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李 佳, 徐 钰, 王 优, 高 占. [Clinical characteristics of influenza pneumonia in the elderly and relationship between D-dimer and disease severity]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2022; 54:153-160. [PMID: 35165483 PMCID: PMC8860641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Indexed: 08/25/2024]
Abstract
OBJECTIVE To clarify the clinical characteristics of influenza pneumonia in the elderly patients and the relationship between D-dimer and the severity of influenza pneumonia. METHODS In the study, 52 hospitalized patients older than 65 years with confirmed influenza pneumonia diagnosed in Peking University People's Hospital on 5 consecutive influenza seasons from 2014 were retrospectively analyzed. General information, clinical symptoms, laboratory data, treatment methods and prognosis of the patients were collected. The relationship between D-dimer and pneumonia severity was analyzed, and receiver operating characteristic (ROC) curve was used to evaluate the predictive value of D-dimer. RESULTS Among the 52 patients, 31 were male (31/52, 59.6%), the average age was (77.1±7.4) years, and 19 of them (36.5%) were diagnosed with severe pneumonia. About 70% patients presenting with fever. In the severe group, the patients were more likely to complain of dyspnea than in the non-severe group (14/19, 73.7% vs. 10/33, 30.3%, P=0.004), severe pneumonia group had higher level of CURB-65 (confusion, urea, respiratory rate, blood pressure, and age>65), pneumonia severity index (PSI), C-reactive protein, urea nitrogen, lactate dehydrogenase, fasting glucose, and D-dimer (P value was 0.004, < 0.001, < 0.001, 0.003, 0.038, 0.018, and < 0.001, respectively), albumin was lower than that in the non-severe group [(35.8±5.6) g/L vs. (38.9±3.5) g/L, t=-2.348, P=0.018]. There was a significant positive correlation between the D-dimer at the first admission and PSI score (r=0.540, 95%CI: 0.302 to 0.714, P < 0.001), while a significant negative correlation with PaO2/FiO2 (r=-0.559, 95%CI: -0.726 to -0.330, P < 0.001). Area under the curve of D-dimer was 0.765 (95%CI: 0.627 to 0.872). Area under the curve of PSI was 0.843 (95%CI: 0.716 to 0.929). There was no statistically significant difference in test efficacy between the two (Z=2.360, P=0.174). D-dimer level over 1 225 μg/L had a positive predict value for influenza pneumonia in hospital death with a sensitivity of 76.92% and a specificity of 74.36%. CONCLUSION Influenza pneumonia in the elderly always has atypical symptoms, dyspnea is a prominent feature in severe cases, D-dimer level is associated with the severity of influenza pneumonia, and greater than 1 200 μg/L has a good predictive value for in-hospital death in the elderly.
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Affiliation(s)
- 佳 李
- 北京大学人民医院急诊科,北京 100044Department of Emergency, Peking University People's Hospital, Beijing 100044, China
| | - 钰 徐
- 北京积水潭医院呼吸与危重症医学科,北京 100035Department of Pulmonary and Critical Care Medicine, Beijing Jishuitan Hospital, Beijing 100035, China
| | - 优雅 王
- 北京大学人民医院呼吸与危重症医学科,北京 100044Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - 占成 高
- 北京大学人民医院呼吸与危重症医学科,北京 100044Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
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李 佳, 徐 钰, 王 优, 高 占. [Clinical characteristics of influenza pneumonia in the elderly and relationship between D-dimer and disease severity]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2022; 54:153-160. [PMID: 35165483 PMCID: PMC8860641 DOI: 10.19723/j.issn.1671-167x.2022.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To clarify the clinical characteristics of influenza pneumonia in the elderly patients and the relationship between D-dimer and the severity of influenza pneumonia. METHODS In the study, 52 hospitalized patients older than 65 years with confirmed influenza pneumonia diagnosed in Peking University People's Hospital on 5 consecutive influenza seasons from 2014 were retrospectively analyzed. General information, clinical symptoms, laboratory data, treatment methods and prognosis of the patients were collected. The relationship between D-dimer and pneumonia severity was analyzed, and receiver operating characteristic (ROC) curve was used to evaluate the predictive value of D-dimer. RESULTS Among the 52 patients, 31 were male (31/52, 59.6%), the average age was (77.1±7.4) years, and 19 of them (36.5%) were diagnosed with severe pneumonia. About 70% patients presenting with fever. In the severe group, the patients were more likely to complain of dyspnea than in the non-severe group (14/19, 73.7% vs. 10/33, 30.3%, P=0.004), severe pneumonia group had higher level of CURB-65 (confusion, urea, respiratory rate, blood pressure, and age>65), pneumonia severity index (PSI), C-reactive protein, urea nitrogen, lactate dehydrogenase, fasting glucose, and D-dimer (P value was 0.004, < 0.001, < 0.001, 0.003, 0.038, 0.018, and < 0.001, respectively), albumin was lower than that in the non-severe group [(35.8±5.6) g/L vs. (38.9±3.5) g/L, t=-2.348, P=0.018]. There was a significant positive correlation between the D-dimer at the first admission and PSI score (r=0.540, 95%CI: 0.302 to 0.714, P < 0.001), while a significant negative correlation with PaO2/FiO2 (r=-0.559, 95%CI: -0.726 to -0.330, P < 0.001). Area under the curve of D-dimer was 0.765 (95%CI: 0.627 to 0.872). Area under the curve of PSI was 0.843 (95%CI: 0.716 to 0.929). There was no statistically significant difference in test efficacy between the two (Z=2.360, P=0.174). D-dimer level over 1 225 μg/L had a positive predict value for influenza pneumonia in hospital death with a sensitivity of 76.92% and a specificity of 74.36%. CONCLUSION Influenza pneumonia in the elderly always has atypical symptoms, dyspnea is a prominent feature in severe cases, D-dimer level is associated with the severity of influenza pneumonia, and greater than 1 200 μg/L has a good predictive value for in-hospital death in the elderly.
