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

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dc.contributor.author Srinivasa Rao, Arni S. R.
dc.contributor.author Chen, Maggie H.
dc.contributor.author Pham, Ba'Z
dc.contributor.author Tricco, Andrea C.
dc.contributor.author Gilca, Vladimir
dc.contributor.author Duval, Bernard
dc.contributor.author Krahn, Murray D.
dc.contributor.author Bauch, Chris T.
dc.date.accessioned 2018-09-04T15:00:27Z
dc.date.available 2018-09-04T15:00:27Z
dc.date.copyright 2006
dc.date.issued 2006-12-05
dc.identifier.citation Srinivasa Rao, Arni S.R., Chen, Maggie H., Pham, Ba' Z, Tricco, Andrea C., Gilca, Vladimir, Duval, Bernard, Krahn, Murray D, and Bauch, Chris T. (2006). Cohort effects in dynamic models and their impact on vaccination programmes: an example from Hepatitis A. BMC Infectious Diseases. 6(1). https://www.doi.org/10.1186/1471-2334-6-174 en_US
dc.identifier.issn 1471-2334
dc.identifier.uri http://hdl.handle.net/10214/14168
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher BioMed Central Ltd. en_US
dc.rights Attribution *
dc.rights.uri http://creativecommons.org/licenses/by/2.0 *
dc.subject transmission rate en_US
dc.subject cohort effect en_US
dc.subject universal vaccination en_US
dc.subject catalytic modelling en_US
dc.subject seroprevalence data en_US
dc.title Cohort effects in dynamic models and their impact on vaccination programmes: an example from Hepatitis A en_US
dc.type Article en_US
dc.rights.holder © 2006 Srinivasa Rao et al; licensee BioMed Central Ltd. en_US


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