Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data.
BACKGROUND
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. 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.


RESULTS
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.


CONCLUSIONS
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.
Departamento de Medicina Social, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brasil.
Jorge Alberto
JA
Achcar
Edson Zangiacomi
EZ
Martinez
Aparecida Doniseti Pires de
AD
Souza
Vilma Mayumi
VM
Tachibana
Edilson Ferreira
EF
Flores
D001938:Brazil; D003247:Conservation of Natural Resources; D004777:Environment; D006801:Humans; D008288:Malaria / Q000453:epidemiology*; D016012:Poisson Distribution; D012044:Regression Analysis; D012307:Risk Factors; D014197:Trees