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. 2013 May 25:6:152.
doi: 10.1186/1756-3305年6月15日2.

Modelling the geographical distribution of soil-transmitted helminth infections in Bolivia

Affiliations

Modelling the geographical distribution of soil-transmitted helminth infections in Bolivia

Frédérique Chammartin et al. Parasit Vectors. .

Abstract

Background: The prevalence of infection with the three common soil-transmitted helminths (i.e. Ascaris lumbricoides, Trichuris trichiura, and hookworm) in Bolivia is among the highest in Latin America. However, the spatial distribution and burden of soil-transmitted helminthiasis are poorly documented.

Methods: We analysed historical survey data using Bayesian geostatistical models to identify determinants of the distribution of soil-transmitted helminth infections, predict the geographical distribution of infection risk, and assess treatment needs and costs in the frame of preventive chemotherapy. Rigorous geostatistical variable selection identified the most important predictors of A. lumbricoides, T. trichiura, and hookworm transmission.

Results: Results show that precipitation during the wettest quarter above 400 mm favours the distribution of A. lumbricoides. Altitude has a negative effect on T. trichiura. Hookworm is sensitive to temperature during the coldest month. We estimate that 38.0%, 19.3%, and 11.4% of the Bolivian population is infected with A. lumbricoides, T. trichiura, and hookworm, respectively. Assuming independence of the three infections, 48.4% of the population is infected with any soil-transmitted helminth. Empirical-based estimates, according to treatment recommendations by the World Health Organization, suggest a total of 2.9 million annualised treatments for the control of soil-transmitted helminthiasis in Bolivia.

Conclusions: We provide estimates of soil-transmitted helminth infections in Bolivia based on high-resolution spatial prediction and an innovative variable selection approach. However, the scarcity of the data suggests that a national survey is required for more accurate mapping that will govern spatial targeting of soil-transmitted helminthiasis control.

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Figures

Figure 1
Figure 1
Acyclic graph of the geostatistical variable selection. Stochastic and logical nodes are represented as ellipses. Dashed arrows are logical links and straight line arrows are stochastic dependencies. Fixed parameters of the prior distributions are highlighted in pink.
Figure 2
Figure 2
Frequency distribution of the survey periods in Bolivia for A. lumbricoides (A), T. trichiura (B), and hookworm (C).
Figure 3
Figure 3
Ascaris lumbricoides infection risk in Bolivia. The maps show the situation before 1995 (A) and from 1995 onwards (B), and provide estimates of the geographical distribution of the infection (1), the observed prevalence (2), and the coefficient of variation (3).
Figure 4
Figure 4
Trichuris trichiura infection risk in Bolivia. The maps show the situation before 1995 (A) and from 1995 onwards (B), and provide estimates of the geographical distribution of the infection (1), the observed prevalence (2), and the coefficient of variation (3).
Figure 5
Figure 5
Hookworm infection risk in Bolivia. The maps show the situation before 1995 (A) and from 1995 onwards (B), and provide estimates of the geographical distribution of the infection (1), the observed prevalence (2), and the coefficient of variation (3).
Figure 6
Figure 6
Major climatic zones and spatial distribution of the remotely sensed predictors in Bolivia.
Figure 7
Figure 7
Proportion of locations with observed prevalence falling within credible intervals of the posterior predictive distribution with probability coverage varying from 1% to 100%.

References

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