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BACKGROUND Hepatitis C virus (HCV) antigen testing is less expensive than quantitative RT-PCR but has lower sensitivity for very low viral loads (VLVL; HCV RNA ≤3,000 IU/ml). Currently the benefits of antigen testing for screening are discussed, but data on prevalence and outcomes of persons with VLVL are scarce. METHODS We assessed prevalence and predictors of VLVL by logistic regression in treatment-naive participants in the Swiss Hepatitis C Cohort Study. We analyzed if the last viral load after VLVL was low, compared cirrhosis and mortality in persons with and without VLVL, and evaluated the number of samples with VLVL that were reactive by antigen testing. RESULTS We included 2,533 treatment-naive persons with available quantitative HCV RNA testing results. Overall, 133 persons (5.3%) had a VLVL. Age 18-40 years, female gender and HIV coinfection were associated with VLVL. Of 72 persons with a viral load available after VLVL, 14% had a VLVL and 17% had spontaneous viral clearance. The prevalence and incidence of cirrhosis and mortality were comparable in persons with and without VLVL; all 24 persons with VLVL and cirrhosis had excessive alcohol consumption or immunosuppression. Overall 33% of samples with VLVL were reactive by antigen testing. CONCLUSIONS The frequency of VLVL was low. Among the persons who would probably be missed by antigen screening, some had a favorable disease course, but some had immunosuppression and liver cirrhosis. The benefit of HCV antigen testing for screening may be limited by the risk of missing patients with severe liver disease.

Background.With direct-acting antiviral agents (DAAs), mortality rates and causes of death among persons with hepatitis C virus (HCV) infection may change over time. However, the emergence of such trends may be delayed by the slow progression of chronic hepatitis C. To date, detailed analyses of cause-specific mortality among HCV-infected persons over time remain limited.Methods.We evaluated changes in causes of death among Swiss Hepatitis C Cohort Study (SCCS) participants from 2008 to 2016. We analyzed risk factors for all-cause and cause-specific mortality, accounting for changes in treatment, fibrosis stage, and use of injectable drugs over time. Mortality ascertainment was completed by linking lost-to-follow-up participants to the Swiss Federal Statistical Office death registry.Results.We included 4700 SCCS participants, of whom 478 died between 2008 and 2016. The proportion of unknown causes of death decreased substantially after linkage, from 42% to 10%. Leading causes of death were liver failure (crude death rate 4.4/1000 person-years), liver cancer (3.4/1000 person-years), and nonliver cancer (2.8/1000 person-years), with an increasing proportion of cancer-related deaths over time. Cause-specific analysis showed that persons with sustained virologic response were less at risk for liver-related mortality than those never treated or treated unsuccessfully.Conclusions.Although the expected decrease in mortality is not yet observable, causes of death among HCV-infected persons have evolved over time. With the wider use of DAAs, liver-related mortality is expected to decline in the future. Continued moni-toring of cause-specific mortality will remain important to assess the long-term effect of DAAs and design effective interventions.Keywords. cohort; hepatitis C; mortality; risk factors; Switzerland.

© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. BACKGROUND: The effect of neuraminidase inhibitor (NAI) treatment on length of stay (LoS) in patients hospitalized with influenza is unclear. METHODS: We conducted a one-stage individual participant data (IPD) meta-analysis exploring the association between NAI treatment and LoS in patients hospitalized with 2009 influenza A(H1N1) virus (A[H1N1]pdm09) infection. Using mixed-effects negative binomial regression and adjusting for the propensity to receive NAI, antibiotic, and corticosteroid treatment, we calculated incidence rate ratios (IRRs) and 95% confidence intervals (CIs). Patients with a LoS of

Late diagnosis and treatment may increase morbidity and mortality among persons with hepatitis C virus (HCV) infection. We included all participants of the Swiss Hepatitis C Cohort Study (SCCS). We used unadjusted and adjusted logistic and Cox regressions to determine the association between the geographic origin of the participants and the following outcomes: antiviral treatment status; sustained virologic response; cirrhosis at enrolment; incident cirrhosis; loss to follow-up (LTFU); and mortality. The analyses were adjusted for sex, baseline age, education, source of income, alcohol consumption, injection drug use (IDU), HCV genotype, HIV or HBV coinfection, duration of HCV infection, time since enrolment, cirrhosis, (type of) HCV treatment, and centre at enrolment. Among 5,356 persons, 1,752 (32.7%) were foreign-born. IDU was more common among Swiss- (64.1%) than foreign-born (36.6%) persons. Cirrhosis at enrolment was more frequent among foreign- than Swiss-born persons, reflecting the high frequency of cirrhosis among Italian-born persons who acquired HCV between 1950 and 1970 in Italian healthcare settings. Although antiviral treatment coverage was similar, the sustained viral response rate was increased and the mortality was lower among foreign-vs. Swiss-born persons, with the lowest mortality in persons from Asia/Oceania. LTFU was more frequent in persons from Germany, Eastern and Southern Europe, and the Americas. In conclusion, in Switzerland, a country with universal healthcare, geographic origin had no influence on hepatitis C treatment access, and the better treatment outcomes among foreign-born persons were likely explained by their lower prevalence of IDU and alcohol consumption than among Swiss-born persons.

