Incidence estimation from sentinel surveillance data; a simulation study and application to data from the Belgian laboratory sentinel surveillance.
Identifieur interne : 000005 ( Main/Exploration ); précédent : 000004; suivant : 000006Incidence estimation from sentinel surveillance data; a simulation study and application to data from the Belgian laboratory sentinel surveillance.
Auteurs : Toon Braeye [Belgique] ; Sophie Quoilin [Belgique] ; Niel Hens [Belgique]Source :
- BMC public health [ 1471-2458 ] ; 2019.
Descripteurs français
- KwdFr :
- Belgique (épidémiologie), Campylobacter (isolement et purification), Humains, Incidence, Laboratoires, Modèles économétriques, Orthomyxoviridae (isolement et purification), Probabilité, Reproductibilité des résultats, Rotavirus (isolement et purification), Surveillance sentinelle, Yersinia enterocolitica (isolement et purification).
- MESH :
- isolement et purification : Campylobacter, Orthomyxoviridae, Rotavirus, Yersinia enterocolitica.
- épidémiologie : Belgique.
- Humains, Incidence, Laboratoires, Modèles économétriques, Probabilité, Reproductibilité des résultats, Surveillance sentinelle.
- Wicri :
- geographic : Belgique.
English descriptors
- KwdEn :
- Belgium (epidemiology), Campylobacter (isolation & purification), Humans, Incidence, Laboratories, Models, Econometric, Orthomyxoviridae (isolation & purification), Probability, Reproducibility of Results, Rotavirus (isolation & purification), Sentinel Surveillance, Yersinia enterocolitica (isolation & purification).
- MESH :
- geographic , epidemiology : Belgium.
- isolation & purification : Campylobacter, Orthomyxoviridae, Rotavirus, Yersinia enterocolitica.
- Humans, Incidence, Laboratories, Models, Econometric, Probability, Reproducibility of Results, Sentinel Surveillance.
Abstract
BACKGROUND
Inverse probability weighting (IPW) methods can be used to estimate the total number of cases from the sample collected through sentinel surveillance. Central to these methods are the inverse weights which can be derived in several ways and, in this case, represent the probability that laboratory (lab) sentinel surveillance detects a lab-confirmed case.
METHODS
We compare different weights in a simulation study. Weights are obtained from the proportion of participating labs over all labs. We adjust these weights for attractiveness and density of labs over population. The market share of sentinel labs, as estimated by the econometric Huff-model, is also considered. Additionally, we investigate the effect of not recognizing sentinel labs as sentinel labs when they report no cases. We estimate the bias associated with the different weights as the difference between the simulated number of cases and the estimate of this total from the sentinel sample. As motivating data examples, we apply an extended Huff-model to four pathogens under laboratory sentinel surveillance in Belgium between 2010 and 2015 and discuss the model fit. We estimate the total number of lab-confirmed cases associated with Rotavirus, influenza virus, Y. enterocolitica and Campylobacter spp.. The extended Huff-model takes the lab-concept, the number of reimbursements and the number of departments, lab-density, regional borders, distance and competition between labs in account.
RESULTS
Estimates obtained with the Huff-model were most accurate in the more complex simulation scenarios as compared to other weights. In the data examples, several significant coefficients are identified, but the fit of the Huff-model to the Belgian sentinel surveillance data leaves much variability in market shares unexplained.
CONCLUSION
The Huff-model allows for estimation of the spatial and population coverage of sentinel surveillance and through IPW-methods also for the estimation of the total number of cases. The Huff-model's gravity function allows us to differentiate inside an area while estimating from the full dataset. Our data examples show that additional data on the participation to surveillance and practices of labs is necessary for a more accurate estimation.
DOI: 10.1186/s12889-019-7279-y
PubMed: 31337363
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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xml:lang="fr">Belgique</country><wicri:regionArea>Epidemiology of Infectious Diseases, Epidemiology, Sciensano, Juliette Wytsmanstraat 14, Brussels, 1050</wicri:regionArea><wicri:noRegion>1050</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium. toonbraeye@gmail.com.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500, Hasselt</wicri:regionArea><wicri:noRegion>Hasselt</wicri:noRegion></affiliation></author><author><name sortKey="Quoilin, Sophie" sort="Quoilin, Sophie" uniqKey="Quoilin S" first="Sophie" last="Quoilin">Sophie Quoilin</name><affiliation wicri:level="1"><nlm:affiliation>Epidemiology of Infectious Diseases, Epidemiology, Sciensano, Juliette Wytsmanstraat 14, Brussels, 1050, 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xml:lang="fr"><term>Belgique (épidémiologie)</term><term>Campylobacter (isolement et purification)</term><term>Humains</term><term>Incidence</term><term>Laboratoires</term><term>Modèles économétriques</term><term>Orthomyxoviridae (isolement et purification)</term><term>Probabilité</term><term>Reproductibilité des résultats</term><term>Rotavirus (isolement et purification)</term><term>Surveillance sentinelle</term><term>Yersinia enterocolitica (isolement et purification)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Belgium</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Campylobacter</term><term>Orthomyxoviridae</term><term>Rotavirus</term><term>Yersinia enterocolitica</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Campylobacter</term><term>Orthomyxoviridae</term><term>Rotavirus</term><term>Yersinia enterocolitica</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Belgique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Incidence</term><term>Laboratories</term><term>Models, Econometric</term><term>Probability</term><term>Reproducibility of Results</term><term>Sentinel Surveillance</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Incidence</term><term>Laboratoires</term><term>Modèles économétriques</term><term>Probabilité</term><term>Reproductibilité des résultats</term><term>Surveillance sentinelle</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Belgique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Inverse probability weighting (IPW) methods can be used to estimate the total number of cases from the sample collected through sentinel surveillance. Central to these methods are the inverse weights which can be derived in several ways and, in this case, represent the probability that laboratory (lab) sentinel surveillance detects a lab-confirmed case.