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Can Telehealth Ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health?

Published online by Cambridge University Press:  21 May 2015

Adam van Dijk*
Affiliation:
Queen's University Emergency Syndromic Surveillance Team (QUESST), Kingston, Ont.
Don McGuinness
Affiliation:
Queen's University Emergency Syndromic Surveillance Team (QUESST), Kingston, Ont.
Elizabeth Rolland
Affiliation:
Queen's University Emergency Syndromic Surveillance Team (QUESST), Kingston, Ont. Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK
Kieran M. Moore
Affiliation:
Queen's University Emergency Syndromic Surveillance Team (QUESST), Kingston, Ont. Department of Emergency Medicine and Community Health and Epidemiology, Queen's University, Kingston, Ont.
*
Syndromic Surveillance KFL&A Public Health, 221 Portsmouth Ave., Kingston ON K7M 1V5; avandijk@kflapublichealth.ca

Abstract

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Objective:

There is a paucity of information regarding the usefulness of non-traditional data streams for real-time syndromic surveillance systems. The objective of this paper is to examine the temporal relation between Ontario's emergency department (ED) visits and telephone health line (Telehealth) call volume for respiratory illnesses to test the feasibility of using Ontario's Telehealth system for real-time surveillance.

Methods:

Retrospective time-series data from the National Ambulatory Care Reporting System (NACRS) and the Telehealth Ontario program from June 1, 2004, to March 31, 2006, were analyzed. The added value of Telehealth Ontario data was determined by comparing it temporally with NACRS data, which uses the International Classification of Diseases (ICD) 10-Canadian Enhancement coding system for discharge diagnoses.

Results:

Telehealth Ontario had 216 105 calls for respiratory complaints, while 819 832 ICD-coded complaints from NACRS were identified with a comparable diagnosis of respiratory illness. Telehealth Ontario call volume was heavily weighted for the 0–4 years age group (49%), while the NACRS visits were mainly from those 18–64 years old (44%). The Spearman rank correlation coefficient was calculated to be 0.97, with the time-series analysis also resulting in significant correlations at lags (semi-monthly) 0 and 1, indicating that increases in Telehealth Ontario call volume correlate with increases in NACRS discharge diagnosis data for respiratory illnesses.

Conclusion:

Telehealth Ontario call volume fluctuation reflects directly on ED respiratory visit data on a provincial basis. These call complaints are a timely, useful and representative data stream that shows promise for integration into a real-time syndromic surveillance system.

Résumé

RÉSUMÉObjectifs:

Il y a une insuffisance d'information concernant l'utilité de flux de données non conventionnels pour les systèmes de surveillance syndromique en temps réel. Cet article vise à examiner la relation temporelle entre les visites aux salles d'urgence en Ontario et le volume d'appels au service téléphonique de conseils-santé Télésanté Ontario concernant des troubles respiratoires afin de mesurer la faisabilité d'utiliser Télésanté Ontario aux fins de surveillance en temps réel.

Méthodes:

Nous avons fait une analyse rétrospective d'une série chronologique de données provenant du Système national d'information sur les soins ambulatoires (SNISA) et de Télésanté Ontario couvrant la période du 1er juin 2004 au 31 mars 2006. La valeur ajoutée des données de Télésanté Ontario a été déterminée en comparant ces données temporellement à celles du SNISA, qui utilise le système de codification de la version élargie (CIM-10-CA) de la Classification internationale des maladies (CIM-10), pour les diagnostics de congé.

Résultats:

Télésanté Ontario a reçu 216 105 appels relatifs à des troubles respiratoires alors que 819 832 plaintes codées selon la CIM du SNISA portaient un diagnostic comparable d'une maladie respiratoire. Le volume d'appels de Télésanté Ontario était nettement plus élevé pour le groupe des 0 à 4 ans (49 %), alors que les visites consignées dans le SNISA étaient principalement du groupe des 18 à 64 ans (44 %). Le coefficient de corrélation de Spearman était de 0,97, et les analyses de série chronologique ont montré des corrélations significatives dans l'intervalle (bimensuel) 0 et 1. Cela signifie qu'il y a une corrélation entre la hausse du volume d'appels à Télésanté Ontario et la hausse des données de diagnostics de congé du SNISA relatives aux maladies respiratoires.

