Loading…

Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis

Abstract Background Northeast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region. Methods To advance the study of the Zika ep...

Full description

Saved in:
Bibliographic Details
Published in:Transactions of the Royal Society of Tropical Medicine and Hygiene 2023-03, Vol.117 (3), p.189-196
Main Authors: Freitas, Laís Picinini, Lowe, Rachel, Koepp, Andrew E, Alves, Sandra Valongueiro, Dondero, Molly, Marteleto, Letícia J
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83
cites cdi_FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83
container_end_page 196
container_issue 3
container_start_page 189
container_title Transactions of the Royal Society of Tropical Medicine and Hygiene
container_volume 117
creator Freitas, Laís Picinini
Lowe, Rachel
Koepp, Andrew E
Alves, Sandra Valongueiro
Dondero, Molly
Marteleto, Letícia J
description Abstract Background Northeast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region. Methods To advance the study of the Zika epidemic, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, using Aedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used Kulldorff's Poisson purely spatial scan statistic to detect low- and high-risk clusters for Aedes-borne diseases (2014–2017) and for microcephaly (2015–2017), separately. Municipalities were classified according to a proposed gradient of Zika burden during the epidemic, based on the combination of cluster status in each analysis and considering the strength of the evidence. Results We identified 26 Aedes-borne diseases clusters (11 high-risk) and 5 microcephaly clusters (3 high-risk) in Pernambuco. According to the proposed Zika burden gradient, our results indicate that the northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area of Pernambuco, while the second has one of the highest rates of social and economic inequality in Brazil. Conclusion We successfully identified possible hidden Zika hotspots using a simple methodology combining Aedes-borne diseases and microcephaly information.
doi_str_mv 10.1093/trstmh/trac099
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9977212</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/trstmh/trac099</oup_id><sourcerecordid>2731717695</sourcerecordid><originalsourceid>FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83</originalsourceid><addsrcrecordid>eNqFkMtLAzEQxoMotlavHiVHBbfmsZuHB0GLj0pBDz15Cdnsbhvdl8muUP96I61FTx6GYZjffN_wAXCM0RgjSS8657tqGZo2SModMMSCi4gmiO6CIUI0iSRBdAAOvH9FiCQ4kftgQBkljItkCB6nWV53tljZegGXNgsTfLFvGi6bzrehoK3hc-5qXaW9ac7hjdOftryEGvpWd1aXUNe6XHnrD8FeoUufH236CMzvbueTh2j2dD-dXM8iE5O4iwgtjMniVBeGM0RxXGSYMpYyKQTBGSFGFCSVjDPJkIilNFhgTgpMdRKngo7A1Vq27dMqz0x43-lStc5W2q1Uo636u6ntUi2aDyUl5wSTIHC6EXDNe5_7TlXWm7wsdZ03vVeEU8xxsE8COl6jxjXeu7zY2mCkvvNX6_zVJv9wcPL7uS3-E3gAztZA07f_iX0BR5GS8w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2731717695</pqid></control><display><type>article</type><title>Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis</title><source>Oxford Journals Online</source><creator>Freitas, Laís Picinini ; Lowe, Rachel ; Koepp, Andrew E ; Alves, Sandra Valongueiro ; Dondero, Molly ; Marteleto, Letícia J</creator><creatorcontrib>Freitas, Laís Picinini ; Lowe, Rachel ; Koepp, Andrew E ; Alves, Sandra Valongueiro ; Dondero, Molly ; Marteleto, Letícia J</creatorcontrib><description>Abstract Background Northeast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region. Methods To advance the study of the Zika epidemic, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, using Aedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used Kulldorff's Poisson purely spatial scan statistic to detect low- and high-risk clusters for Aedes-borne diseases (2014–2017) and for microcephaly (2015–2017), separately. Municipalities were classified according to a proposed gradient of Zika burden during the epidemic, based on the combination of cluster status in each analysis and considering the strength of the evidence. Results We identified 26 Aedes-borne diseases clusters (11 high-risk) and 5 microcephaly clusters (3 high-risk) in Pernambuco. According to the proposed Zika burden gradient, our results indicate that the northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area of Pernambuco, while the second has one of the highest rates of social and economic inequality in Brazil. Conclusion We successfully identified possible hidden Zika hotspots using a simple methodology combining Aedes-borne diseases and microcephaly information.