Hospitalizations related to respiratory viral infections during the 2017/18 season in the Valencia Region of Spain

Objectives To describe the impact and severity of hospitalizations related to respiratory viral infections using data from weeks 2017-37 to 2018-19 in the Valencia Region of Spain. Methods All consenting admitted patients were included in the study if they were non-institutionalized, residents in any of the participating hospitals' catchment areas, not discharged from a previous hospital admission within 30 days, had an influenza-like illness (ILI, only for patients ≥5 years old) and were hospitalized within 7 days of the onset of symptoms. Demographic and clinical information was collected by interviewing and/or from clinical records. Swabs were tested by real-time reverse transcription polymerase chain reaction (RT-PCR) for influenza, respiratory syncytial virus (RSV), metapneumovirus, parainfluenza, rhinovirus/enterovirus, adenovirus, coronavirus and bocavirus. Hospitalization incidence rates were calculated by virus and age. Severity was explored through Intensive Care Unit (ICU) admission, death in hospital, mechanical ventilation, extracorporeal membrane oxygenation and the length of hospitalization. Results The hospitalization incidence rates related to respiratory viral infections were 1062.89, 31.38, 36.12 and 434.64 per 100,000 in patients <5, 5 to <18, 18 to <65 and ≥65 years old (y.o.), respectively. The incidence rates were especially high in children <1 y.o. and in adults ≥85 y.o.: 3311.94 and 1044.77 per 100,000, respectively. The highest rates were detected for influenza, rhinovirus/enterovirus and RSV: 63.11, 40.20 and 25.81 per 100,000, respectively. Among infected patients, 2% were admitted to the ICU, 4% needed mechanical ventilation and 4% died in hospital. No differences in severity were detected among viruses. The median length of hospitalization among infected patients was 5 days (Interquartile Range, IQR: 3-8 days). Conclusions Respiratory viral infections affected mainly young children and elderly people. Influenza, rhinovirus/enterovirus and RSV were the most commonly detected infections. No differences in severity were detected between the assessed viruses.Medicin

Evidence for magnetic clusters in BaCoO3_3

Magnetic properties of the transition metal oxide BaCoO$_3$ are analyzed on the basis of the experimental and theoretical literature available via ab inito calculations. These can be explained by assuming the material to be formed by noninteracting ferromagnetic clusters of about 1.2 nm in diameter separated by about 3 diameters. Above about 50 K, the so-called blocking temperature, superparamagnetic behavior of the magnetic clusters occurs and, above 250 K, paramagnetism sets in.Comment: 4 pages, 1 figur

