Application of Pharmacovigilance Methods in Occupational Health Surveillance: Comparison of Seven Disproportionality Metrics
Identifieur interne : 000090 ( Pmc/Checkpoint ); précédent : 000089; suivant : 000091Application of Pharmacovigilance Methods in Occupational Health Surveillance: Comparison of Seven Disproportionality Metrics
Auteurs : Vincent Bonneterre [France] ; Dominique Joseph Bicout [France] ; Regis De Gaudemaris [France]Source :
- Safety and Health at Work [ 2093-7911 ] ; 2012.
Abstract
The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease × exposure associations in RNV3P database (by analogy with the detection of potentially new health event × drug associations in the spontaneous reporting databases from pharmacovigilance).
2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease × exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease × exposure associations, are compared.
RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested.
This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.
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DOI: 10.5491/SHAW.2012.3.2.92
PubMed: 22993712
PubMed Central: 3440466
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wicri:curation="lc">France</country><wicri:regionArea>Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="A3">The French National Occupational Diseases Surveillance and Prevention Network (RNV3P), France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P)</wicri:regionArea></affiliation></author><author><name sortKey="Bicout, Dominique Joseph" sort="Bicout, Dominique Joseph" uniqKey="Bicout D" first="Dominique Joseph" last="Bicout">Dominique Joseph Bicout</name><affiliation wicri:level="1"><nlm:aff id="A1">UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble, France.</nlm:aff><country xml:lang="fr" 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Teaching Hospital (CHU Grenoble), Grenoble, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="A3">The French National Occupational Diseases Surveillance and Prevention Network (RNV3P), France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P)</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22993712</idno><idno type="pmc">3440466</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3440466</idno><idno type="RBID">PMC:3440466</idno><idno type="doi">10.5491/SHAW.2012.3.2.92</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000229</idno><idno type="wicri:Area/Pmc/Curation">000227</idno><idno type="wicri:Area/Pmc/Checkpoint">000090</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Application of Pharmacovigilance Methods in Occupational Health Surveillance: Comparison of Seven Disproportionality Metrics</title><author><name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name><affiliation wicri:level="1"><nlm:aff id="A1">UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="A2">Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="A3">The French National Occupational Diseases Surveillance and Prevention Network (RNV3P), France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P)</wicri:regionArea></affiliation></author><author><name sortKey="Bicout, Dominique Joseph" sort="Bicout, Dominique Joseph" uniqKey="Bicout D" first="Dominique Joseph" last="Bicout">Dominique Joseph Bicout</name><affiliation wicri:level="1"><nlm:aff id="A1">UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation></author><author><name sortKey="De Gaudemaris, Regis" sort="De Gaudemaris, Regis" uniqKey="De Gaudemaris R" first="Regis" last="De Gaudemaris">Regis De Gaudemaris</name><affiliation wicri:level="1"><nlm:aff id="A1">UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="A2">Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble</wicri:regionArea><placeName><settlement type="city">Grenoble</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="A3">The French National Occupational Diseases Surveillance and Prevention Network (RNV3P), France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P)</wicri:regionArea></affiliation></author></analytic><series><title level="j">Safety and Health at Work</title><idno type="ISSN">2093-7911</idno><idno type="eISSN">2093-7997</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Objectives</title><p>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease × exposure associations in RNV3P database (by analogy with the detection of potentially new health event × drug associations in the spontaneous reporting databases from pharmacovigilance).</p></sec><sec><title>Methods</title><p>2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease × exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease × exposure associations, are compared.</p></sec><sec><title>Results</title><p>RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested.