Emergency medical and fire calls during severe weather events
Emergency Medical and Fire Callsduring Severe Weather EventsLaura McLay, PhDlamclay@vcu.edu@lauramclay on twitterpunkrockOR.wordpress.comThis material is based upon work supported by the National Science Foundation under Award No. CMMI-1054148. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation. 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)Significant during snow events Not significant during snow events Cardiac arrest Behavior/psychological Natural death Diabetes Stroke Abdominal pain Altered consciousness Cardiac rhythm Chest pain Trauma
New Jersey (2010)Significant during snow on ground Not significant during snow on ground Cardiac arrest (weekdays) Cardiac arrest (weekends) Natural death Diabetes Behavior/psychological Stroke Abdominal pain Altered consciousness Cardiac rhythm Chest pain Trauma
New Hampshire & Maine (2010)Significant during snow events Not significant during snow events Cardiac arrest Natural Death Behavior/psychological Diabetes Cardiac rhythm Stroke Abdominal pain Altered consciousness Chest pain Trauma
New Hampshire & Maine (2010)Significant during snow on ground Not significant during snow on ground Behavior/psychological Natural death Cardiac arrest Diabetes Stroke Abdominal pain Altered consciousness Cardiac rhythm Chest pain Trauma
New Hampshire & Maine (2010) Mutual aid responses more than Few medical transport and double in rural areas during snow interfacility transfers during snow events Rural Urban 1 10.9 0.90.8 Medical 0.8 Medical Transport Transport0.7 0.7 Interfacility Interfacility0.6 Transfer 0.6 Transfer0.5 Intercept 0.5 Intercept0.4 0.4 Mutual Aid Mutual Aid0.3 0.30.2 911 0.2 9110.1 0.1 0 0 No Snow Snow Snow on Thunderstorm No Snow Snow Snow on Thunderstorm Ground Ground 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
New Hampshire & Maine (2010) Response times: Rural Response times: Suburban12 1010 88 66 +12% +2% 4 +16% +6%4 220 0 No Snow Snow Snow on Ground No Snow Snow Snow on Ground Service times: Rural Service times: Suburban60 5050 4040 3030 +3% +4% +5% +1% 202010 10 0 0 No Snow Snow Snow on Ground No Snow Snow Snow on Ground
Response and Service TimesNew Hampshire & MaineOn average, snow adds 40 seconds to response time 116 seconds to service timeOn average, snow on the ground adds 27 seconds to response time 100 seconds to service time
Suburban Richmond EMS and Fire callsDecember 2009 blizzardFriday-Saturday 80 Totals 70 60 50 40 Historic average Snowmaggedon 30 20 10 0 EMS Fire Heart Seizure Car accidents Diabetes
Richmond police callsDecember 2009 blizzardFriday-Saturday 140.0 Total 120.0 100.0 80.0 60.0 Average Snowmaggedon 40.0 20.0 0.0
Richmond Police Time Series Number of calls per week 1000 800calls 600 Christmas 400 Snowmaggedon 200 2010.0 2010.5 2011.0 2011.5 yr
Weather and calls for serviceWe apply regression to examine how weathereffects the volume and nature of fire and EMScalls as well as 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 callsPut the models together to compare the workload for typical fall day and a blizzard day.
Dependent variable values for the base caseand 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.
Ambulance staffingChanges in the volume and nature of EMS callsand an impaired transportation network affectthe number of ambulances needed to reliablydelivery public service commodities.
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 Awaits Response Ambulance Responds- Call Arrival Time - Unit Response Decision - Unit Arrival- District - Queue Time - Hospital Transport Decision- Priority - 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 ScenariosMany of the models use the “weather scenario” as a collection of independent variables
Unreliability for the blizzard scenarios andsystem 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 inthe 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 / firstname.lastname@example.org@lauramclay on twitterpunkrockOR.wordpress.com