The Childhood Immunization Schedule and Safety Stakeholder Concerns, Scientific Evidence, and Future Studies (2013) / Chapter Skim
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Appendix D: Study Designs for the Safety Evaluation of Different Childhood Immunization Schedules--Martin Kulldorff
Appendix D: Study Designs for the Safety Evaluation of Different Childhood Immunization Schedules--Martin Kulldorff
Pages 161-200
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From page 161... ...
A number of possible study designs are presented in this review to evaluate different features or components of the vaccine schedule. These include the timing of individual vaccines, the timing between doses of the same vaccine, the interaction effect between vaccines and concurrent health conditions or pharmaceutical medications, the interaction effects of different vaccines given on the same day, the ordering of different vaccines, and the effect of cumulative summary metrics such as the total number of vaccines or the total amount of some vaccine ingredient.
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From page 162... ...
For the latter, there exist several postmarketing vaccine safety surveillance systems using observational data on children who receive the vaccines as part of their general care. In the United States, these include the Vaccine Adverse Event Reporting System (VAERS)
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From page 163... ...
The core of this paper is a set of proposals for the type of study designs and methods that would be appropriate for the comparative evaluation of vaccine adverse events under different vaccine schedules, and the paper is written in the context of the many difficulties raised by the speakers at the committee meetings held in February and March 2012. Note, though, that it is not a synthesis, an evaluation, or a review of the many excellent presentations made at those meetings.
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From page 164... ...
Comparison of Complete Vaccine Schedules • Whether or not the child has approximately followed the CDC recommended vaccine schedule. • The comparative safety of a specific alternative vaccine schedule, such as Dr.
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From page 165... ...
For example, a child receiving vaccine A at an early age may be more likely to also receive vaccine B at an early age, and the timing of vaccine A will then be correlated with the number of adverse events even if it is the timing of vaccine B that is the culprit. It could also be that there are two different vaccine schedule components that cause adverse events but that they cancel each other out when one looks at the difference between two complete schedules, making it impossible to detect the problem if only the complete schedules are studied.
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From page 166... ...
The most suitable study designs and analysis methods are greatly dependent on whether the potential adverse event has an early or late onset, and in the description below, separate methods are proposed for the two outcome types. This is a little bit of a simplification, since there are, of course, also potential adverse events that fall somewhere in between on this spectrum.
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From page 167... ...
They are also able to find common adverse events, but their sample size is typically not large enough to evaluate rare but serious adverse events. Their primary use for postmarketing vaccine safety surveillance is to generate study hypotheses.
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From page 168... ...
Together, these health plans have about 9.5 million members and an annual birth cohort of more than 100,000. The VSD system is used both for retrospective studies and for near-real-time vaccine safety surveillance with weekly analyses of newly approved vaccines.
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From page 169... ...
Because of their similarities, EMRs and health insurance claims data will be treated as the same type of data in this appendix under the name "health plan data." Study-Specific Data Collection Sometimes, new data are collected specifically for vaccine safety studies, such as a self-controlled case series, a case-control study, a cohort study, or a postmarketing randomized trial. An intermediate option is to obtain some of the data from health plans, disease registries, and/or vaccine registries, while the remaining data are collected from study-specific patient surveys or measurements.
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From page 170... ...
All three studies found that the risk of the adverse event varied greatly by age. Data EMRs from health plans and health insurance claims from health plans are ideally suited for studying this question.
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From page 171... ...
. The second step is to evaluate the relationship between age at vaccination and excess risk of the adverse event.
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From page 172... ...
(2006) used data from VSD to conduct an influenza vaccine safety study specific to this age group, looking at a wide variety of potential adverse events.
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From page 173... ...
For some adverse events where the incidence rate changes rapidly from one week of age to the next, an age adjustment must be made. If the age distribution of the disease is know, this can easily be done by use of an offset term in a logistic regression model.
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From page 174... ...
Early-Onset Adverse Events Data The use of electronic health data is suitable for vaccine doses that are at most a few years apart. If the time between doses is too long, health plan data are less suitable, as only some members will have been enrolled long enough to have information about all the doses of interest.
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From page 175... ...
For late-onset events and longer times between doses, health plan data may be less suitable, as only some members will have been enrolled long enough to be informative. Methods The same methods used for early-onset events can be used for late-onset adverse events, with some modifications.
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From page 176... ...
Early-Onset Adverse Events Data Electronic health plan data are suitable for early-onset adverse events.
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From page 177... ...
This design is more prone to bias than the self-controlled design described above. One way to partially adjust for this is to include only children that had both the vaccine and the potential adverse event of interest, at any time, and compare the children who had the vaccination at the same time as the health event with those that had them at different times.
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From page 178... ...
Hence, the estimates obtained indicated that it is safer to give the two vaccines on separate days rather than on the same day. Early-Onset Adverse Events Data Both VAERS and electronic health plan data can be used to evaluate early-onset adverse events due to vaccine-vaccine interaction.
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From page 179... ...
