Objective: To analyze the trend in the detection rate of syphilis in elderly people in Brazil from 2011 to 2019. Methods: An ecological, time-series study with data from the Notifiable Diseases Information System. The temporal trend of syphilis detection rates was analyzed according to the Prais-Winsten linear regression method. Results: 62,765 cases of syphilis in aged people were reported. There was a growing trend in the rate of syphilis detection in aged people in Brazil. The increase was of approximately six times, with a mean increase of 25% each year (annual percent change [APC]: 25.0; 95%CI 22.1–28.1). The increase in the detection rate was identified in both genders and for all age groups, with emphasis on the increase in females (APC: 49.1; 95%CI 21.9–26.8) and in the group aged 70 to 79 years old (APC: 25.8; 95%CI 23.3–28.3). All macro-regions of the country showed an increasing trend, with emphasis on the Northeast (APC: 51.2; 95%CI 43.0–59.8) and South (APC: 49.2; 95%CI 32.3–68.3). Conclusion: The growing trend in the detection rate of syphilis in aged people throughout Brazil highlights the need for planning and developing effective and multidisciplinary prevention actions and assistance adapted to this public.

A common approach to modelling extreme data are to consider the distribution of the exceedance value over a high threshold. This approach is based on the distribution of excess, which follows the generalized Pareto distribution (GPD) and has shown to be adequate for this type of situation. As with all data involving analysis in time, excesses above a threshold may also vary and suffer from the influence of covariates. Thus, the GPD distribution can be modelled by entering the presence of these factors. This paper presents a new model for extreme values, where GPD parameters are written on the basis of a dynamic regression model. The estimation of the model parameters is made under the Bayesian paradigm, with sampling points via MCMC. As with environmental data, behaviour data are related to other factors such as time and covariates such as latitude and distance from the sea. Simulation studies have shown the efficiency and identifiability of the model, and applying real rain data from the state of Piaui, Brazil, shows the advantage in predicting and interpreting the model against other similar models proposed in the literature.