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Respiratory Bacteria Vaccines: Model Analyses for Vaccine and Vaccine Trial Design. Jim Koopman MD MPH Ximin Lin MD MPH Tom Riggs MD MPH Dept. of Epidemiology & Center for Study of Complex Systems University of Michigan. Questions Addressed.

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respiratory bacteria vaccines model analyses for vaccine and vaccine trial design

Respiratory Bacteria Vaccines: Model Analyses for Vaccine and Vaccine Trial Design

Jim Koopman MD MPH

Ximin Lin MD MPH

Tom Riggs MD MPH

Dept. of Epidemiology &

Center for Study of Complex Systems

University of Michigan

questions addressed
Questions Addressed
  • What role does immunity affecting pathogenicity vs. transmission play in the sharp drop with age in NTHi otitis media?
  • What vaccine effects should be sought and measured in trials?
  • How should vaccine trials be designed to insure adequate power to detect important effects?
general issues regarding nthi
General Issues Regarding NTHi
  • Causes 20-40% of acute otitis media
  • Vaccine market 1 billion $ per year in U.S.
  • Infection, immunity, and disease data is meager, non-specific, & highly variable
  • Knowledge of natural history of infection and immunity is deficient
  • Unquestioned assumption that vaccine trials will be individual based and assess disease outcomes
aspects of nt hi many other bacterial infections
Aspects of NTHi (& many other bacterial) infections
  • Partial immunity, rarely sterilizing
    • IgA proteases show evolutionary importance of immunity
  • Many variants arise due to transformation competency
    • No permanent strains yet identified
  • Immunity to colonization or infection, disease, & transmission can be distinct
using nt hi models for inference
Using NTHi Models for Inference
  • Models with diverse natural Hx of infection and immunity, age groupings, and contact patterns were constructed
    • Deterministic compartmental (DC) models built first
    • Gradual acquisition of immunity with each colonization and continuous loss over time
  • All models were fit to the full range of data conformations deemed plausible using least squares
  • Projections of vaccine effects made for all fits of all models (about 1000 total)
  • Individual event history stochastic models corresponding to the DC models were used for vaccine trial design
modeling partial immunity
Modeling partial immunity

Model agent variation and host response as single process

Assumptions

  • equal immunity from each colonization
  • multiplicative effects of sequential infections
  • immunity limit (m levels)
  • immunity waning
aspects of immunity modeled
Aspects of Immunity Modeled
  • Susceptibility
  • Contagiousness
  • Pathogenicity
  • Duration
population structure
Population structure
  • Preschool children (0.5-5 years)
      • Day-care + Non-day-care
      • 9 age groups with 6-month interval
  • School children (5-15 years)
  • Adults
population parameters
Population parameters

* The units of all rates are year-1.

limited highly variable epidemiologic data
Limited & Highly Variable Epidemiologic data
  • NTHi prevalence by age & daycare attendance (diverse methods)
  • AOM incidence < age 5 by daycare (combine incidence studies & fraction with NTHi studies)
  • Antibody levels by age (diverse methods)
  • Colonization duration (quite limited)
  • Daycare risk ratios for AOM
slide16

