Michael Beeler Presentation

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Estimation and Management of Pandemic Influenza Transmission Risk at Mass Immunization Clinics Michael F. Beeler† , Dionne M. Aleman† , Michael W. Carter† † Department

of Mechanical and Industrial Engineering, University of Toronto

December 11, 2011

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Mass Immunization Clinics (MICs) I

MICs are the primary mode of administering vaccines during influenza pandemics (i.e., H1N1)

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

MIC Management Challenges

1. Long waits discourage people from coming back for future vaccination 2. Large social/economic loss with thousands of people-hours spent standing in line 3. Unknown risk of infection in crowded lines by asymptomatic individuals

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

MIC Management Goals

1. Reduce wait times 2. Reduce infections occuring within facility 3. Control costs 4. Increase daily vaccination capacity

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Why Simulation?

I

Client data revealed that service times depend on: I I I I I I I

I

Patient age group Group arrival size Queue length Time of day Type of nursing staff (general RN or ID/VPD) Whether the vaccine used is adjuvanted Whether priority group screening is in effect

Infection transmission risk varies with age, duration of exposure, and the effectiveness of registration staff at screening

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

MIC Model in Simul8

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Patient Flow Through MIC

Patient Arrival (time-dependent distribution)

Go home

Go home

Go home

Yes

Yes

No

Line too long?

No

Wait Outside

Waited too long?

No

Wait Inside

Registration and Screening

Eligibility?

No Family members done?

Yes

Deemed sick?

No Recovery Area

Vaccination

Wait for vaccination Yes

Yes Go home

Pandemic Simulation

Flu Assessment

Wait for flu assessment

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Data From Public Health Unit1

I

Time-stamped electronic patient health information system generated arrival times, registration times, and wait times

I

Each patient’s age and the size of the group they arrived with

I

Detailed staff schedule for nurses and clerks that showed hourly productivity per worker, and worker type (ID/VPD)

I

Facility floor plans

I

Costs for staffing, vaccines, space rental

1

Niagara Public Health Departments MICs during H1N1, Oct-Nov 2009

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Parameters Drawn from Secondary Sources

I

Age-specific infection risk per minute of proximity to infectious person

I

Changes in the demographic structure and group-arrival size resulting from government priority restrictions Approximate number of infections averted per 100 vaccinations

I

I

Pandemic Simulation

Assumes shot is received during second wave, when pandemic influenza vaccine usually becomes available

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Infection Risk: Much Is Still Unknown

I

Probability influenza transmission per minute of “contact� is uncertain: modes of transmission are still disputed

I

Estimates from literature assume constant age-dependent risk within 2m radius

I

But hazard rates should increase with proximity

I

Precise effect of other factors is subject to debate: humidity, ventilation, strength of symptoms

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Probability of Infection

Prj (S, I) = 1 − e

−

P

i∈Ij

λij tij

λij The per minute hazard rate (probability of infection) based on the age of the susceptible and infectious patients making contact tij The duration of contact between patient i and patient j I

Contact in MIC defined as being within three positions from another patient in a queue

Ij The set of infectious people who make contact with person j during the simulation

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Staff Productivity by Time of Day

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Average Productivity vs. Maximum Productivity

Clients per staff member per hour

30 25

20 15

10 5 0

Avg Oct 26

Pandemic Simulation

Avg Oct 28

Avg Max Oct 28

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Staff Productivity Patterns

Question Is staff productivity a function of perceived future workload (queue length)? Time remaining in shift?

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Scenario

I

Same staffing and patient turnout conditions as an actual MIC

I

No priority screening

I

An average of nine registration staff and 11 vaccination nurses on staff throughout the day

I

Hours of service were: 8:30AM to 8:00PM

I

Assumed that 2% of population was infectious, but without symptoms

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Validation

After incorporating a 5% efficiency loss at service stations (perhaps representing staff breaks and disruptions), the total patient time in system, total patient throughput, and wait times for each specific station, came to resemble the values for the actual MIC

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Results

Response Variable Expected Infections Number Vaccinated Cost per Vaccination Average Time in Facility

Value 7.91 1558 17.9 51.5

Initial Observations: I

Expected infections is a small fraction of number of people treated, but not negligible

I

80% of time in facility is spent waiting—not an efficient use of the public’s time

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Five-factor Design of Experiments

1. Whether the demographic profile and family arrival size resembled i) The Canadian census, ii) Patient turnout when no priority restrictions were in place, iii) Patient turnout under priority restiction excluding seniors, iv) Patient turnout under priority restriction including seniors 2. Increasing the percentage of asymptomatic infectious patients from 2% to 3% 3. Extending operating hours by half an hour on both ends 4. Decreasing clerical staff by one full-day equivalent 5. Decreasing nursing staff by one full-day equivalent

