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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
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Mass Immunization Clinics (MICs) I
MICs are the primary mode of administering vaccines during influenza pandemics (i.e., H1N1)
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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
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MIC Management Goals
1. Reduce wait times 2. Reduce infections occuring within facility 3. Control costs 4. Increase daily vaccination capacity
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Why Simulation?
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Client data revealed that service times depend on: I I I I I I I
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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
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MIC Model in Simul8
Pandemic Simulation
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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
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Data From Public Health Unit1
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Time-stamped electronic patient health information system generated arrival times, registration times, and wait times
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Each patient’s age and the size of the group they arrived with
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Detailed staff schedule for nurses and clerks that showed hourly productivity per worker, and worker type (ID/VPD)
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Facility floor plans
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Costs for staffing, vaccines, space rental
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Niagara Public Health Departments MICs during H1N1, Oct-Nov 2009
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Parameters Drawn from Secondary Sources
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Age-specific infection risk per minute of proximity to infectious person
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Changes in the demographic structure and group-arrival size resulting from government priority restrictions Approximate number of infections averted per 100 vaccinations
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Pandemic Simulation
Assumes shot is received during second wave, when pandemic influenza vaccine usually becomes available
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Infection Risk: Much Is Still Unknown
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Probability influenza transmission per minute of “contact� is uncertain: modes of transmission are still disputed
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Estimates from literature assume constant age-dependent risk within 2m radius
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But hazard rates should increase with proximity
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Precise effect of other factors is subject to debate: humidity, ventilation, strength of symptoms
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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
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Staff Productivity by Time of Day
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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
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Staff Productivity Patterns
Question Is staff productivity a function of perceived future workload (queue length)? Time remaining in shift?
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Scenario
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Same staffing and patient turnout conditions as an actual MIC
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No priority screening
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An average of nine registration staff and 11 vaccination nurses on staff throughout the day
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Hours of service were: 8:30AM to 8:00PM
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Assumed that 2% of population was infectious, but without symptoms
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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
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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
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80% of time in facility is spent waiting—not an efficient use of the public’s time
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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
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Factor Interactions
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None of the interactions tested were both statistically and economically significant, so a main effects model was used
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The scenario with fewer staff, 2% infectious, normal hours, and a representative demographic was used to define the intercept for all linear models generated
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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
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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
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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
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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
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Experiment 2: Overwhelmed Facility with Waiting Penalty
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20% increase in patient arrival rate
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Additional consideration: $10/hr penalty per hour of waiting Factors varied:
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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)
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Change in Expected Infections
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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
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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
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Factors Affecting Time in System
Intercept: 101.9 Significant Factors Add one nurse
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Value -13.4
p-value <0.001
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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
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General Implications for MIC Management
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Can test adequacy of staff scheduling
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Can estimate the marginal return of adding MIC staff under range of circumstances
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Method for estimating infection risk, and how faster services can reduce it
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Important Observations I
The demographic make-up of patients has a large effect on service times, and therefore on wait times and infection risk
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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
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Time cost to public could exceed direct costs of service provision; should be considered
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Relationship between queue length, time of day and faster service times in input data
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Input analysis: perception of staff working at full capacity may not be correct; use data to detect
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Future Research
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Observational study: I
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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
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Test effect of process re-design: non-adjuvanted nasal spray vaccine, and batching consent process to save time
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Test on data from other Public Health Unitsâ&#x20AC;&#x2122; MICs
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Acknowledgments
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Niagara Region Public Health Department (Ontario, Canada)
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Dr. Brian Schwartz and Sarah Levitt, Public Health Ontario
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Gerilynne Carroll, Emergency Management Branch, Ministry of Health and Long Term Care (Ontario, Canada)
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Canadian Institutes of Health Research (CIHR)
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CIHR-Strategic Training Program in Public Health and the Rural Agricultural Ecosystem
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CIHR-Strategic Training Program in Public Health Policy
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