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We compare different weights in a simulation study. Weights are obtained from the proportion of participating labs over all labs. We adjust these weights for attractiveness and density of labs over population. The market share of sentinel labs, as estimated by the econometric Huff-model, is also considered. Additionally, we investigate the effect of not recognizing sentinel labs as sentinel labs when they report no cases. We estimate the bias associated with the different weights as the difference between the simulated number of cases and the estimate of this total from the sentinel sample. As motivating data examples, we apply an extended Huff-model to four pathogens under laboratory sentinel surveillance in Belgium between 2010 and 2015 and discuss the model fit. We estimate the total number of lab-confirmed cases associated with Rotavirus, influenza virus, Y. enterocolitica and Campylobacter spp.. The extended Huff-model takes the lab-concept, the number of reimbursements and the number of departments, lab-density, regional borders, distance and competition between labs in account.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Estimates obtained with the Huff-model were most accurate in the more complex simulation scenarios as compared to other weights. In the data examples, several significant coefficients are identified, but the fit of the Huff-model to the Belgian sentinel surveillance data leaves much variability in market shares unexplained.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>The Huff-model allows for estimation of the spatial and population coverage of sentinel surveillance and through IPW-methods also for the estimation of the total number of cases. The Huff-model's gravity function allows us to differentiate inside an area while estimating from the full dataset. Our data examples show that additional data on the participation to surveillance and practices of labs is necessary for a more accurate estimation.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">31337363</PMID><DateCompleted><Year>2019</Year><Month>10</Month><Day>24</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2458</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>Jul</Month><Day>23</Day></PubDate></JournalIssue><Title>BMC public health</Title><ISOAbbreviation>BMC Public Health</ISOAbbreviation></Journal><ArticleTitle>Incidence estimation from sentinel surveillance data; a simulation study and application to data from the Belgian laboratory sentinel surveillance.</ArticleTitle><Pagination><MedlinePgn>982</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12889-019-7279-y</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Inverse probability weighting (IPW) methods can be used to estimate the total number of cases from the sample collected through sentinel surveillance. Central to these methods are the inverse weights which can be derived in several ways and, in this case, represent the probability that laboratory (lab) sentinel surveillance detects a lab-confirmed case.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">We compare different weights in a simulation study. Weights are obtained from the proportion of participating labs over all labs. We adjust these weights for attractiveness and density of labs over population. The market share of sentinel labs, as estimated by the econometric Huff-model, is also considered. Additionally, we investigate the effect of not recognizing sentinel labs as sentinel labs when they report no cases. We estimate the bias associated with the different weights as the difference between the simulated number of cases and the estimate of this total from the sentinel sample. As motivating data examples, we apply an extended Huff-model to four pathogens under laboratory sentinel surveillance in Belgium between 2010 and 2015 and discuss the model fit. We estimate the total number of lab-confirmed cases associated with Rotavirus, influenza virus, Y. enterocolitica and Campylobacter spp.. The extended Huff-model takes the lab-concept, the number of reimbursements and the number of departments, lab-density, regional borders, distance and competition between labs in account.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Estimates obtained with the Huff-model were most accurate in the more complex simulation scenarios as compared to other weights. In the data examples, several significant coefficients are identified, but the fit of the Huff-model to the Belgian sentinel surveillance data leaves much variability in market shares unexplained.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">The Huff-model allows for estimation of the spatial and population coverage of sentinel surveillance and through IPW-methods also for the estimation of the total number of cases. The Huff-model's gravity function allows us to differentiate inside an area while estimating from the full dataset. Our data examples show that additional data on the participation to surveillance and practices of labs is necessary for a more accurate estimation.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Braeye</LastName><ForeName>Toon</ForeName><Initials>T</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-5637-4613</Identifier><AffiliationInfo><Affiliation>Epidemiology of Infectious Diseases, Epidemiology, Sciensano, Juliette Wytsmanstraat 14, Brussels, 1050, Belgium. toonbraeye@gmail.com.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium. toonbraeye@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Quoilin</LastName><ForeName>Sophie</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Epidemiology of Infectious Diseases, Epidemiology, Sciensano, Juliette Wytsmanstraat 14, Brussels, 1050, Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hens</LastName><ForeName>Niel</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Health Economic Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (WHO Collaborating Centre), University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Epidemiology and social medicine (ESOC), University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>07</Month><Day>23</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Public Health</MedlineTA><NlmUniqueID>100968562</NlmUniqueID><ISSNLinking>1471-2458</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001530" MajorTopicYN="N" Type="Geographic">Belgium</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002167" MajorTopicYN="N">Campylobacter</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007753" MajorTopicYN="Y">Laboratories</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017059" MajorTopicYN="N">Models, Econometric</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009975" MajorTopicYN="N">Orthomyxoviridae</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011336" MajorTopicYN="N">Probability</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012401" MajorTopicYN="N">Rotavirus</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018571" MajorTopicYN="Y">Sentinel Surveillance</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015008" MajorTopicYN="N">Yersinia enterocolitica</DescriptorName><QualifierName UI="Q000302" 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uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name><name sortKey="Quoilin, Sophie" sort="Quoilin, Sophie" uniqKey="Quoilin S" first="Sophie" last="Quoilin">Sophie Quoilin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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