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Affiliation(s)
- 佳 李
- 北京大学人民医院急诊科,北京 100044Department of Emergency, Peking University People's Hospital, Beijing 100044, China
| | - 钰 徐
- 北京积水潭医院呼吸与危重症医学科,北京 100035Department of Pulmonary and Critical Care Medicine, Beijing Jishuitan Hospital, Beijing 100035, China
| | - 优雅 王
- 北京大学人民医院呼吸与危重症医学科,北京 100044Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - 占成 高
- 北京大学人民医院呼吸与危重症医学科,北京 100044Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
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Khanh NC, Fowlkes AL, Nghia ND, Duong TN, Tu NH, Tu TA, McFarland JW, Nguyen TTM, Ha NT, Gould PL, Thanh PN, Trang NTH, Mai VQ, Thi PN, Otsu S, Azziz-Baumgartner E, Anh DD, Iuliano AD. Burden of Influenza-Associated Respiratory Hospitalizations, Vietnam, 2014-2016. Emerg Infect Dis 2021; 27:2648-2657. [PMID: 34545793 PMCID: PMC8462305 DOI: 10.3201/eid2710.204765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Influenza burden estimates are essential to informing prevention and control policies. To complement recent influenza vaccine production capacity in Vietnam, we used acute respiratory infection (ARI) hospitalization data, severe acute respiratory infection (SARI) surveillance data, and provincial population data from 4 provinces representing Vietnam’s major regions during 2014–2016 to calculate provincial and national influenza-associated ARI and SARI hospitalization rates. We determined the proportion of ARI admissions meeting the World Health Organization SARI case definition through medical record review. The mean influenza-associated hospitalization rates per 100,000 population were 218 (95% uncertainty interval [UI] 197–238) for ARI and 134 (95% UI 119–149) for SARI. Influenza-associated SARI hospitalization rates per 100,000 population were highest among children <5 years of age (1,123; 95% UI 946–1,301) and adults >65 years of age (207; 95% UI 186–227), underscoring the need for prevention and control measures, such as vaccination, in these at-risk populations.
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Roguski KM, Rolfes MA, Reich JS, Owens Z, Patel N, Fitzner J, Cozza V, Lafond KE, Azziz-Baumgartner E, Iuliano AD. Variability in published rates of influenza-associated hospitalizations: A systematic review, 2007-2018. J Glob Health 2021; 10:020430. [PMID: 33274066 PMCID: PMC7699004 DOI: 10.7189/jogh.10.020430] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background Influenza burden estimates help provide evidence to support influenza prevention and control programs at local and international levels. Methods Through a systematic review, we aimed to identify all published articles estimating rates of influenza-associated hospitalizations, describe methods and data sources used, and identify regions of the world where estimates are still lacking. We evaluated study heterogeneity to determine if we could pool published rates to generate global estimates of influenza-associated hospitalization. Results We identified 98 published articles estimating influenza-associated hospitalization rates from 2007-2018. Most articles (65%) identified were from high-income countries, with 34 of those (53%) presenting estimates from the United States. While we identified fewer publications (18%) from low- and lower-middle-income countries, 50% of those were published from 2015-2018, suggesting an increase in publications from lower-income countries in recent years. Eighty percent (n = 78) used a multiplier approach. Regression modelling techniques were only used with data from upper-middle or high-income countries where hospital administrative data was available. We identified variability in the methods, case definitions, and data sources used, including 91 different age groups and 11 different categories of case definitions. Due to the high observed heterogeneity across articles (I2>99%), we were unable to pool published estimates. Conclusions The variety of methods, data sources, and case definitions adapted locally suggests that the current literature cannot be synthesized to generate global estimates of influenza-associated hospitalization burden.
