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Estimating underreporting of leprosy in Brazil using a Bayesian approach
BACKGROUND: Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinde...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423270/ https://www.ncbi.nlm.nih.gov/pubmed/34432805 http://dx.doi.org/10.1371/journal.pntd.0009700 |
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author | de Oliveira, Guilherme L. Oliveira, Juliane F. Pescarini, Júlia M. Andrade, Roberto F. S. Nery, Joilda S. Ichihara, Maria Y. Smeeth, Liam Brickley, Elizabeth B. Barreto, Maurício L. Penna, Gerson O. Penna, Maria L. F. Sanchez, Mauro N. |
author_facet | de Oliveira, Guilherme L. Oliveira, Juliane F. Pescarini, Júlia M. Andrade, Roberto F. S. Nery, Joilda S. Ichihara, Maria Y. Smeeth, Liam Brickley, Elizabeth B. Barreto, Maurício L. Penna, Gerson O. Penna, Maria L. F. Sanchez, Mauro N. |
author_sort | de Oliveira, Guilherme L. |
collection | PubMed |
description | BACKGROUND: Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions. METHODOLOGY/PRINCIPAL FINDINGS: Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States. CONCLUSIONS/SIGNIFICANCE: The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission. |
format | Online Article Text |
id | pubmed-8423270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84232702021-09-08 Estimating underreporting of leprosy in Brazil using a Bayesian approach de Oliveira, Guilherme L. Oliveira, Juliane F. Pescarini, Júlia M. Andrade, Roberto F. S. Nery, Joilda S. Ichihara, Maria Y. Smeeth, Liam Brickley, Elizabeth B. Barreto, Maurício L. Penna, Gerson O. Penna, Maria L. F. Sanchez, Mauro N. PLoS Negl Trop Dis Research Article BACKGROUND: Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions. METHODOLOGY/PRINCIPAL FINDINGS: Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States. CONCLUSIONS/SIGNIFICANCE: The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission. Public Library of Science 2021-08-25 /pmc/articles/PMC8423270/ /pubmed/34432805 http://dx.doi.org/10.1371/journal.pntd.0009700 Text en © 2021 de Oliveira 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 de Oliveira, Guilherme L. Oliveira, Juliane F. Pescarini, Júlia M. Andrade, Roberto F. S. Nery, Joilda S. Ichihara, Maria Y. Smeeth, Liam Brickley, Elizabeth B. Barreto, Maurício L. Penna, Gerson O. Penna, Maria L. F. Sanchez, Mauro N. Estimating underreporting of leprosy in Brazil using a Bayesian approach |
title | Estimating underreporting of leprosy in Brazil using a Bayesian approach |
title_full | Estimating underreporting of leprosy in Brazil using a Bayesian approach |
title_fullStr | Estimating underreporting of leprosy in Brazil using a Bayesian approach |
title_full_unstemmed | Estimating underreporting of leprosy in Brazil using a Bayesian approach |
title_short | Estimating underreporting of leprosy in Brazil using a Bayesian approach |
title_sort | estimating underreporting of leprosy in brazil using a bayesian approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423270/ https://www.ncbi.nlm.nih.gov/pubmed/34432805 http://dx.doi.org/10.1371/journal.pntd.0009700 |
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