Dataset Information

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters.

ABSTRACT:

Background

Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs.

Results

Our landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin.

Conclusion

Our analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks.

SUBMITTER: Wagner T

PROVIDER: S-EPMC2423183 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Publications

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters.

Wagner Tyler T Benbow M Eric ME Brenden Travis O TO Qi Jiaguo J Johnson R Christian RC

International journal of health geographics 20080527

<h4>Background</h4>Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies pe ...[more]

PMID: 18505567

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Project description: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.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.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.

Project description:BackgroundIn the Cameroon, previous efforts to identify Buruli ulcer (BU) through the mobilization of community health workers (CHWs) yielded poor results. In this paper, we describe the successful creation of a BU community of practice (BUCOP) in Bankim, Cameroon composed of hospital staff, former patients, CHWs, and traditional healers.Methods and principle findingsAll seven stages of a well-defined formative research process were conducted during three phases of research carried out by a team of social scientists working closely with Bankim hospital staff. Phase one ethnographic research generated interventions tested in a phase two proof of concept study followed by a three- year pilot project. In phase three the pilot project was evaluated. An outcome evaluation documented a significant rise in BU detection, especially category I cases, and a shift in case referral. Trained CHW and traditional healers initially referred most suspected cases of BU to Bankim hospital. Over time, household members exposed to an innovative and culturally sensitive outreach education program referred the greatest number of suspected cases. Laboratory confirmation of suspected BU cases referred by community stakeholders was above 30%. An impact and process evaluation found that sustained collaboration between health staff, CHWs, and traditional healers had been achieved. CHWs came to play a more active role in organizing BU outreach activities, which increased their social status. Traditional healers found they gained more from collaboration than they lost from referral.Conclusion/ significanceSetting up lines of communication, and promoting collaboration and trust between community stakeholders and health staff is essential to the control of neglected tropical diseases. It is also essential to health system strengthening and emerging disease preparedness. The BUCOP model described in this paper holds great promise for bringing communities together to solve pressing health problems in a culturally sensitive manner.

Dataset Information

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters.

Background

Results

Conclusion

Publications

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters.

Similar Datasets

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