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Affiliation(s)
| | - Melissa A Rolfes
- US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA
| | - Jeremy S Reich
- US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA
| | - Zachary Owens
- Emory University, Rollins School of Public Health, Department of Epidemiology, Atlanta, Georgia, USA
| | - Neha Patel
- US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA
| | - Julia Fitzner
- World Health Organization, Global Influenza Programme, Geneva, Switzerland
| | - Vanessa Cozza
- World Health Organization, Global Influenza Programme, Geneva, Switzerland
| | - Kathryn E Lafond
- US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA
| | | | - A Danielle Iuliano
- US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA
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Tsopra R, Frappe P, Streit S, Neves AL, Honkoop PJ, Espinosa-Gonzalez AB, Geroğlu B, Jahr T, Lingner H, Nessler K, Pesolillo G, Sivertsen ØS, Thulesius H, Zoitanu R, Burgun A, Kinouani S. Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries. BMC FAMILY PRACTICE 2021; 22:96. [PMID: 34000985 PMCID: PMC8127252 DOI: 10.1186/s12875-021-01413-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/10/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND General practitioners (GPs) play a key role in managing the COVID-19 outbreak. However, they may encounter difficulties adapting their practices to the pandemic. We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the pandemic from 15 countries. METHODS A network of GPs collaborated together in a three-step process: (i) identification of key recommendations of GP surgery reorganisation, according to WHO, CDC and health professional resources from health care facilities; (ii) collection of key recommendations included in the guidelines published in 15 countries; (iii) analysis, comparison and synthesis of the results. RESULTS Recommendations for the reorganisation of GP surgeries of four types were identified: (i) reorganisation of GP consultations (cancelation of non-urgent consultations, follow-up via e-consultations), (ii) reorganisation of GP surgeries (area partitioning, visual alerts and signs, strict hygiene measures), (iii) reorganisation of medical examinations by GPs (equipment, hygiene, partial clinical examinations, patient education), (iv) reorganisation of GP staff (equipment, management, meetings, collaboration with the local community). CONCLUSIONS We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the COVID-19 outbreak from 15 countries. These guidelines focus principally on clinical care, with less attention paid to staff management, and the area of epidemiological surveillance and research is largely neglected. The differences of guidelines between countries and the difficulty to apply them in routine care, highlight the need of advanced research in primary care. Thereby, primary care would be able to provide recommendations adapted to the real-world settings and with stronger evidence, which is especially necessary during pandemics.
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Affiliation(s)
- Rosy Tsopra
- INSERM, Université de Paris, Sorbonne Université, Centre de Recherche des Cordeliers, Information Sciences to support Personalized Medicine, F-75006, Paris, France. .,Department of Medical Informatics, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France.
| | - Paul Frappe
- Department of general practice, Faculty of medicine Jacques Lisfranc, University of Lyon, Saint-Etienne, France.,Inserm UMR 1059, Sainbiose DVH, University of Lyon, Saint-Etienne, France.,Inserm CIC-EC 1408, University of Lyon, Saint-Etienne, France.,College of General Practice / Collège de la Médecine Générale, Paris, France
| | - Sven Streit
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Ana Luisa Neves
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, London, UK.,Center for Health Technology and Services Research / Department of Community Medicine, Health Information and Decision, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Persijn J Honkoop
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Berk Geroğlu
- İzmir Karşıyaka District Health Directorate, İzmir, Turkey
| | - Tobias Jahr
- Medizinische Hochschule Hannover, OE 5430, Carl Neuberg Str. 1, 30625, Hannover, Germany
| | - Heidrun Lingner
- Medizinische Hochschule Hannover, Medizinische Psychologie, OE 5430, Hannover, Germany.,Member of the German Center for Lung Research (DZL)/ BREATH - Biomedical Research in Endstage and Obstructive Lung Disease Hannover, Carl Neuberg Str. 1, 30625, Hannover, Germany
| | - Katarzyna Nessler
- Department of Family Medicine, Jagiellonian University Medical College, Kraków, Poland.,Vasco da Gama Movement, Wonca Europe, Kraków, Poland
| | | | - Øyvind Stople Sivertsen
- Torshovdalen Health Center, Oslo, Norway.,Editor of the Journal of the Norwegian Medical Association, Oslo, Norway
| | | | - Raluca Zoitanu
- National Federation of Family Medicine Employers in Romania (FNPMF), București, Romania
| | - Anita Burgun
- INSERM, Université de Paris, Sorbonne Université, Centre de Recherche des Cordeliers, Information Sciences to support Personalized Medicine, F-75006, Paris, France.,Department of Medical Informatics, Hôpital Européen Georges-Pompidou & Necker Children's Hospital, AP-HP, Paris, France
| | - Shérazade Kinouani
- INSERM, Bordeaux Population Health Research Center, team HEALTHY, UMR 1219, university of Bordeaux, F-33000, Bordeaux, France.,Department of General Practice, University of Bordeaux, 146 rue Léo Saignat, F-33000, Bordeaux, France
