Challenges in studying alternative vaccination schedules

Group Health Research Institute and

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Group Health Research Institute and the Vaccine Safety Datalink

8th March 2012

Funding

This work was funded by the Centers for Disease Control and Prevention (CDC), through a contract to America’s Health

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through a contract to America’s Health Insurance Plans (AHIP)

Motivation for current work• Parental concern about immunization safety is

increasing• More than 10% of parents refuse or delay one or

more vaccinations for their children• Independent authors are proposing alternatives

to the ACIP recommended schedule

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to the ACIP recommended schedule• Although the rationale behind such alternative

schedules has been extensively critiqued, parents may believe these safety claims

• Thus, there is a public health need to compare the relative safety of recommended and alternate schedules

Dempsey et al; Pediatr 2011; 128(5):848-56Gust et al; Pediatr 2008; 122(4):718-25

Offit and Moser; Pediatr 2009; 123(1): e164-9Offit and Jew; Pediatr 2003; 112(6):1394-7

ACIP recommended schedule: birth through 7 months of age

Vaccine Birth1

Month2

Months3

Months4

Months5

Months6

Months7

Months

Hepatitis B

Rotavirus

DTaP*

HepB HepB HepB à

RV RV RV

DTaP DTaP DTaP

Age

4

Hib**

Pneumococcal

Inactivated polio

Influenza

*Diphtheria, tetanus, pertussis**Haemophilus influenzae type b

Hib Hib Hib

PCV PCV PCV

IPV IPV IPV à

Flu à

Adapted from:MMWR Weekly Rep 2012; 61(5):1-4

“Dr. Bob’s alternative schedule”: birth through 7 months of age

Vaccine Birth1

Month2

Months3

Months4

Months5

Months6

Months7

Months

Hepatitis B

Rotavirus

DTaP*

Age

RV RV RV

DTaP DTaP DTaP

5

Hib**

Pneumococcal

Inactivated polio

Influenza

*Diphtheria, tetanus, pertussis**Haemophilus influenzae type b

Hib Hib Hib

PCV PCV PCV

Adapted from:Sears; The Vaccine Book; 2007, Little, Brown and Company

“Dr. Bob’s alternative schedule” with ACIP doses missed

Vaccine Birth1

Month2

Months3

Months4

Months5

Months6

Months7

Months

Hepatitis B

Rotavirus

DTaP*

Age

RV RV RV

DTaP DTaP DTaP

HepB HepB HepB à

6

Hib**

Pneumococcal

Inactivated polio

Influenza

*Diphtheria, tetanus, pertussis**Haemophilus influenzae type b

Hib Hib Hib

Adapted from:MMWR Weekly Rep 2012; 61(5):1-4

Sears; The Vaccine Book; 2007, Little, Brown and Company

PCV PCV PCV

IPV IPV IPV à

Flu à

Challenges to comparing safety of different vaccination schedules

• How to characterize vaccination• How to define safety• Age effects• Modeling interactions

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• Modeling interactions

How to characterize vaccination (1)

• Option 1: classify children based on the entire vaccination schedule

• Directly compares safety of different schedules

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schedules• Limitations

– Only well suited to outcomes that manifest after completion of the schedule

– Requires sufficient number of children in specified alternative schedule(s)

How to characterize vaccination (2)

• Option 2: classify children based on timing of individual vaccinations

• Allows evaluation of outcomes that occur before completion of the schedule

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before completion of the schedule• Limitations

– Relative safety of different schedules must be inferred from presence or absence of interactions between vaccines

– Requires decisions about classifying combination vaccines, which affect interpretation of results

What do we mean by “safer”?

