r = 0.421) and length of stay for discharged patients (r = 0.548), had moderate correlation with GFT. CONCLUSIONS: City-level GFT shows strong correlation with influenza cases and ED ILI visits, validating its use as an ED surveillance tool. GFT correlated with several pediatric ED crowding measures and those for low-acuity adult patients. SN - 1537-6591 UR - https://www.unboundmedicine.com/medline/citation/22230244/Google_Flu_Trends:_correlation_with_emergency_department_influenza_rates_and_crowding_metrics_ L2 - https://academic.oup.com/cid/article-lookup/doi/10.1093/cid/cir883 DB - PRIME DP - Unbound Medicine ER -