Emergency Medical and Fire Calls during Severe Weather Events
Laura McLay, [email protected]@lauramclay on twitterpunkrockOR.wordpress.com
This material is based upon work supported by the National Science Foundation under Award No. CMMI-1054148. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Virginia Commonwealth University
Research interests
My research interest is to understand how to use operations research methodologies allocate limited public resources for responding to health and fire emergencies during severe weather events
Resource allocation decisions—such as staffing levels—is important for system performance and patient outcomes.
First, we have to understand what is different during severe weather: the volume and nature of calls for service may be different, critical infrastructure is impaired or destroyed, and there are cascading failures in the system.…these issues are not as predictable as they would be on a
“normal” day
Data sets
My research models often use data from the metro-Richmond area.
State-level data sheds light on the impact of weather in other regions.
Weather data from airports and National Weather Service (recorded hourly or daily) captures actual weather conditions when calls for service are made.
National emergency medical service (EMS) data set from 2010 NEMSIS data set is a collection of EMS calls from agencies
throughout the United States EMS operations and calls vary between localities E.g., 1.6% of NJ, 5.8% of ME/NH, and 14.1% of Hanover (VA),
calls are motor vehicle accident responses Limitation: not all calls included from all municipalities Focus on 2010 data from
New Jersey: 736K calls, urban New Hampshire/Maine: 232K calls,
Urban/Suburban/Rural/Wilderness Examine whether snow affected the types of calls Binomial test to evaluate significant differences in
proportions of calls (at the 0.05 level)
New Jersey (2010)
Cardiac arrest Natural death
Behavior/psychological Diabetes Stroke Abdominal pain Altered consciousness Cardiac rhythm Chest pain Trauma
Significant during snow events Not significant during snow events
New Jersey (2010)
Cardiac arrest (weekdays) Natural death Behavior/psychological
Cardiac arrest (weekends) Diabetes Stroke Abdominal pain Altered consciousness Cardiac rhythm Chest pain Trauma
Significant during snow on ground Not significant during snow on ground
New Hampshire & Maine (2010)
Cardiac arrest Behavior/psychological Cardiac rhythm
Natural Death Diabetes Stroke Abdominal pain Altered consciousness Chest pain Trauma
Significant during snow events Not significant during snow events
New Hampshire & Maine (2010)
Behavior/psychological Natural death Cardiac arrest Diabetes Stroke Abdominal pain Altered consciousness Cardiac rhythm Chest pain Trauma
Significant during snow on ground Not significant during snow on ground
New Hampshire & Maine (2010)
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Thunderstorm
Rural
Medical Transport
Interfacility Transfer
Intercept
Mutual Aid
911
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Thunderstorm
Urban
Medical Transport
Interfacility Transfer
Intercept
Mutual Aid
911
Mutual aid responses more than double in rural areas during snow
events
Few medical transport and interfacility transfers during snow
Mass casualty events are more likely during and after snow events- increase by 90% during snow- increase by 36% while there is snow on the ground
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Service times: Suburban
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Service times: Rural
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Response times: Suburban
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Response times: Rural
New Hampshire & Maine (2010)
+12% +2%
+3% +4%
+6%+16%
+5% +1%
Response and Service TimesNew Hampshire & Maine
On average, snow adds 40 seconds to response time 116 seconds to service time
On average, snow on the ground adds 27 seconds to response time 100 seconds to service time
Snowmageddon
December 2009 – February 2010
Suburban Richmond EMS and Fire callsDecember 2009 blizzardFriday-Saturday
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EMS Fire Heart Seizure Car accidents Diabetes
Historic average
Snowmaggedon
Totals
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Snowmaggedon
Richmond police callsDecember 2009 blizzardFriday-Saturday
Total
2010.0 2010.5 2011.0 2011.5
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Number of calls per week
yr
calls
Richmond Police Time Series
Snowmaggedon
Christmas
We apply regression to examine how weather effects the volume and nature of fire and EMS calls as well as service.
