Skip to main content
Top
Published in: BMC Emergency Medicine 1/2016

Open Access 01-12-2016 | Technical advance

Human temperatures for syndromic surveillance in the emergency department: data from the autumn wave of the 2009 swine flu (H1N1) pandemic and a seasonal influenza outbreak

Authors: Samantha F. Bordonaro, Daniel C. McGillicuddy, Francesco Pompei, Dmitriy Burmistrov, Charles Harding, Leon D. Sanchez

Published in: BMC Emergency Medicine | Issue 1/2016

Login to get access

Abstract

Background

The emergency department (ED) increasingly acts as a gateway to the evaluation and treatment of acute illnesses. Consequently, it has also become a key testing ground for systems that monitor and identify outbreaks of disease. Here, we describe a new technology that automatically collects body temperatures during triage. The technology was tested in an ED as an approach to monitoring diseases that cause fever, such as seasonal flu and some pandemics.

Methods

Temporal artery thermometers that log temperature measurements were placed in a Boston ED and used for initial triage vital signs. Time-stamped measurements were collected from the thermometers to investigate the performance a real-time system would offer. The data were summarized in terms of rates of fever (temperatures ≥100.4 °F [≥38.0 °C]) and were qualitatively compared with regional disease surveillance programs in Massachusetts.

Results

From September 2009 through August 2011, 71,865 body temperatures were collected and included in our analysis, 2073 (2.6 %) of which were fevers. The period of study included the autumn–winter wave of the 2009–2010 H1N1 (swine flu) pandemic, during which the weekly incidence of fever reached a maximum of 5.6 %, as well as the 2010–2011 seasonal flu outbreak, during which the maximum weekly incidence of fever was 6.6 %. The periods of peak fever rates corresponded with the periods of regionally elevated flu activity.