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Pană A, Pistol A, Streinu-Cercel A, Ileanu BV. Burden of influenza in Romania. A retrospective analysis of 2014/15 - 2018/19 seasons in Romania. Germs 2020; 10:201-209. [PMID: 33134198 DOI: 10.18683/germs.2020.1206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/04/2020] [Accepted: 07/20/2020] [Indexed: 11/08/2022]
Abstract
Introduction Influenza is a seasonal epidemic with a heavy negative impact both on population health, and healthcare system utilization; until now, there are only two burden of disease studies in the Romanian context. This study aims to quantify the burden of influenza for the Romanian population for the seasons 2014/15 to 2018/19, using health administrative databases. Methods Incidence, hospitalization and mortality rates attributable to influenza as well as total number of influenza cases and deaths were estimated, for each season in the analyzed period, by combining the new cases reported by General Practitioners, Emergency Department presentations, hospitalizations, number of deaths, positivity rate of influenza, and probability to be consulted by a physician. Years of life lost due to premature death attributable to influenza complications were also computed. Results On average, 591,151 cases/season attributable to influenza were estimated during the period 2014/15 - 2018/19. The highest rates for incidence, hospitalization and presentation to emergency department were found in the age groups 0-4 years and 65 years and above. Influenza mortality rate was estimated at 3 per 100,000 persons and the 65 and above age group had the highest rate. Conclusions About 3% of the total Romanian population is estimated to develop an influenza attributable disease in a non-pandemic season. An overall increasing trend of the mortality rate attributable to influenza may be also underlined. On average, a person loses 12 years due to premature death caused by complications of influenza.
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Affiliation(s)
- Adrian Pană
- MD, MPH, Center for Health Outcomes & Evaluation, Splaiul Unirii 45, Bloc M15, Ap. 55, District no. 3, Bucharest, Romania
| | - Adriana Pistol
- MD, Researcher, National Institute of Public Health, Doctor Leonte Anastasievici No. 1-3, Bucharest Romania
| | - Adrian Streinu-Cercel
- MD, PhD, Professor, Carol Davila University of Medicine and Pharmacy Bucharest, National Institute for Infectious Diseases "Prof. Dr. Matei Balş, No. 1 Dr. Calistrat Grozovici street, Bucharest, Romania
| | - Bogdan-Vasile Ileanu
- PhD, Researcher at Center for Health Outcomes & Evaluation, Lecturer at Bucharest University of Economic Studies, Piața Romană 6, 010374, Bucharest, Romania
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Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17144979. [PMID: 32664331 PMCID: PMC7400312 DOI: 10.3390/ijerph17144979] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 06/29/2020] [Accepted: 07/07/2020] [Indexed: 12/22/2022]
Abstract
The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.
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Marbus SD, Schweitzer VA, Groeneveld GH, Oosterheert JJ, Schneeberger PM, van der Hoek W, van Dissel JT, van Gageldonk-Lafeber AB, Mangen MJ. Incidence and costs of hospitalized adult influenza patients in The Netherlands: a retrospective observational study. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2020; 21:775-785. [PMID: 32180069 PMCID: PMC7095032 DOI: 10.1007/s10198-020-01172-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 02/25/2020] [Indexed: 05/14/2023]
Abstract
OBJECTIVE Influenza virus infections cause a high disease and economic burden during seasonal epidemics. However, there is still a need for reliable disease burden estimates to provide a more detailed picture of the impact of influenza. Therefore, the objectives of this study is to estimate the incidence of hospitalisation for influenza virus infection and associated hospitalisation costs in adult patients in the Netherlands during two consecutive influenza seasons. METHODS We conducted a retrospective study in adult patients with a laboratory confirmed influenza virus infection in three Dutch hospitals during respiratory seasons 2014-2015 and 2015-2016. Incidence was calculated as the weekly number of hospitalised influenza patients divided by the total population in the catchment populations of the three hospitals. Arithmetic mean hospitalisation costs per patient were estimated and included costs for emergency department consultation, diagnostics, general ward and/or intensive care unit admission, isolation, antibiotic and/or antiviral treatment. These hospitalisation costs were extrapolated to national level and expressed in 2017 euros. RESULTS The study population consisted of 380 hospitalised adult influenza patients. The seasonal cumulative incidence was 3.5 cases per 10,000 persons in respiratory season 2014-2015, compared to 1.8 cases per 10,000 persons in 2015-2016. The arithmetic mean hospitalisation cost per influenza patient was €6128 (95% CI €4934-€7737) per patient in 2014-2015 and €8280 (95% CI €6254-€10,665) in 2015-2016, potentially reaching total hospitalisation costs of €28 million in 2014-2015 and €20 million in 2015-2016. CONCLUSIONS Influenza virus infections lead to 1.8-3.5 hospitalised patients per 10,000 persons, with mean hospitalisation costs of €6100-€8300 per adult patient, resulting in 20-28 million euros annually in The Netherlands. The highest arithmetic mean hospitalisation costs per patient were found in the 45-64 year age group. These influenza burden estimates could be used for future influenza cost-effectiveness and impact studies.
