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BMC Public Health
Published in: BMC Public Health 1/2015

Open Access 01-12-2015 | Research article

Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models

Authors: Ta-Chien Chan, Yung-Chu Teng, Jing-Shiang Hwang

Published in: BMC Public Health | Issue 1/2015

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Abstract

Background

Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI.

Methods

The health insurance claims data during 2004–2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM.

Results

The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods.

Conclusions

The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.
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Literature
1.
Jhung MA, Swerdlow D, Olsen SJ, Jernigan D, Biggerstaff M, Kamimoto L, et al. Epidemiology of 2009 pandemic influenza A (H1N1) in the United States. Clin Infect Dis. 2011;52 Suppl 1:S13–26.CrossRefPubMed
2.
Viboud C, Simonsen L. Global mortality of 2009 pandemic influenza A H1N1. Lancet Infect Dis. 2012;12(9):651–3.CrossRefPubMed
3.
Li Q, Zhou L, Zhou M, Chen Z, Li F, Wu H, et al. Epidemiology of human infections with avian influenza A(H7N9) virus in China. N Engl J Med. 2014;370(6):520–32.CrossRefPubMed
4.
Keech M, Beardsworth P. The impact of influenza on working days lost: a review of the literature. PharmacoEconomics. 2008;26(11):911–24.CrossRefPubMed
5.
Paul Glezen W, Schmier JK, Kuehn CM, Ryan KJ, Oxford J. The burden of influenza B: a structured literature review. Am J Public Health. 2013;103(3):e43–51.CrossRefPubMedPubMedCentral
6.
Eisenberg KW, Szilagyi PG, Fairbrother G, Griffin MR, Staat M, Shone LP, et al. Vaccine effectiveness against laboratory-confirmed influenza in children 6 to 59 months of age during the 2003–2004 and 2004–2005 influenza seasons. Pediatrics. 2008;122(5):911–9.CrossRefPubMedPubMedCentral
7.
Nichol KL, Nordin JD, Nelson DB, Mullooly JP, Hak E. Effectiveness of influenza vaccine in the community-dwelling elderly. N Engl J Med. 2007;357(14):1373–81.CrossRefPubMed
8.
Zhang G, Xia Z, Liu Y, Li X, Tan X, Tian Y, et al. Epidemiological and clinical features of 308 hospitalized patients with novel 2009 influenza A (H1N1) virus infection in China during the first pandemic wave. Intervirology. 2011;54(3):164–70.CrossRefPubMed
9.
Fitzner KA, McGhee SM, Hedley AJ, Shortridge KF. Influenza surveillance in Hong Kong: results of a trial Physician Sentinel Programme. Hong Kong Med J. 1999;5(1):87–94.PubMed
10.
Wallace LA, Collins TC, Douglas JD, McIntyre S, Millar J, Carman WF. Virological surveillance of influenza-like illness in the community using PCR and serology. J Clin Virol. 2004;31(1):40–5.CrossRefPubMed
11.
Irvin CB, Nouhan PP, Rice K. Syndromic analysis of computerized emergency department patients’ chief complaints: an opportunity for bioterrorism and influenza surveillance. Ann Emerg Med. 2003;41(4):447–52.CrossRefPubMed
12.
Ritzwoller DP, Kleinman K, Palen T, Abrams A, Kaferly J, Yih W, et al. Comparison of syndromic surveillance and a sentinel provider system in detecting an influenza outbreak–Denver, Colorado, 2003. Morb Mortal Wkly Rep. 2005;54(Suppl):151–6.
13.
Moore K, Black J, Rowe S, Franklin L. Syndromic surveillance for influenza in two hospital emergency departments. Relationships between ICD-10 codes and notified cases, before and during a pandemic. BMC Public Health. 2011;11:338.CrossRefPubMedPubMedCentral
14.
Shimoni Z, Rodrig J, Dusseldorp N, Niven M, Froom P. Increased emergency department chief complaints of fever identified the influenza (H1N1) pandemic before outpatient symptom surveillance. Environ Health Prev Med. 2012;17(1):69–72.CrossRefPubMed
15.
Lee YC, Huang YT, Tsai YW, Huang SM, Kuo KN, McKee M, et al. The impact of universal National Health Insurance on population health: the experience of Taiwan. BMC Health Serv Res. 2010;10:225.CrossRefPubMedPubMedCentral
16.
Cowling BJ, Wong IO, Ho LM, Riley S, Leung GM. Methods for monitoring influenza surveillance data. Int J Epidemiol. 2006;35(5):1314–21.CrossRefPubMed
17.
18.
