Epidemiology
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Risk Factors, Seroprevalence and Infectivity of Hepatitis B Virus
ISSN: 2474-3658 Ogbonna et al. J Infect Dis Epidemiol 2021, 7:186 DOI: 10.23937/2474-3658/1510186 Volume 7 | Issue 1 Journal of Open Access Infectious Diseases and Epidemiology Research ArticlE Risk Factors, Seroprevalence and Infectivity of Hepatitis B Virus amongst Children Resident in Orphanages in a Developing Country Chioma Paulina Ogbonna, MBBS, FMCPaed1, Christian Chukwukere Ogoke, MBBS, FWACP2* , Anthony Nnaemeka Ikefuna, MBBS, FMCPaed, FRCP3,4, Tochukwu 1 Check for Chukwukadibia Ezeofor, MBBS, FMCpaed, FRCP , Emeka Charles Nwolisa, MBBS, updates FWACP, FRCP5, Franklin Chime Emerenini, MBBch, FMCpaed1, and Christopher Bismarck Eke MBBS, FWACP4,6 1Consultant Paediatrician, Department of Paediatrics, Federal Medical Centre, Owerri, Nigeria 2Paediatric Neurology Unit, Department of Paediatrics, King Fahad Central Hospital, Abu Arish 82666, Kingdom of Saudi Arabia 3Professor of Paediatrics, Faculty of Medical Sciences, Department of Paediatrics, University of Nigeria, Nigeria 4Consultant Paediatrician, University of Nigeria Teaching Hospital, Nigeria 5Chief Consultant Paediatrician, Federal Medical Centre, Owerri, Nigeria 6Senior Lecturer, Department of Paediatrics, University of Nigeria, Nigeria *Corresponding authors: Dr. Christian Chukwukere Ogoke, Paediatric Neurology Unit, Department of Paediatrics, King Fahad Central Hospital, Abu Arish 82666, Kingdom of Saudi Arabia Abstract Results: Seroprevalence of HBsAg in children in orphanag- es was 20.0% versus 10.7% in the controls (P = 0.031). In- Background: Hepatitis B infection (HBV) remains a signifi- fectivity of HBV was higher in subjects (42.9%) than controls cant clinical and public health problem and is hyperendemic (20.0%) (P = 0.245). Sharing of towel (P = 0.014) sharing of in Nigeria. In highly endemic regions, infections spread from barbing devices (P = 0.001) and circumcision (P = 0.005) mother to child, or by horizontal transmission, with the bur- were the significant risk factors for Hepatitis B virus infec- den of infection being highest in under-fives. -
Scientific Committee Animal Health and Animal Welfare
EUROPEAN COMMISSION HEALTH and CONSUMER PROTECTION DIRECTORATE-GENERAL Directorate C - Scientific Opinions C2 - Management of scientific committees; scientific co-operation and networks Diagnostic Techniques and Vaccines for Foot-and-Mouth Disease, Classical Swine Fever, Avian Influenza and some other important OIE List A Diseases Report of the Scientific Committee on Animal Health and Animal Welfare Adopted 24-25th April 2003 TABLE OF CONTENTS 1. ABBREVIATIONS.....................................................................................................6 2. MANDATE.................................................................................................................9 3. BACKGROUND.........................................................................................................9 4. PREAMBLE..............................................................................................................10 5. RECENT DEVELOPMENTS IN THE DIAGNOSIS OF INFECTIOUS DISEASES ................................................................................................................14 5.1. Introduction .....................................................................................................14 5.2. Screening of animal products ..........................................................................19 5.3. Nucleic acid amplification methods ................................................................19 5.3.1. Extraction of nucleic acid from the test sample ................................20 5.3.2. Target amplification -
The Comparative Clinical Performance of Four SARS-Cov-2 Rapid Antigen Tests and Their Correlation to Infectivity in Vitro
