Optimizing Influenza Vaccine Distribution Jan Medlock Clemson - - PowerPoint PPT Presentation

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Optimizing Influenza Vaccine Distribution Jan Medlock Clemson - - PowerPoint PPT Presentation

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Introduction Model Outcome Measures Results Conclusions Optimizing Influenza Vaccine Distribution Jan Medlock Clemson University Department of Mathematical Sciences 03 August 2009 Introduction Model Outcome Measures Results Conclusions

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Introduction Model Outcome Measures Results Conclusions

Optimizing Influenza Vaccine Distribution

Jan Medlock

Clemson University Department of Mathematical Sciences

03 August 2009

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Introduction Model Outcome Measures Results Conclusions

Rather than thinking only about saving the most lives when considering vaccine rationing strategies, a better approach would be to maximize individuals’ life span and

  • pportunity to reach life goals.

Who Should Get Influenza Vaccine When Not All Can?

Ezekiel J. Emanuel* and Alan Wertheimer

T

he potential threat of pandemic influenza is staggering: 1.9 million deaths, 90 mil- lion people sick, and nearly 10 million people hospitalized, with almost 1.5 million production is just 425 million doses per annum, if all available factories would run at full capac- ity after a vaccine was developed. Under cur- rently existing capabilities for manufacturing beds despite the presentation of another patient who is equally or even more sick; “Save the most quality life years” is central to cost-effec- tiveness rationing. “Save the worst-off ”

Science 2006

  • Should value people “on the basis of the amount the person

invested in his or her life balanced by the amount left to live.”

  • Then vaccinate the most-valued people!
  • Misses epidemiology: Transmission, Case mortality, Vaccine

efficacy

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SLIDE 3

Introduction Model Outcome Measures Results Conclusions

Rather than thinking only about saving the most lives when considering vaccine rationing strategies, a better approach would be to maximize individuals’ life span and

  • pportunity to reach life goals.

Who Should Get Influenza Vaccine When Not All Can?

Ezekiel J. Emanuel* and Alan Wertheimer

T

he potential threat of pandemic influenza is staggering: 1.9 million deaths, 90 mil- lion people sick, and nearly 10 million people hospitalized, with almost 1.5 million production is just 425 million doses per annum, if all available factories would run at full capac- ity after a vaccine was developed. Under cur- rently existing capabilities for manufacturing beds despite the presentation of another patient who is equally or even more sick; “Save the most quality life years” is central to cost-effec- tiveness rationing. “Save the worst-off ”

Science 2006

  • Should value people “on the basis of the amount the person

invested in his or her life balanced by the amount left to live.”

  • Then vaccinate the most-valued people!
  • Misses epidemiology: Transmission, Case mortality, Vaccine

efficacy

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SLIDE 4

Introduction Model Outcome Measures Results Conclusions

Rather than thinking only about saving the most lives when considering vaccine rationing strategies, a better approach would be to maximize individuals’ life span and

  • pportunity to reach life goals.

Who Should Get Influenza Vaccine When Not All Can?

Ezekiel J. Emanuel* and Alan Wertheimer

T

he potential threat of pandemic influenza is staggering: 1.9 million deaths, 90 mil- lion people sick, and nearly 10 million people hospitalized, with almost 1.5 million production is just 425 million doses per annum, if all available factories would run at full capac- ity after a vaccine was developed. Under cur- rently existing capabilities for manufacturing beds despite the presentation of another patient who is equally or even more sick; “Save the most quality life years” is central to cost-effec- tiveness rationing. “Save the worst-off ”

Science 2006

  • Should value people “on the basis of the amount the person

invested in his or her life balanced by the amount left to live.”

  • Then vaccinate the most-valued people!
  • Misses epidemiology: Transmission, Case mortality, Vaccine

efficacy

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Introduction Model Outcome Measures Results Conclusions

Problem Setup

  • For influenza
  • Age structure but not risk or occupation
  • Given an outcome measure
  • How to distribute limited vaccine doses?
  • Nonlinear constrained optimization
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Introduction Model Outcome Measures Results Conclusions

Model

UR US UI UE VR VI VS VE νU γ λ νV (1 − ǫ)λ τ τ γ

Age structured (0, 1–4, 5–9, 10–14, 15–19, . . . , 70–74, 75+) No birth or natural death

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Introduction Model Outcome Measures Results Conclusions

2007 US Population Age Structure

Number Age (years) 0M 1M 2M 3M 4M 5M 20 40 60 80 100

Sources: US Census, US Census.

