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. 2014 Sep 4;8(9):e3123.
doi: 10.1371/journal.pntd.0003123. eCollection 2014 Sep.

Spatio-temporal patterns and landscape-associated risk of Buruli ulcer in Akonolinga, Cameroon

Affiliations

Spatio-temporal patterns and landscape-associated risk of Buruli ulcer in Akonolinga, Cameroon

Jordi Landier et al. PLoS Negl Trop Dis. .

Abstract

Background: Buruli ulcer (BU) is an extensively damaging skin infection caused by Mycobacterium ulcerans, whose transmission mode is still unknown. The focal distribution of BU and the absence of interpersonal transmission suggest a major role of environmental factors, which remain unidentified. This study provides the first description of the spatio-temporal variations of BU in an endemic African region, in Akonolinga, Cameroon. We quantify landscape-associated risk of BU, and reveal local patterns of endemicity.

Methodology/principal findings: From January 2002 to May 2012, 787 new BU cases were recorded in 154 villages of the district of Akonolinga. Incidence per village ranged from 0 (n = 59 villages) to 10.4 cases/1000 person.years (py); median incidence was 0.4 cases/1,000 py. Villages neighbouring the Nyong River flood plain near Akonolinga town were identified as the highest risk zone using the SPODT algorithm. We found a decreasing risk with increasing distance to the Nyong and identified 4 time phases with changes in spatial distribution. We classified the villages into 8 groups according to landscape characteristics using principal component analysis and hierarchical clustering. We estimated the incidence ratio (IR) associated with each landscape using a generalised linear model. BU risk was highest in landscapes with abundant wetlands, especially cultivated ones (IR = 15.7, 95% confidence interval [95%CI] = 15.7[4.2-59.2]), and lowest in reference landscape where primary and secondary forest cover was abundant. In intermediate-risk landscapes, risk decreased with agriculture pressure (from IR[95%CI] = 7.9[2.2-28.8] to 2.0[0.6-6.6]). We identified landscapes where endemicity was stable and landscapes where incidence increased with time.

Conclusion/significance: Our study on the largest series of BU cases recorded in a single endemic region illustrates the local evolution of BU and identifies the Nyong River as the major driver of BU incidence. Local differences along the river are explained by wetland abundance and human modification of the environment.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Identification of the Nyong as a major risk factor for BU incidence in Akonolinga 2002–2012 (spatial analysis on time-aggregated incidence rate of BU in Akonolinga).
A: Incidence rate per village (cases/1,000py). B: Decreasing risk of BU with increasing distance to the Nyong River. Homogenous risk areas of Akonolinga district were identified using the SPODT algorithm. Associated odds-ratio and 95% CI are provided.
Figure 2
Figure 2. Maps of spatio-temporal variations of BU incidence in Akonolinga district.
A–D: Incidence rate maps for the periods, phases 1 to 4, identified in the time-series (cases/1,000py).
Figure 3
Figure 3. Landscape-associated risk of BU in Akonolinga district, 2002–2012.
A: Classification of Akonolinga area villages according to landscape group and associated BU incidence ratio with 95% confidence interval. B: Predicted cumulative incidence for each village of the district according to the landscape model (cases/1,000py). C: Observed cumulative incidence rate for each village of the district (cases/1,000py).

References

    1. Silva MT, Portaels F, Pedrosa J (2009) Pathogenetic mechanisms of the intracellular parasite Mycobacterium ulcerans leading to Buruli ulcer. Lancet Infect Dis 9: 699–710 10.1016/S1473-3099(09)70234-8 - DOI - PubMed
    1. Wansbrough-Jones M, Phillips R (2006) Buruli ulcer: emerging from obscurity. Lancet 367: 1849–1858 10.1016/S0140-6736(06)68807-7 - DOI - PubMed
    1. Johnson PDR, Stinear T, Small PLC, Pluschke G, Merritt RW, et al. (2005) Buruli ulcer (M. ulcerans infection): new insights, new hope for disease control. PLoS Med 2: e108 10.1371/journal.pmed.0020108 - DOI - PMC - PubMed
    1. World Health Organisation - Global Buruli Ulcer Initiative Meeting (2012) Buruli ulcer control and research: Summary Data. Available: http://www.who.int/buruli/Summary_data_and_new_target_2013.pdf. Accessed 12 December 2013.
    1. Merritt RW, Walker ED, Small PLC, Wallace JR, Johnson PDR, et al. (2010) Ecology and transmission of Buruli ulcer disease: a systematic review. PLoS Negl Trop Dis 4: e911 10.1371/journal.pntd.0000911 - DOI - PMC - PubMed

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