Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

  • Uwem F. Ekpo | ekpouf@unaab.edu.ng Spatial Parasitology and Health GIS Group, Department of Biological Sciences, Federal University of Agriculture, Abeokuta, Nigeria.
  • Eveline Hürlimann Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Nadine Schur Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Akinola. S. Oluwole Spatial Parasitology and Health GIS Group, Department of Biological Sciences, Federal University of Agriculture, Abeokuta, Nigeria.
  • Eniola M. Abe Spatial Parasitology and Health GIS Group, Department of Biological Sciences, Federal University of Agriculture, Abeokuta, Nigeria.
  • Margaret A. Mafe Department of Public Health, National Institute for Medical Research, Yaba, Lagos, Nigeria.
  • Obiageli J. Nebe Schistosomiasis/STH Control Programme, Department of Public Health, Federal Ministry of Health, Abuja, Nigeria.
  • Sunday Isiyaku Sightsavers, Nigeria Country Office, Kaduna, Nigeria.
  • Francisca Olamiju Mission to Save the Helpless (MITOSATH), Jos, Nigeria.
  • Mukaila Kadiri Spatial Parasitology and Health GIS Group, Department of Biological Sciences, Federal University of Agriculture, Abeokuta, Nigeria.
  • Temitope O.S. Poopola Department of Microbiology, Federal University of Agriculture, Abeokuta, Nigeria.
  • Eka I. Braide Department of Animal and Environmental Biology, University of Calabar, Calabar, Nigeria.
  • Yisa Saka National Onchocerciasis Control Programme (NOCP), Department of Public Health, Federal Ministry of Health, Abuja, Nigeria.
  • Chiedu F. Mafiana National Universities Commission, Abuja, Nigeria.
  • Thomas K. Kristensen DBL, Department of Veterinary Disease Biology, University of Copenhagen, Frederiksberg C, Denmark; School of Biological and Conservation Sciences, Faculty of Science and Agriculture, University of KwaZulu-Natal, KawaZulu-Natal, South Africa.
  • Jürg Utzinger Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Penelope Vounatsou Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.

Abstract

Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country’s 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is <10% in most states with a few reaching as high as 50%. It was estimated that 11.3 million children require praziquantel annually (95% CI: 10.3-12.2 million).

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Published
2013-05-01
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Original Articles
Keywords:
schistosomiasis, prevalence, geo-referencing, geographical information system, risk mapping, Bayesian geospatial modelling, control, Nigeria.
Statistics
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How to Cite
Ekpo, U. F., Hürlimann, E., Schur, N., Oluwole, A. S., Abe, E. M., Mafe, M. A., Nebe, O. J., Isiyaku, S., Olamiju, F., Kadiri, M., Poopola, T. O., Braide, E. I., Saka, Y., Mafiana, C. F., Kristensen, T. K., Utzinger, J., & Vounatsou, P. (2013). Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. Geospatial Health, 7(2), 355-366. https://doi.org/10.4081/gh.2013.92

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