</description><identifier>ISSN: 0035-9203</identifier><identifier>ISSN: 1878-3503</identifier><identifier>EISSN: 1878-3503</identifier><identifier>DOI: 10.1093/trstmh/trac099</identifier><identifier>PMID: 36326785</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Aedes ; Animals ; Brazil - epidemiology ; Humans ; Microcephaly - epidemiology ; Original ; Spatial Analysis ; Zika Virus ; Zika Virus Infection - epidemiology</subject><ispartof>Transactions of the Royal Society of Tropical Medicine and Hygiene, 2023-03, Vol.117 (3), p.189-196</ispartof><rights>The Author(s) 2022. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83</citedby><cites>FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83</cites><orcidid>0000-0001-9012-9382</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36326785$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Freitas, Laís Picinini</creatorcontrib><creatorcontrib>Lowe, Rachel</creatorcontrib><creatorcontrib>Koepp, Andrew E</creatorcontrib><creatorcontrib>Alves, Sandra Valongueiro</creatorcontrib><creatorcontrib>Dondero, Molly</creatorcontrib><creatorcontrib>Marteleto, Letícia J</creatorcontrib><title>Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis</title><title>Transactions of the Royal Society of Tropical Medicine and Hygiene</title><addtitle>Trans R Soc Trop Med Hyg</addtitle><description>Abstract Background Northeast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region. Methods To advance the study of the Zika epidemic, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, using Aedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used Kulldorff's Poisson purely spatial scan statistic to detect low- and high-risk clusters for Aedes-borne diseases (2014–2017) and for microcephaly (2015–2017), separately. Municipalities were classified according to a proposed gradient of Zika burden during the epidemic, based on the combination of cluster status in each analysis and considering the strength of the evidence. Results We identified 26 Aedes-borne diseases clusters (11 high-risk) and 5 microcephaly clusters (3 high-risk) in Pernambuco. According to the proposed Zika burden gradient, our results indicate that the northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area of Pernambuco, while the second has one of the highest rates of social and economic inequality in Brazil. Conclusion We successfully identified possible hidden Zika hotspots using a simple methodology combining Aedes-borne diseases and microcephaly information.</description><subject>Aedes</subject><subject>Animals</subject><subject>Brazil - epidemiology</subject><subject>Humans</subject><subject>Microcephaly - epidemiology</subject><subject>Original</subject><subject>Spatial Analysis</subject><subject>Zika Virus</subject><subject>Zika Virus Infection - epidemiology</subject><issn>0035-9203</issn><issn>1878-3503</issn><issn>1878-3503</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFkMtLAzEQxoMotlavHiVHBbfmsZuHB0GLj0pBDz15Cdnsbhvdl8muUP96I61FTx6GYZjffN_wAXCM0RgjSS8657tqGZo2SModMMSCi4gmiO6CIUI0iSRBdAAOvH9FiCQ4kftgQBkljItkCB6nWV53tljZegGXNgsTfLFvGi6bzrehoK3hc-5qXaW9ac7hjdOftryEGvpWd1aXUNe6XHnrD8FeoUufH236CMzvbueTh2j2dD-dXM8iE5O4iwgtjMniVBeGM0RxXGSYMpYyKQTBGSFGFCSVjDPJkIilNFhgTgpMdRKngo7A1Vq27dMqz0x43-lStc5W2q1Uo636u6ntUi2aDyUl5wSTIHC6EXDNe5_7TlXWm7wsdZ03vVeEU8xxsE8COl6jxjXeu7zY2mCkvvNX6_zVJv9wcPL7uS3-E3gAztZA07f_iX0BR5GS8w</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Freitas, Laís Picinini</creator><creator>Lowe, Rachel</creator><creator>Koepp, Andrew E</creator><creator>Alves, Sandra Valongueiro</creator><creator>Dondero, Molly</creator><creator>Marteleto, Letícia J</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9012-9382</orcidid></search><sort><creationdate>20230301</creationdate><title>Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis</title><author>Freitas, Laís