The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya.
Questionnaires of reported blood in urine (BIU) distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium, thereby helping in the targeting of schistosomiasis control. However, not all schools return...
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ftdoajarticles:oai:doaj.org/article:792cbcd590b44c9691a8d1ff2f3b9181 2023-05-15T15:13:21+02:00 The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. Hugh J W Sturrock Rachel L Pullan Jimmy H Kihara Charles Mwandawiro Simon J Brooker 2013-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0002016 https://doaj.org/article/792cbcd590b44c9691a8d1ff2f3b9181 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC3554572?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0002016 https://doaj.org/article/792cbcd590b44c9691a8d1ff2f3b9181 PLoS Neglected Tropical Diseases, Vol 7, Iss 1, p e2016 (2013) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2013 ftdoajarticles https://doi.org/10.1371/journal.pntd.0002016 2022-12-31T03:24:38Z Questionnaires of reported blood in urine (BIU) distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium, thereby helping in the targeting of schistosomiasis control. However, not all schools return questionnaires and it is unclear whether treatment is warranted in such schools. This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.Data from a questionnaire survey conducted by the Kenya Ministry of Education in Coast Province in 2009 were combined with available parasitological and environmental data in a Bayesian bivariate spatial model. This modeled the relationship between BIU data and environmental covariates, as well as the relationship between BIU and S. haematobium infection prevalence, to predict S. haematobium infection prevalence at all schools in the study region. Validation procedures were implemented to assess the predictive accuracy of endemicity classification.The prevalence of BIU was negatively correlated with distance to nearest river and there was considerable residual spatial correlation at small (~15 km) spatial scales. There was a predictable relationship between the prevalence of reported BIU and S. haematobium infection. The final model exhibited excellent sensitivity (0.94) but moderate specificity (0.69) in identifying low (<10%) prevalence schools, and had poor performance in differentiating between moderate and high prevalence schools (sensitivity 0.5, specificity 1).Schistosomiasis is highly focal and there is a need to target treatment on a school-by-school basis. The use of bivariate spatial modelling can supplement questionnaire data to identify schools requiring mass treatment, but is unable to distinguish between moderate and high prevalence schools. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLoS Neglected Tropical Diseases 7 1 e2016 |
institution |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Hugh J W Sturrock Rachel L Pullan Jimmy H Kihara Charles Mwandawiro Simon J Brooker The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Questionnaires of reported blood in urine (BIU) distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium, thereby helping in the targeting of schistosomiasis control. However, not all schools return questionnaires and it is unclear whether treatment is warranted in such schools. This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.Data from a questionnaire survey conducted by the Kenya Ministry of Education in Coast Province in 2009 were combined with available parasitological and environmental data in a Bayesian bivariate spatial model. This modeled the relationship between BIU data and environmental covariates, as well as the relationship between BIU and S. haematobium infection prevalence, to predict S. haematobium infection prevalence at all schools in the study region. Validation procedures were implemented to assess the predictive accuracy of endemicity classification.The prevalence of BIU was negatively correlated with distance to nearest river and there was considerable residual spatial correlation at small (~15 km) spatial scales. There was a predictable relationship between the prevalence of reported BIU and S. haematobium infection. The final model exhibited excellent sensitivity (0.94) but moderate specificity (0.69) in identifying low (<10%) prevalence schools, and had poor performance in differentiating between moderate and high prevalence schools (sensitivity 0.5, specificity 1).Schistosomiasis is highly focal and there is a need to target treatment on a school-by-school basis. The use of bivariate spatial modelling can supplement questionnaire data to identify schools requiring mass treatment, but is unable to distinguish between moderate and high prevalence schools. |
format |
Article in Journal/Newspaper |
author |
Hugh J W Sturrock Rachel L Pullan Jimmy H Kihara Charles Mwandawiro Simon J Brooker |
author_facet |
Hugh J W Sturrock Rachel L Pullan Jimmy H Kihara Charles Mwandawiro Simon J Brooker |
author_sort |
Hugh J W Sturrock |
title |
The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. |
title_short |
The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. |
title_full |
The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. |
title_fullStr |
The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. |
title_full_unstemmed |
The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. |
title_sort |
use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of schistosoma haematobium in coastal kenya. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2013 |
url |
https://doi.org/10.1371/journal.pntd.0002016 https://doaj.org/article/792cbcd590b44c9691a8d1ff2f3b9181 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 7, Iss 1, p e2016 (2013) |
op_relation |
http://europepmc.org/articles/PMC3554572?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0002016 https://doaj.org/article/792cbcd590b44c9691a8d1ff2f3b9181 |
op_doi |
https://doi.org/10.1371/journal.pntd.0002016 |
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PLoS Neglected Tropical Diseases |
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7 |
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1 |
container_start_page |
e2016 |
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1766343920027959296 |