Scheduling of measles vaccination in low-income countries: Projections of a dynamic model
Introduction
In the 1990s, endemic measles was dramatically reduced and – according to many – completely eliminated in the Americas through large-scale supplemental immunization activities (SIAs) and efforts to strengthen routine immunization (RI) programs by improving coverage of the first scheduled dose of measles-containing vaccine (MCV1) and by introducing a second scheduled dose (MCV2) [1]. Subsequently, these “second opportunity” strategies were extended to 45 priority countries, mostly in Africa and Asia [1]. As a result, the estimated number of worldwide measles-related deaths declined from 873,000 in 1999 to 345,000 in 2005 [1]. However, measles disease burden remains high in absolute terms, and many of these countries only recently held their first SIA and are far from introducing MCV2 [1], [2]. Therefore, decision-makers continue to face challenges in determining the optimal interval between SIAs and the optimal criteria for introducing MCV2 in the priority countries. At the same time, new measles vaccine technologies are in development. These include DNA primers that make conventional measles vaccine more effective in infants [3]. Other technologies simplify administration and waste disposal by administering vaccine via aerosolized droplets [4], dry powder formulations [5], and needle-free syringes [6]. Decision-makers therefore also face challenges in determining how to invest in these new technologies, particularly given the rapidly changing landscape of global measles disease burden.
To meet these challenges, credible projections of future measles cases under various possible vaccination strategies are needed. Such projections can be obtained using dynamic disease transmission models [7]. Dynamic models incorporate transmission mechanisms and can thereby capture herd immunity effects, whereby vaccination also protects unvaccinated individuals by reducing disease transmission in the population and thus reducing the force of infection (the rate at which a susceptible person is infected) [8]. As a result, an infectious disease can be eliminated in a population with an imperfectly efficacious vaccine, and without vaccinating everyone. Herd immunity effects become particularly pronounced at higher coverage levels, near the elimination threshold in vaccine coverage [8]. Hence, dynamic models are excellent choices for projecting the impact of vaccination programs when coverage levels are high, as might occur under successful measles SIA and RI efforts. Dynamic models are referred to as dynamic because they can capture how the force of infection evolves over time due to factors such as the introduction of vaccination. By comparison, widely used static models (e.g. cohort models) assume a fixed, unchanging force of infection and do not capture herd immunity [9]. Therefore, they incorrectly predict that disease elimination can only occur when a perfectly efficacious vaccine is given to everyone in a population, which contradicts the local elimination of measles that has been observed in many countries immediately following SIA efforts.
A dynamic model of particular relevance for measles is the age-structured SEIR compartmental model. This model and variations thereof have long been applied for assessing vaccination programs for pediatric infectious diseases, and has been validated against pre- and post-vaccination age-stratified case reports and seroprevalence surveys for measles [8], [10], [11], [12]. Previous analyses have tailored measles models to settings where birth rates and transmission rates are high [13], [14] as well as campaign settings [15], [16]. The SEIR model has also been applied in very policy-specific settings, such as in New Zealand where an age-structured measles model was used by the Ministry of Health to predict an epidemic of measles and to design optimal vaccination schedules for that country [17]. The SEIR model has since been developed into metapopulation models of measles transmission, such as have been used recently to understand the spatio-temporal dynamics of measles outbreaks in western Africa [18].
The objective of this study was to develop a simplified compartmental model of measles transmission and vaccination in low-income settings. The model was developed for use in a pilot project that will assess the cost-effectiveness of potential innovations in measles vaccination technologies in low-income countries. Here, we demonstrate the stand-alone utility of the model for assessing the effectiveness of second opportunity strategies with the current vaccine. A model with relatively simple structure was developed because there are often not sufficient data for many low-income countries to populate the parameters of more complex models. As a result, we did not include stochastic, spatial, seasonal or other effects that are relevant in certain contexts [8], [12], [18], [19]. However, because of its limited data requirements, the model can be easily adapted to any given country. The present model requires country-specific vaccine coverage, vital statistics, and (optionally) measles case fatality rate. Most other parameters are specific to low-income countries generally.
Accordingly we developed country-specific versions of the model for six countries, chosen on the basis of their relatively high annual number of cases (India, Nigeria), the availability of vaccine cost data that will be used in the pilot project on cost-effectiveness that will use this model (Cambodia, Ghana, Morocco), or because they provide illustrative examples of cases where data availability is quite limited (India, Uganda). These countries also represent a wide range of measles disease burden and vaccine coverage. To illustrate the utility and specific policy implications of the model, we explored projected cases in these countries under various possible scenarios for (1) interval between SIAs, (2) criteria for introducing MCV2, and (3) rate of improvement of MCV1 coverage. We also compared the projections of the compartmental model to those of the corresponding static model.
Section snippets
Model structure
We developed an age-structured MSEIRV compartmental model, whereby individuals are allocated into one of a number of mutually exclusive categories based on their epidemiological status and age. Epidemiological categories were: maternally immune (naturally derived), maternally immune (vaccine-derived), susceptible, exposed, infectious, recovered, and vaccinated. We distinguished between vaccine- and naturally derived maternal immunity because they wane at different rates [20], [21]. We define
Results
In order to capture long-term health outcomes, we projected the total number of cases from 2008 to 2050 for all six countries except Morocco, which introduced MCV2 several years ago.
Discussion
Here we have developed a model to investigate optimal second opportunity vaccination programs in low-income countries. The model was parameterized with seroprevalence data from low-income settings and exhibits broad agreement with data on measles incidence in low-income countries. If RI coverage continues improving, the model projects that the recent low in measles incidence (Fig. 2) will continue, as long as SIAs are held at least every 4 years in India and Nigeria, and at least every 8 years
Acknowledgements
This study was supported by the Bill & Melinda Gates Foundation Global Health Program and the University of Washington. The authors are grateful to Colleen Burgess for comments on an earlier draft and to our program officer Girindre Beeharry and colleagues at the Bill & Melinda Gates foundation for their support and feedback.
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