BACKGROUND: It is cost-effective to perform an HIV test in people with specific indicator conditions (IC) with an undiagnosed HIV prevalence of at least 0.1%. Our aim was to determine the HIV prevalence for 14 different conditions across 20 European countries.METHODS: Individuals aged 18-65 years presenting for care with one of 14 ICs between January 2012 and June 2014 were included and routinely offered an HIV test. Logistic regression assessed factors associated with testing HIV positive. Patients presenting with infectious mononucleosis-like syndrome (IMS) were recruited up until September 2015.RESULTS: Of 10,877 patients presenting with an IC and included in the analysis, 303 tested positive (2.8%; 95% CI 2.5-3.1%). People presenting with an IC in Southern and Eastern Europe were more likely to test HIV positive as were people presenting with IMS, lymphadenopathy and leukocytopenia/ thrombocytopenia. One third of people diagnosed with HIV after presenting with IMS reported a negative HIV test in the preceding 12 months. Of patients newly diagnosed with HIV where data was available, 92.6% were promptly linked to care; of these 10.4% were reported lost to follow up or dead 12 months after diagnosis.CONCLUSION: The study showed that 10 conditions had HIV prevalences > 0.1%. These 10 ICs should be adopted into HIV testing and IC specialty guidelines. As IMS presentation can mimic acute HIV sero-conversion and has the highest positivity rate, this IC in particular affords opportunities for earlier diagnosis and public health benefit.

Patients who start combination antiretroviral therapy (cART) during primary HIV-1 infection show a smaller HIV-1 latent reservoir, less immune activation and a smaller viral diversity compared to patients who start cART during chronic infection. We conducted a pilot study to test whether these properties would allow sustained virological suppression after simplification of cART to dolutegravir monotherapy.

Background The effect of neuraminidase inhibitor (NAI) treatment on length of stay (LoS) in patients hospitalized with influenza is unclear. Methods We conducted a one-stage individual participant data (IPD) meta-analysis exploring the association between NAI treatment and LoS in patients hospitalized with 2009 influenza A(H1N1) virus (A[H1N1]pdm09) infection. Using mixed-effects negative binomial regression and adjusting for the propensity to receive NAI, antibiotic, and corticosteroid treatment, we calculated incidence rate ratios (IRRs) and 95% confidence intervals (CIs). Patients with a LoS of, We found that neuraminidase inhibitor (NAI) treatment initiated on hospital admission to patients with clinically diagnosed or laboratory-confirmed A(H1N1)pdm09 virus infection was associated with a reduction in hospital length of stay when compared to later or no NAI treatment.

