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Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.

Karagiannis-Voules DA, Scholte RG, Guimarães LH, Utzinger J, Vounatsou P - PLoS Negl Trop Dis (2013)

Bottom Line: Brazil is one of the most severely affected countries.Particular emphasis was placed on spatial and temporal patterns.The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland ; University of Basel, Basel, Switzerland.

ABSTRACT

Background: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries.

Methodology: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis.

Principal findings: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively.

Conclusions/significance: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.

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Related in: MedlinePlus

Geostatistical model-based predicted incidence rates per 10,000 for cutaneous leishmaniasis in Brazil in 2001.
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pntd-0002213-g005: Geostatistical model-based predicted incidence rates per 10,000 for cutaneous leishmaniasis in Brazil in 2001.

Mentions: Model-based predictions were obtained over a grid of 136,841 pixels at 88 km spatial resolution. The rates (per 10,000 people) of the predictions for CL and VL in 2010 are depicted in Figures 3 and 4, respectively. The decreasing trend of CL cases is apparent by comparing the maps for the year of 2010 (Figure 3) with that of 2001 (Figure 5). For instance, in 2010 lower rates were observed in west and north-west Brazil in the states of Amazonas and Roraima. Incidence maps under the assumption that missing cases were zeros are provided in supporting information text S3.


Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.

Karagiannis-Voules DA, Scholte RG, Guimarães LH, Utzinger J, Vounatsou P - PLoS Negl Trop Dis (2013)

Geostatistical model-based predicted incidence rates per 10,000 for cutaneous leishmaniasis in Brazil in 2001.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3649962&req=5

pntd-0002213-g005: Geostatistical model-based predicted incidence rates per 10,000 for cutaneous leishmaniasis in Brazil in 2001.
Mentions: Model-based predictions were obtained over a grid of 136,841 pixels at 88 km spatial resolution. The rates (per 10,000 people) of the predictions for CL and VL in 2010 are depicted in Figures 3 and 4, respectively. The decreasing trend of CL cases is apparent by comparing the maps for the year of 2010 (Figure 3) with that of 2001 (Figure 5). For instance, in 2010 lower rates were observed in west and north-west Brazil in the states of Amazonas and Roraima. Incidence maps under the assumption that missing cases were zeros are provided in supporting information text S3.

Bottom Line: Brazil is one of the most severely affected countries.Particular emphasis was placed on spatial and temporal patterns.The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland ; University of Basel, Basel, Switzerland.

ABSTRACT

Background: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries.

Methodology: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis.

Principal findings: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively.

Conclusions/significance: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.

Show MeSH
Related in: MedlinePlus