Visibility graphs of fractional Wu-Baleanu time series

[EN] We study time series generated by the parametric family of fractional discrete maps introduced by Wu and Baleanu, presenting an alternative way of introducing these maps. For the values of the parameters that yield chaotic time series, we have studied the Shannon entropy of the degree distribution of the natural and horizontal visibility graphs associated to these series. In these cases, the degree distribution can be fitted with a power law. We have also compared the Shannon entropy and the exponent of the power law fitting for the different values of the fractionary exponent and the scaling factor of the model. Our results illustrate a connection between the fractionary exponent and the scaling factor of the maps, with the respect to the onset of the chaos.J.A. Conejero is supported Ministerio de Economia y Competitividad Grant Project MTM2016-75963-P. Carlos Lizama is supported by CONICYT, under Fondecyt Grant number 1180041. Cristobal Rodero-Gomez is funded by European Commission H2020 research and Innovation programme under the Marie Sklodowska-Curie grant agreement No. 764738.Conejero, JA.; Lizama, C.; Mira-Iglesias, A.; Rodero-Gómez, C. (2019). Visibility graphs of fractional Wu-Baleanu time series. The Journal of Difference Equations and Applications. 25(9-10):1321-1331. https://doi.org/10.1080/10236198.2019.1619714S13211331259-10Anand, K., & Bianconi, G. (2009). Entropy measures for networks: Toward an information theory of complex topologies. Physical Review E, 80(4). doi:10.1103/physreve.80.045102Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Brzeziński, D. W. (2017). Comparison of Fractional Order Derivatives Computational Accuracy - Right Hand vs Left Hand Definition. Applied Mathematics and Nonlinear Sciences, 2(1), 237-248. doi:10.21042/amns.2017.1.00020Brzeziński, D. W. (2018). Review of numerical methods for NumILPT with computational accuracy assessment for fractional calculus. Applied Mathematics and Nonlinear Sciences, 3(2), 487-502. doi:10.2478/amns.2018.2.00038DONNER, R. V., SMALL, M., DONGES, J. F., MARWAN, N., ZOU, Y., XIANG, R., & KURTHS, J. (2011). RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS. International Journal of Bifurcation and Chaos, 21(04), 1019-1046. doi:10.1142/s0218127411029021Edelman, M. (2015). On the fractional Eulerian numbers and equivalence of maps with long term power-law memory (integral Volterra equations of the second kind) to Grünvald-Letnikov fractional difference (differential) equations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(7), 073103. doi:10.1063/1.4922834Edelman, M. (2018). On stability of fixed points and chaos in fractional systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(2), 023112. doi:10.1063/1.5016437Gao, Z.-K., Small, M., & Kurths, J. (2016). Complex network analysis of time series. EPL (Europhysics Letters), 116(5), 50001. doi:10.1209/0295-5075/116/50001Iacovacci, J., & Lacasa, L. (2016). Sequential visibility-graph motifs. Physical Review E, 93(4). doi:10.1103/physreve.93.042309Indahl, U. G., Naes, T., & Liland, K. H. (2018). A similarity index for comparing coupled matrices. Journal of Chemometrics, 32(10), e3049. doi:10.1002/cem.3049Kantz, H., & Schreiber, T. (2003). Nonlinear Time Series Analysis. doi:10.1017/cbo9780511755798Lacasa, L., & Iacovacci, J. (2017). Visibility graphs of random scalar fields and spatial data. Physical Review E, 96(1). doi:10.1103/physreve.96.012318Lacasa, L., Luque, B., Ballesteros, F., Luque, J., & Nuño, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13), 4972-4975. doi:10.1073/pnas.0709247105Lizama, C. (2015). lp-maximal regularity for fractional difference equations on UMD spaces. Mathematische Nachrichten, 288(17-18), 2079-2092. doi:10.1002/mana.201400326Lizama, C. (2017). The Poisson distribution, abstract fractional difference equations, and stability. Proceedings of the American Mathematical Society, 145(9), 3809-3827. doi:10.1090/proc/12895Luque, B., Lacasa, L., Ballesteros, F., & Luque, J. (2009). Horizontal visibility graphs: Exact results for random time series. Physical Review E, 80(4). doi:10.1103/physreve.80.046103Luque, B., Lacasa, L., Ballesteros, F. J., & Robledo, A. (2011). Feigenbaum Graphs: A Complex Network Perspective of Chaos. PLoS ONE, 6(9), e22411. doi:10.1371/journal.pone.0022411Luque, B., Lacasa, L., & Robledo, A. (2012). Feigenbaum graphs at the onset of chaos. Physics Letters A, 376(47-48), 3625-3629. doi:10.1016/j.physleta.2012.10.050May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261(5560), 459-467. doi:10.1038/261459a0Núñez, Á. M., Luque, B., Lacasa, L., Gómez, J. P., & Robledo, A. (2013). Horizontal visibility graphs generated by type-I intermittency. Physical Review E, 87(5). doi:10.1103/physreve.87.052801Ravetti, M. G., Carpi, L. C., Gonçalves, B. A., Frery, A. C., & Rosso, O. A. (2014). Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph. PLoS ONE, 9(9), e108004. doi:10.1371/journal.pone.0108004Robledo, A. (2013). Generalized Statistical Mechanics at the Onset of Chaos. Entropy, 15(12), 5178-5222. doi:10.3390/e15125178Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.xSong, C., Havlin, S., & Makse, H. A. (2006). Origins of fractality in the growth of complex networks. Nature Physics, 2(4), 275-281. doi:10.1038/nphys266West, J., Lacasa, L., Severini, S., & Teschendorff, A. (2012). Approximate entropy of network parameters. Physical Review E, 85(4). doi:10.1103/physreve.85.046111Wu, G.-C., & Baleanu, D. (2013). Discrete fractional logistic map and its chaos. Nonlinear Dynamics, 75(1-2), 283-287. doi:10.1007/s11071-013-1065-7Wu, G.-C., & Baleanu, D. (2014). Discrete chaos in fractional delayed logistic maps. Nonlinear Dynamics, 80(4), 1697-1703. doi:10.1007/s11071-014-1250-3Zhang, J., & Small, M. (2006). Complex Network from Pseudoperiodic Time Series: Topology versus Dynamics. Physical Review Letters, 96(23). doi:10.1103/physrevlett.96.23870

Predictors of influenza severity among hospitalized adults with laboratory confirmed influenza: Analysis of nine influenza seasons from the Valencia region, Spain