Background and objective: An estimated 40,000 people were chronically infected with Hepatitis C virus (HCV) in Switzerland in 2016. HCV is one of the leading causes of liver disease, but a considerable proportion of the infected people may remain unaware of their infections until the onset of severe symptoms. A few years ago, the new effective therapy with direct acting antivirals (DAA) became available, and since October 2017, all HCV infected patients in Switzerland are eligible to be treated. The aim of our project was to estimate the effect of various screening strategies on identifying the currently undiagnosed patients, and to project the number of annual new diagnoses, treated patients achieving sustained virological response (SVR), liver related deaths among HCV infected people, and the size of the HCV viremic population, between 2018 and 2029. We compared the following screening interventions with the current practice of screening (baseline scenario): intensified testing of current injection drug users (IDU); screening of former IDU; screening of people originating in high prevalence regions (South Europe, Asia, Africa); screening of people born 1951-1985; and universal screening of the entire population. Methods: We developed a mathematical model of HCV disease progression that simulates individual patients from HCV infection until death. The progression of the disease is represented using health states that account for the current stage of liver disease (F0-F4, decompensated cirrhosis, hepatocellular carcinoma, transplanted liver) and stage of the infection and care (acute, chronic undiagnosed, diagnosed, on treatment, SVR/cured). Patients are assigned demographic and behavioral baseline characteristics. Transition times between health states are sampled from hazard functions, which were parameterized based on a comprehensive literature search and consulting experts. Because of uncertainty in input parameters, we conducted four alternative analyses, combining two assumptions about the rate of fibrosis progression (dynamic age- and stage-dependent vs. constant) and past diagnosis rate among IDU (low increasing vs. constant high). The outputs of the model were converted into the assumed HCV infected population of Switzerland by giving each simulated patient a weight based on his/her baseline characteristics, corresponding to the representativeness of this simulated patient among the true infected population. We used the notification database of the Federal Office of Public Health and the data collected by the Swiss Hepatitis C Cohort Study to estimate the distribution of the characteristics among the individuals diagnosed by 2016. We estimated the size of the undiagnosed population in 2016 by assuming a total infected population of 40,000 individuals. We assumed that the distribution of the characteristics was the same among the individuals infected in a particular year regardless of being diagnosed or not by 2016, and that the number of annual new cases of HCV would continue in the future on the same level as in the recent years. We also conducted sensitivity analyses where we either increased or decreased the total size of the infected population, or the proportion of individuals with high-risk behavior among the undiagnosed, or increased the liver related mortality rate. Results: In this summary, we present the results comparing the future strategies from the main analysis assuming dynamic fibrosis progression and low diagnosis rate among IDU in the past (see Section 6 and Appendix E of the full report for the other analyses). The expected number of new diagnoses in 2018 was about 700 in the baseline scenario, which represents a substantial drop from 2017 due to the decreasing number of undiagnosed patients in the easy-to-identify population groups (Figure i). Afterwards, the annual new diagnoses continued to slightly decrease. More intensive screening of current IDU did not considerably change the number of new diagnoses. With origin based screening, the new diagnoses were slightly above the baseline scenario, with similar pattern across the years. The number of diagnoses in 2018 was considerably higher with birth cohort screening (3,000) and universal screening (3,900). After the first years, the diagnoses decreased rapidly. The model predicted in the baseline scenario that over 7,000 patients would achieve SVR in 2018. Afterwards, the number decreased fast, with only about 200 patients achieving SVR in 2029. The differences in annual number of SVR across the scenarios followed those of the new diagnoses. In particular universal and birth cohort screening scenarios will be able to cure over 1,000 patients more than the baseline scenario in the first years.

