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Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study
Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in es...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759231/ https://www.ncbi.nlm.nih.gov/pubmed/35027002 http://dx.doi.org/10.1186/s12874-021-01477-6 |
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author | Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Basáñez, María-Gloria Nouvellet, Pierre |
author_facet | Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Basáñez, María-Gloria Nouvellet, Pierre |
author_sort | Ledien, Julia |
collection | PubMed |
description | Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty. In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia. A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions. Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01477-6. |
format | Online Article Text |
id | pubmed-8759231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87592312022-01-18 Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Basáñez, María-Gloria Nouvellet, Pierre BMC Med Res Methodol Research Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty. In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia. A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions. Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01477-6. BioMed Central 2022-01-13 /pmc/articles/PMC8759231/ /pubmed/35027002 http://dx.doi.org/10.1186/s12874-021-01477-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Basáñez, María-Gloria Nouvellet, Pierre Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study |
title | Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study |
title_full | Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study |
title_fullStr | Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study |
title_full_unstemmed | Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study |
title_short | Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study |
title_sort | spatiotemporal variations in exposure: chagas disease in colombia as a case study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759231/ https://www.ncbi.nlm.nih.gov/pubmed/35027002 http://dx.doi.org/10.1186/s12874-021-01477-6 |
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