Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach
Identifieur interne : 001052 ( Main/Exploration ); précédent : 001051; suivant : 001053Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach
Auteurs : Camille Pelat [Royaume-Uni] ; Neil M. Ferguson [Royaume-Uni] ; Peter J. White [Royaume-Uni] ; Carrie Reed [États-Unis] ; Lyn Finelli [États-Unis] ; Simon Cauchemez [Royaume-Uni, France] ; Christophe Fraser [Royaume-Uni]Source :
- 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.
- Pascal (Inist)
- Humains, Informatique mathématique, Maladie émergente, Infection, Mortalité, Méthodologie, Pandémies, Probabilité, Surveillance de la population, Surveillance sanitaire, Homme, Taux de létalité, Estimation, Grippe, Santé publique, Monde, Statistique, Planification, Epidémiologie, Pandémie, Taux de survie.
- Wicri :
- geographic : États-Unis.
- topic : Homme, Santé publique, Statistique.
English descriptors
- KwdEn :
- Case fatality rate, Emerging disease, Epidemiology, Estimation, Human, Humans, Infection, Influenza, Influenza A Virus, H1N1 Subtype (pathogenicity), Influenza, Human (diagnosis), Influenza, Human (epidemiology), Mathematical Computing, Methodology, Mortality, Pandemics, Planning, Population Surveillance (methods), Probability, Public health, Sanitary surveillance, Statistics, Survival Rate, United States (epidemiology), World.
- 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:
Affiliations:
- France, Royaume-Uni, États-Unis
- Angleterre, Grand Londres, Géorgie (États-Unis), Île-de-France
- Londres, Paris
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Le document en format XML
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Ferguson</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>MRC Centre for Outbreak Analysis and Modelling and National Institute for Health Research Health Protection Research Unit in Modelling Methodology at Imperial College London, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London</s1><s2>London</s2><s3>GBR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>6 aut.</sZ><sZ>7 aut.</sZ></inist:fA14><country>Royaume-Uni</country><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation></author><author><name sortKey="White, Peter J" sort="White, Peter J" uniqKey="White P" first="Peter J." last="White">Peter J. 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type="région" nuts="1">Grand Londres</region></placeName></affiliation></author></analytic><series><title level="j" type="main">American journal of epidemiology</title><title level="j" type="abbreviated">Am. j. epidemiol.</title><idno type="ISSN">0002-9262</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">American journal of epidemiology</title><title level="j" type="abbreviated">Am. j. epidemiol.</title><idno type="ISSN">0002-9262</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Case fatality rate</term><term>Emerging disease</term><term>Epidemiology</term><term>Estimation</term><term>Human</term><term>Humans</term><term>Infection</term><term>Influenza</term><term>Influenza A Virus, H1N1 Subtype (pathogenicity)</term><term>Influenza, Human (diagnosis)</term><term>Influenza, Human (epidemiology)</term><term>Mathematical Computing</term><term>Methodology</term><term>Mortality</term><term>Pandemics</term><term>Planning</term><term>Population Surveillance (methods)</term><term>Probability</term><term>Public health</term><term>Sanitary surveillance</term><term>Statistics</term><term>Survival Rate</term><term>United States (epidemiology)</term><term>World</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="Pascal" xml:lang="fr"><term>Humains</term><term>Informatique mathématique</term><term>Maladie émergente</term><term>Infection</term><term>Mortalité</term><term>Méthodologie</term><term>Pandémies</term><term>Probabilité</term><term>Surveillance de la population</term><term>Surveillance sanitaire</term><term>Homme</term><term>Taux de létalité</term><term>Estimation</term><term>Grippe</term><term>Santé publique</term><term>Monde</term><term>Statistique</term><term>Planification</term><term>Epidémiologie</term><term>Pandémie</term><term>Taux de survie</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Homme</term><term>Santé publique</term><term>Statistique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>France</li><li>Royaume-Uni</li><li>États-Unis</li></country><region><li>Angleterre</li><li>Grand Londres</li><li>Géorgie (États-Unis)</li><li>Île-de-France</li></region><settlement><li>Londres</li><li>Paris</li></settlement></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Pelat, Camille" sort="Pelat, Camille" uniqKey="Pelat C" first="Camille" last="Pelat">Camille Pelat</name></region><name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name><name sortKey="Ferguson, Neil M" sort="Ferguson, Neil M" uniqKey="Ferguson N" first="Neil M." last="Ferguson">Neil M. Ferguson</name><name sortKey="Fraser, Christophe" sort="Fraser, Christophe" uniqKey="Fraser C" first="Christophe" last="Fraser">Christophe Fraser</name><name sortKey="White, Peter J" sort="White, Peter J" uniqKey="White P" first="Peter J." last="White">Peter J. White</name><name sortKey="White, Peter J" sort="White, Peter J" uniqKey="White P" first="Peter J." last="White">Peter J. White</name></country><country name="États-Unis"><region name="Géorgie (États-Unis)"><name sortKey="Reed, Carrie" sort="Reed, Carrie" uniqKey="Reed C" first="Carrie" last="Reed">Carrie Reed</name></region><name sortKey="Finelli, Lyn" sort="Finelli, Lyn" uniqKey="Finelli L" first="Lyn" last="Finelli">Lyn Finelli</name></country><country name="France"><region name="Île-de-France"><name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach
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