Stella G. Muthuri,1 Sudhir Venkatesan,1 Puja R. Myles,1 Jo Leonardi-Bee,1 Wei Shen Lim,2 Abdullah Al Mamun,3 Ashish P. Anovadiya,4 Wildo N. Ara ujo,5 Eduardo Azziz-Baumgartner,6 Clarisa B aez,7 Carlos Bantar,8 Mazen M. Barhoush,9 Matteo Bassetti,10 Bojana Beovic,11 Roland Bingisser,12 Isabelle Bonmarin,13 Victor H. Borja-Aburto,14 Bin Cao,15 Jordi Carratala,16 Mar ıa R. Cuezzo,17 Justin T. Denholm,18 Samuel R. Dominguez,19 Pericles A. D. Duarte,20 Gal Dubnov-Raz,21 Marcela Echavarria,22 Sergio Fanella,23 James Fraser,24 Zhancheng Gao,25 Patrick G erardin,26,27,28,29 Maddalena Giannella,30 Sophie Gubbels,31 Jethro Herberg,32 Anjarath L. Higuera Iglesias,33 Peter H. Hoeger,34 Matthias Hoffmann,35 Xiaoyun Hu,36 Quazi T. Islam,37 Mirela F. Jim enez,38 Amr Kandeel,39 Gerben Keijzers,40 Hossein Khalili,41 Gulam Khandaker,42 Marian Knight,43 Gabriela Kusznierz,44 Ilija Kuzman,45 Arthur M. C. Kwan,46 Idriss Lahlou Amine,47 Eduard Langenegger,48 Kamran B. Lankarani,49 Yee-Sin Leo,50 Rita Linko,51 Pei Liu,52 Faris Madanat,53 Toshie Manabe,54 Elga Mayo-Montero,55 Allison McGeer,56 Ziad A. Memish,57,58 Gokhan Metan,59 Dragan Miki c,60 Kristin G. I. Mohn,61,62 Ahmadreza Moradi,63,64 Pagbajabyn Nymadawa,65 Bulent Ozbay,66 Mehpare Ozkan,67 Dhruv Parekh,68 Mical Paul,69 Wolfgang Poeppl,70 Fernando P. Polack,71,72 Barbara A. Rath,73 Alejandro H. Rodr ıguez,74 Marilda M. Siqueira,75 Joanna Skre zt-Magierło,76 Ewa Talarek,77 Julian W. Tang,78,79,80 Antoni Torres,81 Selda H. T€ or€un,82 Dat Tran,83 Timothy M. Uyeki,84 Annelies van Zwol,85 Wendy Vaudry,86 Daiva Velyvyte,87 Tjasa Vidmar,88 Paul Zarogoulidis,89 PRIDE Consortium Investigators* Jonathan S. Nguyen-Van-Tam1 1Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK. 2Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK. 3International Centre for Diarrhoeal Diseases, Research Bangladesh (ICDDRB), Dhaka, Bangladesh. 4Department of Pharmacology, Government Medical College and Sir Takhtsinhji General Hospital, Bhavnagar, Gujarat, India. 5University of Bras ılia, Bras ılia, DF, Brazil. 6Centers for Disease Control and Prevention, Atlanta, GA, USA. 7Ministerio de Salud de la Provincia de Buenos Aires, Buenos Aires, Argentina. 8Department of Infection Control, Hospital San Mart ın de Paran a, Entre R ıos, Argentina. 9Department of Medicine, King Saud Medical City, Riyadh, Saudi Arabia. 10Santa Maria Misericordia Hospital, Udine, Italy. 11Department of Infectious Diseases, University Medical Centre, Ljubljana, Slovenia. 12Department of Emergency Medicine, University Hospital Basel, Basel, Switzerland. 13Institut de Veille Sanitaire, Saint-Maurice, France. 14Instituto Mexicano del Seguro Social (IMSS), Mexico City, Mexico. 15Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. 16Department of Infectious Diseases, Hospital Universitari de Bellvitge, Bellvitge Institute for Biomedical Research, L’Hospitalet de Llobregat, Red Espa~nola de Investigaci on en Patolog ıa Infecciosa, University of Barcelona, Barcelona, Spain. 17Ministerio de Salud de Tucum an, Tucum an, Argentina. 18Victorian Infectious Diseases Service and Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, Parkville, Vic., Australia. 19Department of Pediatric Infectious Diseases, Children’s Hospital Colorado, University of Colorado School of Medicine, Aurora, CO, USA. 20Universidade Estadual do Oeste do Parana ´, UNIOESTE, Cascavel, PR, Brazil. 21The Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Tel-Hashomer, Israel. 22Clinical Virology Laboratory, CEMIC University Hospital, Buenos Aires, Argentina. 23Section of Pediatric Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada. 24Paediatric Intensive Care Unit, Bristol Children’s Hospital, Bristol, UK. 25Department of Respiratory & Critical Care Medicine, Peking University People’s Hospital, Beijing, China. 26NICU/PICU, PFME, CHU Saint Pierre, Saint Pierre, La R eunion, France. 27CIC 1410 (CHU/Inserm/University of La Re ´union/URML-OI), CHU Saint Pierre, Saint Pierre, La Réunion, France. 28UMR PIMIT (CHU/Inserm/University of La Re ´union/IRD/CNRS), CYROI, Saint Denis – Reunion Island, Saint Denis, France. 29NICU/PICU CHU of La Re ´union, Groupe Hospitalier Sud Re ´union, Saint Pierre, La Re ´union, France. 30Department of Clinical Microbiology and Infectious Diseases, Hospital General Universitario Gregorio Maran ˜o ´n, Madrid, Spain. 