Purpose Influenza hospitalizations contribute substantially to healthcare disruption. We explored the impact of ageing, comorbidities and other risk factors to better understand associations with severe clinical outcomes in adults hospitalized with influenza. Methods We analysed multi-season data from adults ≥18 years, hospitalized with laboratory-confirmed influenza in Valencia, Spain. Severity was defined as intensive care unit (ICU) admission, assisted ventilation and/or death. Generalized estimating equations were used to estimate associations between risk factors and severity. Rate of hospital discharge was analysed with a cumulative incidence function. Results Only 26% of influenza patients had their primary discharge diagnosis coded as influenza. Comorbidities were associated with severity among adults aged 50–79 years, with the highest odds ratio (OR) in patients with ≥3 comorbidities aged 50–64 years (OR = 6.7; 95% CI: 1.0–44.6). Morbid obesity and functional dependencies were also identified risk factors (ORs varying from 3 to 5 depending on age). The presence of increasing numbers of comorbidities was associated with prolonged hospital stay. Conclusions Influenza clinical outcomes are aggravated by the presence of comorbidities and ageing. Increased awareness of influenza among hospitalized patients could prompt clinical and public health interventions to reduce associated burden

Effectiveness of influenza vaccination programme in preventing hospital admissions, Valencia, 2014/15 early results

Preliminary results for the 2014/15 season indicate low to null effect of vaccination against influenza A(H3N2)-related disease. As of week 5 2015, there have been 1,136 hospital admissions, 210 were due to influenza and 98% of subtype A strains were H3. Adjusted influenza vaccine effectiveness was 33% (range: 6–53%) overall and 40% (range: 13% to 59%) in those 65 years and older. Vaccination reduced by 44% (28–68%) the probability of admission with influenza.The study was funded by a contract between FISABIO and Sanofi-Pasteur

Increased 30-Day Mortality in Very Old ICU Patients with COVID-19 Compared to Patients with Respiratory Failure without COVID-19

Purpose: The number of patients ≥ 80 years admitted into critical care is increasing. Coronavirus disease 2019 (COVID-19) added another challenge for clinical decisions for both admission and limitation of life-sustaining treatments (LLST). We aimed to compare the characteristics and mortality of very old critically ill patients with or without COVID-19 with a focus on LLST. Methods: Patients 80 years or older with acute respiratory failure were recruited from the VIP2 and COVIP studies. Baseline patient characteristics, interventions in intensive care unit (ICU) and outcomes (30-day survival) were recorded. COVID patients were matched to non-COVID patients based on the following factors: age (± 2 years), Sequential Organ Failure Assessment (SOFA) score (± 2 points), clinical frailty scale (± 1 point), gender and region on a 1:2 ratio. Specific ICU procedures and LLST were compared between the cohorts by means of cumulative incidence curves taking into account the competing risk of discharge and death. Results: 693 COVID patients were compared to 1393 non-COVID patients. COVID patients were younger, less frail, less severely ill with lower SOFA score, but were treated more often with invasive mechanical ventilation (MV) and had a lower 30-day survival. 404 COVID patients could be matched to 666 non-COVID patients. For COVID patients, withholding and withdrawing of LST were more frequent than for non-COVID and the 30-day survival was almost half compared to non-COVID patients. Conclusion: Very old COVID patients have a different trajectory than non-COVID patients. Whether this finding is due to a decision policy with more active treatment limitation or to an inherent higher risk of death due to COVID-19 is unclear.info:eu-repo/semantics/publishedVersio

Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018: a systematic review and modelling study

