Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection

• Autor(a) principal: de Sousa, Bruno
• Data de Publicação: 2020
• Outros Autores: Pires, Carlos, Gomes, Dulce, Filipe, Patrícia A., Costa-Veiga, A, Nunes, Carla
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/29383
• topic: Structured Additive Regression Models; Pulmonary Tuberculosis; SpatialTemporal Epidemiology; Full Bayesian; Empirical Bayesian

Tuberculosis (TB) is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). In 2017, the World Health Organization (WHO) estimated 10.0 million people developed TB and 1.3 million deaths (range, 1.2–1.4 million) among HIV-negative people with an additional 300 000 deaths from TB (range, 266 000–335 000) among HIVpositive people. Studies that understand the socio-demographic characteristics, time and spatial distribution of the disease are vital to allocating resources in order to improve National TB Programs. The database includes information from all confirmed Pulmonary TB (PTB) cases notified in Continental Portugal between 2000 and 2010. Following a descriptive analysis of the main risk factors of the disease, a Structured Additive Regression (STAR) model is presented exploring possible spatial and temporal correlations in PTB incidence rates in order to identify the regions of increased incidence rates. Three main regions are identified as statistically significant areas of increased PTB incidence rates in Continental Portugal. STAR models proved to be a valuable and effective approach in identifying PTB incidence rates and will be used in future research to identify the associated risk factors in Continental Portugal, yielding high-level information for decision-making in TB control.