31Department of Infectious Disease Epidemiology, Sector for National Health Documentation and Research, Statens Serum Institut, Copenhagen, Denmark. 32Section of Paediatrics, Division of Infectious Disease, Imperial College, London, UK. 33Epidemiology Research Unit, Instituto Nacional de Enfermedades Respiratorias, Ismael Cosı ´o Villegas, Mexico City, Mexico. 34Cath. Children’s Hospital Wilhelmstift, Hamburg, Germany. 35Division of Infectious Diseases and Hospital Epidemiology, Kantonsspital St. Gallen, St. Gallen, Switzerland. 36Peking Union Medical College Hospital, Beijing, China. 37Dhaka Medical College Hospital, Dhaka, Bangladesh. 38Departamento de Ginecologia e Obstetrı ´cia – UFCSPA, Preceptora da Reside ˆncia Me ´dica do Hospital Fe ˆmina, Porto Alegre, Brazil. 39Ministry of Health in Egypt, Cairo, Egypt. 40Gold Coast Hospital, Gold Coast, Qld, Australia. 41Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran. 42National Centre for Immunisation Research and Surveillance (NCIRS), The Children’s Hospital at Westmead, University of Sydney, Sydney, NSW, Australia. 43National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 44National Institute of Respiratory Diseases ‘Emilio Coni’ ANLIS “C. Malbran”, Santa Fe, Argentina. 45School of Medicine, University Hospital for Infectious Diseases, University of Zagreb, Zagreb, Croatia. 46Department of Intensive Care, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong. 47Faculty of Medicine and Pharmacy, Mohammed V Military Teaching Hospital, Biosafety Level 3 and Research Laboratory, University Mohammed V-Souissi, Rabat, Morocco. 48Department of Obstetrics and Gynaecology, Stellenbosch University and Tygerberg, Stellenbosch, South Africa. 49Health Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 50Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore, Singapore. 51Helsinki University Hospital, Helsinki, Finland. 52Department of Infectious Diseases, The First Affiliated Hospital, China Medical University, Shenyang, China. 53Department of Pediatrics, King Hussein Cancer Center, Amman, Jordan. 54Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan. 55Instituto de Medicina Preventiva de la Defensa, Capitan Medico Ramon y Cajal (IMPDEF), Ministerio de Defensa, Madrid, Spain. 56Toronto Invasive Bacterial Diseases Network, University of Toronto, Toronto, ON, Canada. 57Ministry of Health, Riyadh, Saudi Arabia. 58College of Medicine, Alfaisal University, Riyadh, Saudi Arabia. 59Department of Infectious Diseases and Clinical Microbiology, Erciyes University Faculty of Medicine, Kayseri, Turkey. 60Military Medical Academy, Clinic for Infectious and Tropical Diseases, Belgrade, Serbia. 61Section for Infectious Diseases, Medical Department, and Department of Research and Development, Haukeland University Hospital, Bergen, Norway. 62Department of Clinical Science, The Influenza Centre, University of Bergen, Bergen, Norway. 63The Division of Ocular Immunology, Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 64National Research Institute for Tuberculosis and Lung Disease, Massih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 65National Influenza Center, National Center of Communicable Diseases, Ministry of Health, Ulaanbaatar, Mongolia. 66Department of Pulmonary and Critical Care, Yuzuncu Yil University Medical Faculty, Van, Turkey. 67Clinic of Pediatric Neurology, Dr. Sami Ulus Research and Training Hospital of Women’s and Children’s Health and Diseases, Ankara, Turkey. 68Critical Care and Pain Perioperative, Critical Care and Trauma Trials Group, School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK. 69Division of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel. 70Medical University of Vienna, Vienna, Austria. 71Department of Pediatrics, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA. 72Fundacion INFANT, Buenos Aires, Argentina. 73Division of Pneumonology-Immunology, Department of Pediatrics, Charite ´ University Medical Center, Berlin, Germany. 74Critical Care Department, Hospital Joan XXIII, IISPV, URV, CIBERES, Tarragona, Spain. 75Laboratory of Respiratory Viruses, Oswaldo Cruz Institute/Fiocruz, Rio de Janeiro, Brazil. 76Uniwersytet Rzeszowski, Rzeszo ´w, Poland. 77Department of Children’s Infectious Diseases, Medical University of Warsaw, Warsaw, Poland. 78Division of Microbiology/Molecular Diagnostic Centre, Department of Laboratory Medicine, National University Hospital, Singapore, Singapore. 