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. 2006 Apr;11(4):490-503.
doi: 10.1111/j.1365-3156.2006.01594.x.

Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania

Archie C A Clements  1 , Nicholas J S Lwambo, Lynsey Blair, Ursuline Nyandindi, Godfrey Kaatano, Safari Kinung'hi, Joanne P Webster, Alan Fenwick, Simon Brooker
Affiliations

Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania

Archie C A Clements et al. Trop Med Int Health. 2006 Apr.

Abstract

Objective: To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania.

Methods: Bayesian geostatistical models were developed using parasitological data from 143 schools.

Results: In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation.

Conclusion: Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.

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Figures

Figure 1
Figure 1
Geographical distribution of schools surveyed in northwest Tanzania and prevalence of (a) Schistosoma haematobium and (b) S. mansoni infections.
Figure 2
Figure 2
Distance decay of spatial correlation for Schistosoma haematobium in northwest Tanzania. Note: at the equator, 1 decimal degree equates to approximately 120 kilometres.
Figure 3
Figure 3
Distance decay of spatial correlation for Schistosoma mansoni in northwest Tanzania. Note: at the equator, 1 decimal degree equates to approximately 120 kilometres.
Figure 4
Figure 4
Prevalence predictions for Schistosoma haematobium in northwest Tanzania: a) posterior median; b) lower 95% Bayesian credible interval limit; c) upper 95% Bayesian credible interval limit.
Figure 5
Figure 5
Prevalence predictions for Schistosoma mansoni in northwest Tanzania: a) posterior median; b) lower 95% Bayesian credible interval limit; c) upper 95% Bayesian credible interval limit.
Figure 6
Figure 6
Geostatistical random-effect component (θi) of the spatially-explicit logistic regression model for Schistosoma mansoni in northwest Tanzania.
Figure 7
Figure 7
Intervention map for northwest Tanzania with prevalence contours defining areas to be excluded from mass treatment (prevalence of Schistosoma mansoni or S. haematobium <0.1), areas to receive mass treatment of school-age children (prevalence of S. mansoni or S. haematobium >0.1) and areas to receive priority for mass treatment (prevalence of S. mansoni or S. haematobium >0.5), including possible targeting of other high-prevalence groups in addition to school-age children.
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References

    1. Agumya A, Hunter GJ. Responding to the consequences of uncertainty in geographical data. International Journal of Geographical Information Science. 2002;16:405–417.
    1. Alexander N, Moyeed R, Stander J. Spatial modelling of individual-level parasite counts using the negative binomial distribution. Biostatistics. 2000;1:453–463. - PubMed
    1. Anderson RM, May RM. Infectious Diseases of Humans: dynamics and control. Oxford: Oxford University Press; 1991.
    1. Appleton CC. Review of Literature on abiotic factors influencing the distribution and life-cycles of Bilharziasis intermediate host snails. Malacological Review. 1978;11:1–25.
    1. Best N, Richardson S, Thomson A. A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research. 2005;14:35–59. - PubMed

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