Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.

BACKGROUND: Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than...

Full description

Bibliographic Details
Main Authors: Nadine Schur, Eveline Hürlimann, Amadou Garba, Mamadou S Traoré, Omar Ndir, Raoult C Ratard, Louis-Albert Tchuem Tchuenté, Thomas K Kristensen, Jürg Utzinger, Penelope Vounatsou
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-06-01
Series:PLoS Neglected Tropical Diseases
Online Access:http://europepmc.org/articles/PMC3114755?pdf=render
_version_ 1831685195288281088
author Nadine Schur
Eveline Hürlimann
Amadou Garba
Mamadou S Traoré
Omar Ndir
Raoult C Ratard
Louis-Albert Tchuem Tchuenté
Thomas K Kristensen
Jürg Utzinger
Penelope Vounatsou
author_facet Nadine Schur
Eveline Hürlimann
Amadou Garba
Mamadou S Traoré
Omar Ndir
Raoult C Ratard
Louis-Albert Tchuem Tchuenté
Thomas K Kristensen
Jürg Utzinger
Penelope Vounatsou
author_sort Nadine Schur
collection DOAJ
description BACKGROUND: Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps. METHODOLOGY: We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i) to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤ 20 years in West Africa, including Cameroon, and (ii) to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging. PRINCIPAL FINDINGS: Our models revealed that 50.8 million individuals aged ≤ 20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia) and 37.1% (Liberia) for S. mansoni, and between 17.6% (The Gambia) and 51.6% (Sierra Leone) for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3%) than reported before (30.0%). Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection. CONCLUSION/SIGNIFICANCE: We present the first empirical estimates for S. mansoni and S. haematobium prevalence at high spatial resolution throughout West Africa. Our prediction maps allow prioritizing of interventions in a spatially explicit manner, and will be useful for monitoring and evaluation of schistosomiasis control programs.
first_indexed 2024-12-20T08:14:52Z
format Article
id doaj.art-7a0abe0ca4f54279aa7f82340b50ff29
institution Directory Open Access Journal
issn 1935-2727
1935-2735
language English
last_indexed 2024-12-20T08:14:52Z
publishDate 2011-06-01
publisher Public Library of Science (PLoS)
record_format Article
series PLoS Neglected Tropical Diseases
spelling doaj.art-7a0abe0ca4f54279aa7f82340b50ff292022-12-21T19:47:09ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352011-06-0156e119410.1371/journal.pntd.0001194Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.Nadine SchurEveline HürlimannAmadou GarbaMamadou S TraoréOmar NdirRaoult C RatardLouis-Albert Tchuem TchuentéThomas K KristensenJürg UtzingerPenelope VounatsouBACKGROUND: Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps. METHODOLOGY: We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i) to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤ 20 years in West Africa, including Cameroon, and (ii) to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging. PRINCIPAL FINDINGS: Our models revealed that 50.8 million individuals aged ≤ 20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia) and 37.1% (Liberia) for S. mansoni, and between 17.6% (The Gambia) and 51.6% (Sierra Leone) for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3%) than reported before (30.0%). Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection. CONCLUSION/SIGNIFICANCE: We present the first empirical estimates for S. mansoni and S. haematobium prevalence at high spatial resolution throughout West Africa. Our prediction maps allow prioritizing of interventions in a spatially explicit manner, and will be useful for monitoring and evaluation of schistosomiasis control programs.http://europepmc.org/articles/PMC3114755?pdf=render
spellingShingle Nadine Schur
Eveline Hürlimann
Amadou Garba
Mamadou S Traoré
Omar Ndir
Raoult C Ratard
Louis-Albert Tchuem Tchuenté
Thomas K Kristensen
Jürg Utzinger
Penelope Vounatsou
Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.
PLoS Neglected Tropical Diseases
title Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.
title_full Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.
title_fullStr Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.
title_full_unstemmed Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.
title_short Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.
title_sort geostatistical model based estimates of schistosomiasis prevalence among individuals aged ≤ 20 years in west africa
url http://europepmc.org/articles/PMC3114755?pdf=render
work_keys_str_mv AT nadineschur geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT evelinehurlimann geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT amadougarba geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT mamadoustraore geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT omarndir geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT raoultcratard geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT louisalberttchuemtchuente geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT thomaskkristensen geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT jurgutzinger geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica
AT penelopevounatsou geostatisticalmodelbasedestimatesofschistosomiasisprevalenceamongindividualsaged20yearsinwestafrica