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Introduction Model Outcome Measures Results Conclusions

Parameters

Parameter Ages Value Ref Latent period, 1/τ all 1.2 d

[1]

Infectious period, 1/γ all 4.1 d

[1]

Vaccine efficacy 0–64 0.80

[2, 3]

against infection, ǫa 65+ 0.60 Vaccine efficacy 0–19 0.75 against death 20–64 0.70

[4, 2]

65+ 0.60

[1] Longini et al, Science, 2005; [2] Galvani, Reluga, & Chapman, PNAS, 2007; [3] CDC, ACIP, 2007; [4] Meltzer, Cox, & Fukuda, Emerg Infect Dis, 1999.

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Introduction Model Outcome Measures Results Conclusions

Death Rate

Influenza death rate (per day) Age (years) 1957, unvaccinated 1957, vaccinated 1918, unvaccinated 1918, vaccinated 0.000 0.002 0.004 0.006 0.008 0.010 20 40 60 80

Sources: Serfling, Sherman, & Houseworth, Am J Epidemiol, 1967; Luk, Gross, & Thompson, Clin Infect Dis, 2001; Glezen, Epidemiol Rev, 1996.

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Introduction Model Outcome Measures Results Conclusions

Contacts

Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases

Joe ¨l Mossong1,2*, Niel Hens3, Mark Jit4, Philippe Beutels5, Kari Auranen6, Rafael Mikolajczyk7, Marco Massari8, Stefania Salmaso8, Gianpaolo Scalia Tomba9, Jacco Wallinga10, Janneke Heijne10, Malgorzata Sadkowska-Todys11, Magdalena Rosinska11, W. John Edmunds4

PLoS MEDICINE

PLoS Med 2008

Surveyed 7,290 Europeans for daily contacts

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Introduction Model Outcome Measures Results Conclusions

Contacts

0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70+ Age (years) 0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70+ Age (years) 10 100 Contact rate (per person per day)

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Introduction Model Outcome Measures Results Conclusions

R0

  • R0 = 1.4 for Swine Flu (Fraser et al, Science, 2009)
  • R0 = 2.0 for 1918 Pandemic (Mills et al, Nature, 2004)
  • We considered R0 = 1.4 and also R0 = 1.2, 1.6, 1.8, 2.0
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Introduction Model Outcome Measures Results Conclusions

Outcome Measures

Map outcome (number infected, dead, etc) to objective

  • Total Infections
  • Total Deaths
  • Years of Life Lost: Using expectation of life (NCHS, US Life Tables, 2003)
  • Contingent Valuation: Indirect assessment of value of lives of

different ages

  • Total Cost: Converts deaths, infections, etc into dollars
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Introduction Model Outcome Measures Results Conclusions

Contingent Valuation

  • Survey asked about

20, 30, 40, 60 year

  • lds and fit

va = aω−1 exp (−ψaω)

(Cropper et al, J Risk Uncertain, 1994)

  • Alternative:

wage–risk market data, but only for working-aged adults

Relative disutility of death Age (years) 0.0 0.2 0.4 0.6 0.8 1.0 20 40 60 80

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Introduction Model Outcome Measures Results Conclusions

Total Cost

  • Monetary cost of

illness (Meltzer, Cox, &

Fukuda, Emerg Infect Dis, 1999)

  • Monetary cost of

death

  • Future lifetime

earnings (Haddix et

al, 1996)

  • Alternatives:

Include value of non-work time

Future lifetime earnings Age (years) 0.0 0.5 1.0 1.5 20 40 60 80

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Introduction Model Outcome Measures Results Conclusions

Outcome Measures

Relative disutility of death Age (years) Total Deaths Years of Life Lost Contingent Valuation Total Cost 0.0 0.2 0.4 0.6 0.8 1.0 20 40 60 80

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Introduction Model Outcome Measures Results Conclusions

No Vaccination

Number infected Time (days) 1957 1918 0M 2M 4M 6M 8M 10M 60 120 180 240 300 360

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Introduction Model Outcome Measures Results Conclusions

Current Vaccination

CDC estimate

  • 84M doses used in

2007

  • 100M+ doses

annually

  • 600M doses for Swine

Flu

Vaccine coverage Age (years) 0% 20% 40% 60% 20 40 60 80

Sources: CDC, ACIP, 2008; NHIS, 2007.