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Affiliation(s)
- Sierk D. Marbus
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
| | - Valentijn A. Schweitzer
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geert H. Groeneveld
- Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan J. Oosterheert
- Department of Internal Medicine and Infectious Diseases, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Peter M. Schneeberger
- Regional Laboratory for Medical Microbiology and Infection Prevention, ‘s-Hertogenbosch, The Netherlands
| | - Wim van der Hoek
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
| | - Jaap T. van Dissel
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
- Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Arianne B. van Gageldonk-Lafeber
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
| | - Marie-Josée Mangen
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
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9
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Pițigoi D, Streinu-Cercel A, Ivanciuc AE, Lazãr M, Cherciu CM, Mihai ME, Nițescu M, Aramă V, Crăciun MD, Streinu-Cercel A, Săndulescu O. Surveillance of medically-attended influenza in elderly patients from Romania-data from three consecutive influenza seasons (2015/16, 2016/17, and 2017/18). Influenza Other Respir Viruses 2020; 14:530-540. [PMID: 32410402 PMCID: PMC7431641 DOI: 10.1111/irv.12752] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 04/14/2020] [Accepted: 04/16/2020] [Indexed: 12/11/2022] Open
Abstract
Background Influenza is an acute infection affecting all age groups; however, elderly patients are at an increased risk. We aim to describe the clinical characteristics and the circulation of influenza virus types in elderly patients admitted for severe acute respiratory infection (SARI) to a tertiary care hospital in Bucharest, Romania, part of the I‐MOVE+ hospital network. Methods We conducted an active surveillance study at the National Institute for Infectious Diseases “Prof. Dr Matei Balș,” Bucharest, Romania, during three consecutive influenza seasons: 2015/16, 2016/17, and 2017/18. All patients aged 65 and older admitted to our hospital for SARI were tested for influenza by PCR. Results A total of 349 eligible patients were tested during the study period, and 149 (42.7%) were confirmed with influenza. Most patients, 321 (92.5%) presented at least one underlying condition at the time of hospital admission, the most frequent being cardiovascular disease, 270 (78.3%). The main influenza viral subtype circulating in 2015/16 was A(H1N1)pdm09, followed by A(H3N2) in 2016/17 and B influenza in 2017/18. Case fatality was highest in the 2015/16 season (3.7%), 0% in 2016/17, and 1.0% in 2017/18. Vaccination coverage in elderly patients with SARI from our study population was 22 (6.3%) over the three seasons. Conclusions Our study has highlighted a high burden of comorbidities in elderly patients presenting with SARI during winter season in Romania. The influenza vaccine coverage rate needs to be substantially increased in the elderly population, through targeted interventions.
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Affiliation(s)
- Daniela Pițigoi
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
| | - Anca Streinu-Cercel
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
| | - Alina Elena Ivanciuc
- "Cantacuzino" National Medico-Military Institute for Research and Development, Bucharest, Romania
| | - Mihaela Lazãr
- "Cantacuzino" National Medico-Military Institute for Research and Development, Bucharest, Romania
| | - Carmen Maria Cherciu
- "Cantacuzino" National Medico-Military Institute for Research and Development, Bucharest, Romania
| | - Maria Elena Mihai
- "Cantacuzino" National Medico-Military Institute for Research and Development, Bucharest, Romania
| | - Maria Nițescu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
| | - Victoria Aramă
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
| | - Maria Dorina Crăciun
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,Grigore Alexandrescu Clinical Children's Emergency Hospital, Bucharest, Romania
| | - Adrian Streinu-Cercel
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
| | - Oana Săndulescu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
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10
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Feng L, Feng S, Chen T, Yang J, Lau YC, Peng Z, Li L, Wang X, Wong JYT, Qin Y, Bond HS, Zhang J, Fang VJ, Zheng J, Yang J, Wu P, Jiang H, He Y, Cowling BJ, Yu H, Shu Y, Lau EHY. Burden of influenza-associated outpatient influenza-like illness consultations in China, 2006-2015: A population-based study. Influenza Other Respir Viruses 2020; 14:162-172. [PMID: 31872547 PMCID: PMC7040965 DOI: 10.1111/irv.12711] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 12/04/2019] [Accepted: 12/08/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Human influenza virus infections cause a considerable burden of morbidity and mortality worldwide each year. Understanding regional influenza-associated outpatient burden is crucial for formulating control strategies against influenza viruses. METHODS We extracted the national sentinel surveillance data on outpatient visits due to influenza-like-illness (ILI) and virological confirmation of sentinel specimens from 30 provinces of China from 2006 to 2015. Generalized additive regression models were fitted to estimate influenza-associated excess ILI outpatient burden for each individual province, accounting for seasonal baselines and meteorological factors. RESULTS Influenza was associated with an average of 2.5 excess ILI consultations per 1000 person-years (py) in 30 provinces of China each year from 2006 to 2015. Influenza A(H1N1)pdm09 led to a higher number of influenza-associated ILI consultations in 2009 across all provinces compared with other years. The excess ILI burden was 4.5 per 1000 py among children aged below 15 years old, substantially higher than that in adults. CONCLUSIONS Human influenza viruses caused considerable impact on population morbidity, with a consequent healthcare and economic burden. This study provided the evidence for planning of vaccination programs in China and a framework to estimate burden of influenza-associated outpatient consultations.