Journal of Clinical Medicine Article The Comparative Clinical Performance of Four SARS-CoV-2 Rapid Antigen Tests and Their Correlation to Infectivity In Vitro Niko Kohmer 1,†, Tuna Toptan 1,† , Christiane Pallas 1, Onur Karaca 1, Annika Pfeiffer 1 , Sandra Westhaus 1, Marek Widera 1 , Annemarie Berger 1, Sebastian Hoehl 1 , Martin Kammel 2,3, Sandra Ciesek 1,4,5,*,‡ and Holger F. Rabenau 1,*,‡ 1 Institute for Medical Virology, University Hospital, Goethe University Frankfurt, 60596 Frankfurt, Germany; [email protected] (N.K.); [email protected] (T.T.); [email protected] (C.P.); [email protected] (O.K.); [email protected] (A.P.); [email protected] (S.W.); [email protected] (M.W.); [email protected] (A.B.); [email protected] (S.H.) 2 Institut fuer Qualitaetssicherung in der Virusdiagnostik-IQVD der GmbH, 14129 Berlin, Germany; [email protected] 3 INSTAND Gesellschaft zur Foerderung der Qualitaetssicherung in Medizinischen Laboratorien e.V., 40223 Duesseldorf, Germany 4 German Centre for Infection Research, External Partner Site, 60323 Frankfurt, Germany 5 Fraunhofer Institute for Molecular Biology and Applied Ecology (IME), Branch Translational Medicine and Pharmacology, 60596 Frankfurt, Germany * Correspondence: [email protected] (S.C.); [email protected] (H.F.R.); Tel.: +49-696-301-5219 (S.C.); Tel.: +49-696-301-5312 (H.F.R.) † These authors contributed equally to this work. ‡ These authors contributed equally to this work. Abstract: Citation: Kohmer, N.; Toptan, T.; Due to globally rising numbers of severe acute respiratory syndrome coronavirus 2 (SARS- Pallas, C.; Karaca, O.; Pfeiffer, A.; CoV-2) infections, resources for real-time reverse-transcription polymerase chain reaction (rRT-PCR)- Westhaus, S.; Widera, M.; Berger, A.; based testing have been exhausted. -
Inferring the Association Between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-Cov-2
viruses Article Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2 Shi Zhao 1,2,* , Jingzhi Lou 1, Marc K. C. Chong 1,2 , Lirong Cao 1,2 , Hong Zheng 1, Zigui Chen 3 , Renee W. Y. Chan 4,5,6,7 , Benny C. Y. Zee 1,2 , Paul K. S. Chan 3 and Maggie H. Wang 1,2,* 1 JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; [email protected] (J.L.); [email protected] (M.K.C.C.); [email protected] (L.C.); [email protected] (H.Z.); [email protected] (B.C.Y.Z.) 2 CUHK Shenzhen Research Institute, Shenzhen 518000, China 3 Department of Microbiology, Chinese University of Hong Kong, Hong Kong, China; [email protected] (Z.C.); [email protected] (P.K.S.C.) 4 Department of Pediatrics, Chinese University of Hong Kong, Hong Kong, China; [email protected] 5 Hong Kong Hub of Pediatric Excellence, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China 6 CUHK-UMCU Joint Research Laboratory of Respiratory Virus & Immunobiology, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China 7 Li Ka Shing Institute of Health Sciences, Faculty of Medicine, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China * Correspondence: [email protected] (S.Z.); [email protected] (M.H.W.) Abstract: As COVID-19 is posing a serious threat to global health, the emerging mutation in SARS- CoV-2 genomes, for example, N501Y substitution, is one of the major challenges against control of the Citation: Zhao, S.; Lou, J.; Chong, pandemic. -
FDA Oncology Experience in Innovative Adaptive Trial Designs
Innovative Adaptive Trial Designs Rajeshwari Sridhara, Ph.D. Director, Division of Biometrics V Office of Biostatistics, CDER, FDA 9/3/2015 Sridhara - Ovarian cancer workshop 1 Fixed Sample Designs • Patient population, disease assessments, treatment, sample size, hypothesis to be tested, primary outcome measure - all fixed • No change in the design features during the study Adaptive Designs • A study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (interim data) from subjects in the study 9/3/2015 Sridhara - Ovarian cancer workshop 2 Bayesian Designs • In the Bayesian paradigm, the parameter measuring treatment effect is regarded as a random variable • Bayesian inference is based on the posterior distribution (Bayes’ Rule – updated based on observed data) – Outcome adaptive • By definition adaptive design 9/3/2015 Sridhara - Ovarian cancer workshop 3 Adaptive Designs (Frequentist or Bayesian) • Allows for planned design modifications • Modifications based on data accrued in the trial up to the interim time • Unblinded or blinded interim results • Control probability of false positive rate for multiple options • Control operational bias • Assumes independent increments of information 9/3/2015 Sridhara - Ovarian cancer workshop 4 Enrichment Designs – Prognostic or Predictive • Untargeted or All comers design: – post-hoc enrichment, prospective-retrospective designs – Marker evaluation after randomization (example: KRAS in cetuximab -
