Influenza pandemic preparedness in France: modelling the impact of interventions
Identifieur interne : 000A30 ( France/Analysis ); précédent : 000A29; suivant : 000A31Influenza pandemic preparedness in France: modelling the impact of interventions
Auteurs : Aoife Doyle [France] ; Isabelle Bonmarin ; Daniel Lévy-Bruhl ; Yann Le Strat ; Jean-Claude DesenclosSource :
- Journal of Epidemiology and Community Health [ 0143-005X ] ; 2006-05.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Antiviraux (usage thérapeutique), Enfant, Enfant d'âge préscolaire, Femelle, Flambées de maladies (), France (épidémiologie), Grippe humaine (), Grippe humaine (mortalité), Grippe humaine (épidémiologie), Hospitalisation (), Humains, Maladie chronique, Modèles théoriques, Mâle, Nourrisson, Nouveau-né, Oséltamivir (usage thérapeutique), Simulation numérique, Sujet âgé, Sujet âgé de 80 ans ou plus, Vaccins antigrippaux (usage thérapeutique).
- MESH :
- mortalité : Grippe humaine.
- usage thérapeutique : Antiviraux, Oséltamivir, Vaccins antigrippaux.
- épidémiologie : France, Grippe humaine.
- Pascal (Inist)
- MESH :
- Wicri :
- geographic : France.
- topic : Enseignement, Médecine, Santé publique, Simulation, Stockage.
English descriptors
- KwdEn :
- Adolescent, Adult, Aged, Aged, 80 and over, Antiviral, Antiviral Agents (therapeutic use), Child, Child, Preschool, Chronic Disease, Computer Simulation, Disease Outbreaks (prevention & control), Female, France, France (epidemiology), Hospitalization (statistics & numerical data), Humans, Infant, Infant, Newborn, Influenza, Influenza Vaccines (therapeutic use), Influenza, Human (epidemiology), Influenza, Human (mortality), Influenza, Human (prevention & control), Male, Medicine, Middle Aged, Modeling, Models, Models, Theoretical, Monte Carlo method, Oseltamivir (therapeutic use), Preparation, Public health, Simulation, Storage, Teaching, Treatment, antiviral agents, disaster planning, influenza, models, pandemic.
- MESH :
- chemical , therapeutic use : Antiviral Agents, Influenza Vaccines, Oseltamivir.
- geographic , epidemiology : France.
- epidemiology : Influenza, Human.
- mortality : Influenza, Human.
- prevention & control : Disease Outbreaks, Influenza, Human.
- statistics & numerical data : Hospitalization.
- Teeft :
- Adolescent, Adult, Aged, Aged, 80 and over, Antiviral, Antiviral drugs, Antiviral strategies, Antiviral treatment, Appropriate interventions, Attack rate, Child, Child, Preschool, Chronic Disease, Clinical attack rate, Clinical evidence, Computer Simulation, Contributor, Control strategies, Death rates, Direct cost, Dos, Economic impact, Epidemiol community health, Essential healthcare, Euro, Female, France metropole, French pandemic preparedness plan, General population, Gross attack rate, Health event, Health events, Health outcomes, Healthcare professionals, High risk, High risk groups, Highest risk, Hospitalisation, Humans, Infant, Infant, Newborn, Influenza, Influenza pandemic, Influenza pandemic preparedness, Influenza vaccination, Influenza vaccine, Input variables, Large number, Limited availability, Limited stocks, Lower limit, Male, Middle Aged, Modelled influenza pandemic, Models, Theoretical, Monte carlo simulation model, Next pandemic, Oseltamivir, Pandemic, Pandemic influenza, Pandemic plan, Pandemic strain, Pandemic virus, Pandemie grippale, Peer reviewer, Peer reviewers, Preparedness, Previous pandemics, Previous studies, Priority groups, Priority population, Priority populations, Probability distributions, Prophylaxis, Relative benefits, Risk group, Risk groups, Seasonal prophylaxis, Target group, Therapeutic treatment, Total population, Treatment effectiveness, Vaccination, Vaccine, World health organisation, Year olds.
