Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in western Africa
Clements, Archie, Firth, Sonja, Dembele, Robert, Garba, Amadou, Toure, Seydou, Sacko, Moussa, Landoure, Aly, Bosque-Oliva, Elisa, Barnett, Adrian G., Brooker, Simon, & Fenwick, Alan (2009) Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in western Africa. World Health Organisation. Bulletin, 87(12), pp. 921-929.
Objective We aimed to predict sub-national spatial variation in numbers of people infected with Schistosoma haematobium, and associated uncertainties, in Burkina Faso, Mali and Niger, prior to implementation of national control programmes.
Methods We used national field survey datasets covering a contiguous area 2,750 × 850 km, from 26,790 school-aged children (5–14 years) in 418 schools. Bayesian geostatistical models were used to predict prevalence of high and low intensity infections and associated 95% credible intervals (CrI). Numbers infected were determined by multiplying predicted prevalence by numbers of school-aged children in 1 km2 pixels covering the study area. Findings Numbers of school-aged children with low-intensity infections were: 433,268 in Burkina Faso, 872,328 in Mali and 580,286 in Niger. Numbers with high-intensity infections were: 416,009 in Burkina Faso, 511,845 in Mali and 254,150 in Niger. 95% CrIs (indicative of uncertainty) were wide; e.g. the mean number of boys aged 10–14 years infected in Mali was 140,200 (95% CrI 6200, 512,100). Conclusion National aggregate estimates for numbers infected mask important local variation, e.g. most S. haematobium infections in Niger occur in the Niger River valley. Prevalence of high-intensity infections was strongly clustered in foci in western and central Mali, north-eastern and northwestern Burkina Faso and the Niger River valley in Niger. Populations in these foci are likely to carry the bulk of the urinary schistosomiasis burden and should receive priority for schistosomiasis control. Uncertainties in predicted prevalence and numbers infected should be acknowledged and taken into consideration by control programme planners.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Keywords:||Schistosomiasis, Schistosoma Haematobium, Spatial Analysis, Bayesian Methods, Burden of Disease|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Epidemiology (111706)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > CLINICAL SCIENCES (110300) > Infectious Diseases (110309)
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2009 World Health Organisation|
|Deposited On:||21 Jan 2010 23:57|
|Last Modified:||29 Feb 2012 14:01|
Repository Staff Only: item control page