[Determination of vaccination coverage and disease incidence using statutory health insurance data].
Identifieur interne : 000335 ( Main/Exploration ); précédent : 000334; suivant : 000336[Determination of vaccination coverage and disease incidence using statutory health insurance data].
Auteurs : A. Reuss [Allemagne] ; M. Feig ; L. Kappelmayer ; T. Eckmanns ; G. PoggenseeSource :
- Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) [ 1439-4421 ] ; 2010.
Descripteurs français
- KwdFr :
- Allemagne (épidémiologie), Contrôle des maladies transmissibles (statistiques et données numériques), Examen des demandes de remboursement d'assurance (statistiques et données numériques), Humains (MeSH), Incidence (MeSH), Surveillance de la population (méthodes), Vaccination de masse (statistiques et données numériques), Épidémies de maladies (prévention et contrôle), Épidémies de maladies (statistiques et données numériques).
- MESH :
- méthodes : Surveillance de la population.
- prévention et contrôle : Épidémies de maladies.
- statistiques et données numériques : Contrôle des maladies transmissibles, Examen des demandes de remboursement d'assurance, Vaccination de masse, Épidémies de maladies.
- épidémiologie : Allemagne.
- Humains, Incidence.
- Wicri :
- geographic : Allemagne.
English descriptors
- KwdEn :
- Communicable Disease Control (statistics & numerical data), Disease Outbreaks (prevention & control), Disease Outbreaks (statistics & numerical data), Germany (epidemiology), Humans (MeSH), Incidence (MeSH), Insurance Claim Review (statistics & numerical data), Mass Vaccination (statistics & numerical data), Population Surveillance (methods).
- MESH :
- geographic , epidemiology : Germany.
- methods : Population Surveillance.
- prevention & control : Disease Outbreaks.
- statistics & numerical data : Communicable Disease Control, Disease Outbreaks, Insurance Claim Review, Mass Vaccination.
- Humans, Incidence.
Abstract
BACKGROUND
The KV-Sentinel, established in 2004, is a joint project of the Robert Koch Institute and the 17 associations of statutory health insurance physicians (ASHIPs) in Germany. The ASHIPs provide anonymous physicians billing data to the Robert Koch Institute. The aim of this article is to describe methodological approaches for processing these routine data to determine vaccination coverage and incidence of vaccine preventable diseases. Furthermore, we discuss limitations in interpreting these data.
METHODS
The ASHIPs perform a data query of all vaccinations and of ICD-10 codes for pertussis, measles, mumps, varicella and herpes zoster and send anonymous data to the Robert Koch Institute. We perform routine tests to ensure data quality. Study population is the statutory health insured population (85.5% of the German population). Vaccination coverage is determined by the number of vaccinated persons and the number of statutory health insured persons. Incidence is calculated by the number of diseased persons per 100 000 statutory health insured persons.
RESULTS
All 17 ASHIPs participate in the project. In total, 95 905 605 data records for vaccinations and 4 570 919 data records for pertussis, measles, mumps, varicella and herpes zoster were provided from 2004 to 2007. After performing routine tests with regard to structure and content of data, more than 99% of the data records can be analysed. In 2007, the majority of given vaccinations were monovalent vaccinations against influenza (39%) and tick-borne encephalitis (17%). In 2006 and 2007, 1 893 790 data records for diagnoses were provided. Of these, 75% were acute diagnoses and of these 70% were confirmed diagnoses. Most often, ICD-10 codes for herpes zoster (57%) and varicella (35%) were reported.
CONCLUSION
Nationwide vaccination coverage of statutory health insured persons by age group can be determined by using billing data. It is possible to validate billing data of vaccinations with available data from other studies. Interpretation of billing data of acute vaccine preventable diseases remains challenging because it is difficult to assess potential under- or overestimation without the possibility of external validation. Therefore, further research is needed.