Abstract
Background: Influenza pandemics result in excess mortality and social disruption. To assist health authorities update the French pandemic plan, the authors estimated the number of health events (cases, hospitalisations, and deaths) in a pandemic and compared interventions in terms of impact and efficiency. Method: A Monte Carlo simulation model, incorporating probability distributions of key variables, provided estimates of health events (HE) by age and risk group. Input variables were set after literature and expert consultation. The impact of targeted influenza vaccination and antiviral prophylaxis/treatment (oseltamivir) in high risk groups (elderly, chronic diseases), priority (essential professionals), and total populations was compared. Outcome measures were HE avoided, number of doses needed, and direct cost per HE avoided. Results: Without intervention, an influenza pandemic could result in 14.9 million cases, 0.12 million deaths, and 0.6 million hospitalisations in France. Twenty four per cent of deaths and 40% of hospitalisations would be among high risk groups. With a 25% attack rate, 2000–86 000 deaths could be avoided, depending on population targeted and intervention. If available initially, vaccination of the total population is preferred. If not, for priority populations, seasonal prophylaxis seems the best strategy. For high risk groups, antiviral treatment, although less effective, seems more feasible and cost effective than prophylaxis (respectively 29% deaths avoided; 1800 doses/death avoided and 56% deaths avoided; 18 500 doses/death avoided) and should be chosen, especially if limited drug availability. Conclusion: The results suggest a strong role for antivirals in an influenza pandemic. While this model can compare the impact of different intervention strategies, there remains uncertainty surrounding key variables.
Url:
- https://api.istex.fr/ark:/67375/NVC-6PWXHVCZ-7/fulltext.pdf
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2563983
DOI: 10.1136/jech.2005.034082
Affiliations:
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<term>Adult</term>
<term>Aged</term>
<term>Aged, 80 and over</term>
<term>Antiviral</term>
<term>Antiviral Agents (therapeutic use)</term>
<term>Child</term>
<term>Child, Preschool</term>
<term>Chronic Disease</term>
<term>Computer Simulation</term>
<term>Disease Outbreaks (prevention & control)</term>
<term>Female</term>
<term>France</term>
<term>France (epidemiology)</term>
<term>Hospitalization (statistics & numerical data)</term>
<term>Humans</term>
<term>Infant</term>
<term>Infant, Newborn</term>
<term>Influenza</term>
<term>Influenza Vaccines (therapeutic use)</term>
<term>Influenza, Human (epidemiology)</term>
<term>Influenza, Human (mortality)</term>
<term>Influenza, Human (prevention & control)</term>
<term>Male</term>
<term>Medicine</term>
<term>Middle Aged</term>
<term>Modeling</term>
<term>Models</term>
<term>Models, Theoretical</term>
<term>Monte Carlo method</term>
<term>Oseltamivir (therapeutic use)</term>
<term>Preparation</term>
<term>Public health</term>
<term>Simulation</term>
<term>Storage</term>
<term>Teaching</term>
<term>Treatment</term>
<term>antiviral agents</term>
<term>disaster planning</term>
<term>influenza</term>
<term>models</term>
<term>pandemic</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Antiviraux (usage thérapeutique)</term>
<term>Enfant</term>
<term>Enfant d'âge préscolaire</term>
<term>Femelle</term>
<term>Flambées de maladies ()</term>
<term>France (épidémiologie)</term>
<term>Grippe humaine ()</term>
<term>Grippe humaine (mortalité)</term>
<term>Grippe humaine (épidémiologie)</term>
<term>Hospitalisation ()</term>
<term>Humains</term>
<term>Maladie chronique</term>
<term>Modèles théoriques</term>
<term>Mâle</term>
<term>Nourrisson</term>
<term>Nouveau-né</term>
<term>Oséltamivir (usage thérapeutique)</term>
<term>Simulation numérique</term>
<term>Sujet âgé</term>
<term>Sujet âgé de 80 ans ou plus</term>
<term>Vaccins antigrippaux (usage thérapeutique)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Antiviral Agents</term>
<term>Influenza Vaccines</term>
<term>Oseltamivir</term>
</keywords>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>France</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term>
</keywords>
<keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Influenza, Human</term>
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<keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Grippe humaine</term>
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<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Disease Outbreaks</term>
<term>Influenza, Human</term>
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<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Hospitalization</term>
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<keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Antiviraux</term>
<term>Oséltamivir</term>
<term>Vaccins antigrippaux</term>
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<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>France</term>
<term>Grippe humaine</term>
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<keywords scheme="Pascal" xml:lang="fr"><term>Antiviral</term>
<term>Enseignement</term>
<term>Etat de préparation</term>
<term>France</term>
<term>Grippe</term>
<term>Modèle</term>
<term>Modélisation</term>
<term>Médecine</term>
<term>Méthode Monte Carlo</term>
<term>Pandémie</term>
<term>Plan pandémie</term>
<term>Préparation</term>
<term>Santé publique</term>
<term>Simulation</term>
<term>Stockage</term>
<term>Traitement</term>
