Cohort effects in dynamic models and their impact on vaccination programmes: an example from Hepatitis A

Arni SR Srinivasa Rao¹ , Maggie H Chen² , Ba' Z Pham⁵ , Andrea C Tricco⁵ , Vladimir Gilca⁴ , Bernard Duval⁴ , Murray D Krahn³ and Chris T Bauch¹

¹Department of Mathematics and Statistics, University of Guelph, Guelph, Canada

²Toronto General Hospital and University Health Network, Toronto, Canada

³Department of Health Policy, Management and Evaluation, University of Toronto and University Health Network, Toronto, Canada

⁴Institut national de santé publique Québec, Québec City, Canada, and Research Centre, Centre hospitalier universitaire de Québec, Québec City, Canada

⁵BioMedical Data Sciences, GlaxoSmithKline, Toronto, Canada

author email corresponding author email

BMC Infectious Diseases 2006, 6:174doi:10.1186/1471-2334-6-174


Published:	5 December 2006

Abstract

Background

Infection rates for many infectious diseases have declined over the past century. This has created a cohort effect, whereby older individuals experienced a higher infection rate in their past than younger individuals do now. As a result, age-stratified seroprevalence profiles often differ from what would be expected from constant infection rates.

Methods

Here, we account for the cohort effect by fitting an age-structured compartmental model with declining transmission rates to Hepatitis A seroprevalence data for Canadian-born individuals. We compare the predicted impact of universal vaccination with and without including the cohort effect in the dynamic model.

Results

We find that Hepatitis A transmissibility has declined by a factor of 2.8 since the early twentieth century. When the cohort effect is not included in the model, incidence and mortality both with and without vaccination are significantly over-predicted. Incidence (respectively mortality) over a 20 year period of universal vaccination is 34% (respectively 90%) higher than if the cohort effect is included. The percentage reduction in incidence and mortality due to vaccination are also over-predicted when the cohort effect is not included. Similar effects are likely for many other infectious diseases where infection rates have declined significantly over past decades and where immunity is lifelong.

Conclusion

Failure to account for cohort effects has implications for interpreting seroprevalence data and predicting the impact of vaccination programmes with dynamic models. Cohort effects should be included in dynamic modelling studies whenever applicable.