Conclusion:

Les fluctuations du volume d'appels à Télésanté Ontario ont des répercussions directes sur les données relatives aux visites à l'urgence pour des troubles respiratoires à l'échelle provinciale. Ces appels constituent un flux de données représentatif, ponctuel et utile, qui est prometteur pour son intégration dans un système de surveillance syndromique en temps réel.

Type
Original Research • Recherche originale
Copyright
Copyright © Canadian Association of Emergency Physicians 2008

References

1. Wagner, MM, Tsui, F-C, Espino, JU, et al. The emerging science of very early detection of disease outbreaks. J Public Health Manag Pract 2001;7:51–9.10.1097/00124784-200107060-00006Google Scholar
2. Henning, KJ. Overview of syndromic surveillance — what is syndromic surveillance. MMWR Morb Mortal Wkly Rep 2004; 53(Suppl):511.Google Scholar
3. Fienberg, SE, Shmueli, G. Statistical issues and challenges associated with rapid detection of bio-terrorist attacks. Stat Med 2005;24:513–29.Google Scholar
4. Wang, L, Ramoni, MF, Mandl, KD, et al. Factors affecting automated syndromic surveillance. Artif Intell Med 2005;34:269–78.Google Scholar
5. Rolland, E, Moore, K, Robinson, VA, et al. Using Ontario’s “Telehealth” health telephone helpline as an early-warning system: a study protocol. BMC Health Serv Res 2006;6:10.Google Scholar
6. Moore, K. Real-time syndrome surveillance in Ontario, Canada: the potential use of emergency departments and Telehealth. Eur J Emerg Med 2004;11:311.Google Scholar
7. Harcourt, SE, Smith, GE, Hollyoak, V, et al. Can calls to NHS Direct be used for syndromic surveillance? Commun Dis Public Health 2001;4:178–82.Google Scholar
8. Chapman, RS, Smith, GE, Warburton, F, et al. Impact of NHS Direct on general practice consultations during the winter of 19992000: analysis of routinely collected data. BMJ 2002;325:1397–8.Google Scholar
9. Cooper, DL, Smith, G, Baker, M, et al. National symptom surveillance using calls to a telephone health advice service — United Kingdom, December 2001-February 2003. MMWR Morb Mortal Wkly Rep 2004;53(Suppl):179–83.Google Scholar
10. Cooper, DL, Smith, GE, Hollyoak, VA, et al. Use of NHS direct calls for surveillance of influenza — a second year’s experience. Commun Dis Public Health 2002;5:127–31.Google Scholar
11. Cooper, DL, Smith, GE, O’Brien, SJ, et al. What can analysis of calls to NHS Direct Tell us about the epidemiology of gastrointestinal infections in the community? J Infect 2003;46:101–5.10.1053/jinf.2002.1090Google Scholar
12. Payne, F, Jessopp, L. NHS Direct: review of activity data for the first year of operation at one site. J Public Health Med 2001;23:155–8.Google Scholar
13. Leonardi, GS, Hajat, S, Kovats, RS, et al. Syndromic surveillance use to detect the early effects of heat-waves: an analysis of NHS Direct data in England. Soz Praventivmed 2005;51:194201.Google Scholar
14. Miller, E. A. Solving the disjuncture between research and practice: Telehealth trends in the 21st century. Health Policy (New York) 2007;82:133–41.Google Scholar
15. Ontario Ministry of Health and Long-Term Care. Ontario health plan for an influenza pandemic 2006. Available: http://www.health.gov.on.ca/english/providers/program/emu/pan_flu/pan_flu_plan.html#whole (accessed 2007 Mar 2).Google Scholar
16. Reis, BY, Mandl, KD. Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak 2003;3:2.Google Scholar
17. Beitel, AJ, Olson, KL, Reis, BY, et al. Use of emergency department chief complaint and diagnostic codes for identifying respiratory illness in a pediatric population. Pediatr Emerg Care 2004;20:355–60.10.1097/01.pec.0000133608.96957.b9Google Scholar
18. Bourgeois, FT, Olson, KL, Brownstein, JS, et al. Validation of syndromic surveillance of respiratory infections. Ann Emerg Med 2006;47:265–71.Google Scholar
19. Townes, JM, Kohn, MA, Southwick, KL, et al. Investigation of an electronic emergency department information system as a data source for respiratory syndrome surveillance. J Public Health Manag Pract 2004;10:299307.Google Scholar