Conclusions

Temperature measurements were monitored at triage in the ED over a period of 2 years. The resulting data showed promise as a potential surveillance tool for febrile disease that could complement current disease surveillance systems. Because temperature can easily be measured by non-experts, it might also be suitable for monitoring febrile disease activity in schools, workplaces, and transportation hubs, where many traditional syndromic indicators are impractical. However, the system’s validity and generalizability should be evaluated in additional years and settings.
Appendix
Available only for authorised users
Literature
1.
go back to reference Hiller KM, Stoneking L, Min A, Rhodes SM. Syndromic surveillance for influenza in the emergency department-A systematic review. PLoS One. 2013;8, e73832.CrossRefPubMedPubMedCentral Hiller KM, Stoneking L, Min A, Rhodes SM. Syndromic surveillance for influenza in the emergency department-A systematic review. PLoS One. 2013;8, e73832.CrossRefPubMedPubMedCentral
2.
go back to reference Rosenkötter N, Ziemann A, Riesgo LG-C, Gillet JB, Vergeiner G, Krafft T, Brand H. Validity and timeliness of syndromic influenza surveillance during the autumn/winter wave of A (H1N1) influenza 2009: results of emergency medical dispatch, ambulance and emergency department data from three European regions. BMC Public Health. 2013;13:905. Rosenkötter N, Ziemann A, Riesgo LG-C, Gillet JB, Vergeiner G, Krafft T, Brand H. Validity and timeliness of syndromic influenza surveillance during the autumn/winter wave of A (H1N1) influenza 2009: results of emergency medical dispatch, ambulance and emergency department data from three European regions. BMC Public Health. 2013;13:905.
3.
go back to reference Muscatello DJ, Amin J, MacIntyre CR, Newall AT, Rawlinson WD, Sintchenko V, et al. Inaccurate ascertainment of morbidity and mortality due to influenza in administrative databases: a population-based record linkage study. PLoS One. 2014;9, e98446.CrossRefPubMedPubMedCentral Muscatello DJ, Amin J, MacIntyre CR, Newall AT, Rawlinson WD, Sintchenko V, et al. Inaccurate ascertainment of morbidity and mortality due to influenza in administrative databases: a population-based record linkage study. PLoS One. 2014;9, e98446.CrossRefPubMedPubMedCentral
5.
go back to reference Thompson WW, Comanor L, Shay DK. Epidemiology of seasonal influenza: use of surveillance data and statistical models to estimate the burden of disease. J Infect Dis. 2006;194(Suppl):S82–91.CrossRefPubMed Thompson WW, Comanor L, Shay DK. Epidemiology of seasonal influenza: use of surveillance data and statistical models to estimate the burden of disease. J Infect Dis. 2006;194(Suppl):S82–91.CrossRefPubMed
6.
go back to reference Olson DR, Paladini M, Lober WB, Buckeridge DL. Applying a new model for sharing population health data to national syndromic influenza surveillance: DiSTRIBuTE project proof of concept, 2006 to 2009. PLoS Curr. 2011;3, RRN1251.CrossRefPubMedPubMedCentral Olson DR, Paladini M, Lober WB, Buckeridge DL. Applying a new model for sharing population health data to national syndromic influenza surveillance: DiSTRIBuTE project proof of concept, 2006 to 2009. PLoS Curr. 2011;3, RRN1251.CrossRefPubMedPubMedCentral
7.
go back to reference Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012–4.CrossRefPubMed Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012–4.CrossRefPubMed
8.
go back to reference Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014;343:1203–5.CrossRefPubMed Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014;343:1203–5.CrossRefPubMed
9.
go back to reference Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol. 2013;9, e1003256.CrossRefPubMedPubMedCentral Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol. 2013;9, e1003256.CrossRefPubMedPubMedCentral
11.
go back to reference Lazer D, Kennedy R. What we can learn from the epic failure of Google Flu Trends. Wired. 2015. Lazer D, Kennedy R. What we can learn from the epic failure of Google Flu Trends. Wired. 2015.
12.
go back to reference Gluskin RT, Johansson MA, Santillana M, Brownstein JS. Evaluation of Internet-Based Dengue Query Data: Google Dengue Trends. PLoS Negl Trop Dis. 2014;8, e2713.CrossRefPubMedPubMedCentral Gluskin RT, Johansson MA, Santillana M, Brownstein JS. Evaluation of Internet-Based Dengue Query Data: Google Dengue Trends. PLoS Negl Trop Dis. 2014;8, e2713.CrossRefPubMedPubMedCentral
13.
go back to reference Marx J, Hockberger R, Walls R, Adams J. Rosen’s Emergency Medicine: Concepts and Clinical Practice. 7th ed. Philidelphia: Mosby/Elsevier; 2010. Marx J, Hockberger R, Walls R, Adams J. Rosen’s Emergency Medicine: Concepts and Clinical Practice. 7th ed. Philidelphia: Mosby/Elsevier; 2010.
14.
go back to reference Trautner BW, Caviness AC, Gerlacher GR, Demmler G, Macias CG. Prospective evaluation of the risk of serious bacterial infection in children who present to the emergency department with hyperpyrexia (temperature of 106 degrees F or higher). Pediatrics. 2006;118:34–40.CrossRefPubMedPubMedCentral Trautner BW, Caviness AC, Gerlacher GR, Demmler G, Macias CG. Prospective evaluation of the risk of serious bacterial infection in children who present to the emergency department with hyperpyrexia (temperature of 106 degrees F or higher). Pediatrics. 2006;118:34–40.CrossRefPubMedPubMedCentral