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Affiliation(s)
- Luzhao Feng
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
| | - Shuo Feng
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Tao Chen
- National Institute for Viral Disease Control and PreventionCollaboration Innovation Center for Diagnosis and Treatment of Infectious DiseasesChinese Center for Disease Control and PreventionBeijingChina
| | - Juan Yang
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
- Key Laboratory of Public Health SafetyMinistry of EducationSchool of Public HealthFudan UniversityShanghaiChina
| | - Yiu Chung Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Zhibin Peng
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
| | - Li Li
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Xiling Wang
- Key Laboratory of Public Health SafetyMinistry of EducationSchool of Public HealthFudan UniversityShanghaiChina
| | - Jessica Y. T. Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Ying Qin
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
| | - Helen S. Bond
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Juanjuan Zhang
- Key Laboratory of Public Health SafetyMinistry of EducationSchool of Public HealthFudan UniversityShanghaiChina
| | - Vicky J. Fang
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Jiandong Zheng
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
| | - Jing Yang
- National Institute for Viral Disease Control and PreventionCollaboration Innovation Center for Diagnosis and Treatment of Infectious DiseasesChinese Center for Disease Control and PreventionBeijingChina
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Hui Jiang
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
| | - Yangni He
- Key Laboratory of Public Health SafetyMinistry of EducationSchool of Public HealthFudan UniversityShanghaiChina
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Hongjie Yu
- Key Laboratory of Surveillance and Early‐warning on Infectious DiseaseDivision of Infectious DiseaseChinese Center for Disease Control and PreventionBeijingChina
- Key Laboratory of Public Health SafetyMinistry of EducationSchool of Public HealthFudan UniversityShanghaiChina
| | - Yuelong Shu
- National Institute for Viral Disease Control and PreventionCollaboration Innovation Center for Diagnosis and Treatment of Infectious DiseasesChinese Center for Disease Control and PreventionBeijingChina
- School of Public Health (Shenzhen)Sun Yat‐sen UniversityShenzhenChina
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and ControlSchool of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina
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11
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Drăgănescu A, Săndulescu O, Florea D, Vlaicu O, Streinu-Cercel A, Oțelea D, Luminos ML, Aramă V, Abrudan S, Streinu-Cercel A, Pițigoi D. The 2017-2018 influenza season in Bucharest, Romania: epidemiology and characteristics of hospital admissions for influenza-like illness. BMC Infect Dis 2019; 19:967. [PMID: 31718578 PMCID: PMC6852761 DOI: 10.1186/s12879-019-4613-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 10/31/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Seasonal influenza causes a considerable burden to healthcare services every year. To better measure the impact of severe influenza cases in Romania, we analyzed active surveillance data collected during the 2017-2018 season from patients admitted for influenza-like illness (ILI) at a tertiary care hospital in Bucharest. METHODS Patients admitted for acute ILI were included if they were resident in the Bucharest-Ilfov region, had been hospitalized for at least 24 h, and had onset of symptoms within 7 days before admission. Patient demographics, healthcare use, vaccination status, and outcome data were collected by questionnaire or by searching clinical records. Respiratory swabs were also obtained from each patient to confirm influenza A (A/H1 and A/H3 subtypes) or influenza B (Yamagata and Victoria lineages) infection by real-time reverse-transcription polymerase chain reaction assay. RESULTS The study included 502 patients, many (45.2%) of whom were aged < 5 years. Overall, 108 patients (21.5%) had one or more comorbidities. Seventeen adults aged 18-64 years (3.4%) had been vaccinated against influenza. Patients were hospitalized for a median of 5 days and most (90.4%) were prescribed antiviral treatment. More than one-half of the patients (n = 259, 51.6%) were positive for influenza. Most influenza cases were caused by B viruses (172/259, 66.4%), which were mostly of the B/Yamagata lineage (85 of 94 characterized, 90.4%). Most of the subtyped A viruses were A/H1 (59/74, 79.7%). A/H1 viruses were frequently detected in influenza-positive admissions throughout the 2017-2018 season, whereas the predominant B/Yamagata viruses were detected around the middle of the season, with a peak in cases at week 7 of 2018. Eleven patients were admitted to an intensive care unit; of these, one patient with confirmed B/Yamagata infection died. CONCLUSIONS These results show that seasonal influenza results in considerable hospitalization in Bucharest-Ilfov, Romania and suggest vaccine coverage should be extended, especially to the youngest age groups. The data from this study should help inform and optimize national influenza healthcare policies.