FORMULAS from EPIDEMIOLOGY KEPT SIMPLE (3E) Chapter 3: Epidemiologic Measures
FORMULAS FROM EPIDEMIOLOGY KEPT SIMPLE (3e) Chapter 3: Epidemiologic Measures Basic epidemiologic measures used to quantify: • frequency of occurrence • the effect of an exposure • the potential impact of an intervention. Epidemiologic Measures Measures of disease Measures of Measures of potential frequency association impact (“Measures of Effect”) Incidence Prevalence Absolute measures of Relative measures of Attributable Fraction Attributable Fraction effect effect in exposed cases in the Population Incidence proportion Incidence rate Risk difference Risk Ratio (Cumulative (incidence density, (Incidence proportion (Incidence Incidence, Incidence hazard rate, person- difference) Proportion Ratio) Risk) time rate) Incidence odds Rate Difference Rate Ratio (Incidence density (Incidence density difference) ratio) Prevalence Odds Ratio Difference Macintosh HD:Users:buddygerstman:Dropbox:eks:formula_sheet.doc Page 1 of 7 3.1 Measures of Disease Frequency No. of onsets Incidence Proportion = No. at risk at beginning of follow-up • Also called risk, average risk, and cumulative incidence. • Can be measured in cohorts (closed populations) only. • Requires follow-up of individuals. No. of onsets Incidence Rate = ∑person-time • Also called incidence density and average hazard. • When disease is rare (incidence proportion < 5%), incidence rate ≈ incidence proportion. • In cohorts (closed populations), it is best to sum individual person-time longitudinally. It can also be estimated as Σperson-time ≈ (average population size) × (duration of follow-up). Actuarial adjustments may be needed when the disease outcome is not rare. • In an open populations, Σperson-time ≈ (average population size) × (duration of follow-up). Examples of incidence rates in open populations include: births Crude birth rate (per m) = × m mid-year population size deaths Crude mortality rate (per m) = × m mid-year population size deaths < 1 year of age Infant mortality rate (per m) = × m live births No. -
The Global Case-Fatality Rate of COVID-19 Has Been Declining Since May 2020
Am. J. Trop. Med. Hyg., 104(6), 2021, pp. 2176–2184 doi:10.4269/ajtmh.20-1496 Copyright © 2021 by The American Society of Tropical Medicine and Hygiene The Global Case-Fatality Rate of COVID-19 Has Been Declining Since May 2020 Mohammad Nayeem Hasan,1† Najmul Haider,2† Florian L. Stigler,3 Rumi Ahmed Khan,4 David McCoy,5 Alimuddin Zumla,6,7 Richard A. Kock,2 and Md. Jamal Uddin1 1Department of Statistics, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh; 2The Royal Veterinary College, University of London, Hawkshead Lane, North Mymms, Hatfield, Hertfordshire, United Kingdom; 3Austrian Sickness Fund, Vienna, Austria; 4Department of Critical Care Medicine, Orlando Regional Medical Centre, Orlando, Florida; 5Institute of Population Health Sciences, Barts and London Medical and Dental School, Queen Mary University of London, London, United Kingdom; 6Department of Infection, Division of Infection and Immunity, Centre for Clinical Microbiology, Royal Free Campus, University College London, London, United Kingdom; 7National Institute for Health Research Biomedical Research Centre, University College London Hospitals, London, United Kingdom Abstract. The objective of this study was to evaluate the trend of reported case fatality rate (rCFR) of COVID-19 over time, using globally reported COVID-19 cases and mortality data. We collected daily COVID-19 diagnoses and mortality data from the WHO’s daily situation reports dated January 1 to December 31, 2020. We performed three time-series models [simple exponential smoothing, auto-regressive integrated moving average, and automatic forecasting time- series (Prophet)] to identify the global trend of rCFR for COVID-19. We used beta regression models to investigate the association between the rCFR and potential predictors of each country and reported incidence rate ratios (IRRs) of each variable. -