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factor Interactions

I

None of the interactions tested were both statistically and economically significant, so a main effects model was used

I

The scenario with fewer staff, 2% infectious, normal hours, and a representative demographic was used to define the intercept for all linear models generated

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Expected Infections

Intercept: 12.37 Significant Factors Unrestricted demographic Priority restriction excl. seniors Priority restriction incl. seniors Increment % infectious Add one clerical staff

Pandemic Simulation

Value -2.22 -8.50 -6.02 2.11 -1.22

p-value 0.0025 <0.001 <0.001 <0.001 0.017

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Number Vaccinated

Intercept: 1538.7 Significant Factors Unrestricted demographic Priority restriction excl. seniors Priority restriction incl. seniors Increment % infectious Extend hours Add one nurse Add one clerical staff

Pandemic Simulation

Value -51.56 -305.8 -261.6 -30.81 66.13 36.56 31.56

p-value <0.001 .0012 <0.001 0.0055 <0.001 0.0012 0.0045

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Cost per Vaccination

Intercept: $17.13 Significant Factors Priority restriction excl. seniors Priority restriction incl. seniors Increment % infectious

Pandemic Simulation

Value 0.543 0.642 0.541

p-value <0.001 <0.001 <0.001

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Average Time in Facility

Intercept: 58.63 minutes Significant Factors Unrestricted demographic Priority restriction excl. seniors Priority restriction incl. seniors Extend hours Add one nurse

Pandemic Simulation

Value -4.25 -20.73 -17.38 -3.03 -4.22

p-value <0.001 <0.001 <0.001 <0.001 <0.001

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Experiment 2: Overwhelmed Facility with Waiting Penalty

I

20% increase in patient arrival rate

I

Additional consideration: $10/hr penalty per hour of waiting Factors varied:

I

I I I

Pandemic Simulation

% Infectious patients (0, 2) Increasing clerical staff by one full-day equivalent (9 to 10) Increasing nursing staff by one full-day equivalent (11 to 12)

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Change in Expected Infections

I

Increasing patient turnout pushed expected infections to 14.5 out of â&#x2C6;ź 1700 I I

Thatâ&#x20AC;&#x2122;s 0.85% of patients Suppose 5-10% of patients vaccinated would have become infected without vaccination I I

Pandemic Simulation

Infection risk could reduce MIC efficacy by up to 17% Infection risk, even with severe crowding, does not offset the benefits of the MIC

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Number of Vaccines Administered

Intercept: 1699 Significant Factors Add one nurse

Value 105.1

p-value <0.001

Increasing clerical staff had no impact because of consistent bottleneck at injection stations

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Time in System

Intercept: 101.9 Significant Factors Add one nurse

Pandemic Simulation

Value -13.4

p-value <0.001

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Factors Affecting Direct + Social Cost per Vaccination

Intercept: $35.26, vs. $17.13 without publicâ&#x20AC;&#x2122;s waiting costs considered Significant Factors Add one nurse

Pandemic Simulation

Value -2.31

p-value <0.001

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

General Implications for MIC Management

I

Can test adequacy of staff scheduling

I

Can estimate the marginal return of adding MIC staff under range of circumstances

I

Method for estimating infection risk, and how faster services can reduce it

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Important Observations I

The demographic make-up of patients has a large effect on service times, and therefore on wait times and infection risk

I

In the worst scenario, fewer than 1% of people became infected at the MIC even with an extremely large 3% of the population being infectious and asymptomatic

I

Time cost to public could exceed direct costs of service provision; should be considered

I

Relationship between queue length, time of day and faster service times in input data

I

Input analysis: perception of staff working at full capacity may not be correct; use data to detect

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Future Research

I

Observational study: I

I

Quantify relationship between queue size, queue visibility, and staff productivity Compare open concept and sectioned MIC layouts to see if there are differences in work-rate responsiveness to backlog

I

Test effect of process re-design: non-adjuvanted nasal spray vaccine, and batching consent process to save time

I

Test on data from other Public Health Unitsâ&#x20AC;&#x2122; MICs

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)

Introduction

The Model

Input Analysis

Baseline Runs

Experiment 1

Experiment 2

Conclusions

Acknowledgments

I

Niagara Region Public Health Department (Ontario, Canada)

I

Dr. Brian Schwartz and Sarah Levitt, Public Health Ontario

I

Gerilynne Carroll, Emergency Management Branch, Ministry of Health and Long Term Care (Ontario, Canada)

I

Canadian Institutes of Health Research (CIHR)

I

CIHR-Strategic Training Program in Public Health and the Rural Agricultural Ecosystem

I

CIHR-Strategic Training Program in Public Health Policy

Pandemic Simulation

Medical Operations Research Laboratory (morLAB)