Background: Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018. Methods: We estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries. Findings: In 2018, among children under 5 years globally, there were an estimated 109·5 million influenza virus episodes (uncertainty range [UR] 63·1–190·6), 10·1 million influenza-virus-associated ALRI cases (6·8–15·1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000–1 415 000), 15 300 in-hospital deaths (5800–43 800), and up to 34 800 (13 200–97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries. Interpretation: A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries. Funding: WHO; Bill & Melinda Gates Foundation.Fil: Wang, Xin. University of Edinburgh; Reino UnidoFil: Li, You. University of Edinburgh; Reino UnidoFil: O'Brien, Katherine L.. University Johns Hopkins; Estados UnidosFil: Madhi, Shabir A.. University of the Witwatersrand; SudáfricaFil: Widdowson, Marc Alain. Centers for Disease Control and Prevention; Estados UnidosFil: Byass, Peter. Umea University; SueciaFil: Omer, Saad B.. Yale School Of Public Health; Estados UnidosFil: Abbas, Qalab. Aga Khan University; PakistánFil: Ali, Asad. Aga Khan University; PakistánFil: Amu, Alberta. Dodowa Health Research Centre; GhanaFil: Azziz-Baumgartner, Eduardo. Centers for Disease Control and Prevention; Estados UnidosFil: Bassat, Quique. University Of Barcelona; EspañaFil: Abdullah Brooks, W.. University Johns Hopkins; Estados UnidosFil: Chaves, Sandra S.. Centers for Disease Control and Prevention; Estados UnidosFil: Chung, Alexandria. University of Edinburgh; Reino UnidoFil: Cohen, Cheryl. National Institute For Communicable Diseases; SudáfricaFil: Echavarría, Marcela Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Fasce, Rodrigo A.. Public Health Institute; ChileFil: Gentile, Angela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Gordon, Aubree. University of Michigan; Estados UnidosFil: Groome, Michelle. University of the Witwatersrand; SudáfricaFil: Heikkinen, Terho. University Of Turku; FinlandiaFil: Hirve, Siddhivinayak. Kem Hospital Research Centre; IndiaFil: Jara, Jorge H.. Universidad del Valle de Guatemala; GuatemalaFil: Katz, Mark A.. Clalit Research Institute; IsraelFil: Khuri Bulos, Najwa. University Of Jordan School Of Medicine; JordaniaFil: Krishnan, Anand. All India Institute Of Medical Sciences; IndiaFil: de Leon, Oscar. Universidad del Valle de Guatemala; GuatemalaFil: Lucero, Marilla G.. Research Institute For Tropical Medicine; FilipinasFil: McCracken, John P.. Universidad del Valle de Guatemala; GuatemalaFil: Mira-Iglesias, Ainara. Fundación Para El Fomento de la Investigación Sanitaria; EspañaFil: Moïsi, Jennifer C.. Agence de Médecine Préventive; FranciaFil: Munywoki, Patrick K.. No especifíca;Fil: Ourohiré, Millogo. No especifíca;Fil: Polack, Fernando Pedro. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Rahi, Manveer. University of Edinburgh; Reino UnidoFil: Rasmussen, Zeba A.. National Institutes Of Health; Estados UnidosFil: Rath, Barbara A.. Vienna Vaccine Safety Initiative; AlemaniaFil: Saha, Samir K.. Child Health Research Foundation; BangladeshFil: Simões, Eric A.F.. University of Colorado; Estados UnidosFil: Sotomayor, Viviana. Ministerio de Salud de Santiago de Chile; ChileFil: Thamthitiwat, Somsak. Thailand Ministry Of Public Health; TailandiaFil: Treurnicht, Florette K.. University of the Witwatersrand; SudáfricaFil: Wamukoya, Marylene. African Population & Health Research Center; KeniaFil: Lay-Myint, Yoshida. Nagasaki University; JapónFil: Zar, Heather J.. University of Cape Town; SudáfricaFil: Campbell, Harry. University of Edinburgh; Reino UnidoFil: Nair, Harish. University of Edinburgh; Reino Unid

Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018: a systematic review and modelling study

BackgroundSeasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018.MethodsWe estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries.FindingsIn 2018, among children under 5 years globally, there were an estimated 109·5 million influenza virus episodes (uncertainty range [UR] 63·1–190·6), 10·1 million influenza-virus-associated ALRI cases (6·8–15·1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000–1 415 000), 15 300 in-hospital deaths (5800–43 800), and up to 34 800 (13 200–97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries.InterpretationA large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries.</div

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Background rates of 41 adverse events of special interest for COVID-19 vaccines in 10 European healthcare databases - an ACCESS cohort study

BACKGROUND: In May 2020, the ACCESS (The vACCine covid-19 monitoring readinESS) project was launched to prepare real-world monitoring of COVID-19 vaccines. Within this project, this study aimed to generate background incidence rates of 41 adverse events of special interest (AESI) to contextualize potential safety signals detected following administration of COVID-19 vaccines. METHODS: A dynamic cohort study was conducted using a distributed data network of 10 healthcare databases from 7 European countries (Italy, Spain, Denmark, The Netherlands, Germany, France and United Kingdom) over the period 2017 to 2020. A common protocol (EUPAS37273), common data model, and common analytics programs were applied for syntactic, semantic and analytical harmonization. Incidence rates (IR) for each AESI and each database were calculated by age and sex by dividing the number of incident cases by the total person-time at risk. Age-standardized rates were pooled using random effect models according to the provenance of the events. FINDINGS: A total number of 63,456,074 individuals were included in the study, contributing to 211.7 million person-years. A clear age pattern was observed for most AESIs, rates also varied by provenance of disease diagnosis (primary care, specialist care). Thrombosis with thrombocytopenia rates were extremely low ranging from 0.06 to 4.53/100,000 person-years for cerebral venous sinus thrombosis (CVST) with thrombocytopenia (TP) and mixed venous and arterial thrombosis with TP, respectively. INTERPRETATION: Given the nature of the AESIs and the setting (general practitioners or hospital-based databases or both), background rates from databases that show the highest level of completeness (primary care and specialist care) should be preferred, others can be used for sensitivity. The study was designed to ensure representativeness to the European population and generalizability of the background incidence rates. FUNDING: The project has received support from the European Medicines Agency under the Framework service contract nr EMA/2018/28/PE