Predictive vs. empiric assessment of schistosomiasis: implications for treatment projections in Ghana

Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and deter...

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Published inPLoS neglected tropical diseases Vol. 7; no. 3; p. e2051
Main Authors Kabore, Achille, Biritwum, Nana-Kwadwo, Downs, Philip W, Soares Magalhaes, Ricardo J, Zhang, Yaobi, Ottesen, Eric A
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.03.2013
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Abstract Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined 'high-risk' schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection - translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.
AbstractList The challenge of accurately mapping schistosomiasis is a daunting one - particularly because of the highly focal distribution of the disease. Ideally, of course, each specific treatment area would be assessed for infection prevalence and then treated appropriately based on guidelines of the World Health Organization. In practice, however, this is not possible, and a variety of short-cutting techniques have been developed to meet these mapping needs, including geospatial predictive mapping. This paper assesses the accuracy of model-based geostatistics (MBG) predictions for determining treatments projections in Ghana by comparing previously published data using MBG predictions with empirically derived prevalence values for schistosomiasis from school surveys completed at 79 sites. We found that using predictive mapping alone would not have provided reliable information for mass drug administration (MDA) planning - resulting in overtreatment in some areas and most importantly under-treatment in areas that needed it most. Based on our findings, predictive risk mapping cannot be a one-time exercise but must instead be a process that incorporates empiric testing and model refining to create optimised spatial decision support tools that meet the needs of disease control operational managers.
Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined 'high-risk' schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection - translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.
BACKGROUNDMapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. METHODOLOGY/PRINCIPAL FINDINGSTo assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined 'high-risk' schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection - translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. CONCLUSIONS/SIGNIFICANCEUsing the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.
Background: Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. Methodology/Principal Findings: To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined 'high-risk' schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection--translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. Conclusions/Significance: Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible;modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.
  Background Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. Methodology/Principal Findings To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined 'high-risk' schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection - translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. Conclusions/Significance Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.
Audience Academic
Author Soares Magalhaes, Ricardo J
Downs, Philip W
Kabore, Achille
Ottesen, Eric A
Zhang, Yaobi
Biritwum, Nana-Kwadwo
AuthorAffiliation 3 University of Queensland, Infectious Disease Epidemiology Unit, School of Population Health, Brisbane, Australia
4 Helen Keller International, Regional Office for Africa, Dakar, Senegal
2 Neglected Tropical Diseases Control Programme, Ghana Health Service, Accra, Ghana
1 RTI International, Washington, District of Columbia, United States of America
London School of Hygiene & Tropical Medicine, United Kingdom
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ContentType Journal Article
Copyright COPYRIGHT 2013 Public Library of Science
2013 Kabore et al 2013 Kabore et al
2013 Kabore et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Kabore A, Biritwum N-K, Downs PW, Soares Magalhaes RJ, Zhang Y, et al. (2013) Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana. PLoS Negl Trop Dis 7(3): e2051. doi:10.1371/journal.pntd.0002051
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– notice: 2013 Kabore et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Kabore A, Biritwum N-K, Downs PW, Soares Magalhaes RJ, Zhang Y, et al. (2013) Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana. PLoS Negl Trop Dis 7(3): e2051. doi:10.1371/journal.pntd.0002051
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Issue 3
Keywords Schistosomiasis
Animals
Schistosoma haematobium
Epidemiologic Methods
Humans
Adolescent
Female
Male
Ghana
Models, Statistical
Child
Topography, Medical
Language English
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Conceived and designed the experiments: NKB AK PWD EAO. Performed the experiments: NKB AK PWD. Analyzed the data: NKB AK PWD EAO RJSM YZ. Contributed reagents/materials/analysis tools: NKB AK PWD RJSM. Wrote the paper: NKB AK PWD EAO RJSM YZ.
The authors state they have no competing interests.
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Snippet Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection...
Background: Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to...
BACKGROUNDMapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess...
The challenge of accurately mapping schistosomiasis is a daunting one - particularly because of the highly focal distribution of the disease. Ideally, of...
The challenge of accurately mapping schistosomiasis is a daunting one – particularly because of the highly focal distribution of the disease. Ideally, of...
BACKGROUND: Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to...
  Background Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to...
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StartPage e2051
SubjectTerms Adolescent
Animals
Care and treatment
Chemotherapy
Child
Distribution
Elementary school students
Epidemiologic Methods
Female
Geostatistics
Ghana - epidemiology
Health aspects
Humans
Infections
Male
Medicine
Methods
Models, Statistical
Prevalence studies (Epidemiology)
Schistosoma
Schistosoma haematobium - isolation & purification
Schistosomiasis
Schistosomiasis - epidemiology
Schistosomiasis - prevention & control
Schools
Studies
Topography, Medical
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Title Predictive vs. empiric assessment of schistosomiasis: implications for treatment projections in Ghana
URI https://www.ncbi.nlm.nih.gov/pubmed/23505584
https://search.proquest.com/docview/1317857012
https://search.proquest.com/docview/1520366718
https://pubmed.ncbi.nlm.nih.gov/PMC3591348
https://doaj.org/article/75ade1175e264347afd3bab38551c322
http://dx.doi.org/10.1371/journal.pntd.0002051
Volume 7
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