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

Abstract

Introduction: 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 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. 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.

The changing distribution of malaria in the Brazilian Amazon, 2003-2004 and 2008-2009

“…Of all of the adverse effects resulting from the modern occupation of the Brazilian Amazon, perhaps the most notable was deforestation and its paradoxical effect on malaria transmission. Initially, deforestation was not only harmful to the ecosystem but also resulted in increases in vector-borne diseases; this detrimental effect was well documented by Achcar et al 12 .…”

“…According to these authors, deforestation was accompanied by a loss of biodiversity and alterations in the ecosystem, which, in the initial phase, led to vector proliferation and a rise in the diseases transmitted by those vectors, one being malaria 12 . Afterwards, the later effect of deforestation appeared.…”

Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence

“…The ambiguity in the latter cases arose from varying results based on mosquito species in question and the landscape context [42], the type of forest studied (sustainable forest reserve versus protected forest reserve; [45]), or the metric used to measure malaria (entomological inoculation rate (EIR) versus human-biting rate (HBR); [46]). Also, five of these 12 papers supported the idea that initial deforestation in new settlements increases malaria risk, but as deforestation proceeds it can translate into lower malaria risk [13,17,37–39]. Fifteen per cent of reviewed articles (7 of 47) specifically evaluated deforestation rate [10,16,25,39,43,47,48], but only three of these found a positive association between deforestation and malaria [39,47,48].…”

“…Also, five of these 12 papers supported the idea that initial deforestation in new settlements increases malaria risk, but as deforestation proceeds it can translate into lower malaria risk [13,17,37–39]. Fifteen per cent of reviewed articles (7 of 47) specifically evaluated deforestation rate [10,16,25,39,43,47,48], but only three of these found a positive association between deforestation and malaria [39,47,48]. In short, we fail to find overwhelming evidence supporting a consistent simple and straightforward relationship between forests, deforestation rate and malaria.…”

Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models

“…The approach in many fields of study is to use a count regression model instead of a linear regression model. For example, in health-related studies, count regression models have been used to model the number of incidents of physical aggression or substance abuse (Gagnon, Doron-LaMarca, Bell, O'Farrell, & Taft, 2008), the number of malaria cases (Achcar, Martinez, Pires de Souza, Tachibana, & Flores, 2011), the number of medically attended childhood injuries (Karazsia & van Dulmen, 2008), number of health benefits received per patient (Czado, Schabenberger, & Erhardt, 2014), and number of sub-health symptoms (Xu, Li, & Chen, 2011). In other fields of study, they have also been used to estimate recreational trip demands (Wang, Li, Little, & Yang, 2009), number of auto insurance claims (Meng, 2009), number of roadway accidents (Nassiri, Najaf, & Amiri, 2014), and number of hardware failures or occurrences of disease or death (Gulkema & Goffelt, 2008).…”