Chan TC, King CC, Yen MY, Chiang PH, Huang CS, Hsiao CK. Probabilistic daily ILI syndromic surveillance with a spatio-temporal Bayesian hierarchical model. PLoS One. 2010;5(7):e11626.CrossRefPubMedPubMedCentral
19.
Fricker Jr RD, Hegler BL, Dunfee DA. Comparing syndromic surveillance detection methods: EARS’ versus a CUSUM-based methodology. Stat Med. 2008;27(17):3407–29.CrossRefPubMed
20.
Sparks RS, Keighley T, Muscatello D. Early warning CUSUM plans for surveillance of negative binomial daily disease counts. J Appl Stat. 2010;37(11):1911–29.CrossRef
21.
Sparks RS, Keighley T, Muscatello D. Optimal exponentially weighted moving average (EWMA) plans for detecting seasonal epidemics when faced with non-homogeneous negative binomial counts. J Appl Stat. 2011;38(10):2165–81.CrossRef
22.
Cooper DL, Smith GE, Regan M, Large S, Groenewegen PP. Tracking the spatial diffusion of influenza and norovirus using telehealth data: A spatiotemporal analysis of syndromic data. BMC Med. 2008;6:16.CrossRefPubMedPubMedCentral
23.
Lowen AC, Mubareka S, Steel J, Palese P. Influenza virus transmission is dependent on relative humidity and temperature. PLoS Pathog. 2007;3(10):1470–6.CrossRefPubMed
24.
Lombardo J, Burkom H, Elbert E, Magruder S, Lewis SH, Loschen W, et al. A systems overview of the electronic surveillance system for the early notification of community-based epidemics (ESSENCE II). J Urban Health. 2003;80(2 Suppl 1):i32–42.PubMedPubMedCentral
25.
Marsden-Haug N, Foster VB, Gould PL, Elbert E, Wang H, Pavlin JA. Code-based syndromic surveillance for influenzalike illness by International Classification of Diseases, Ninth Revision. Emerg Infect Dis. 2007;13(2):207–16.CrossRefPubMedPubMedCentral
26.
Centers for Disease Control and Prevention. Clinical Signs and Symptoms of Influenza. Atlanta, United States: Centers for Disease Control and Prevention; 2009.
27.
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
28.
Venables WN, Ripley BD. Modern Applied Statistics with S. 4th ed. New York: Springer; 2002.CrossRef
29.
Hutwagner LC, Thompson WW, Seeman GM, Treadwell T. A simulation model for assessing aberration detection methods used in public health surveillance for systems with limited baselines. Stat Med. 2005;24(4):543–50.CrossRefPubMed
30.
Viboud C, Boelle PY, Carrat F, Valleron AJ, Flahault A. Prediction of the spread of influenza epidemics by the method of analogues. Am J Epidemiol. 2003;158(10):996–1006.CrossRefPubMed
31.
van Noort SP, Aguas R, Ballesteros S, Gomes MG. The role of weather on the relation between influenza and influenza-like illness. J Theor Biol. 2012;298:131–7.CrossRefPubMed
32.
Wu TS, Shih FY, Yen MY, Wu JS, Lu SW, Chang KC, et al. Establishing a nationwide emergency department-based syndromic surveillance system for better public health responses in Taiwan. BMC Public Health. 2008;8:18.CrossRefPubMedPubMedCentral
33.
Kass-Hout TA, Buckeridge D, Brownstein J, Xu Z, McMurray P, Ishikawa CK, et al. Self-reported fever and measured temperature in emergency department records used for syndromic surveillance. J Am Med Inform Assoc. 2012;19(5):775–6.CrossRefPubMedPubMedCentral
34.
Westheimer E, Paladini M, Balter S, Weiss D, Fine A, Nguyen TQ. Evaluating the New York City Emergency Department syndromic surveillance for monitoring influenza activity during the 2009–10 influenza season. PLoS Currents 2012, 4:e500563f500563ea500181.
35.
Schrell S, Ziemann A, Garcia-Castrillo Riesgo L, Rosenkotter N, Llorca J, Popa D, et al. Local implementation of a syndromic influenza surveillance system using emergency department data in Santander, Spain. J Public Health. 2013;35(3):397–403.CrossRef
36.
Bradley CA, Rolka H, Walker D, Loonsk J. BioSense: implementation of a National early event detection and situational awareness system. Morb Mortal Wkly Rep. 2005;54(Suppl):11–9.
37.
Liao CM, Chang SY, Chen SC, Chio CP. Influenza-associated morbidity in subtropical Taiwan. Int J Infect Dis. 2009;13(5):589–99.CrossRefPubMed
Metadata
Title
Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models
Authors
Ta-Chien Chan
Yung-Chu Teng
Jing-Shiang Hwang
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2015
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-015-1500-4

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