Low Values

High Values

Colonization prevalence values fitted

Colonization prevalence ages 0-5 when in daycare

23%

51%

Colonization prevalence ages 0-5 when not in daycare

9.5%

21%

Colonization prevalence ages 6-15

7%

15%

Colonization prevalence in adults

4%

9%

AOM Incidence values fitted

Annual NTHi AOM incidence age* <1

0.08

0.22

Annual NTHi AOM incidence age 1-2

0.13

0.33

Annual NTHi AOM incidence age 2-3

0.08

0.22

Annual NTHi AOM incidence age 3-4

0.06

0.18

Annual NTHi AOM incidence age 4-5

0.05

0.17

other data
Other Data
  • Antibody levels peak during elementary school
  • Daycare Risk Ratios from 2 to 3
  • Colonization mean of 2 months but many transient episodes and some long (limited data)
  • Waning “seems” to be relatively fast
presumptions before our work
Presumptions Before Our Work
  • Very different from Hi Type B
  • Colonization is so frequent, even at older ages, that immunity to transmission cannot be important
  • Trials should assess effects on AOM, not colonization
general assumptions of our model
General assumptions of our model
  • Every colonized individual is infectious
  • Acute otitis media (AOM) is the only relevant disease (Unlike Hi Type B or Strep pneumo)
  • Maternal immunity (Children aged 0-6 months totally immune from colonization)
fitting model to epidemiologic data
Fitting model to epidemiologic data
  • Berkeley Madonna: “boundary value ODE…” & optimize functions
  • Empirical identifiability checking
  • Extensive robustness assessment for both data conformation and model conformation rather than estimating variance of estimates
fitting results
Fitting Results
  • Most efficient level # is 4
  • Needed immunity profile includes
    • Susceptibility
    • Contagiousness
    • Pathogenicity
  • Contagiousness and Duration Effects are highly co-linear when fitting equilibrium
parameter values that fit nthi prevalence aom incidence for models without all immunity effects
Parameter values that fit NTHi prevalence & AOM incidence for models without all immunity effects.
slide25

Age 0-1

Age 1-2

Age 2-3

Age 3-4

Age 4-5

Further Sensitivity Analysis

immunity acquisition waning for p vaccine vaccine effects don t exceed natural immunity effects
Immunity acquisition & waning for P vaccine (Vaccine effects don’t exceed natural immunity effects)

Vaccination

vaccination strategy
Vaccination strategy

All children at age of 6 months vaccinated

slide31
Absolute reduction of AOM incidence by age and daycare attendance among preschool children due to vaccination at birth.
slide32
AOM cases among daycare and non-daycare children from a population of 1,000,000 before and after vaccination at birth with SIP vaccines.
summary of deterministic model findings
Summary of Deterministic Model Findings
  • Wide range of feasible models fit to a wide range of feasible data
  • Over this entire huge range, the intuition that immune effects on pathogenicity are the major determinants of AOM incidence proves to be wrong
  • Trials must assess transmission
model refinements desirable
Model Refinements Desirable
  • Model agent strains with different degrees of cross reacting immunity
  • Incorporate evolution of agent into vaccine effect assessment
  • Make maternal immunity and acquisition time for vaccine immunity more realistic
additional practical need for indirect effects
Additional Practical Need for Indirect Effects
  • Very young age of highest risk means little time to get all the booster effects needed
using nt hi models for inference about vaccine trial design
Using NTHi Models for Inference About Vaccine Trial Design
  • Convert deterministic compartmental model to individual event history model
  • Add distinct daycare units and families
  • Construct vaccine trials assessing colonization in the IEH models with varying randomization schemes, vaccine effects exceeding natural immunity, sample collection periods, serology & typing results
  • Hundreds of thousands of vaccine trial simulations performed
conclusions from vaccine trial simulations
Conclusions from Vaccine Trial Simulations
  • Most efficient randomization unit is daycare
    • Individual randomized trials run too much risk of missing important vaccine effects
  • Standard power calculation methods for Group Randomized Trials are far off because they are based on individual effect
  • Role of inside vs. outside transmission in daycare significantly affects power
  • Molecular assessment of transmission worthwhile
standard variance calculation in group randomized trials grts
Standard variance calculation in Group Randomized Trials (GRTs)
  • variance:
  • ICC: intraclass correlation
  • Assumes objective is measurement of individual effects
simple model for insight
Simple Model For Insight

S

I

S*

Equilibrium distribution of states solved theoretically for daycare with 12 children

Vaccine effect decreases susceptibility by 50%

slide43

Unvacc mostly within trans 30%Prev

Unvacc mostly outside trans

Vacc mostly within trans

Vacc mostly outside trans

slide44

Unvacc mostly within trans 50%Prev

Unvacc mostly outside trans

Vacc mostly within trans

Vacc mostly outside trans

significance of s s contribution to power calculation
Significance of S & S* Contribution to Power Calculation
  • Serological ability to assess cumulative infection level would contribute considerably to power
why standard power calculations for grts are way off
Why standard power calculations for GRTs are way off
  • ICC is determined by transmission dynamics
  • Effect is determined by transmission dynamics
  • Power is not just determined a single outcome state but by correlated infection and immunity states