79Alberta Provincial Laboratory for Public Health, University of Alberta Hospital, Edmonton, Canada. 80Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada. 81Hospital Clinic, University of Barcelona, IDIBAPS, CIBERES, Barcelona, Spain. 82Department of Pediatric Infectious Diseases, Istanbul Medical Faculty, Istanbul, Turkey. 83Division of Infectious Diseases, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Canada. 84Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA. 85Department of Pediatric Intensive Care, VU University Medical Center, Amsterdam, The Netherlands. 86Division of Infectious Diseases, Department of Pediatrics, Stollery Children’s Hospital, University of Alberta, Edmonton, AB, Canada. 87Lithuanian University of Health Sciences, Kaunas, Lithuania. 88General Hospital, Slovenj Gradec, Slovenia. 89Unit of Infectious Diseases, University General Hospital of Alexandroupolis, Democritus University Thrace, Dragana, Greece. Correspondence: Jonathan S. Nguyen-Van-Tam, University of Nottingham, City Hospital, DM, Room A28b, Clinical Sciences Building, Nottingham NG5 1PB, UK. E-mail: jvt@nottingham.ac.uk *List of PRIDE Consortium Investigators are in Appendix 1. For affiliations, please see Table S1. Múltipla - ver em notas Background The impact of neuraminidase inhibitors (NAIs) on influenza-related pneumonia (IRP) is not established. Our objective was to investigate the association between NAI treatment and IRP incidence and outcomes in patients hospitalised with A(H1N1) pdm09 virus infection. Methods A worldwide meta-analysis of individual participant data from 20 634 hospitalised patients with laboratory-confirmed A (H1N1)pdm09 (n = 20 021) or clinically diagnosed (n = 613) ‘pandemic influenza’. The primary outcome was radiologically confirmed IRP. Odds ratios (OR) were estimated using generalised linear mixed modelling, adjusting for NAI treatment propensity, antibiotics and corticosteroids. Results Of 20 634 included participants, 5978 (29 0%) had IRP; conversely, 3349 (16 2%) had confirmed the absence of radiographic pneumonia (the comparator). Early NAI treatment (within 2 days of symptom onset) versus no NAI was not significantly associated with IRP [adj. OR 0 83 (95% CI 0 64–1 06; P = 0 136)]. Among the 5978 patients with IRP, early NAI treatment versus none did not impact on mortality [adj. OR = 0 72 (0 44–1 17; P = 0 180)] or likelihood of requiring ventilatory support [adj. OR = 1 17 (0 71– 1 92; P = 0 537)], but early treatment versus later significantly reduced mortality [adj. OR = 0 70 (0 55–0 88; P = 0 003)] and likelihood of requiring ventilatory support [adj. OR = 0 68 (0 54– 0 85; P = 0 001)]. Conclusions Early NAI treatment of patients hospitalised with A (H1N1)pdm09 virus infection versus no treatment did not reduce the likelihood of IRP. However, in patients who developed IRP, early NAI treatment versus later reduced the likelihood of mortality and needing ventilatory support.

BACKGROUND The incidence of anal cancer is high in HIV-positive men who have sex with men (MSM). We modeled the impact of screening strategies and combination antiretroviral therapy (cART) coverage on anal cancer incidence in Switzerland. METHODS Individual-based, dynamic simulation model parameterized with Swiss HIV Cohort Study (SHCS) and literature data. We assumed all men to be HPV infected. CD4 cell count trajectories were the main predictors of anal cancer. From 2016 we modeled cART coverage either as below 100% (corresponding to 2010-2015) or as 100%, and the following four screening strategies: (i) no screening, (ii) yearly anal cytology (Pap smears), (iii) yearly anoscopy and (iv) targeted anoscopy five years after CD4 count dropped below 200 cells/μl. RESULTS Median nadir CD4 cell count of 6,411 MSM increased from 229 cells/μl during 1980-89 to 394 cells/μl during 2010-15; cART coverage increased from 0% to 83.4%. Modeled anal cancer incidence peaked at 81.7/100,000 in 2009, plateaued 2010-2015 and decreased to 58.7 by 2030 with stable cART coverage, and to 52.0 with 100% cART coverage. With yearly cytology, incidence declined to 38.2/100,000 by 2030, with yearly anoscopy to 32.8 and with CD4 count guided anoscopy to 51.3. The numbers needed to screen over 15 years to prevent one anal cancer case (NNS) were 384 for yearly cytology, 313 for yearly anoscopy and 242 for CD4 count dependent screening. CONCLUSIONS Yearly screening of HIV-positive MSM may reduce anal cancer incidence substantially, with a NNS that is comparable to other screening interventions to prevent cancer.

