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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.

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

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  1. 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

  2. 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?

  3. 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

  4. 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

  5. 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

  6. Natural history of NTHi colonization

  7. FA model

  8. 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

  9. Modeling partial immunity:S1I1S2I2S3I3……Sm-1Im-1SmImvs. SIR/SIRS/SIS

  10. Aspects of Immunity Modeled • Susceptibility • Contagiousness • Pathogenicity • Duration

  11. 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

  12. Population structure

  13. Contact structure

  14. Population parameters * The units of all rates are year-1.

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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)

  20. 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

  21. 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

  22. Parameter values that fit NTHi prevalence & AOM incidence for models without all immunity effects.

  23. Sensitivity Analysis to 10% Change In Pathogenicity or Transmission Immunity

  24. Age 0-1 Age 1-2 Age 2-3 Age 3-4 Age 4-5 Further Sensitivity Analysis

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

  26. Immunity acquiring & waning in vaccinated population: SIP vaccine Vaccination

  27. Vaccination strategy All children at age of 6 months vaccinated

  28. % reduction in AOM incidence among all preschool children as the result of vaccination at birth

  29. % reduction in AOM incidence among preschool children due to vaccination at birth.

  30. Absolute reduction of AOM incidence by age and daycare attendance among preschool children due to vaccination at birth.

  31. AOM cases among daycare and non-daycare children from a population of 1,000,000 before and after vaccination at birth with SIP vaccines.

  32. 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

  33. 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

  34. Additional Practical Need for Indirect Effects • Very young age of highest risk means little time to get all the booster effects needed

  35. 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

  36. 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

  37. Standard variance calculation in Group Randomized Trials (GRTs) • variance: • ICC: intraclass correlation • Assumes objective is measurement of individual effects

  38. ICC & Vaccine effect

  39. Change in Variance with Daycare Size & Sample Size

  40. Preliminary results (1): variance & immunity

  41. Simple Model For Insight S I S* Equilibrium distribution of states solved theoretically for daycare with 12 children Vaccine effect decreases susceptibility by 50%

  42. Unvacc mostly within trans 30%Prev Unvacc mostly outside trans Vacc mostly within trans Vacc mostly outside trans

  43. Unvacc mostly within trans 50%Prev Unvacc mostly outside trans Vacc mostly within trans Vacc mostly outside trans

  44. Significance of S & S* Contribution to Power Calculation • Serological ability to assess cumulative infection level would contribute considerably to power

  45. Empirical power calculation

  46. Empirical power & the number of the pairs of daycare centers

  47. 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

  48. Thank You

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