Detecting infection hotspots: Modeling the surveillance challenge for elimination of lymphatic filariasis.
Identifieur interne : 000183 ( PubMed/Corpus ); précédent : 000182; suivant : 000184Detecting infection hotspots: Modeling the surveillance challenge for elimination of lymphatic filariasis.
Auteurs : Julie R. Harris ; Ryan E. WiegandSource :
- PLoS neglected tropical diseases [ 1935-2735 ] ; 2017.
English descriptors
- KwdEn :
- Adolescent, Adult, Africa South of the Sahara (epidemiology), Aged, Aged, 80 and over, Child, Child, Preschool, Computer Simulation, Disease Eradication (methods), Disease Transmission, Infectious (prevention & control), Elephantiasis, Filarial (diagnosis), Elephantiasis, Filarial (epidemiology), Elephantiasis, Filarial (prevention & control), Epidemiological Monitoring, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Young Adult.
- MESH :
- diagnosis : Elephantiasis, Filarial.
- epidemiology : Africa South of the Sahara, Elephantiasis, Filarial.
- methods : Disease Eradication.
- prevention & control : Disease Transmission, Infectious, Elephantiasis, Filarial.
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Computer Simulation, Epidemiological Monitoring, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Young Adult.
Abstract
During the past 20 years, enormous efforts have been expended globally to eliminate lymphatic filariasis (LF) through mass drug administration (MDA). However, small endemic foci (microfoci) of LF may threaten the presumed inevitable decline of infections after MDA cessation. We conducted microsimulation modeling to assess the ability of different types of surveillance to identify microfoci in these settings.
DOI: 10.1371/journal.pntd.0005610
PubMed: 28542274
Links to Exploration step
pubmed:28542274Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Detecting infection hotspots: Modeling the surveillance challenge for elimination of lymphatic filariasis.</title><author><name sortKey="Harris, Julie R" sort="Harris, Julie R" uniqKey="Harris J" first="Julie R" last="Harris">Julie R. Harris</name><affiliation><nlm:affiliation>Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Wiegand, Ryan E" sort="Wiegand, Ryan E" uniqKey="Wiegand R" first="Ryan E" last="Wiegand">Ryan E. Wiegand</name><affiliation><nlm:affiliation>Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:28542274</idno><idno type="pmid">28542274</idno><idno type="doi">10.1371/journal.pntd.0005610</idno><idno type="wicri:Area/PubMed/Corpus">000183</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000183</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Detecting infection hotspots: Modeling the surveillance challenge for elimination of lymphatic filariasis.</title><author><name sortKey="Harris, Julie R" sort="Harris, Julie R" uniqKey="Harris J" first="Julie R" last="Harris">Julie R. Harris</name><affiliation><nlm:affiliation>Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Wiegand, Ryan E" sort="Wiegand, Ryan E" uniqKey="Wiegand R" first="Ryan E" last="Wiegand">Ryan E. Wiegand</name><affiliation><nlm:affiliation>Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PLoS neglected tropical diseases</title><idno type="eISSN">1935-2735</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Africa South of the Sahara (epidemiology)</term><term>Aged</term><term>Aged, 80 and over</term><term>Child</term><term>Child, Preschool</term><term>Computer Simulation</term><term>Disease Eradication (methods)</term><term>Disease Transmission, Infectious (prevention & control)</term><term>Elephantiasis, Filarial (diagnosis)</term><term>Elephantiasis, Filarial (epidemiology)</term><term>Elephantiasis, Filarial (prevention & control)</term><term>Epidemiological Monitoring</term><term>Female</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Male</term><term>Middle Aged</term><term>Young Adult</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Elephantiasis, Filarial</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Africa South of the Sahara</term><term>Elephantiasis, Filarial</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Disease Eradication</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Disease Transmission, Infectious</term><term>Elephantiasis, Filarial</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Aged, 80 and over</term><term>Child</term><term>Child, Preschool</term><term>Computer Simulation</term><term>Epidemiological Monitoring</term><term>Female</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Male</term><term>Middle Aged</term><term>Young Adult</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">During the past 20 years, enormous efforts have been expended globally to eliminate lymphatic filariasis (LF) through mass drug administration (MDA). However, small endemic foci (microfoci) of LF may threaten the presumed inevitable decline of infections after MDA cessation. We conducted microsimulation modeling to assess the ability of different types of surveillance to identify microfoci in these settings.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28542274</PMID><DateCreated><Year>2017</Year><Month>05</Month><Day>25</Day></DateCreated><DateCompleted><Year>2017</Year><Month>07</Month><Day>10</Day></DateCompleted><DateRevised><Year>2017</Year><Month>07</Month><Day>13</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1935-2735</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><Issue>5</Issue><PubDate><Year>2017</Year><Month>May</Month></PubDate></JournalIssue><Title>PLoS neglected tropical diseases</Title><ISOAbbreviation>PLoS Negl Trop Dis</ISOAbbreviation></Journal><ArticleTitle>Detecting infection hotspots: Modeling the surveillance challenge for elimination of lymphatic filariasis.