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<term>Adult</term>
<term>Aged</term>
<term>Aged, 80 and over</term>
<term>Antiviral</term>
<term>Antiviral drugs</term>
<term>Antiviral strategies</term>
<term>Antiviral treatment</term>
<term>Appropriate interventions</term>
<term>Attack rate</term>
<term>Child</term>
<term>Child, Preschool</term>
<term>Chronic Disease</term>
<term>Clinical attack rate</term>
<term>Clinical evidence</term>
<term>Computer Simulation</term>
<term>Contributor</term>
<term>Control strategies</term>
<term>Death rates</term>
<term>Direct cost</term>
<term>Dos</term>
<term>Economic impact</term>
<term>Epidemiol community health</term>
<term>Essential healthcare</term>
<term>Euro</term>
<term>Female</term>
<term>France metropole</term>
<term>French pandemic preparedness plan</term>
<term>General population</term>
<term>Gross attack rate</term>
<term>Health event</term>
<term>Health events</term>
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<term>High risk groups</term>
<term>Highest risk</term>
<term>Hospitalisation</term>
<term>Humans</term>
<term>Infant</term>
<term>Infant, Newborn</term>
<term>Influenza</term>
<term>Influenza pandemic</term>
<term>Influenza pandemic preparedness</term>
<term>Influenza vaccination</term>
<term>Influenza vaccine</term>
<term>Input variables</term>
<term>Large number</term>
<term>Limited availability</term>
<term>Limited stocks</term>
<term>Lower limit</term>
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<term>Middle Aged</term>
<term>Modelled influenza pandemic</term>
<term>Models, Theoretical</term>
<term>Monte carlo simulation model</term>
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<term>Oseltamivir</term>
<term>Pandemic</term>
<term>Pandemic influenza</term>
<term>Pandemic plan</term>
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<term>Peer reviewers</term>
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<term>Previous pandemics</term>
<term>Previous studies</term>
<term>Priority groups</term>
<term>Priority population</term>
<term>Priority populations</term>
<term>Probability distributions</term>
<term>Prophylaxis</term>
<term>Relative benefits</term>
<term>Risk group</term>
<term>Risk groups</term>
<term>Seasonal prophylaxis</term>
<term>Target group</term>
<term>Therapeutic treatment</term>
<term>Total population</term>
<term>Treatment effectiveness</term>
<term>Vaccination</term>
<term>Vaccine</term>
<term>World health organisation</term>
<term>Year olds</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Enfant</term>
<term>Enfant d'âge préscolaire</term>
<term>Femelle</term>
<term>Flambées de maladies</term>
<term>Grippe humaine</term>
<term>Hospitalisation</term>
<term>Humains</term>
<term>Maladie chronique</term>
<term>Modèles théoriques</term>
<term>Mâle</term>
<term>Nourrisson</term>
<term>Nouveau-né</term>
<term>Simulation numérique</term>
<term>Sujet âgé</term>
<term>Sujet âgé de 80 ans ou plus</term>
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<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>France</term>
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<keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Enseignement</term>
<term>Médecine</term>
<term>Santé publique</term>
<term>Simulation</term>
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<front><div type="abstract" xml:lang="en">Background: Influenza pandemics result in excess mortality and social disruption. To assist health authorities update the French pandemic plan, the authors estimated the number of health events (cases, hospitalisations, and deaths) in a pandemic and compared interventions in terms of impact and efficiency. Method: A Monte Carlo simulation model, incorporating probability distributions of key variables, provided estimates of health events (HE) by age and risk group. Input variables were set after literature and expert consultation. The impact of targeted influenza vaccination and antiviral prophylaxis/treatment (oseltamivir) in high risk groups (elderly, chronic diseases), priority (essential professionals), and total populations was compared. Outcome measures were HE avoided, number of doses needed, and direct cost per HE avoided. Results: Without intervention, an influenza pandemic could result in 14.9 million cases, 0.12 million deaths, and 0.6 million hospitalisations in France. Twenty four per cent of deaths and 40% of hospitalisations would be among high risk groups. With a 25% attack rate, 2000–86 000 deaths could be avoided, depending on population targeted and intervention. If available initially, vaccination of the total population is preferred. If not, for priority populations, seasonal prophylaxis seems the best strategy. For high risk groups, antiviral treatment, although less effective, seems more feasible and cost effective than prophylaxis (respectively 29% deaths avoided; 1800 doses/death avoided and 56% deaths avoided; 18 500 doses/death avoided) and should be chosen, especially if limited drug availability. Conclusion: The results suggest a strong role for antivirals in an influenza pandemic. While this model can compare the impact of different intervention strategies, there remains uncertainty surrounding key variables.</div>
</front>
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<name sortKey="Levy Bruhl, Daniel" sort="Levy Bruhl, Daniel" uniqKey="Levy Bruhl D" first="Daniel" last="Lévy-Bruhl">Daniel Lévy-Bruhl</name>
<name sortKey="Strat, Yann Le" sort="Strat, Yann Le" uniqKey="Strat Y" first="Yann Le" last="Strat">Yann Le Strat</name>
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