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Affiliation(s)
- Anca Drăgănescu
- Children X Department, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
| | - Oana Săndulescu
- Adults II Department, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
- Department of Infectious Diseases I, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Dragoș Florea
- Molecular Diagnosis Laboratory, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
- Department of Microbiology I, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Ovidiu Vlaicu
- Molecular Diagnosis Laboratory, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
| | - Anca Streinu-Cercel
- Adults II Department, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
- Department of Infectious Diseases I, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Dan Oțelea
- Molecular Diagnosis Laboratory, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
| | - Monica Luminița Luminos
- Children X Department, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
- Department of Infectious Diseases, Faculty of Dental Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Victoria Aramă
- Department of Infectious Diseases I, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Adults III Department, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
| | | | - Adrian Streinu-Cercel
- Adults II Department, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
- Department of Infectious Diseases I, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Daniela Pițigoi
- Compartment for Surveillance and Prevention of Healthcare-associated Infections, National Institute for Infectious Diseases ‘Prof. Dr. Matei Balş’, Bucharest, Romania
- Department of Epidemiology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
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12
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Ng Y, Chua LAV, Ma S, Jian Ming Lee V. Estimates of influenza-associated hospitalisations in tropical Singapore, 2010-2017: Higher burden estimated in more recent years. Influenza Other Respir Viruses 2019; 13:574-581. [PMID: 31433131 PMCID: PMC6800300 DOI: 10.1111/irv.12676] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 08/06/2019] [Accepted: 08/06/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND We previously estimated Singapore's influenza-associated hospitalisation rate for pneumonia and influenza (P&I) in 2010-2012 to be 29.6 per 100 000 person-years, which corresponds to 11.2% of all P&I hospitalisations. OBJECTIVES This study aims to update Singapore's estimates of the influenza-associated pneumonia and influenza (P&I) hospitalisation burden using the latest data from 2010 to 2017. METHODS We estimated the number of P&I hospitalisations associated with influenza using generalised additive models. We specified the weekly number of admissions for P&I and the weekly influenza positivity in the models, along with potential confounders such as weekly respiratory syncytial virus (RSV) positivity and meteorological data. RESULTS In 2010-2017, 16.3% of all P&I hospitalisations in Singapore were estimated to be attributed to influenza, corresponding to an excess influenza-associated P&I hospitalisation rate of 50.1 per 100 000 person-years. Higher excess rates were estimated for children aged 0-4 years (186.8 per 100 000 person-years) and elderly aged ≥ 65 years (338.0 per 100 000 person-years). Higher influenza-associated hospitalisation rates were estimated for 2016 and 2017 (67.9 and 75.1 per 100 000 persons, respectively) years when the influenza A(H3N2) subtype was dominant. CONCLUSION Influenza burden in Singapore has increased since 2010. Influenza vaccination programmes should continue to be prioritised for the young and the elderly.
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Affiliation(s)
- Yixiang Ng
- Epidemiology and Disease Control DivisionMinistry of HealthSingapore CitySingapore
| | - Lily Ai Vee Chua
- Epidemiology and Disease Control DivisionMinistry of HealthSingapore CitySingapore
| | - Stefan Ma
- Epidemiology and Disease Control DivisionMinistry of HealthSingapore CitySingapore
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13
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The incidence of symptomatic infection with influenza virus in the Netherlands 2011/2012 through 2016/2017, estimated using Bayesian evidence synthesis. Epidemiol Infect 2018; 147:e30. [PMID: 30348244 PMCID: PMC6518592 DOI: 10.1017/s095026881800273x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Due to differences in the circulation of influenza viruses, distribution and antigenic drift of A subtypes and B lineages, and susceptibility to infection in the population, the incidence of symptomatic influenza infection can vary widely between seasons and age-groups. Our goal was to estimate the symptomatic infection incidence in the Netherlands for the six seasons 2011/2012 through 2016/2017, using Bayesian evidence synthesis methodology to combine season-specific sentinel surveillance data on influenza-like illness (ILI), virus detections in sampled ILI cases and data on healthcare-seeking behaviour. Estimated age-aggregated incidence was 6.5 per 1000 persons (95% uncertainty interval (UI): 4.7–9.0) for season 2011/2012, 36.7 (95% UI: 31.2–42.8) for 2012/2013, 9.1 (95% UI: 6.3–12.9) for 2013/2014, 41.1 (95% UI: 35.0–47.7) for 2014/2015, 39.4 (95% UI: 33.4–46.1) for 2015/2016 and 27.8 (95% UI: 22.7–33.7) for season 2016/2017. Incidence varied substantially between age-groups (highest for the age-group <5 years: 23 to 47/1000, but relatively low for 65+ years: 2 to 34/1000 over the six seasons). Integration of all relevant data sources within an evidence synthesis framework has allowed the estimation – with appropriately quantified uncertainty – of the incidence of symptomatic influenza virus infection. These estimates provide valuable insight into the variation in influenza epidemics across seasons, by virus subtype and lineage, and between age-groups.
