Predicting Fluctuating Rates of Hospitalizations in Relation to Influenza Epidemics and Meteorological Factors
| Autor: | Mireille Batton-Hubert, Marianne Sarazin, Radia Spiga |
|---|---|
| Přispěvatelé: | Service de Santé Publique et d'Information Médicale, Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Département Décision en Entreprise : Modélisation, Optimisation (DEMO-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Henri Fayol, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT), Institut Henri Fayol (FAYOL-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Ingénierie et Santé (CIS-ENSMSE), Département d’Information Médical [Centre Hospitalier de Firminy], Centre Hospitalier de Firminy, CHU Saint-Etienne, Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), HAL-UPMC, Gestionnaire, Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Male Pediatrics Viral Diseases Atmospheric Science Critical Care and Emergency Medicine Epidemiology Linear Discriminant Analysis Geographical Locations 0302 clinical medicine Mathematical and Statistical Techniques Statistics Medicine and Health Sciences 030212 general & internal medicine Child Principal Component Analysis Multidisciplinary Physics Electromagnetic Radiation Temperature Middle Aged Markov Chains Hospitals 3. Good health Hospitalization Europe Infectious Diseases Child Preschool Physical Sciences symbols Epidemiological Methods and Statistics Medicine Female Solar Radiation France Seasons Statistics (Mathematics) Research Article Adult medicine.medical_specialty Adolescent Science Time lag Influenza epidemics Research and Analysis Methods 03 medical and health sciences symbols.namesake Meteorology Influenza Human medicine Humans Solar Activity Statistical Methods Epidemics Aged Hospitalizations Models Statistical business.industry Infant Newborn Infant Humidity Emergency department Linear discriminant analysis Infant newborn Pearson product-moment correlation coefficient Influenza Health Care 030104 developmental biology [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie Health Care Facilities People and Places Earth Sciences Classification methods [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie business Mathematics |
| Zdroj: | PLoS ONE PLoS ONE, 2016, 11 (6), pp.e0157492. ⟨10.1371/journal.pone.0157492⟩ PLoS ONE, Vol 11, Iss 6, p e0157492 (2016) PLoS ONE, Public Library of Science, 2016, 11 (6), pp.e0157492. ⟨10.1371/journal.pone.0157492⟩ |
| ISSN: | 2007-2015 1932-6203 |
| Popis: | IntroductionIn France, rates of hospital admissions increase at the peaks of influenza epidemics. Predicting influenza-associated hospitalizations could help to anticipate increased hospital activity. The purpose of this study is to identify predictors of influenza epidemics through the analysis of meteorological data, and medical data provided by general practitioners.MethodsHistorical data were collected from Meteo France, the Sentinelles network and hospitals' information systems for a period of 8 years (2007-2015). First, connections between meteorological and medical data were estimated with the Pearson correlation coefficient, Principal component analysis and classification methods (Ward and k-means). Epidemic states of tested weeks were then predicted for each week during a one-year period using linear discriminant analysis. Finally, transition probabilities between epidemic states were calculated with the Markov Chain method.ResultsHigh correlations were found between influenza-associated hospitalizations and the variables: Sentinelles and emergency department admissions, and anti-correlations were found between hospitalizations and each of meteorological factors applying a time lag of: -13, -12 and -32 days respectively for temperature, absolute humidity and solar radiation. Epidemic weeks were predicted accurately with the linear discriminant analysis method; however there were many misclassifications about intermediate and non-epidemic weeks. Transition probability to an epidemic state was 100% when meteorological variables were below: 2°C, 4 g/m3 and 32 W/m2, respectively for temperature, absolute humidity and solar radiation. This probability was 0% when meteorological variables were above: 6°C, 5.8g/m3 and 74W/m2.ConclusionThese results confirm a good correlation between influenza-associated hospitalizations, meteorological factors and general practitioner's activity, the latter being the strongest predictor of hospital activity. |
| Databáze: | OpenAIRE |
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