Estimating underreporting of leprosy in Brazil using a Bayesian approach
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-m...
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| creator | 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 |
| description | 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.
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.
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. |
| doi_str_mv | 10.1371/journal.pntd.0009700 |
| format | Article |
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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.
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.</description><identifier>ISSN: 1935-2735</identifier><identifier>ISSN: 1935-2727</identifier><identifier>EISSN: 1935-2735</identifier><identifier>DOI: 10.1371/journal.pntd.0009700</identifier><identifier>PMID: 34432805</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Bayes Theorem ; Bayesian analysis ; Blindness ; Brazil - epidemiology ; Computer and Information Sciences ; Decision making ; Disabilities ; Disease ; Distribution ; Earth Sciences ; Engineering and Technology ; Households ; Humans ; Incidence ; Information systems ; Leprosy ; Leprosy - economics ; Leprosy - epidemiology ; Medicine and Health Sciences ; People and places ; Population ; Probability theory ; Random variables ; Resource allocation ; Sentinel health events ; Social factors ; Social Sciences ; Socioeconomic data ; Socioeconomic Factors ; Socioeconomics ; Spatial analysis ; Spatial variations ; Statistics ; Transmission ; Tropical diseases ; Urban areas</subject><ispartof>PLoS neglected tropical diseases, 2021-08, Vol.15 (8), p.e0009700-e0009700</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 de Oliveira et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 de Oliveira et al 2021 de Oliveira et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c624t-2e768e3acf8c0c71c4a72d0d73a3283b7fcbccaeae80d5b1b0271ab2f58ef12a3</citedby><cites>FETCH-LOGICAL-c624t-2e768e3acf8c0c71c4a72d0d73a3283b7fcbccaeae80d5b1b0271ab2f58ef12a3</cites><orcidid>0000-0002-9323-1400 ; 0000-0002-0472-1804 ; 0000-0001-8590-6212 ; 0000-0002-9168-6022 ; 0000-0002-7167-8754 ; 0000-0003-0371-8037 ; 0000-0001-8711-9589 ; 0000-0001-8967-536X ; 0000-0003-3220-6356 ; 0000-0003-0280-2288 ; 0000-0002-0215-4930</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423270/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423270/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2929,23868,27926,27927,53793,53795</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34432805$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Nardi, Susilene Maria Tonelli</contributor><creatorcontrib>de Oliveira, Guilherme L</creatorcontrib><creatorcontrib>Oliveira, Juliane F</creatorcontrib><creatorcontrib>Pescarini, Júlia M</creatorcontrib><creatorcontrib>Andrade, Roberto F S</creatorcontrib><creatorcontrib>Nery, Joilda S</creatorcontrib><creatorcontrib>Ichihara, Maria Y</creatorcontrib><creatorcontrib>Smeeth, Liam</creatorcontrib><creatorcontrib>Brickley, Elizabeth B</creatorcontrib><creatorcontrib>Barreto, Maurício L</creatorcontrib><creatorcontrib>Penna, Gerson O</creatorcontrib><creatorcontrib>Penna, Maria L F</creatorcontrib><creatorcontrib>Sanchez, Mauro N</creatorcontrib><title>Estimating underreporting of leprosy in Brazil using a Bayesian approach</title><title>PLoS neglected tropical diseases</title><addtitle>PLoS Negl Trop Dis</addtitle><description>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.
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.
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.</description><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Blindness</subject><subject>Brazil - epidemiology</subject><subject>Computer and Information Sciences</subject><subject>Decision making</subject><subject>Disabilities</subject><subject>Disease</subject><subject>Distribution</subject><subject>Earth Sciences</subject><subject>Engineering and Technology</subject><subject>Households</subject><subject>Humans</subject><subject>Incidence</subject><subject>Information systems</subject><subject>Leprosy</subject><subject>Leprosy - economics</subject><subject>Leprosy - epidemiology</subject><subject>Medicine and Health Sciences</subject><subject>People and places</subject><subject>Population</subject><subject>Probability theory</subject><subject>Random variables</subject><subject>Resource 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underreporting of leprosy in Brazil using a Bayesian approach</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c624t-2e768e3acf8c0c71c4a72d0d73a3283b7fcbccaeae80d5b1b0271ab2f58ef12a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Blindness</topic><topic>Brazil - epidemiology</topic><topic>Computer and Information Sciences</topic><topic>Decision making</topic><topic>Disabilities</topic><topic>Disease</topic><topic>Distribution</topic><topic>Earth Sciences</topic><topic>Engineering and 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neglected tropical diseases</jtitle><addtitle>PLoS Negl Trop Dis</addtitle><date>2021-08-01</date><risdate>2021</risdate><volume>15</volume><issue>8</issue><spage>e0009700</spage><epage>e0009700</epage><pages>e0009700-e0009700</pages><issn>1935-2735</issn><issn>1935-2727</issn><eissn>1935-2735</eissn><abstract>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.
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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34432805</pmid><doi>10.1371/journal.pntd.0009700</doi><orcidid>https://orcid.org/0000-0002-9323-1400</orcidid><orcidid>https://orcid.org/0000-0002-0472-1804</orcidid><orcidid>https://orcid.org/0000-0001-8590-6212</orcidid><orcidid>https://orcid.org/0000-0002-9168-6022</orcidid><orcidid>https://orcid.org/0000-0002-7167-8754</orcidid><orcidid>https://orcid.org/0000-0003-0371-8037</orcidid><orcidid>https://orcid.org/0000-0001-8711-9589</orcidid><orcidid>https://orcid.org/0000-0001-8967-536X</orcidid><orcidid>https://orcid.org/0000-0003-3220-6356</orcidid><orcidid>https://orcid.org/0000-0003-0280-2288</orcidid><orcidid>https://orcid.org/0000-0002-0215-4930</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | PLoS neglected tropical diseases, 2021-08, Vol.15 (8), p.e0009700-e0009700 |
| issn | 1935-2735 1935-2727 1935-2735 |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; PubMed Central Open Access; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Bayes Theorem Bayesian analysis Blindness Brazil - epidemiology Computer and Information Sciences Decision making Disabilities Disease Distribution Earth Sciences Engineering and Technology Households Humans Incidence Information systems Leprosy Leprosy - economics Leprosy - epidemiology Medicine and Health Sciences People and places Population Probability theory Random variables Resource allocation Sentinel health events Social factors Social Sciences Socioeconomic data Socioeconomic Factors Socioeconomics Spatial analysis Spatial variations Statistics Transmission Tropical diseases Urban areas |
| title | Estimating underreporting of leprosy in Brazil using a Bayesian approach |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T02%3A19%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estimating%20underreporting%20of%20leprosy%20in%20Brazil%20using%20a%20Bayesian%20approach&rft.jtitle=PLoS%20neglected%20tropical%20diseases&rft.au=de%20Oliveira,%20Guilherme%20L&rft.date=2021-08-01&rft.volume=15&rft.issue=8&rft.spage=e0009700&rft.epage=e0009700&rft.pages=e0009700-e0009700&rft.issn=1935-2735&rft.eissn=1935-2735&rft_id=info:doi/10.1371/journal.pntd.0009700&rft_dat=%3Cgale_plos_%3EA673929878%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2573455481&rft_id=info:pmid/34432805&rft_galeid=A673929878&rft_doaj_id=oai_doaj_org_article_6715060094bf4746963bfd7311ecd685&rfr_iscdi=true |