Picinini ; Lowe, Rachel ; Koepp, Andrew E ; Alves, Sandra Valongueiro ; Dondero, Molly ; Marteleto, Letícia J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aedes</topic><topic>Animals</topic><topic>Brazil - epidemiology</topic><topic>Humans</topic><topic>Microcephaly - epidemiology</topic><topic>Original</topic><topic>Spatial Analysis</topic><topic>Zika Virus</topic><topic>Zika Virus Infection - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Freitas, Laís Picinini</creatorcontrib><creatorcontrib>Lowe, Rachel</creatorcontrib><creatorcontrib>Koepp, Andrew E</creatorcontrib><creatorcontrib>Alves, Sandra Valongueiro</creatorcontrib><creatorcontrib>Dondero, Molly</creatorcontrib><creatorcontrib>Marteleto, Letícia J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Transactions of the Royal Society of Tropical Medicine and Hygiene</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Freitas, Laís Picinini</au><au>Lowe, Rachel</au><au>Koepp, Andrew E</au><au>Alves, Sandra Valongueiro</au><au>Dondero, Molly</au><au>Marteleto, Letícia J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis</atitle><jtitle>Transactions of the Royal Society of Tropical Medicine and Hygiene</jtitle><addtitle>Trans R Soc Trop Med Hyg</addtitle><date>2023-03-01</date><risdate>2023</risdate><volume>117</volume><issue>3</issue><spage>189</spage><epage>196</epage><pages>189-196</pages><issn>0035-9203</issn><issn>1878-3503</issn><eissn>1878-3503</eissn><abstract>Abstract Background Northeast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region. Methods To advance the study of the Zika epidemic, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, using Aedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used Kulldorff's Poisson purely spatial scan statistic to detect low- and high-risk clusters for Aedes-borne diseases (2014–2017) and for microcephaly (2015–2017), separately. Municipalities were classified according to a proposed gradient of Zika burden during the epidemic, based on the combination of cluster status in each analysis and considering the strength of the evidence. Results We identified 26 Aedes-borne diseases clusters (11 high-risk) and 5 microcephaly clusters (3 high-risk) in Pernambuco. According to the proposed Zika burden gradient, our results indicate that the northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area of Pernambuco, while the second has one of the highest rates of social and economic inequality in Brazil. Conclusion We successfully identified possible hidden Zika hotspots using a simple methodology combining Aedes-borne diseases and microcephaly information.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36326785</pmid><doi>10.1093/trstmh/trac099</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-9012-9382</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0035-9203
ispartof Transactions of the Royal Society of Tropical Medicine and Hygiene, 2023-03, Vol.117 (3), p.189-196
issn 0035-9203
1878-3503
1878-3503
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9977212
source Oxford Journals Online
subjects Aedes
Animals
Brazil - epidemiology
Humans
Microcephaly - epidemiology
Original
Spatial Analysis
Zika Virus
Zika Virus Infection - epidemiology
title Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T18%3A59%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identifying%20hidden%20Zika%20hotspots%20in%20Pernambuco,%20Brazil:%20a%20spatial%20analysis&rft.jtitle=Transactions%20of%20the%20Royal%20Society%20of%20Tropical%20Medicine%20and%20Hygiene&rft.au=Freitas,%20La%C3%ADs%20Picinini&rft.date=2023-03-01&rft.volume=117&rft.issue=3&rft.spage=189&rft.epage=196&rft.pages=189-196&rft.issn=0035-9203&rft.eissn=1878-3503&rft_id=info:doi/10.1093/trstmh/trac099&rft_dat=%3Cproquest_pubme%3E2731717695%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c424t-23fccd4bafc760314fd1366b698821d22c8f2b96769608499c18172f13a54b83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2731717695&rft_id=info:pmid/36326785&rft_oup_id=10.1093/trstmh/trac099&rfr_iscdi=true