For electronic health plan data, a different methodological approach is needed. With a self-controlled risk interval analysis, it is possible to evaluate the effect of a vaccine on an adverse event by comparing the number of adverse events in a risk interval right after the vaccine is given with the number in a control interval long after vaccination.
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From page 180... ...
In the study, MMRV was given 6 weeks prior to, on the same day, or 6 weeks after the fourth dose of PCV7. The incidence of local and systemic adverse events was comparable among the groups, while no serious adverse events were reported in any group.
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From page 181... ...
Late-Onset Adverse Events Methods The following is a study design for comparing the order of vaccines A and B with health plan data. For simplicity, this description assumes a single dose of each vaccine, but it can be generalized to multiple doses.
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From page 182... ...
CUMULATIVE SUMMARY METRICS OF THE VACCINE SCHEDULES Background It is conceivable that it is neither the timing of individual vaccines nor the interaction between vaccines that is responsible for adverse events but, rather, some more general component of the vaccine schedule, such as the total number of vaccines given or the cumulative amount of immunestimulating content, immunogenic adjuvants, or preservatives in all vaccines received. Similar study designs and statistical methods can be used for most of these types of summary measures or metrics of the vaccine schedule, so they are considered together.
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From page 183... ...
It also makes it impossible to look for a doseresponse relationship, as described below. Data Electronic health plan data provide one of the best opportunities to study the safety of vaccine schedules with respect to cumulative summary metrics.
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From page 184... ...
If the potential adverse event is rare, cases can be identified through the health plan data, together with a set of matched controls. Chart review can then be conducted on this limited population to obtain more detailed information about each of the vaccines given, about the exact nature of the potential adverse event, or about various potential confounders.
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From page 185... ...
The number of potential vaccine schedule summary metrics is large. Here are some examples: • The maximum number of vaccines received on a single day.
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From page 186... ...
Data Electronic health plan data are the most suitable for studying vaccine schedule metrics. Since the complete vaccination history is needed to calculate the metric of interest (such as average age at vaccinations)
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From page 187... ...
To avoid ethical issues, observational health plan data can be used as an alternative to randomized trials. As in the previous section, complete vaccination histories are required to classify children into alternative vaccine schedules, so the length of enrollment must be long enough for a sufficiently large number of children.
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From page 188... ...
If the potential adverse event under study is such that there is little risk that its presence will change any aspect of the vaccination schedule, then one could include adverse events that occur before the end of the vaccine schedule considered, but such an approach is risky. Since different children will have different lengths of follow-up, timeto-event data will best be analyzed by survival analysis methods, adjusting for possible confounders.
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From page 189... ...
Combining Health Plan Data with Study-Specific Data Collection In the data sections presented above, health plans are often recommended as the best source of data to use. There are some potential adverse events that are not fully captured in the electronic health plan data, though, such as neuropsychological performance or immune function.
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From page 190... ...
If one vaccine schedule is safer than an alternative vaccine schedule in terms of a specific outcome but they both have the same average age at vaccination, then the effect size will be attenuated and go undetected. If a statistically significant excess number of adverse events is found, a second problem with these designs is that it can be hard to know which aspect of the schedule caused the excess or reduced risk.
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From page 191... ...
Information from such studies will greatly facilitate the design and understanding of subsequent studies evaluating the more general components discussed earlier as well as the comparison of complete vaccines schedules described above. Cross-National Comparisons Different countries have different recommended vaccine schedules, so it may seem natural to do cross-national studies to compare the safety of the schedules in an ecological study design.
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From page 192... ...
Data Mining Most vaccine safety studies evaluate a specific vaccine-event pair. For VAERS data, data mining methods are also used, where thousands of potential vaccine and adverse event pairs are evaluated simultaneously, without there being any prior hypothesis about their being an excess risk of the event.
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From page 193... ...
Unfortunately, VAERS data are of limited use when one is studying vaccine schedules. The cost is independent of the adverse event.
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From page 194... ...
One way to reduce this cost is to first do a study on fully automated data and do chart review only when that study shows an excess risk of adverse events, to confirm or dismiss that finding. The next level of cost is incurred by study designs that combine health plan data with specially collected outcome data that are not available as part of the EMRs.
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From page 195... ...
Somewhere in between these two extremes there is a gray zone where randomized trials may or may not be ethical, depending on the vaccine schedules being compared and on the available strength of the evidence regarding efficacy and potential adverse events. Experts on medical ethics should then be consulted.
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From page 196... ...
Hypotheses about potential adverse events may come from Phase III trials or from observational postmarketing studies with data from health plans or spontaneous reporting systems. The comparative safety evaluation of different vaccine schedules is a complex and multifaceted task, and all aspects of the vaccine schedule are currently understudied with regards to potential adverse events.
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From page 197... ...
What the paper attempts to show is that the comparative safety evaluation of vaccine schedules is complex and multifaceted and that a wide variety of study designs and statistical methods are available to a scientist who wishes to conduct such studies.
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From page 198... ...
2007. Real time vaccine safety surveillance for the early detection of adverse events.
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From page 199... ...
2011. Active surveillance for adverse events: The experience of the Vaccine Safety Datalink project.
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