Incidence and Secondary Transmission of SARS-Cov-2 Infections in Schools
Prepublication Release Incidence and Secondary Transmission of SARS-CoV-2 Infections in Schools Kanecia O. Zimmerman, MD; Ibukunoluwa C. Akinboyo, MD; M. Alan Brookhart, PhD; Angelique E. Boutzoukas, MD; Kathleen McGann, MD; Michael J. Smith, MD, MSCE; Gabriela Maradiaga Panayotti, MD; Sarah C. Armstrong, MD; Helen Bristow, MPH; Donna Parker, MPH; Sabrina Zadrozny, PhD; David J. Weber, MD, MPH; Daniel K. Benjamin, Jr., MD, PhD; for The ABC Science Collaborative DOI: 10.1542/peds.2020-048090 Journal: Pediatrics Article Type: Regular Article Citation: Zimmerman KO, Akinboyo IC, Brookhart A, et al. Incidence and secondary transmission of SARS-CoV-2 infections in schools. Pediatrics. 2021; doi: 10.1542/peds.2020- 048090 This is a prepublication version of an article that has undergone peer review and been accepted for publication but is not the final version of record. This paper may be cited using the DOI and date of access. This paper may contain information that has errors in facts, figures, and statements, and will be corrected in the final published version. The journal is providing an early version of this article to expedite access to this information. The American Academy of Pediatrics, the editors, and authors are not responsible for inaccurate information and data described in this version. Downloaded from©2021 www.aappublications.org/news American Academy by of guest Pediatrics on September 27, 2021 Prepublication Release Incidence and Secondary Transmission of SARS-CoV-2 Infections in Schools Kanecia O. Zimmerman, MD1,2,3; Ibukunoluwa C. Akinboyo, MD1,2; M. Alan Brookhart, PhD4; Angelique E. Boutzoukas, MD1,2; Kathleen McGann, MD2; Michael J. -
Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests Document Issued On: March 13, 2007
Guidance for Industry and FDA Staff Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests Document issued on: March 13, 2007 The draft of this document was issued on March 12, 2003. For questions regarding this document, contact Kristen Meier at 240-276-3060, or send an e-mail to [email protected]. U.S. Department of Health and Human Services Food and Drug Administration Center for Devices and Radiological Health Diagnostic Devices Branch Division of Biostatistics Office of Surveillance and Biometrics Contains Nonbinding Recommendations Preface Public Comment Written comments and suggestions may be submitted at any time for Agency consideration to the Division of Dockets Management, Food and Drug Administration, 5630 Fishers Lane, Room 1061, (HFA-305), Rockville, MD, 20852. Alternatively, electronic comments may be submitted to http://www.fda.gov/dockets/ecomments. When submitting comments, please refer to Docket No. 2003D-0044. Comments may not be acted upon by the Agency until the document is next revised or updated. Additional Copies Additional copies are available from the Internet at: http://www.fda.gov/cdrh/osb/guidance/1620.pdf. You may also send an e-mail request to [email protected] to receive an electronic copy of the guidance or send a fax request to 240-276-3151 to receive a hard copy. Please use the document number 1620 to identify the guidance you are requesting. Contains Nonbinding Recommendations Table of Contents 1. Background....................................................................................................4 -
Estimated HIV Incidence and Prevalence in the United States
Volume 26, Number 1 Estimated HIV Incidence and Prevalence in the United States, 2015–2019 This issue of the HIV Surveillance Supplemental Report is published by the Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Atlanta, Georgia. Estimates are presented for the incidence and prevalence of HIV infection among adults and adolescents (aged 13 years and older) based on data reported to CDC through December 2020. The HIV Surveillance Supplemental Report is not copyrighted and may be used and reproduced without permission. Citation of the source is, however, appreciated. Suggested citation Centers for Disease Control and Prevention. Estimated HIV incidence and prevalence in the United States, 2015–2019. HIV Surveillance Supplemental Report 2021;26(No. 1). http://www.cdc.gov/ hiv/library/reports/hiv-surveillance.html. Published