Introduction Antibiotic resistant Neisseria gonorrhoeae (NG) is a global public health challenge; resistance has emerged to all antibiotics used for empirical treatment. In Switzerland, gonorrhoea reports increased from 404 in 2000 to 1895 in 2015, with about 40% in men who have sex with men (MSM). To our knowledge, no studies have examined factors associated with antibiotic resistant NG in Switzerland. Methods We enrolled MSM at clinics in Zürich and Bern from May 2015 to June 2016 presenting with symptoms suggestive of NG, partners of known cases or had a positive NG screening test. MSM completed an online questionnaire on social and behavioural factors and physicians completed a case report form. Specimens from pharynx, rectum and urethra were tested with a molecular test and culture. For culture positive specimens, the minimum inhibitory concentration (MIC) for different antibiotics was determined, using EUCAST 2017 resistance breakpoints for ciprofloxacin, ceftriaxone, cefixime and spectinomycin, and ≥2mg/L for azithromycin (as in a European study). We also examined reduced susceptibility to ceftriaxone (cutoff ≥0.016 mg/L). Cases were MSM with NG and MIC above the breakpoint, controls were MSM with NG and MIC less than or equal to the breakpoint. We used logistic regression to estimate odds ratios (OR) with 95% confidence intervals (CI). Results In total, 164/230 (71%) MSM were positive for NG; culture was positive in 118/164. We compared 45 (39%) cases with ciprofloxacin resistance with 73 controls. Cases were more likely than controls to have concurrent sexual partners (OR 2.2, 95%Cl 0.876.0, p=0.13), to have received oral sex (OR 5.3, 0.644.2, p=0.08), to have had sex abroad in the last three months (OR 2.3, 1.04.9, p=0.08) and for the most recent partner to be casual (OR 2.6, 0.88.5 p=0.08). One NG isolate was azithromycin resistant. No isolates were resistant to spectinomycin, cefixime or ceftriaxone; 21 (18%) had reduced susceptibility to ceftriaxone, but we found no associations with clinical or behavioural factors. Conclusions This study had a limited sample size but is the first in Switzerland to link behavioural factors with antibiotic resistant NG. Ciprofloxacin resistance was common in MSM and there is evidence of reduced susceptibility to ceftriaxone. Interventions for MSM to promote safer sex, especially whilst abroad, and enhanced surveillance of antimicrobial susceptibility could help to identify and control emerging resistance in NG.

