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Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods
BACKGROUND: American Samoa completed seven rounds of mass drug administration from 2000–2006 as part of the Global Programme to Eliminate Lymphatic Filariasis (LF). However, resurgence was confirmed in 2016 through WHO-recommended school-based transmission assessment survey and a community-based sur...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989349/ https://www.ncbi.nlm.nih.gov/pubmed/35344542 http://dx.doi.org/10.1371/journal.pntd.0010262 |
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author | Wangdi, Kinley Sheel, Meru Fuimaono, Saipale Graves, Patricia M. Lau, Colleen L. |
author_facet | Wangdi, Kinley Sheel, Meru Fuimaono, Saipale Graves, Patricia M. Lau, Colleen L. |
author_sort | Wangdi, Kinley |
collection | PubMed |
description | BACKGROUND: American Samoa completed seven rounds of mass drug administration from 2000–2006 as part of the Global Programme to Eliminate Lymphatic Filariasis (LF). However, resurgence was confirmed in 2016 through WHO-recommended school-based transmission assessment survey and a community-based survey. This paper uses data from the 2016 community survey to compare different spatial and non-spatial methods to characterise clustering and hotspots of LF. METHOD: Non-spatial clustering of infection markers (antigen [Ag], microfilaraemia [Mf], and antibodies (Ab [Wb123, Bm14, Bm33]) was assessed using intra-cluster correlation coefficients (ICC) at household and village levels. Spatial dependence, clustering and hotspots were examined using semivariograms, Kulldorf’s scan statistic and Getis-Ord Gi* statistics based on locations of surveyed households. RESULTS: The survey included 2671 persons (750 households, 730 unique locations in 30 villages). ICCs were higher at household (0.20–0.69) than village levels (0.10–0.30) for all infection markers. Semivariograms identified significant spatial dependency for all markers (range 207–562 metres). Using Kulldorff’s scan statistic, significant spatial clustering was observed in two previously known locations of ongoing transmission: for all markers in Fagali’i and all Abs in Vaitogi. Getis-Ord Gi* statistic identified hotspots of all markers in Fagali’i, Vaitogi, and Pago Pago-Anua areas. A hotspot of Ag and Wb123 Ab was identified around the villages of Nua-Seetaga-Asili. Bm14 and Bm33 Ab hotspots were seen in Maleimi and Vaitogi-Ili’ili-Tafuna. CONCLUSION: Our study demonstrated the utility of different non-spatial and spatial methods for investigating clustering and hotspots, the benefits of using multiple infection markers, and the value of triangulating results between methods. |
format | Online Article Text |
id | pubmed-8989349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89893492022-04-08 Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods Wangdi, Kinley Sheel, Meru Fuimaono, Saipale Graves, Patricia M. Lau, Colleen L. PLoS Negl Trop Dis Research Article BACKGROUND: American Samoa completed seven rounds of mass drug administration from 2000–2006 as part of the Global Programme to Eliminate Lymphatic Filariasis (LF). However, resurgence was confirmed in 2016 through WHO-recommended school-based transmission assessment survey and a community-based survey. This paper uses data from the 2016 community survey to compare different spatial and non-spatial methods to characterise clustering and hotspots of LF. METHOD: Non-spatial clustering of infection markers (antigen [Ag], microfilaraemia [Mf], and antibodies (Ab [Wb123, Bm14, Bm33]) was assessed using intra-cluster correlation coefficients (ICC) at household and village levels. Spatial dependence, clustering and hotspots were examined using semivariograms, Kulldorf’s scan statistic and Getis-Ord Gi* statistics based on locations of surveyed households. RESULTS: The survey included 2671 persons (750 households, 730 unique locations in 30 villages). ICCs were higher at household (0.20–0.69) than village levels (0.10–0.30) for all infection markers. Semivariograms identified significant spatial dependency for all markers (range 207–562 metres). Using Kulldorff’s scan statistic, significant spatial clustering was observed in two previously known locations of ongoing transmission: for all markers in Fagali’i and all Abs in Vaitogi. Getis-Ord Gi* statistic identified hotspots of all markers in Fagali’i, Vaitogi, and Pago Pago-Anua areas. A hotspot of Ag and Wb123 Ab was identified around the villages of Nua-Seetaga-Asili. Bm14 and Bm33 Ab hotspots were seen in Maleimi and Vaitogi-Ili’ili-Tafuna. CONCLUSION: Our study demonstrated the utility of different non-spatial and spatial methods for investigating clustering and hotspots, the benefits of using multiple infection markers, and the value of triangulating results between methods. Public Library of Science 2022-03-28 /pmc/articles/PMC8989349/ /pubmed/35344542 http://dx.doi.org/10.1371/journal.pntd.0010262 Text en © 2022 Wangdi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wangdi, Kinley Sheel, Meru Fuimaono, Saipale Graves, Patricia M. Lau, Colleen L. Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods |
title | Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods |
title_full | Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods |
title_fullStr | Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods |
title_full_unstemmed | Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods |
title_short | Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods |
title_sort | lymphatic filariasis in 2016 in american samoa: identifying clustering and hotspots using non-spatial and three spatial analytical methods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989349/ https://www.ncbi.nlm.nih.gov/pubmed/35344542 http://dx.doi.org/10.1371/journal.pntd.0010262 |
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