Abstract
Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using bayesian spatiotemporal methods. We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the bayesian paradigm is a good strategy for modeling malaria counts.
Highlights
Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities
We investigate the association between environmental variables and the yearly registers of malaria in the Brazilian Amazon region using a Poisson[11,12] spatiotemporal model under the Bayesian paradigm[13,14], with a latent term that captures the spatial effects of neighboring provinces and another latent term that captures the possible temporal correlation in each province through the years
Data were obtained from Sistema de Informações de Malária (SISMAL), Instituto Brasileiro de Geografia e Estatística (IBGE), Departamento de Informática do Sistema Único de Saúde (DATASUS), and Programa de Cálculo do Desflorestamento da Amazônia (PRODES)
Summary
Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. Methods: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. Results: The model proposed here suggests that deforestation rate, the number of inhabitants per km[2], and the human development index (HDI) are important in the prediction of malaria cases. Conclusions: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. Malaria causes about 400-900 million cases of fever and approximately one to three million deaths annually[1,2] It is a disease commonly associated with poverty. Ecological alterations can affect the spread of these insects and, the spread of malaria
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Revista da Sociedade Brasileira de Medicina Tropical
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