</p></sec><sec><title>Conclusion</title><p>This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Carder, M" uniqKey="Carder M">M Carder</name></author><author><name sortKey="Mcnamee, R" uniqKey="Mcnamee R">R McNamee</name></author><author><name sortKey="Turner, S" uniqKey="Turner S">S Turner</name></author><author><name sortKey="Hussey, L" uniqKey="Hussey L">L Hussey</name></author><author><name sortKey="Money, A" uniqKey="Money A">A Money</name></author><author><name sortKey="Agius, R" uniqKey="Agius R">R Agius</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hussey, L" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Saf Health Work</journal-id><journal-id journal-id-type="iso-abbrev">Saf Health Work</journal-id><journal-id journal-id-type="publisher-id">SHAW</journal-id><journal-title-group><journal-title>Safety and Health at Work</journal-title></journal-title-group><issn pub-type="ppub">2093-7911</issn><issn pub-type="epub">2093-7997</issn><publisher><publisher-name>Occupational Safety and Health Research Institute</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22993712</article-id><article-id pub-id-type="pmc">3440466</article-id><article-id pub-id-type="doi">10.5491/SHAW.2012.3.2.92</article-id><article-categories><subj-group subj-group-type="heading"><subject>Selected Papers from International Conference: International Conference on New Occupational Diseases</subject><subj-group><subject>Original Article</subject></subj-group></subj-group></article-categories><title-group><article-title>Application of Pharmacovigilance Methods in Occupational Health Surveillance: Comparison of Seven Disproportionality Metrics</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Bonneterre</surname><given-names>Vincent</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref><xref ref-type="aff" rid="A3">3</xref></contrib><contrib contrib-type="author"><name><surname>Bicout</surname><given-names>Dominique Joseph</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>de Gaudemaris</surname><given-names>Regis</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref><xref ref-type="aff" rid="A3">3</xref></contrib></contrib-group><aff id="A1"><label>1</label>UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble, France.</aff><aff id="A2"><label>2</label>Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble, France.</aff><aff id="A3"><label>3</label>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P), France.</aff><author-notes><corresp>Correspondence to: Vincent BONNETERRE. Equipe EPSP, Laboratoire TIMC-IMAG, Faculte de Medecine Batiment Jean Roget (3e etage), Domaine de la Merci La Tronche cedex France [F-38706]. Tel: +0033476765851, Fax: +0033476768910, <email>VBonneterre@chu-grenoble.fr</email></corresp></author-notes><pub-date pub-type="ppub"><month>6</month><year>2012</year></pub-date><pub-date pub-type="epub"><day>08</day><month>6</month><year>2012</year></pub-date><volume>3</volume><issue>2</issue><fpage>92</fpage><lpage>100</lpage><history><date date-type="received"><day>09</day><month>9</month><year>2011</year></date><date date-type="rev-recd"><day>01</day><month>5</month><year>2012</year></date><date date-type="accepted"><day>07</day><month>5</month><year>2012</year></date></history><permissions><copyright-statement>Copyright © 2012 by Safety and Health at Work (SH@W)</copyright-statement><copyright-year>2012</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc/3.0"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</ext-link>), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><sec><title>Objectives</title><p>The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease × exposure associations in RNV3P database (by analogy with the detection of potentially new health event × drug associations in the spontaneous reporting databases from pharmacovigilance).</p></sec><sec><title>Methods</title><p>2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease × exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease × exposure associations, are compared.</p></sec><sec><title>Results</title><p>RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested.</p></sec><sec><title>Conclusion</title><p>This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.</p></sec></abstract><kwd-group><kwd>Data mining</kwd><kwd>Occupational diseases</kwd><kwd>Occupational diseases network or database</kwd><kwd>Pharmacovigilance methods</kwd></kwd-group></article-meta></front><floats-group><fig id="F1" position="float"><label>Fig. 1</label><caption><p>"Disease × exposure (D × E)" associations according to the number of times they have been notified in the French National Occupational Diseases Surveillance and Prevention Network (i.e., number of similar observations or reports).