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Introduction Model Outcome Measures Results Conclusions

Eradication

Eradication doses R0 1957 1918 25 50 75 100 125 150 1 1.2 1.4 1.6 1.8 2

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Introduction Model Outcome Measures Results Conclusions

1957-like Mortality

0M 20M 40M 60M I n f e c t s D e a t h s Y L L C V C

  • s

t I n f e c t s D e a t h s Y L L C V C

  • s

t I n f e c t s D e a t h s Y L L C V C

  • s

t Number of doses 20M Doses 40M Doses 60M Doses 5–9 10–14 15–19 20–24 30–34 35–39 45–49 65–69 75+

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Introduction Model Outcome Measures Results Conclusions

1918-like Mortality

0M 20M 40M 60M I n f e c t s D e a t h s Y L L C V C

  • s

t I n f e c t s D e a t h s Y L L C V C

  • s

t I n f e c t s D e a t h s Y L L C V C

  • s

t Number of doses 20M Doses 40M Doses 60M Doses 5–9 10–14 15–19 20–24 30–34 35–39 45–49 65–69 75+

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Introduction Model Outcome Measures Results Conclusions

1957-like Mortality

Infections 0.0 0.5 1.0 Deaths 0.0 0.5 1.0 YLL 0.0 0.5 1.0 CV 5–9 10–14 15–19 20–24 25–29 30–34 35–39 45–49 65–69 70–74 75+ 0.0 0.5 1.0 0M 20M 40M 60M Cost Vaccine doses 0.0 0.5 1.0 0M 20M 40M 60M

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Introduction Model Outcome Measures Results Conclusions

1918-like Mortality

Infections 0.0 0.5 1.0 Deaths 0.0 0.5 1.0 YLL 0.0 0.5 1.0 CV Vaccine doses 5–9 10–14 15–19 20–24 30–34 35–39 0.0 0.5 1.0 0M 20M 40M 60M Cost Vaccine doses 0.0 0.5 1.0 0M 20M 40M 60M

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Introduction Model Outcome Measures Results Conclusions

R0 = 2.0, 1957-like Mortality

Infections 0.0 0.5 1.0 Deaths 0.0 0.5 1.0 YLL 0.0 0.5 1.0 CV Vaccine doses 0.0 0.5 1.0 0M 50M 100M Cost Vaccine doses 0.0 0.5 1.0 0M 50M 100M

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Introduction Model Outcome Measures Results Conclusions

R0 = 2.0, 1918-like Mortality

Infections 0.0 0.5 1.0 Deaths 0.0 0.5 1.0 YLL 0.0 0.5 1.0 CV 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 0.0 0.5 1.0 0M 50M 100M Cost Vaccine doses 0.0 0.5 1.0 0M 50M 100M

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Introduction Model Outcome Measures Results Conclusions

Sensitivity Analysis

  • Reduced vaccine efficacy against infection

Shifts to protecting at risk

  • Reduced vaccine efficacy against death

Reduced susceptibility in elderly Reduced infectious period for vaccinees Reduced infectiousness for vaccinees Little change for 50% reduction

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Introduction Model Outcome Measures Results Conclusions

1957-like Mortality, 40M Doses

0% 20% 40% 60% I n f e c t s D e a t h s Y L L C V C

  • s

t Reduction Optimal Current Uniform Former CDC Seasonal Pandemic Ages 5–19

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Introduction Model Outcome Measures Results Conclusions

1918-like Mortality, 40M Doses

0% 20% 40% 60% I n f e c t s D e a t h s Y L L C V C

  • s

t Reduction Optimal Current Uniform Former CDC Seasonal Pandemic Ages 5–19

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Introduction Model Outcome Measures Results Conclusions

Conclusions

  • 65M doses prevents an R0 = 1.4 epidemic
  • 135M doses prevents an R0 = 2.0 epidemic
  • Can improve vaccination policies
  • Infections: Vaccinate transmitters, children (5–19) & parents

(30–39)

  • Deaths, YLL, Contingent, & Cost:
  • When vaccine limited, vaccinate those at risk of death
  • When vaccine plentiful, vaccinate transmitters
  • Transition varies between outcome measures
  • Deaths averted transitions last
  • Joint work with Alison Galvani

Funded by NSF grant SBE-0624117