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14
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Lee VJ, Ho ZJM, Goh EH, Campbell H, Cohen C, Cozza V, Fitzner J, Jara J, Krishnan A, Bresee J. Advances in measuring influenza burden of disease. Influenza Other Respir Viruses 2018; 12:3-9. [PMID: 29460425 PMCID: PMC5818353 DOI: 10.1111/irv.12533] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2017] [Indexed: 12/16/2022] Open
Affiliation(s)
- Vernon J. Lee
- Ministry of HealthSingaporeSingapore
- Saw Swee Hock School of Public HealthNational University of SingaporeSingaporeSingapore
| | | | | | - Harry Campbell
- Centre for Global Health ResearchUsher Institute of Population Health SciencesUniversity of EdinburghEdinburghUK
| | - Cheryl Cohen
- Division of the National Laboratory ServiceCentre for Respiratory Diseases and MeningitisNational Institute for Communicable DiseasesJohannesburgSouth Africa
- Wits School of Public HealthUniversity of the WitwatersrandJohannesburgSouth Africa
| | - Vanessa Cozza
- Global Influenza ProgrammeWorld Health OrganizationGenevaSwitzerland
| | - Julia Fitzner
- Global Influenza ProgrammeWorld Health OrganizationGenevaSwitzerland
| | - Jorge Jara
- Center for Health Studies, Research InstituteUniversidad del Valle de GuatemalaGuatemala CityGuatemala
| | - Anand Krishnan
- Centre for Community MedicineAll India Institute of Medical SciencesNew DelhiIndia
| | - Joseph Bresee
- Influenza DivisionCenters for Disease Control and PreventionAtlantaGAUSA
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15
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Gefenaite G, Pistol A, Popescu R, Popovici O, Ciurea D, Dolk C, Jit M, Gross D. Estimating burden of influenza-associated influenza-like illness and severe acute respiratory infection at public healthcare facilities in Romania during the 2011/12-2015/16 influenza seasons. Influenza Other Respir Viruses 2017; 12:183-192. [PMID: 29144598 PMCID: PMC5818344 DOI: 10.1111/irv.12525] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2017] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Influenza is responsible for substantial morbidity and mortality, but there is limited information on reliable disease burden estimates, especially from middle-income countries in the WHO European Region. OBJECTIVES To estimate the incidence of medically attended influenza-associated influenza-like illness (ILI) and hospitalizations due to severe acute respiratory infection (SARI) presenting to public healthcare facilities in Romania. PATIENTS/METHODS Sentinel influenza surveillance data for ILI and SARI from 2011/12-2015/16, including virological data, were used to estimate influenza-associated ILI and SARI incidence/100 000 and their 95% confidence intervals (95% CI). RESULTS The overall annual incidence of ILI and influenza-associated ILI per 100 000 persons in Romania varied between 68 (95% CI: 61-76) and 318 (95% CI: 298-338) and between 23 (95% CI: 19-29) and 189 (95% CI: 149-240), respectively. The highest ILI and influenza incidence was among children aged 0-4 years. We estimated that SARI incidence per 100 000 persons was 6 (95% CI: 5-7) to 9 (95% CI: 8-10), of which 2 (95% CI: 1-2) to 3 (95% CI: 2-4) were due to influenza. Up to 0.3% of the Romanian population were annually reported with ILI, and 0.01% was hospitalized with SARI, of which as much as one-third could be explained by influenza. CONCLUSIONS This evaluation was the first study estimating influenza burden in Romania. We found that during each influenza season, a substantial number of persons in Romania suffer from influenza-related ILI or are hospitalized due to influenza-associated SARI.
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Affiliation(s)
- Giedre Gefenaite
- Infectious Hazards Management, Division of Health Emergencies and Communicable Diseases, WHO Regional Office for Europe, Copenhagen, Denmark.,Department of Health Sciences, Faculty of Medicine, Lund University, Lund, Sweden
| | - Adriana Pistol
- National Center for Communicable Diseases Surveillance and Control, National Institute of Public Health, Bucharest, Romania
| | - Rodica Popescu
- National Center for Communicable Diseases Surveillance and Control, National Institute of Public Health, Bucharest, Romania
| | - Odette Popovici
- National Center for Communicable Diseases Surveillance and Control, National Institute of Public Health, Bucharest, Romania
| | - Daniel Ciurea
- Center for Health Policies and Services, Bucharest, Romania
| | - Christiaan Dolk
- Infectious Hazards Management, Division of Health Emergencies and Communicable Diseases, WHO Regional Office for Europe, Copenhagen, Denmark.,PharmacoTherapy, - Epidemiology & -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Diane Gross
- Infectious Hazards Management, Division of Health Emergencies and Communicable Diseases, WHO Regional Office for Europe, Copenhagen, Denmark
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