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Cohort effects in dynamic models and their impact on vaccination programmes: an example from hepatitis A.
Arni S R Srinivasa Rao1, Maggie H Chen, Ba' Z Pham
1Department of Mathematics and Statistics, University of Guelph, Guelph, Canada. arni@maths.ox.ac.uk <arni@maths.ox.ac.uk>
BMC Infectious Diseases
|December 7, 2006
Summary
Accounting for cohort effects in infectious disease models is crucial. Failing to do so over-predicts disease incidence and mortality, impacting vaccination program predictions.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Infection rates for many diseases have declined over the last century.
- This decline creates a cohort effect, where older populations have higher past infection rates than younger ones.
- Age-stratified seroprevalence data may not reflect constant infection rates due to this effect.
Purpose of the Study:
- To account for the cohort effect in Hepatitis A seroprevalence data.
- To compare the predicted impact of universal vaccination with and without considering the cohort effect in a dynamic model.
Main Methods:
- An age-structured compartmental model with declining transmission rates was fitted to Hepatitis A seroprevalence data.
- The model incorporated cohort effects to analyze transmission dynamics.
Main Results:
- Hepatitis A transmissibility has decreased by a factor of 2.8 since the early 20th century.
- Excluding the cohort effect led to significant over-prediction of incidence and mortality.
- Incidence and mortality were over-predicted by 34% and 90% respectively over a 20-year vaccination period when the cohort effect was ignored.
Conclusions:
- Failure to account for cohort effects distorts seroprevalence data interpretation.
- Cohort effects must be included in dynamic models for accurate prediction of vaccination program impacts.
- These findings are relevant for other infectious diseases with declining rates and lifelong immunity.
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