Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in western Africa.
OBJECTIVE
To predict the subnational spatial variation in the number of people infected with Schistosoma haematobium in Burkina Faso, Mali and the Niger prior to national control programmes.


METHODS
We used field survey data sets covering a contiguous area 2750 x 850 km and including 26,790 school-age children (5-14 years old) in 418 schools. The prevalence of high- and low-intensity infection and associated 95% credible intervals (CrIs) were predicted using Bayesian geostatistical models. The number infected was determined from the predicted prevalence and the number of school-age children in each km(2).


RESULTS
The predicted number of school-age children with a low-intensity infection was 433,268 in Burkina Faso, 872,328 in Mali and 580 286 in the Niger. The number with a high-intensity infection was 416,009, 511,845 and 254,150 in each country, respectively. The 95% CrIs were wide: e.g. the mean number of boys aged 10-14 years infected in Mali was 140,200 (95% CrI: 6200-512,100).


CONCLUSIONS
National aggregate estimates of infection mask important local variations:: e.g. most S. haematobium infections in the Niger occur in the Niger River valley. High-intensity infection was strongly clustered in western and central Mali, north-eastern and northwestern Burkina Faso and the Niger River valley in the Niger. Populations in these foci will carry the bulk of the urinary schistosomiasis burden and should be prioritized for schistosomiasis control. Uncertainties in the predicted prevalence and the numbers infected should be acknowledged by control programme planners.
Clements
Archie C A
AC
University of Queensland, School of Population Health, Herston Road, Herston, Qld, 4006, Australia. a.clements@uq.edu.au
Firth
Sonja
S
Dembelé
Robert
R
Garba
Amadou
A
Touré
Seydou
S
Sacko
Moussa
M
Landouré
Aly
A
Bosqué-Oliva
Elisa
E
Barnett
Adrian G
AG
Brooker
Simon
S
Fenwick
Alan
A
D000293:Adolescent; D000354:Africa, Western / Q000453:epidemiology; D000818:Animals; D001499:Bayes Theorem; D002648:Child; D005260:Female; D005544:Forecasting; D006306:Health Surveys; D006801:Humans; D008297:Male; D015995:Prevalence; D012548:Schistosoma haematobium; D012553:Schistosomiasis haematobia / Q000453:epidemiology*; D017062:Small-Area Analysis
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