• Adverse events (AEs) following vaccination could be modeled as:– Probability of having at least one AE– Number of AEs per person

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– Number of AEs per person– Rate of AEs per vaccination– Rate of AEs per person-time of follow-up

• The choice of outcome model affects study conclusions

Outcome model example (1)

Vaccines given together:

Follow-up for AEs

Consider two hypothetical vaccines, A and B, that can be given at the same visit or at separate visits:

11 Time à

Visit 1A + B

Visit 1A only

Follow-up for AEs

Visit 2B only

Follow-up for AEs

Vaccines given at separate visits:

Outcome model example (2)Assume that subjects are followed for febrile seizure (FS) in the 7 days after vaccination, and that only one FS episode can occur per 7-day period

Vaccines given together:

Follow-up for AEs

12 Time à

Visit 1A + B

Visit 1A only

Follow-up for AEs

Visit 2B only

Follow-up for AEs

Vaccines given at separate visits:

Outcome model example (3)

Probability of Expected

FS episodes Rate of FS

per 100 Rate of FS

If:• Probability of FS episode after A or B is 5% in 7 days• There is no interaction between A and B on FS

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Vaccines administered:

Probability of at least one

FS

FS episodes per 100 children

per 100 vaccines

administered

Rate of FS per 100

person-days

Together 9.75% 9.75 4.88 1.39

Separately 9.75% 10 5 0.71

Age effects (1)

• Many outcomes used as vaccine safety endpoints are primarily caused by factors other than vaccines

• The incidence of outcomes due to these

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• The incidence of outcomes due to these other factors can be strongly age dependent

• Example: febrile seizures primarily occur between 6 and 35 months of age, with peak incidence around 18 months of age

Waruiru and Appleton; Arch Dis Child 2004; 89:751-6

Age effects (2)Age may be a confounder that cannot be removed either in study design or analysis:

ACIP recommended schedule

“Dr. Bob’s alternative schedule”

Measles 1Age (years) 2 3 4 5 6<1

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“Dr. Bob’s alternative schedule”

ACIP recommended schedule

“Dr. Bob’s alternative schedule”

Hepatitis B

(2x)

Adapted from:MMWR Weekly Rep 2012; 61(5):1-4

Sears; The Vaccine Book; 2007, Little, Brown and Company

Age effects (3)

• Age effects can also matter when defining vaccinations at the level of the entire schedule– A child could complete the ACIP childhood

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– A child could complete the ACIP childhood schedule (except flu) by 4 years of age

– A child would not complete “Dr. Bob’s alternative schedule” until 6 years of age

• This would require carefully defining outcome time periods to avoid introducing bias

Age effects (4)

• If the risk of a vaccine-induced AE varies by age of vaccination, then age is part of the “causal pathway” linking schedule to risk of outcomes, and should not be treated as a

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outcomes, and should not be treated as a confounder.

Modeling interactions (1)Consider a hypothetical cohort study that defines exposure at the level of the individual vaccine, looking at vaccines A and B. We could model the presence of an interaction as:

log(λ) = α + β1(A) + β2(B) + γ(A)(B)

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where λ is the rate of adverse events and eγ is the parameter of interest, the interaction between A and B on λ.

To fit this model, we would need data on four groups: exposed to both A and B, to B only, to A only, and to neither. Therefore, we would need to include unvaccinated children.

Modeling interactions (2)

• Problem: Unvaccinated subjects may differ from vaccinated subjects in numerous ways, including healthcare seeking behavior. This may introduce confounding by factors that

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may introduce confounding by factors that are difficult to measure.

• Case-only methods (self-controlled case series, case-crossover, etc) may remove inter-subject confounding but at the cost of additional, possibly untestable, assumptions.

Summary

• Comparing vaccination schedules presents many problems of study design and analysis.

• Many of these problems would exist even

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• Many of these problems would exist even in an ideal randomized controlled trial.

• Confounding by age and by factors that are difficult to measure is likely.

Acknowledgements

Group Health Research Institute• Lisa Jackson• Jennifer NelsonKaiser Permanente Colorado

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Kaiser Permanente Colorado• Jason Glanz• Simon Hambidge