Assessing Interventions That Prevent Multiple Infectious Diseases: Simple Methods for Multidisease Modeling

Abstract

Background

Many cost-effectiveness analyses (CEAs) only consider outcomes for a single disease when comparing interventions that prevent or treat 1 disease (e.g., vaccination) to interventions that prevent or treat multiple diseases (e.g., vector control to prevent mosquito-borne diseases). An intervention targeted to a single disease may be preferred to a broader intervention in a single-disease model, but this conclusion might change if outcomes from the additional diseases were included. However, multidisease models are often complex and difficult to construct.

Methods

We present conditions for when multiple diseases should be considered in such a CEA. We propose methods for estimating health outcomes and costs associated with control of additional diseases using parallel single-disease models. Parallel modeling can incorporate competing mortality and coinfection from multiple diseases while maintaining model simplicity. We illustrate our approach with a CEA that compares a dengue vaccine, a chikungunya vaccine, and mosquito control via insecticide and mosquito nets, which can prevent dengue, chikungunya, Zika, and yellow fever.

Results

The parallel models and the multidisease model generated similar estimates of disease incidence and deaths with much less complexity. When using this method in our case study, considering only chikungunya and dengue, the preferred strategy was insecticide. A broader strategy—insecticide plus long-lasting insecticide-treated nets—was not preferred when Zika and yellow fever were included, suggesting the conclusion is robust even without the explicit inclusion of all affected diseases.

Limitations

Parallel modeling assumes independent probabilities of infection for each disease.

Conclusions

When multidisease effects are important, our parallel modeling method can be used to model multiple diseases accurately while avoiding additional complexity.