DOI: 10.1055/s-0030-1249691
PubMed: 20446216
Affiliations:
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Kappelmayer</name></author><author><name sortKey="Eckmanns, T" sort="Eckmanns, T" uniqKey="Eckmanns T" first="T" last="Eckmanns">T. Eckmanns</name></author><author><name sortKey="Poggensee, G" sort="Poggensee, G" uniqKey="Poggensee G" first="G" last="Poggensee">G. 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Reuss</name><affiliation wicri:level="3"><nlm:affiliation>Abteilung für Infektionsepidemiologie, Robert Koch-Institut, Berlin, Germany. reussa@rki.de </nlm:affiliation> <country xml:lang="fr">Allemagne</country><wicri:regionArea>Abteilung für Infektionsepidemiologie, Robert Koch-Institut, Berlin</wicri:regionArea><placeName><region type="land" nuts="3">Berlin</region><settlement type="city">Berlin</settlement></placeName></affiliation></author><author><name sortKey="Feig, M" sort="Feig, M" uniqKey="Feig M" first="M" last="Feig">M. Feig</name></author><author><name sortKey="Kappelmayer, L" sort="Kappelmayer, L" uniqKey="Kappelmayer L" first="L" last="Kappelmayer">L. Kappelmayer</name></author><author><name sortKey="Eckmanns, T" sort="Eckmanns, T" uniqKey="Eckmanns T" first="T" last="Eckmanns">T. Eckmanns</name></author><author><name sortKey="Poggensee, G" sort="Poggensee, G" uniqKey="Poggensee G" first="G" last="Poggensee">G. Poggensee</name></author></analytic><series><title level="j">Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))</title><idno type="eISSN">1439-4421</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Communicable Disease Control (statistics & numerical data)</term><term>Disease Outbreaks (prevention & control)</term><term>Disease Outbreaks (statistics & numerical data)</term><term>Germany (epidemiology)</term><term>Humans (MeSH)</term><term>Incidence (MeSH)</term><term>Insurance Claim Review (statistics & numerical data)</term><term>Mass Vaccination (statistics & numerical data)</term><term>Population Surveillance (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Allemagne (épidémiologie)</term><term>Contrôle des maladies transmissibles (statistiques et données numériques)</term><term>Examen des demandes de remboursement d'assurance (statistiques et données numériques)</term><term>Humains (MeSH)</term><term>Incidence (MeSH)</term><term>Surveillance de la population (méthodes)</term><term>Vaccination de masse (statistiques et données numériques)</term><term>Épidémies de maladies (prévention et contrôle)</term><term>Épidémies de maladies (statistiques et données numériques)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Germany</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Surveillance de la population</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Disease Outbreaks</term></keywords><keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Épidémies de maladies</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Communicable Disease Control</term><term>Disease Outbreaks</term><term>Insurance Claim Review</term><term>Mass Vaccination</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Contrôle des maladies transmissibles</term><term>Examen des demandes de remboursement d'assurance</term><term>Vaccination de masse</term><term>Épidémies de maladies</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Allemagne</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Incidence</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Incidence</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Allemagne</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>The KV-Sentinel, established in 2004, is a joint project of the Robert Koch Institute and the 17 associations of statutory health insurance physicians (ASHIPs) in Germany. The ASHIPs provide anonymous physicians billing data to the Robert Koch Institute. The aim of this article is to describe methodological approaches for processing these routine data to determine vaccination coverage and incidence of vaccine preventable diseases. Furthermore, we discuss limitations in interpreting these data.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The ASHIPs perform a data query of all vaccinations and of ICD-10 codes for pertussis, measles, mumps, varicella and herpes zoster and send anonymous data to the Robert Koch Institute. We perform routine tests to ensure data quality. Study population is the statutory health insured population (85.5% of the German population). Vaccination coverage is determined by the number of vaccinated persons and the number of statutory health insured persons. Incidence is calculated by the number of diseased persons per 100 000 statutory health insured persons.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>All 17 ASHIPs participate in the project. In total, 95 905 605 data records for vaccinations and 4 570 919 data records for pertussis, measles, mumps, varicella and herpes zoster were provided from 2004 to 2007. After performing routine tests with regard to structure and content of data, more than 99% of the data records can be analysed. In 2007, the majority of given vaccinations were monovalent vaccinations against influenza (39%) and tick-borne encephalitis (17%). In 2006 and 2007, 1 893 790 data records for diagnoses were provided. Of these, 75% were acute diagnoses and of these 70% were confirmed diagnoses. Most often, ICD-10 codes for herpes zoster (57%) and varicella (35%) were reported.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Nationwide vaccination coverage of statutory health insured persons by age group can be determined by using billing data. It is possible to validate billing data of vaccinations with available data from other studies. Interpretation of billing data of acute vaccine preventable diseases remains challenging because it is difficult to assess potential under- or overestimation without the possibility of external validation. Therefore, further research is needed.