</p></caption><graphic xlink:href="shaw-3-92-g001"></graphic></fig><fig id="F2" position="float"><label>Fig. 2</label><caption><p>Comparison of the behaviour of the proportional reporting ratio (PRR) method (x axis), with the disproportionality metrics reporting odds ratio (ROR), Yules, chi<sup>2</sup>, Poisson, Sequential Probability Ratio Test (SPRT2), Bayesian Confidence Propagation Neural Network (BCPNN), according to the number of reports in each disease × exposure associations (symbols). The associations represented near the origin of the axes have the lowest disproportionality measures, whereas the ones to the opposite have the highest measures and present the strongest signals. When associations are plotted near the bisecting line, a similar rank has been affected by the 2 disproportionality metrics compared. Conversely, when some associations are plotted lower (respectively higher) than the bisecting line, it means that they have been affected lower (respectively higher) disproportionality measures by the method represented on the y axis, than by the PRR method.</p></caption><graphic xlink:href="shaw-3-92-g002"></graphic></fig><fig id="F3" position="float"><label>Fig. 3</label><caption><p>"Systemic Scleroderma × Exposure" associations reported twice or more, their number of reports, their measures with BCPNN method, whether they generate a signal (solid triangles) or not (empty triangles), and overlap with proportional reporting ratio signals (PRR<sub>1</sub> in blue circles and PRR<sub>2</sub> in red squares). BCPNN: Bayesian Confidence Propagation Neural Network, LI<sub>95%</sub> IC BCPNN: lower bound of 95% confidence interval for each BCPNN measure.</p></caption><graphic xlink:href="shaw-3-92-g003"></graphic></fig><fig id="F4" position="float"><label>Fig. 4</label><caption><p>"Systemic Scleroderma × Exposure" associations and their proportional reporting ratio (PRR) measures (squares), whether they generate a signal with either PRR<sub>2</sub> (over the horizontal line LI<sub>95%</sub> IC PRR>1) or PPR<sub>1</sub> (blue circles), and overlap with BCPNN signals (solid triangles). LI<sub>95%</sub> IC PRR: lower bound of 95% confidence interval for each PRR measure.</p></caption><graphic xlink:href="shaw-3-92-g004"></graphic></fig><table-wrap id="T1" position="float"><label>Table 1</label><caption><p>Total number of D × E associations generating signals according to the 7 disproportionality metrics applied to RNV3P database (2001-2009)</p></caption><graphic xlink:href="shaw-3-92-i001"></graphic><table-wrap-foot><fn><p>All: all disease × exposure (D × E) associations generating a signal with the disproportionality metrics tested (number or percentage), C: part of the D × E associations generating a signal that are eligible for compensation according to criteria for French salaried workers (testifying of already well known occupational diseases), NC: part of the D × E associations not eligible for compensation; it's within this group that we might find new occupational diseases, PRR<sub>1</sub>: proportinal reporting ratio (PRR) with the following signal generation criterion; a≥3 & PRR≥2 & χ<sup>2</sup>≥4, PRR<sub>2</sub>: PRR with the following signal generation criteria; LI<sub>95%</sub> (PRR)>1, RNV3P: French National Occupational Diseases Surveillance and Prevention Network.</p><p>These results are based on the definition interval of the methods, which might slightly differ. As percentages are rounded off at the unit level, the sum of the columns C and NC might sometimes differ from one unit of the percentage notified in the All column.</p><p><sup>*</sup>46% (n=221) have been reported only twice. <sup>†</sup>77% (n=585) have been reported only twice.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc><affiliations><list><country><li>France</li></country><settlement><li>Grenoble</li></settlement></list><tree><country name="France"><noRegion><name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name></noRegion><name sortKey="Bicout, Dominique Joseph" sort="Bicout, Dominique Joseph" uniqKey="Bicout D" first="Dominique Joseph" last="Bicout">Dominique Joseph Bicout</name><name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name><name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name><name sortKey="De Gaudemaris, Regis" sort="De Gaudemaris, Regis" uniqKey="De Gaudemaris R" first="Regis" last="De Gaudemaris">Regis De Gaudemaris</name><name sortKey="De Gaudemaris, Regis" sort="De Gaudemaris, Regis" uniqKey="De Gaudemaris R" first="Regis" last="De Gaudemaris">Regis De Gaudemaris</name><name sortKey="De Gaudemaris, Regis" sort="De Gaudemaris, Regis" uniqKey="De Gaudemaris R" first="Regis" last="De Gaudemaris">Regis De Gaudemaris</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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