The prevention of dengue requires a system to identify areas at higher risk, using epidemiological data with spatial and temporal structure. Bayesian approaches, which integrate prior information and handle hierarchical structures, provide a flexible and robust method that allows for more accurate uncertainty estimates, as well as capturing spatial and spatiotemporal correlation, accounting for this variability in disease risk estimates. These hierarchical Bayesian approaches often require sophisticated numerical methods to provide parameter estimates. In this context, methods such as Markov Chain Monte Carlo (MCMC) or Integrated Nested Laplace Approximation (INLA) can be applied, the latter being a more computationally efficient alternative for latent Gaussian models (LGM), including spatial models such as the hierarchical Besag, York, and Molli´e (BYM) model, which can be extended to ...

En este documento se plasma la evaluación de la eficacia y seguridad del uso del secukinumab en adultos con espondilitis anquilosante activa con contraindicación a la familia anti-factor de necrosis tumoral (ANTI-TNF), cuyos resultados sustentaron la aprobación del uso de esta tecnología sanitaria en EsSalud.