Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach
Identifieur interne : 001931 ( Ncbi/Curation ); précédent : 001930; suivant : 001932Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach
Auteurs : Camille Pelat ; Neil M. Ferguson ; Peter J. White ; Carrie Reed ; Lyn Finelli ; Simon Cauchemez ; Christophe FraserSource :
- American Journal of Epidemiology [ 0002-9262 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- diagnostic : Grippe humaine.
- pathogénicité : Sous-type H1N1 du virus de la grippe A.
- épidémiologie : Grippe humaine, États-Unis d'Amérique.
- Humains, Informatique mathématique, Mortalité, Pandémies, Probabilité, Surveillance de la population, Taux de survie.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : United States.
- diagnosis : Influenza, Human.
- epidemiology : Influenza, Human.
- methods : Population Surveillance.
- pathogenicity : Influenza A Virus, H1N1 Subtype.
- Humans, Mathematical Computing, Mortality, Pandemics, Probability, Survival Rate.
Abstract
In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms—the symptomatic case fatality ratio (sCFR)—is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.
Url:
DOI: 10.1093/aje/kwu213
PubMed: 25255809
PubMed Central: 4240167
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PMC:4240167Le document en format XML
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Ferguson</name></author><author><name sortKey="White, Peter J" sort="White, Peter J" uniqKey="White P" first="Peter J." last="White">Peter J. White</name></author><author><name sortKey="Reed, Carrie" sort="Reed, Carrie" uniqKey="Reed C" first="Carrie" last="Reed">Carrie Reed</name></author><author><name sortKey="Finelli, Lyn" sort="Finelli, Lyn" uniqKey="Finelli L" first="Lyn" last="Finelli">Lyn Finelli</name></author><author><name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name></author><author><name sortKey="Fraser, Christophe" sort="Fraser, Christophe" uniqKey="Fraser C" first="Christophe" last="Fraser">Christophe Fraser</name></author></analytic><series><title level="j">American Journal of Epidemiology</title><idno type="ISSN">0002-9262</idno><idno type="eISSN">1476-6256</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Humans</term><term>Influenza A Virus, H1N1 Subtype (pathogenicity)</term><term>Influenza, Human (diagnosis)</term><term>Influenza, Human (epidemiology)</term><term>Mathematical Computing</term><term>Mortality</term><term>Pandemics</term><term>Population Surveillance (methods)</term><term>Probability</term><term>Survival Rate</term><term>United States (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Grippe humaine (diagnostic)</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Informatique mathématique</term><term>Mortalité</term><term>Pandémies</term><term>Probabilité</term><term>Sous-type H1N1 du virus de la grippe A (pathogénicité)</term><term>Surveillance de la population ()</term><term>Taux de survie</term><term>États-Unis d'Amérique (épidémiologie)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>United States</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="pathogenicity" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="pathogénicité" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>États-Unis d'Amérique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Mathematical Computing</term><term>Mortality</term><term>Pandemics</term><term>Probability</term><term>Survival Rate</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Informatique mathématique</term><term>Mortalité</term><term>Pandémies</term><term>Probabilité</term><term>Surveillance de la population</term><term>Taux de survie</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms—the symptomatic case fatality ratio (sCFR)—is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Reed, C" uniqKey="Reed C">C Reed</name></author><author><name sortKey="Biggerstaff, M" uniqKey="Biggerstaff M">M Biggerstaff</name></author><author><name sortKey="Finelli, L" uniqKey="Finelli L">L Finelli</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Donnelly, Ca" uniqKey="Donnelly C">CA Donnelly</name></author><author><name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S Cauchemez</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Collins, Sd" uniqKey="Collins S">SD Collins</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Garske, T" uniqKey="Garske T">T Garske</name></author><author><name sortKey="Legrand, J" 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Es" uniqKey="Ma E">ES Ma</name></author><author><name sortKey="Lee, Ck" uniqKey="Lee C">CK Lee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Payne, Am" uniqKey="Payne A">AM Payne</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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