Emergency Medical Services Surveillance in Toronto……and Beyond

1. Emergency Medical Services Surveillance in Toronto……and Beyond Kate Bassil, PhD June 13, 2008 QPHI Meeting

2. Outline • Part 1: EMS Data and Surveillance • Part 2: EMS Surveillance in Toronto for heat-related illness • Part 3: Future directions, opportunities for collaboration

3. Part 1: EMS Data and Surveillance

4. Advantages of Using EMS Data for Surveillance • Timeliness: in the capture and process of the data • Simplicity: use of pre-existing data • Acceptability: willingness of stakeholders to contribute to data collection and analysis • Portability: system could be duplicated in another setting • Cost: could be done with no significant software or hardware requirements Surveillance principles from Public Health Agency of Canada and CDC Surveillance Principles

5. Added value • EMS data provides geospatial information about the location where the individual has become ill. • Differs from many other traditional medical data sources that use place of residence. • Important for syndromes where place matters e.g. outdoor recreation areas like heat illness.

6. NYC EMS Surveillance • NYC 911 receives 1 mill calls/year • Implemented in 1998 • Particularly useful for ILI and HRI surveillance • ILI codes: RESP, DIFFBR, SICK, SICPED • Use up to 3 years of baseline data • Alarm generated when the ILI rate exceeds the upper confidence limit Mostashari F, et al. 2003. Use of ambulance dispatch data as an early warning system for communitywide influenza like illness, New York City. J Urban Health 80:i43-i49

7. World Youth DayJuly 12-28, 2002: Canada • Biannual international celebration of the Catholic Church • For youth ages 17-35 years old • Past attendance 2 million + • Days in the Dioceses across Canada followed by the major celebrations in Toronto (overall, a 10-day event)

8. Findings from EMS Data World Youth Day, 2002 • From July 15-Aug 7 a total of 11,250 calls were logged • 39% met a syndrome definition • Most useful EMS call-code cluster was for heat-related illness (HRI)

9. Part 2: EMS Surveillance in Toronto for Heat-related Illness

10. Health Impacts of Hot Weather

11. Klinenberg E. 2003. A Social Autopsy of Disaster in Chicago.

12. Mortality Severityof Effect Hospital admission Medical seeking behaviour: ER, physicians office, 911, Telehealth, clinic Heat cramps, heat exhaustion, heat stroke Mild symptoms, discomfort, subtle effects Proportionof Population Heat-related illness (HRI) • Europe, 2003: > 70,000 excess deaths • Chicago, 1995: > 700 excess deaths • Historical analysis of Canadian cities: • Toronto: 120 annual heat-related deaths • Projected that in the future these values will more than double by 2050 and triple by 2080 Pengelly LD, et al. Anatomy of heat waves and mortality in Toronto: Lessons for public health protection. Can J Public Health. 2007 Sep-Oct;98(5):364-8.

13. Canadian Urban Areas • Urban Heat Island • Continued urbanization • Vulnerable population • Aging population • Lack of acclimatization in temperate zones • Future projections of increasing temperature means and variance

14. Urban Heat Island Profile Natural Resources Canada http://adaptation.nrcan.gc.ca/perspective/health

15. Temperature Trends, Toronto Environment Canada. 2006.

16. Heat Health Warning Systems (HHWS) • “A system that uses meteorological forecasts to initiate acute public heath interventions designed to reduce heat-related impacts on human health during atypically hot weather” Koppe C, et al. Heatwaves: impacts and responses. Copenhagen: World Health Organization, 2003. • Surprisingly few countries and cities have a HHWS • Implementation of interventions at municipal/national level

17. Heat Interventions Bassil et al. 2007. What is the evidence on applicability and effectiveness of public health interventions in reducing morbidity and mortality during heat episodes? A review for the National Collaborating Centre in Environmental Health.

18. Gaps • Heat/Health Warning Systems are based on mortality….what about indicators of morbidity (e.g. syndromic surveillance sources)? • Interventions are not currently targeted geographically

19. Toronto Emergency Medical Services (EMS): Communications Centre • Single-provider EMS system • Annual call volume - approx. 425,000 • Fully computerized system • Uses the Medical Priority Dispatch System (MPDS), a widely used EMS call sorting algorithm, to classify calls.

20. Mark Toman, Toronto Emergency Medical Services

21. MPDS Code Categorization EntryQuestions • Key Questions: • Is s/he completely awake? • Is s/he breathing normally? • Is s/he changing colour? • What is her/his skin temperature? Dispatch Codes: 20-D-1 Heat/Cold Exposure, not alert 20-C-1 Heat/Cold Exposure, cardiac history 20-B-1 Heat/Cold Exposure, change in skin colour 20-A-1 Heat/Cold Exposure, alert Medical Priority Dispatch System, Priority Dispatch Corp., Salt Lake City, Utah

22. EMS Variables in Data Set RMI: “Response Master Incident”

23. Toronto EMS Data • Daily call information for all emergency calls to EMS between 2002-2005 (approximately 850,000 calls) • Excludes cancelled calls and inter-facility transfers. • Microsoft Access database format • Quality assurance of MPDS assignation – 98% agreement, call assignation to US National Academy of Emergency Medicine standards

24. Number of All EMS Calls, Toronto, 2002-2005 Rolling Stones Concert World Youth Day Blackout Emergency calls only

25. Defining HRI with EMS Data Unknown trouble (man down) Most sensitive C A L L V O L U M E Sick person Cardiac Abdominal pain Unconscious/fainting Headache Most specific Heat/cold exposure

26. Developing the case definition i) Clinical process: - Approx 500 medical dispatch call categories reviewed. - Series of expert focus groups ii) Empirical process: -Each call category was assessed graphically with daily mean temperature - 4 groups of call categories were selected as ones which may represent HRI: • Heat/cold exposure, • Breathing problems, • Unconscious/fainting, • Unknown problem/“man down”

27. MPDS Card: Heat/cold exposure Solid line: proportion of calls Dotted line: Daily average temperature

28. MPDS Card: Unknown problem/”man down” Solid line: proportion of calls Dotted line: Daily average temperature

29. Call categories that most clearly represent HRI

30. Heat Illness Calls & Temperature, Toronto, Summer 2007

31. Time Series Analysis • On average, for every one degree increase in mean or maximum temperature there was a 30% increase in EMS calls for HRI (p<.0001). • Lag effect of 1 day (ranged from a 7 to 18% increase in calls for max temp, p<.0001) • Ozone: positive but statistically insignificant

32. Public Health Challenges • Technical issues – several days when data was not sent, so occasionally sent in batches every few days. • Timing with current heat health warning system • Requires daily person time – not a fully automated system • Limited demographic information

33. Public Health Advantages • Additional data source to support decisions around declaring heat alerts • New geospatial information to assist in intervention targeting • Situational awareness

34. Part 3: Future directions

35. Future Work • Further exploration of call codes • Breathing, fainting • TEMS data for other syndromes: • Cold-related illness • Influenza-like illness

36. Future Work • Multi-city study: • Niagara, Vancouver, Montreal, Toronto • Focus on EMS and geospatial analysis • Emergency department data • Vulnerability assessment: • Multiple data sources (EMS, ED) • Focus on heat-related illness

37. Acknowledgements • Toronto Public Health – Effie Gournis, Elizabeth Rea, Marco Vittiglio, Eleni Kefalas • University of Toronto – Donald Cole, Wendy Lou, Rahim Moinnedin • Toronto EMS – Dave Lyons, Alan Craig • Sunnybrook Basehospital – Brian Schwartz, Sandra Chad

38. For more information: kate_bassil@sfu.ca Comments and questions?