Neuraminidase inhibitors were widely used during the 2009/10 influenza A H1N1 pandemic, but evidence for their effectiveness in reducing mortality is uncertain. We did a meta-analysis of individual participant data to investigate the association between use of neuraminidase inhibitors and mortality in patients admitted to hospital with pandemic influenza A H1N1pdm09 virus infection. We assembled data for patients (all ages) admitted to hospital worldwide with laboratory confirmed or clinically diagnosed pandemic influenza A H1N1pdm09 virus infection. We identified potential data contributors from an earlier systematic review of reported studies addressing the same research question. In our systematic review, eligible studies were done between March 1, 2009 (Mexico), or April 1, 2009 (rest of the world), until the WHO declaration of the end of the pandemic (Aug 10, 2010); however, we continued to receive data up to March 14, 2011, from ongoing studies. We did a meta-analysis of individual participant data to assess the association between neuraminidase inhibitor treatment and mortality (primary outcome), adjusting for both treatment propensity and potential confounders, using generalised linear mixed modelling. We assessed the association with time to treatment using time-dependent Cox regression shared frailty modelling. We included data for 29?234 patients from 78 studies of patients admitted to hospital between Jan 2, 2009, and March 14, 2011. Compared with no treatment, neuraminidase inhibitor treatment (irrespective of timing) was associated with a reduction in mortality risk (adjusted odds ratio [OR] 0·81; 95% CI 0·70?0·93; p=0·0024). Compared with later treatment, early treatment (within 2 days of symptom onset) was associated with a reduction in mortality risk (adjusted OR 0·48; 95% CI 0·41?0·56; p

International audience; Background: Neuraminidase inhibitors were widely used during the 2009–10 influenza A H1N1 pandemic, but evidence for their effectiveness in reducing mortality is uncertain. We did a meta-analysis of individual participant data to investigate the association between use of neuraminidase inhibitors and mortality in patients admitted to hospital with pandemic influenza A H1N1pdm09 virus infection.Methods: We assembled data for patients (all ages) admitted to hospital worldwide with laboratory confirmed or clinically diagnosed pandemic influenza A H1N1pdm09 virus infection. We identified potential data contributors from an earlier systematic review of reported studies addressing the same research question. In our systematic review, eligible studies were done between March 1, 2009 (Mexico), or April 1, 2009 (rest of the world), until the WHO declaration of the end of the pandemic (Aug 10, 2010); however, we continued to receive data up to March 14, 2011, from ongoing studies. We did a meta-analysis of individual participant data to assess the association between neuraminidase inhibitor treatment and mortality (primary outcome), adjusting for both treatment propensity and potential confounders, using generalised linear mixed modelling. We assessed the association with time to treatment using time-dependent Cox regression shared frailty modelling.Findings: We included data for 29 234 patients from 78 studies of patients admitted to hospital between Jan 2, 2009, and March 14, 2011. Compared with no treatment, neuraminidase inhibitor treatment (irrespective of timing) was associated with a reduction in mortality risk (adjusted odds ratio [OR] 0·81; 95% CI 0·70–0·93; p=0·0024). Compared with later treatment, early treatment (within 2 days of symptom onset) was associated with a reduction in mortality risk (adjusted OR 0·48; 95% CI 0·41–0·56; p

BACKGROUND: Patterns of morbidity and mortality among human immunodeficiency virus (HIV)-infected individuals taking antiretroviral therapy are changing as a result of immune reconstitution and improved survival. We studied the influence of aging on the epidemiology of non-AIDS diseases in the Swiss HIV Cohort Study. METHODS: The Swiss HIV Cohort Study is a prospective observational cohort established in 1988 with continuous enrollment. We determined the incidence of clinical events (per 1000 person-years) from January 2008 (when a new questionnaire on non-AIDS-related morbidity was introduced) through December 2010. Differences across age groups were analyzed using Cox regression, adjusted for CD4 cell count, viral load, sex, injection drug use, smoking, and years of HIV infection. RESULTS: Overall, 8444 (96%) of 8848 participants contributed data from 40,720 semiannual visits; 2233 individuals (26.4%) were aged 50-64 years, and 450 (5.3%) were aged ≥65 years. The median duration of HIV infection was 15.4 years (95% confidence interval [CI], 9.59-22.0 years); 23.2% had prior clinical AIDS. We observed 994 incident non-AIDS events in the reference period: 201 cases of bacterial pneumonia, 55 myocardial infarctions, 39 strokes, 70 cases of diabetes mellitus, 123 trauma-associated fractures, 37 fractures without adequate trauma, and 115 non-AIDS malignancies. Multivariable hazard ratios for stroke (17.7; CI, 7.06-44.5), myocardial infarction (5.89; 95% CI, 2.17-16.0), diabetes mellitus (3.75; 95% CI, 1.80-7.85), bone fractures without adequate trauma (10.5; 95% CI, 3.58-30.5), osteoporosis (9.13; 95% CI, 4.10-20.3), and non-AIDS-defining malignancies (6.88; 95% CI, 3.89-12.2) were elevated for persons aged ≥65 years. CONCLUSIONS: Comorbidity and multimorbidity because of non-AIDS diseases, particularly diabetes mellitus, cardiovascular disease, non-AIDS-defining malignancies, and osteoporosis, become more important in care of HIV-infected persons and increase with older age.