20. Schull, MJ, Mamdani, MM, Fang, J. Community influenza outbreaks and emergency department ambulance diversion. Ann Emerg Med 2004;44:61–7.10.1016/j.annemergmed.2003.12.008Google Scholar
21. Schull, MJ, Mamdani, MM, Fang, J. Influenza and emergency departaient utilization by elders. Acad Emerg Med 2005;12:338–44.Google Scholar
22. Upshur, REG, Moineddin, R, Crighton, EJ, et al. Interactions of viral pathogens on hospital admissions for pneumonia, croup and chronic obstructive pulmonary diseases: results of a multivariate time-series analysis. Epidemiol Infect 2006;134:1174–8.10.1017/S0950268806006236Google Scholar
23. Crighton, EJ, Moineddin, R, Mamdani, MM, et al. Influenza and pneumonia hospitalizations in Ontario: a time-series analysis. Epidemiol Infect 2004;132:1167–74.Google Scholar
24. Upshur, REG, Knight, K, Goel, V. Time-series analysis of the relation between influenza virus and hospital admissions of the elderly in Ontario, Canada, for pneumonia, chronic lung disease, and congestive heart failure. Am J Epidemiol 1999;149:8592.10.1093/oxfordjournals.aje.a009731Google Scholar
25. Menec, VH, Black, C, MacWilliam, L, et al. The impact of influenza-associated respiratory illnesses on hospitalizations, physician visits, emergency room visits, and mortality. Can J Public Health 2003;94:5963.Google Scholar
26. Ontario Ministry of Health and Long-Term Care. Public information — Telehealth Ontario. Available: http://www.health.gov.on.ca/english/public/program/telehealth/tele_faq.html (accessed 2007 Mar 2).Google Scholar
27. Clinidata. Symptom based tele-triage and health information services. Available: http://www.clinidata.com (accessed 2007 Nov 21).Google Scholar
28. Thakore, J, Roach, J, Flaherty, DH. Clinical administratvie databases — privacy impact assessment. Ottawa (ON): Canadian Institute for Health Information; 2005.Google Scholar
29. Executive summary: database background and general data limitations documentation. National Ambulatory Care Reporting System (NACRS) FY 2005–2006. Ottawa (ON): Canadian Institute for Health Information; 2006.Google Scholar
30. Doroshenko, A, Cooper, D, Smith, G, et al. Evaluation of syndromic surveillance based on national health service direct derived data — England and Wales. MMWR Morb Mortal Wkly Rep 2005;54:117–22.Google Scholar
31. Rowe, BH, Bond, K, Ospina, MB, et al. Data collection on patients in emergency departments in Canada. CJEM 2006;8:417–24.Google Scholar
32. Espino, JU, Hogan, WR, Wagner, MM. Telephone triage: a timely data source for surveillance of influenza-like diseases. Paper presented at: AMIA 2003 Symposium Proceedings; November 8, 2003;Washington, DC.Google Scholar
33. Public Health Agency of Canada. Influenza in Canada: 2004–2005 season. Canada Communicable Disease Report 2006;32(6):5774.Google Scholar
34. Public Health Agency of Canada. Influenza in Canada: 2005–2006 season. Canada Communicable Disease Report 2007;33(3):2144.Google Scholar
35. Espino, JU, Wagner, MM. Accuracy of ICD-9-coded chief complaints and diagnoses for the detection of acute respiratory illness. Paper presented at: AMIA 2001 Annual Symposium; November 3, 2001; Washington, DC.Google Scholar
36. Marsden-Haug, N, Foster, VB, Gould, PL, et al. Code-based syndromic surveillance for influenzalike illness by International Classification of Diseases, ninth revision. Emerg Infect Dis 2007;13:207–16.Google Scholar
37. Buehler, JW, Hopkins, RS, Overhage, JM, et al. Framework for evaluating public health surveillance systems for early detection of outbreaks. MMWR Morb Mortal Wkly Rep 2004;53 (RR05):111.Google Scholar
38. Heikkinen, T. Influenza in children. Acta Paediatr 2006;95:778–84.Google Scholar
39. Centers for Disease Control and Prevention. Prevention and control of influenza — recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR Morb Mortal Wkly Rep 2007;55(RR-10):142.Google Scholar
40. Nicholson, KG. Human influenza. In: Nicholson, KG, Webster, RG, Hay, AJ, eds. Textbook of influenza. Oxford (UK): Blackwell Science; 1998.Google Scholar