Emergency Medical Services Surveillance in Toronto

1
Emergency Medical Services Surveillance in
Torontoand 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

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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
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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.

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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 80i43-i49
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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)

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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

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Health Impacts of Hot Weather
11
Klinenberg E. 2003. A Social Autopsy of
Disaster in Chicago.
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Heat-related illness (HRI)

• Europe, 2003 gt 70,000 excess deaths
• Chicago, 1995 gt 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-Oct98(5)364-8.

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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

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Urban Heat Island Profile
Natural Resources Canada http//adaptation.nrcan.g
c.ca/perspective/health
15
Temperature Trends, Toronto
Environment Canada. 2006.
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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

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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.
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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

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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.

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Mark Toman, Toronto Emergency Medical Services
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MPDS Code Categorization
Entry Questions

• 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
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EMS Variables in Data Set
RMI Response Master Incident
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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

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

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MPDS Card Heat/cold exposure
Solid line proportion of calls Dotted line
Daily average temperature
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MPDS Card Unknown problem/man down
Solid line proportion of calls Dotted line
Daily average temperature
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Call categories that most clearly represent HRI
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Heat Illness Calls Temperature, Toronto, Summer
2007
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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 (plt.0001).
• Lag effect of 1 day (ranged from a 7 to 18
  increase in calls for max temp, plt.0001)
• Ozone positive but statistically insignificant

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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

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Public Health Advantages

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

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• Part 3
• Future directions

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Future Work

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

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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

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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

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Comments and questions?

• For more information
• kate_bassil_at_sfu.ca