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Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm
Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling ideas that treat prevalence data from different locations as an independent random sample from the probability distribution induced by a random sampling design. We s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946792/ https://www.ncbi.nlm.nih.gov/pubmed/33587142 http://dx.doi.org/10.1093/trstmh/trab020 |
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author | Diggle, Peter J Amoah, Benjamin Fronterre, Claudio Giorgi, Emanuele Johnson, Olatunji |
author_facet | Diggle, Peter J Amoah, Benjamin Fronterre, Claudio Giorgi, Emanuele Johnson, Olatunji |
author_sort | Diggle, Peter J |
collection | PubMed |
description | Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling ideas that treat prevalence data from different locations as an independent random sample from the probability distribution induced by a random sampling design. We set out an alternative, explicitly geospatial paradigm that can deliver much more precise estimates of the geospatial variation in prevalence over a country or region of interest. We describe the advantages of this approach under three headings: streamlining, whereby more precise results can be obtained with smaller sample sizes; integrating, whereby a joint analysis of data from two or more diseases can bring further gains in precision; and adapting, whereby the choice of future sampling location is informed by past data. |
format | Online Article Text |
id | pubmed-7946792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-79467922021-03-16 Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm Diggle, Peter J Amoah, Benjamin Fronterre, Claudio Giorgi, Emanuele Johnson, Olatunji Trans R Soc Trop Med Hyg Commentary Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling ideas that treat prevalence data from different locations as an independent random sample from the probability distribution induced by a random sampling design. We set out an alternative, explicitly geospatial paradigm that can deliver much more precise estimates of the geospatial variation in prevalence over a country or region of interest. We describe the advantages of this approach under three headings: streamlining, whereby more precise results can be obtained with smaller sample sizes; integrating, whereby a joint analysis of data from two or more diseases can bring further gains in precision; and adapting, whereby the choice of future sampling location is informed by past data. Oxford University Press 2021-02-15 /pmc/articles/PMC7946792/ /pubmed/33587142 http://dx.doi.org/10.1093/trstmh/trab020 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Commentary Diggle, Peter J Amoah, Benjamin Fronterre, Claudio Giorgi, Emanuele Johnson, Olatunji Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
title | Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
title_full | Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
title_fullStr | Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
title_full_unstemmed | Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
title_short | Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
title_sort | rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946792/ https://www.ncbi.nlm.nih.gov/pubmed/33587142 http://dx.doi.org/10.1093/trstmh/trab020 |
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