Elsevier

Vaccine

Volume 32, Issue 30, 24 June 2014, Pages 3780-3786
Vaccine

Modeling the potential impact of vaccination on the epidemiology of congenital cytomegalovirus infection

https://doi.org/10.1016/j.vaccine.2014.05.014Get rights and content

Highlights

  • We model impact of cytomegalovirus (CMV) vaccination in the U.S. and Brazil.

  • We estimate change in congenital infections overall and by maternal infection type.

  • Greater reductions with vaccination of young children than adults.

  • Vaccination of infants could eliminate congenital CMV infection.

  • Under certain scenarios congenital infection from primary maternal infection may increase.

Abstract

Background

Understanding the potential for vaccination to change cytomegalovirus (CMV) epidemiology is important for developing CMV vaccines and designing clinical trials.

Methods

We constructed a deterministic, age-specific and time-dependent mathematical model of pathogen transmission, parameterized using CMV seroprevalence from the United States and Brazil, to predict the impact of vaccination on congenital CMV infection.

Findings

Concurrent vaccination of young children and adolescents would result in the greatest reductions in congenital CMV infections in populations with moderate and high baseline maternal seroprevalence. Such a vaccination strategy, assuming 70% vaccine efficacy, 90% coverage and 5-year duration of protection, could ultimately prevent 30–50% of congenital CMV infections. At equilibrium, this strategy could result in a 30% reduction in congenital CMV infections due to primary maternal infection in the United States but a 3% increase in Brazil. The potential for an increase in congenital CMV infections due to primary maternal infections in Brazil was not predicted with use of a vaccine that confers protection for greater than 5 years.

Interpretation

Modeling suggests that vaccination strategies that include young children will result in greater declines in congenital CMV infection than those restricted to adolescents or women of reproductive age. Our study highlights the critical need for better understanding of the relative contribution of type of maternal infection to congenital CMV infection and disease, the main focus of vaccine prevention.

Introduction

Congenital cytomegalovirus (cCMV) infection occurs when virus from the mother crosses the placenta and infects the immunologically immature fetus, as a result of primary maternal infection, reinfection or reactivation. The consequences of cCMV infection include fetal or infant death or neurological and sensory impairments [1], [2]. Children with cCMV-related disabilities may require extensive medical care, special education services, and interventions. Costs associated with cCMV infections in the United States were estimated in the 1990s to be at least $1.9 billion annually [3]. Population-based epidemiological data are needed to update and provide more complete estimates of the full spectrum of disease and related disabilities caused by cCMV [4]. Because of the burden associated with cCMV disease, a CMV vaccine was rated as a “highest priority” for vaccine development by the Institute of Medicine in the United States [3], [5]. Several CMV vaccines have been evaluated in clinical trials, although none is yet close to licensure [6].

Mathematical modeling has become increasingly useful for investigating the dynamics of infection and potential impact of vaccination and identifying critical knowledge gaps for study [7]. Identifying which populations to target for CMV vaccination that would result in greatest reductions in the burden of cCMV disease may provide additional insight for the development and design of future CMV vaccines and clinical trials globally. Understanding how vaccination strategies might need to be tailored to underlying population epidemiology is important because of substantial differences in CMV seroprevalence and proportion of cCMV infections due to primary maternal infection within and between countries. We used mathematical modeling to explore the potential impact of CMV vaccination in the United States, a population with moderate seroprevalence, and in Brazil, a population with high seroprevalence. We estimated the potential impact of vaccination on cCMV infections, overall and by type of maternal infection, both at equilibrium and with respect to time after vaccine introduction.

Section snippets

Methods

We constructed a deterministic, age-specific and time-dependent mathematical model of pathogen transmission, with six groups in our human population: susceptible, primarily infected, latently infected, reactivated/reinfected, susceptible vaccinated (before primary infection), and latently infected vaccinated (after primary infection) (Fig. 1). The system of differential equations describing the model is provided in the supplementary data (Appendix 1).

We defined susceptible as CMV seronegative

R0 and estimated distribution of cCMV infections by type of maternal infection in the pre-vaccine state

Using the NGM method, we estimated an R0 of 1.94 in the United States, and 5.17 in Brazil, similar to those estimated using the ‘constant force of infection’ method (Supplementary data – Appendix 2). Assuming the modified contact mixing matrix pattern III and 20-year latency duration, the model-generated distribution of cCMV infections by type of maternal infection in a pre-vaccine state was 16% from primary maternal infection, 12% from reinfection and 72% from reactivation for the United

Discussion

Using a mathematical model of CMV epidemiology parameterized with data from the United States and Brazil, we assessed the potential impact of vaccination on CMV seroprevalence and cCMV infections. Concurrent vaccination at ages 12–18 months and 15–19 years would have the greatest impact on reducing the number of cCMV infections overall, both in populations with moderate and high baseline maternal seroprevalence. Our model suggests that such a vaccination strategy, assuming a vaccine with 70%

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    The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

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