The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya.
<h4>Background</h4>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. H...
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0002016&type=printable |
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| 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 |
| collection | DOAJ |
| description | <h4>Background</h4>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.<h4>Methodology</h4>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.<h4>Principal findings</h4>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).<h4>Conclusions</h4>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 |
| id | doaj-art-792cbcd590b44c9691a8d1ff2f3b9181 |
| institution | DOAJ |
| issn | 1935-2727 1935-2735 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj-art-792cbcd590b44c9691a8d1ff2f3b91812025-08-20T03:11:25ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352013-01-0171e201610.1371/journal.pntd.0002016The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya.Hugh J W SturrockRachel L PullanJimmy H KiharaCharles MwandawiroSimon J Brooker<h4>Background</h4>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.<h4>Methodology</h4>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.<h4>Principal findings</h4>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).<h4>Conclusions</h4>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.https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0002016&type=printable |
| spellingShingle | 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. PLoS Neglected Tropical Diseases |
| title | 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_short | 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 |
| url | https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0002016&type=printable |
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