</ArticleTitle><Pagination><MedlinePgn>e0005610</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pntd.0005610</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">During the past 20 years, enormous efforts have been expended globally to eliminate lymphatic filariasis (LF) through mass drug administration (MDA). However, small endemic foci (microfoci) of LF may threaten the presumed inevitable decline of infections after MDA cessation. We conducted microsimulation modeling to assess the ability of different types of surveillance to identify microfoci in these settings.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Five or ten microfoci of radius 1, 2, or 3 km with infection marker prevalence (intensity) of 3, 6, or 10 times background prevalence were placed in spatial simulations, run in R Version 3.2. Diagnostic tests included microfilaremia, immunochromatographic test (ICT), and Wb123 ELISA. Population size was fixed at 360,000 in a 60 x 60 km area; demographics were based on literature for Sub-Saharan African populations. Background ICT prevalence in 6-7 year olds was anchored at 1.0%, and the prevalence in the remaining population was adjusted by age. Adults≥18 years, women aged 15-40 years (WCBA), children aged 6-7 years, or children≤5 years were sampled. Cluster (CS), simple random sampling (SRS), and TAS-like sampling were simulated, with follow-up testing of the nearest 20, 100, or 500 persons around each infection-marker-positive person. A threshold number of positive persons in follow-up testing indicated a suspected microfocus. Suspected microfoci identified during surveillance and actual microfoci in the simulation were compared to obtain a predictive value positive (PVP). Each parameter set was referred to as a protocol. Protocols were scored by efficiency, defined as the most microfoci identified, the fewest persons requiring primary and follow-up testing, and the highest PVP. Negative binomial regression was used to estimate aggregate effects of different variables on efficiency metrics.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">All variables were significantly associated with efficiency metrics. Additional follow-up tests beyond 20 did not greatly increase the number of microfoci detected, but significantly negatively impacted efficiency. Of 3,402 protocols evaluated, 384 (11.3%) identified all five microfoci (PVP 3.4-100.0%) and required testing 0.73-35.6% of the population. All used SRS and 378 (98.4%) only identified all five microfoci if they were 2-3 km diameter or high-intensity (6x or 10x); 374 (97.4%) required ICT or Wb123 testing to identify all five microfoci, and 281 (73.0%) required sampling adults or WCBA. The most efficient CS protocols identified two (40%) microfoci. After limiting to protocols with 1-km radius microfoci of 3x intensity (n = 378), eight identified all five microfoci; all used SRS and ICT and required testing 31.2-33.3% of the population. The most efficient CS and TAS-like protocols as well as those using microfilaremia testing identified only one (20%) microfocus when they were limited to 1-km radius and 3x intensity.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">In this model, SRS, ICT, and sampling of adults maximized microfocus detection efficiency. Follow-up sampling of more persons did not necessarily increase protocol efficiency. Current approaches towards surveillance, including TAS, may not detect small, low-intensity LF microfoci that could remain after cessation of MDA. The model provides many surveillance protocols that can be selected for optimal outcomes.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Harris</LastName><ForeName>Julie R</ForeName><Initials>JR</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-2496-8441</Identifier><AffiliationInfo><Affiliation>Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wiegand</LastName><ForeName>Ryan E</ForeName><Initials>RE</Initials><AffiliationInfo><Affiliation>Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>05</Month><Day>19</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Negl Trop Dis</MedlineTA><NlmUniqueID>101291488</NlmUniqueID><ISSNLinking>1935-2727</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>PLoS Negl Trop Dis. 2012;6(10):e1807</RefSource><PMID Version="1">23071849</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Filaria J. 2004 May 5;3(1):3</RefSource><PMID Version="1">15128461</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS Negl Trop Dis. 2012 Jan;6(1):e1479</RefSource><PMID 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Sahara</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D060740" MajorTopicYN="N">Disease Eradication</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018562" MajorTopicYN="N">Disease Transmission, Infectious</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004605" MajorTopicYN="N">Elephantiasis, Filarial</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062665" MajorTopicYN="Y">Epidemiological Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007231" MajorTopicYN="N">Infant, Newborn</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>09</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>04</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2017</Year><Month>06</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>5</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>7</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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