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">20446216</PMID><DateCompleted><Year>2010</Year><Month>10</Month><Day>08</Day></DateCompleted><DateRevised><Year>2010</Year><Month>06</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1439-4421</ISSN><JournalIssue CitedMedium="Internet"><Volume>72</Volume><Issue>6</Issue><PubDate><Year>2010</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))</Title><ISOAbbreviation>Gesundheitswesen</ISOAbbreviation></Journal><ArticleTitle>[Determination of vaccination coverage and disease incidence using statutory health insurance data].</ArticleTitle><Pagination><MedlinePgn>340-6</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1055/s-0030-1249691</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The KV-Sentinel, established in 2004, is a joint project of the Robert Koch Institute and the 17 associations of statutory health insurance physicians (ASHIPs) in Germany. The ASHIPs provide anonymous physicians billing data to the Robert Koch Institute. The aim of this article is to describe methodological approaches for processing these routine data to determine vaccination coverage and incidence of vaccine preventable diseases. Furthermore, we discuss limitations in interpreting these data.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">The ASHIPs perform a data query of all vaccinations and of ICD-10 codes for pertussis, measles, mumps, varicella and herpes zoster and send anonymous data to the Robert Koch Institute. We perform routine tests to ensure data quality. Study population is the statutory health insured population (85.5% of the German population). Vaccination coverage is determined by the number of vaccinated persons and the number of statutory health insured persons. Incidence is calculated by the number of diseased persons per 100 000 statutory health insured persons.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">All 17 ASHIPs participate in the project. In total, 95 905 605 data records for vaccinations and 4 570 919 data records for pertussis, measles, mumps, varicella and herpes zoster were provided from 2004 to 2007. After performing routine tests with regard to structure and content of data, more than 99% of the data records can be analysed. In 2007, the majority of given vaccinations were monovalent vaccinations against influenza (39%) and tick-borne encephalitis (17%). In 2006 and 2007, 1 893 790 data records for diagnoses were provided. Of these, 75% were acute diagnoses and of these 70% were confirmed diagnoses. Most often, ICD-10 codes for herpes zoster (57%) and varicella (35%) were reported.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Nationwide vaccination coverage of statutory health insured persons by age group can be determined by using billing data. It is possible to validate billing data of vaccinations with available data from other studies. Interpretation of billing data of acute vaccine preventable diseases remains challenging because it is difficult to assess potential under- or overestimation without the possibility of external validation. Therefore, further research is needed.</AbstractText><CopyrightInformation>Georg Thieme Verlag KG Stuttgart * New York.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Reuss</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Abteilung für Infektionsepidemiologie, Robert Koch-Institut, Berlin, Germany. reussa@rki.de </Affiliation> </AffiliationInfo></Author><Author ValidYN="Y"><LastName>Feig</LastName><ForeName>M</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Kappelmayer</LastName><ForeName>L</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Eckmanns</LastName><ForeName>T</ForeName><Initials>T</Initials></Author><Author ValidYN="Y"><LastName>Poggensee</LastName><ForeName>G</ForeName><Initials>G</Initials></Author></AuthorList><Language>ger</Language><PublicationTypeList><PublicationType UI="D004740">English Abstract</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><VernacularTitle>Bestimmung von Impfquoten und Inzidenzen von Erkrankungen anhand von Daten der Kassenärztlichen Vereinigungen.</VernacularTitle><ArticleDate DateType="Electronic"><Year>2010</Year><Month>05</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Gesundheitswesen</MedlineTA><NlmUniqueID>9204210</NlmUniqueID><ISSNLinking>0941-3790</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003140" MajorTopicYN="N">Communicable Disease Control</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005858" MajorTopicYN="N" Type="Geographic">Germany</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007345" MajorTopicYN="N">Insurance Claim Review</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032541" MajorTopicYN="N">Mass Vaccination</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2010</Year><Month>5</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2010</Year><Month>5</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2010</Year><Month>10</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">20446216</ArticleId><ArticleId IdType="doi">10.1055/s-0030-1249691</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Allemagne</li></country><region><li>Berlin</li></region><settlement><li>Berlin</li></settlement></list><tree><noCountry><name sortKey="Eckmanns, T" sort="Eckmanns, T" uniqKey="Eckmanns T" first="T" last="Eckmanns">T. Eckmanns</name><name sortKey="Feig, M" sort="Feig, M" uniqKey="Feig M" first="M" last="Feig">M. Feig</name><name sortKey="Kappelmayer, L" sort="Kappelmayer, L" uniqKey="Kappelmayer L" first="L" last="Kappelmayer">L. Kappelmayer</name><name sortKey="Poggensee, G" sort="Poggensee, G" uniqKey="Poggensee G" first="G" last="Poggensee">G. Poggensee</name></noCountry><country name="Allemagne"><region name="Berlin"><name sortKey="Reuss, A" sort="Reuss, A" uniqKey="Reuss A" first="A" last="Reuss">A. Reuss</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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