Weather and calls for service
Dependent variables Call volume data
Zero inflated Poisson regression Number of EMS calls (per six hour unit of time) Number of Fire calls (per six hour unit of time)
Call data Multiple linear regression
Log response time (measured in minutes) Service time (measured in minutes)
Logistic regression Priority 1 call (binary) No arriving unit (binary) Hospital call (binary) Heart-related call (binary) Seizure/stroke related call (binary)
EMS/Fire call data was provided for time period June 1, 2009 – May 31, 2010 9218 EMS calls and 2352 Fire calls
Put the models together to compare the workload for typical fall day and a blizzard day.
Dependent variable values for the base case and blizzard scenario
Model Base Case Blizzard
EMS call count (count per six hours) 5.20 8.21
Fire call count (count per six hours) 1.16 2.51
Response time (min) 5.47 7.57
Service Time (min) 83.6 95.7
Priority 1 (probability) 0.404 0.255
No unit arriving (probability) 0.033 0.092
Hospital transport (probability) 0.614 0.397
Heart-related patient (probability) 0.003 0.004
Seizure/stroke-related patient (probability) 0.007 0.005
Offered Load (EMS) in hours 5.12 6.78
Offered Load (Fire) in hours 0.48 1.12
Offered Load (Total) in hours 5.60 7.90
The total offered load increases by 41% for the blizzard scenario.
Changes in the volume and nature of EMS calls and an impaired transportation network affect the number of ambulances needed to reliably delivery public service commodities.
Ambulance staffing
Staffing during blizzards
Study the number of calls that arrive when no units are available (NUA scenario).
How many ambulances are needed such that NUA scenario occurs less than 1% of the time?
How does this change based on response policies and system-wide adaptation? Model parameters vary according to the traffic in the
system.
* Joint work with Amber Kunkel, Rice University
Discrete Event Simulation Summary
New Call Arrives- Call Arrival Time
- District
- Priority
Call Awaits Response- Unit Response Decision
- Queue Time
Ambulance Responds- Unit Arrival
- Hospital Transport Decision
- Service Time
• 40 runs per unique scenario
• 10,000 calls per run
• Data analysis in R, simulation in Matlab
• Base case assumes 6 ambulances
• Goal is to be 98% sure that at least 99% of patients can receive an immediate response
Response Policies
• Queue Excess: All calls arriving when NUA are added to a first-come, first-serve queue.
• Drop Excess: All calls arriving when NUA are dropped from the system.
• Priority-Specific Excess: Low priority calls follow a drop excess policy. High priority calls follow a queue excess policy.
• Drop Low Priority: All low priority calls are dropped, regardless of the number of units available. High priority calls follow a queue excess policy.
Regression Models
Call arrival times Negative binomial regression
Call locations Multinomial regression
Call priorities, unit arrival, and hospital transport probabilities Logistic regression
Log(Service times) Linear regression
Weather Scenarios
Many of the models use the “weather scenario” as a collection of independent variables
Unreliability for the blizzard scenarios and system adaptation (6 ambulances)
How many ambulances are needed to immediately respond to 99% of calls?
Taking system adaptation into account is like having one additional ambulance in the system, particularly when the system is busy.
Poor response policies in NYC
You don’t want your EMS service to be on the front page of the paper [NYC December 2010]
In NYC, call volume doubled, sixth worse day on record
Thank you!
References:Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during
snowstorms using an integrated simulation, regression, and reliability model. Health Care Management Science 16(1), 14 - 26.
McLay, L.A., Brooks, J.P., Boone, E.L., 2012. Analyzing the Volume and Nature of Emergency Medical Calls during Severe Weather Events using Regression Methodologies. Socio-Economic Planning Sciences 46, 55 – 66.
Contact info:Laura McLay, [email protected] / [email protected] @lauramclay on twitterpunkrockOR.wordpress.com
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