May 2021. Accessed [date]. On the Web: http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html Confidential information, referrals, and educational material on HIV infection CDC-INFO 1-800-232-4636 (in English, en Español) 1-888-232-6348 (TTY) http://wwwn.cdc.gov/dcs/ContactUs/Form Acknowledgments Publication of this report was made possible by the contributions of the state and territorial health departments and the HIV surveillance programs that provided surveillance data to CDC. This report was prepared by the following staff and contractors of the Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC: Laurie Linley, Anna Satcher Johnson, Ruiguang Song, Sherry Hu, Baohua Wu, H. -
Infectivity of Severe Acute Respiratory Syndrome Coronavirus 2 in Children Compared with Adults
Early release, published at www.cmaj.ca on April 9, 2021. Subject to revision. RESEARCH Infectivity of severe acute respiratory syndrome coronavirus 2 in children compared with adults Jared Bullard MD, Duane Funk MD, Kerry Dust PhD, Lauren Garnett BSc, Kaylie Tran BSc, Alex Bello PhD, James E. Strong MD PhD, Santina J. Lee MD, Jillian Waruk PhD, Adam Hedley BSc, David Alexander PhD, Paul Van Caeseele MD, Carla Loeppky PhD, Guillaume Poliquin MD PhD n Cite as: CMAJ 2021. doi: 10.1503/cmaj.210263; early-released April 9, 2021 ABSTRACT BACKGROUND: The role of children in the infective dose (TCID50/mL) between adults (p < 0.001). The median TCID50/mL transmission and community spread of adults and children. was significantly lower in children aged severe acute respiratory syndrome coro- 11–17 years (316, interquartile range navirus 2 (SARS-CoV-2) is unclear. We RESULTS: Among 305 samples positive [IQR] 178–2125) than adults (5620, IQR aimed to quantify the infectivity of SARS- for SARS-CoV-2 by RT-PCR, 97 samples 1171 to 17 800, p < 0.001). Cycle thresh- CoV-2 in nasopharyngeal samples from were from children aged 10 years or old was an accurate predictor of posi- children compared with adults. younger, 78 were from children aged tive culture in both children and adults 11–17 years and 130 were from adults (area under the receiver-operator curve, METHODS: We obtained nasopharyngeal (≥ 18 yr). Viral growth in culture was 0.87, 95% CI 0.81–0.93 v. 0.89, 95% CI swabs from adult and pediatric cases of present in 31% of samples, including 18 0.83–0.96, p = 0.6). -
Understanding Relative Risk, Odds Ratio, and Related Terms: As Simple As It Can Get Chittaranjan Andrade, MD
Understanding Relative Risk, Odds Ratio, and Related Terms: As Simple as It Can Get Chittaranjan Andrade, MD Each month in his online Introduction column, Dr Andrade Many research papers present findings as odds ratios (ORs) and considers theoretical and relative risks (RRs) as measures of effect size for categorical outcomes. practical ideas in clinical Whereas these and related terms have been well explained in many psychopharmacology articles,1–5 this article presents a version, with examples, that is meant with a view to update the knowledge and skills to be both simple and practical. Readers may note that the explanations of medical practitioners and examples provided apply mostly to randomized controlled trials who treat patients with (RCTs), cohort studies, and case-control studies. Nevertheless, similar psychiatric conditions. principles operate when these concepts are applied in epidemiologic Department of Psychopharmacology, National Institute research. Whereas the terms may be applied slightly differently in of Mental Health and Neurosciences, Bangalore, India different explanatory texts, the general principles are the same. ([email protected]). ABSTRACT Clinical Situation Risk, and related measures of effect size (for Consider a hypothetical RCT in which 76 depressed patients were categorical outcomes) such as relative risks and randomly assigned to receive either venlafaxine (n = 40) or placebo odds ratios, are frequently presented in research (n = 36) for 8 weeks. During the trial, new-onset sexual dysfunction articles. Not all readers know how these statistics was identified in 8 patients treated with venlafaxine and in 3 patients are derived and interpreted, nor are all readers treated with placebo. These results are presented in Table 1.