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Spatial Analysis of Anthropogenic Landscape Disturbance and Buruli Ulcer Disease in Benin.

Campbell LP, Finley AO, Benbow ME, Gronseth J, Small P, Johnson RC, Sopoh GE, Merritt RM, Williamson H, Qi J - PLoS Negl Trop Dis (2015)

Bottom Line: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence.Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales.A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America; Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America.

ABSTRACT

Background: Land use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.

Methodology/principal findings: A landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200 m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.

Conclusions/significance: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

No MeSH data available.


Related in: MedlinePlus

New locations gridded at 5 km intervals for BU rate predictions at unobserved locations.
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pntd.0004123.g004: New locations gridded at 5 km intervals for BU rate predictions at unobserved locations.

Mentions: We generated a total of 1029 new prediction locations in a gridded pattern at 5 km intervals, where BU rates were unobserved (Fig 4). Gaps among these location points represent areas where data were lacking and derivation of predictor variables was not possible for the “best” model class and distance interval. The predictive BU risk surface was created following Finley et al. (2008) and implemented in the spBayes R package. Here, the use of a Bayesian modeling paradigm is particularly advantageous because it provides access to the entire posterior predictive distribution at each new location [50], and hence, we can map any quantity of interest. For our purposes, these quantities include a map of grid cell specific posterior predictive distribution mean and variance.


Spatial Analysis of Anthropogenic Landscape Disturbance and Buruli Ulcer Disease in Benin.

Campbell LP, Finley AO, Benbow ME, Gronseth J, Small P, Johnson RC, Sopoh GE, Merritt RM, Williamson H, Qi J - PLoS Negl Trop Dis (2015)

New locations gridded at 5 km intervals for BU rate predictions at unobserved locations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608567&req=5

pntd.0004123.g004: New locations gridded at 5 km intervals for BU rate predictions at unobserved locations.
Mentions: We generated a total of 1029 new prediction locations in a gridded pattern at 5 km intervals, where BU rates were unobserved (Fig 4). Gaps among these location points represent areas where data were lacking and derivation of predictor variables was not possible for the “best” model class and distance interval. The predictive BU risk surface was created following Finley et al. (2008) and implemented in the spBayes R package. Here, the use of a Bayesian modeling paradigm is particularly advantageous because it provides access to the entire posterior predictive distribution at each new location [50], and hence, we can map any quantity of interest. For our purposes, these quantities include a map of grid cell specific posterior predictive distribution mean and variance.

Bottom Line: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence.Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales.A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America; Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America.

ABSTRACT

Background: Land use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.

Methodology/principal findings: A landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200 m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.

Conclusions/significance: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

No MeSH data available.


Related in: MedlinePlus