Emergency Medical and Fire Calls during Severe Weather Events

Laura McLay, PhDlamclay@vcu.edu@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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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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New Hampshire & Maine (2010)

+12% +2%

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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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calls

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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, PhDlamclay@vcu.edu / lmclay@wisc.edu @lauramclay on twitterpunkrockOR.wordpress.com