15.
go back to reference Massachusetts Department of Public Health. Weekly Influenza Update, May 27, 2010. Boston; 2010. Massachusetts Department of Public Health. Weekly Influenza Update, May 27, 2010. Boston; 2010.
16.
go back to reference Massachusetts Department of Public Health. Weekly Influenza Update, May 26, 2011. Boston; 2011. Massachusetts Department of Public Health. Weekly Influenza Update, May 26, 2011. Boston; 2011.
17.
go back to reference Massachusetts Department of Public Health. Weekly Influenza Update, May 24, 2012. Boston; 2012. Massachusetts Department of Public Health. Weekly Influenza Update, May 24, 2012. Boston; 2012.
18.
go back to reference Yih WK, Cocoros NM, Crockett M, Klompas M, Kruskal BA, Kulldorff M, Lazarus R, Madoff LC, Morrison MJ, Smole S, Platt R. Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance. Public Health Rep. 2014;129:55–63. Yih WK, Cocoros NM, Crockett M, Klompas M, Kruskal BA, Kulldorff M, Lazarus R, Madoff LC, Morrison MJ, Smole S, Platt R. Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance. Public Health Rep. 2014;129:55–63.
19.
go back to reference Hyndman RJ, Koehler AB, Ord JK, Snyder RD. Forecasting with Exponential Smoothing. 2008. Hyndman RJ, Koehler AB, Ord JK, Snyder RD. Forecasting with Exponential Smoothing. 2008.
20.
go back to reference Hyndman RJ, Khandakar Y. Automatic time series forecasting: The forecast package for R. J Stat Softw. 2008;27:C3–3. Hyndman RJ, Khandakar Y. Automatic time series forecasting: The forecast package for R. J Stat Softw. 2008;27:C3–3.
21.
go back to reference Sund-Levander M, Forsberg C, Wahren LK. Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review. Scand J Caring Sci. 2002;16:122–8.CrossRefPubMed Sund-Levander M, Forsberg C, Wahren LK. Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review. Scand J Caring Sci. 2002;16:122–8.CrossRefPubMed
22.
go back to reference Mackowiak PA, Wasserman SS, Levine MM. A critical appraisal of 98.6 degrees F, the upper limit of the normal body temperature, and other legacies of Carl Reinhold August Wunderlich. JAMA. 1992;268:1578–80.CrossRefPubMed Mackowiak PA, Wasserman SS, Levine MM. A critical appraisal of 98.6 degrees F, the upper limit of the normal body temperature, and other legacies of Carl Reinhold August Wunderlich. JAMA. 1992;268:1578–80.CrossRefPubMed
23.
go back to reference Manitz J, Höhle M. Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany. Biometrical J. 2013;55:509–26.CrossRef Manitz J, Höhle M. Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany. Biometrical J. 2013;55:509–26.CrossRef
24.
go back to reference Maëlle S, Dirk S, Höhle M. Monitoring count time series in R: aberration detection in public health surveillance. arXiv 2014:1411.1292. Maëlle S, Dirk S, Höhle M. Monitoring count time series in R: aberration detection in public health surveillance. arXiv 2014:1411.1292.
25.
go back to reference Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, Fukuda K. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003;289:179–86. Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, Fukuda K. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003;289:179–86.
26.
go back to reference Thompson WW, Shay DK, Weintraub E, Brammer L, Bridges CB, Cox NJ, Fukuda K. Influenza-associated hospitalizations in the United States. JAMA. 2004;292:1333–40. Thompson WW, Shay DK, Weintraub E, Brammer L, Bridges CB, Cox NJ, Fukuda K. Influenza-associated hospitalizations in the United States. JAMA. 2004;292:1333–40.
27.
go back to reference Molinari N-AM, Ortega-Sanchez IR, Messonnier ML, Thompson WW, Wortley PM, Weintraub E, et al. The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine. 2007;25:5086–96.CrossRefPubMed Molinari N-AM, Ortega-Sanchez IR, Messonnier ML, Thompson WW, Wortley PM, Weintraub E, et al. The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine. 2007;25:5086–96.CrossRefPubMed
28.
go back to reference Schanzer DL, Zheng H, Gilmore J. Statistical estimates of absenteeism attributable to seasonal and pandemic influenza from the Canadian Labour Force Survey. BMC Infect Dis. 2011;11:90.CrossRefPubMedPubMedCentral Schanzer DL, Zheng H, Gilmore J. Statistical estimates of absenteeism attributable to seasonal and pandemic influenza from the Canadian Labour Force Survey. BMC Infect Dis. 2011;11:90.CrossRefPubMedPubMedCentral
31.
go back to reference Wang L-M, Chen Y-C, Tung S-P, Chen C-Y, Chang S-C, Chiang S-C, Lee C-H. The rationale of fever surveillance to identify patients with severe acute respiratory syndrome in Taiwan. Emerg Med J. 2006;23:202–5. Wang L-M, Chen Y-C, Tung S-P, Chen C-Y, Chang S-C, Chiang S-C, Lee C-H. The rationale of fever surveillance to identify patients with severe acute respiratory syndrome in Taiwan. Emerg Med J. 2006;23:202–5.
Metadata
Title
Human temperatures for syndromic surveillance in the emergency department: data from the autumn wave of the 2009 swine flu (H1N1) pandemic and a seasonal influenza outbreak
Authors
Samantha F. Bordonaro
Daniel C. McGillicuddy
Francesco Pompei
Dmitriy Burmistrov
Charles Harding
Leon D. Sanchez
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Emergency Medicine / Issue 1/2016
Electronic ISSN: 1471-227X
DOI
https://doi.org/10.1186/s12873-016-0080-7

Other articles of this Issue 1/2016

BMC Emergency Medicine 1/2016 Go to the issue