Last data update: Sep 16, 2024. (Total: 47680 publications since 2009)
Records 1-30 (of 112 Records) |
Query Trace: Andrea W [original query] |
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A case-cohort study of per- and polyfluoroalkyl substance concentrations and incident prostate cancer in the Cancer Prevention Study-II LifeLink Cohort study
Alyssa NT , Lauren RT , James MH , Rodriguez J , Ying W , Johnni D , WRyan D , Andrea W . Environ Res 2024 119560 INTRODUCTION: Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent, potentially carcinogenic chemicals. Previous studies investigating PFAS exposure and prostate cancer yielded mixed findings. We aimed to investigate associations between PFAS exposure and incident prostate cancer in a large cohort of U.S. men, overall and by selected demographic, lifestyle, and medical-related characteristics. METHODS: We conducted a case-cohort study among Cancer Prevention Study-II LifeLink Cohort participants who, at baseline (1998-2001), had serum specimens collected and no prior cancer diagnosis. The study included all men diagnosed with prostate cancer (n=1610) during follow-up (baseline-June 30, 2015) and a random sub-cohort of 500 men. PFAS concentrations [perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorooctanoic acid (PFOA)] were measured in stored serum specimens. We used multivariable Cox proportional hazards models to estimate associations between PFAS concentrations and prostate cancer, overall and by selected characteristics (grade, stage, family history, age, education, smoking status, and alcohol consumption). RESULTS: Prostate cancer hazards were slightly higher among men with concentrations in the highest (Q4) vs lowest quartile (Q1) for PFHxS [hazard ratio (HR) (95% CI): 1.18 (0.88-1.59)] and PFOS [HR (95% CI): 1.18 (0.89-1.58)], but not for PFNA or PFOA. However, we observed heterogeneous associations by age, family history of prostate cancer (PFHxS), alcohol consumption (PFHxS), and education (PFNA). For example, no meaningful associations were observed among men aged <70 years at serum collection, but among men aged ≥70 years, HRs (95% CIs) comparing Q4 to Q1 were PFHxS 1.54 (1.02-2.31) and PFOS 1.62 (1.08-2.44). No meaningful heterogeneity in associations were observed by tumor grade or stage. CONCLUSIONS: Our findings do not clearly support an association between the PFAS considered and prostate cancer. However, positive associations observed in some subgroups, and consistently positive associations observed for PFHxS warrant further investigation. |
The United States COVID-19 Forecast Hub dataset (preprint)
Cramer EY , Huang Y , Wang Y , Ray EL , Cornell M , Bracher J , Brennen A , Rivadeneira AJC , Gerding A , House K , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mody V , Mody V , Niemi J , Stark A , Shah A , Wattanchit N , Zorn MW , Reich NG , US COVID-19 Forecast Hub Consortium , Lopez VK , Walker JW , Slayton RB , Johansson MA , Biggerstaff M . medRxiv 2021 2021.11.04.21265886 Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub |
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)
Cramer EY , Ray EL , Lopez VK , Bracher J , Brennen A , Castro Rivadeneira AJ , Gerding A , Gneiting T , House KH , Huang Y , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mühlemann A , Niemi J , Shah A , Stark A , Wang Y , Wattanachit N , Zorn MW , Gu Y , Jain S , Bannur N , Deva A , Kulkarni M , Merugu S , Raval A , Shingi S , Tiwari A , White J , Abernethy NF , Woody S , Dahan M , Fox S , Gaither K , Lachmann M , Meyers LA , Scott JG , Tec M , Srivastava A , George GE , Cegan JC , Dettwiller ID , England WP , Farthing MW , Hunter RH , Lafferty B , Linkov I , Mayo ML , Parno MD , Rowland MA , Trump BD , Zhang-James Y , Chen S , Faraone SV , Hess J , Morley CP , Salekin A , Wang D , Corsetti SM , Baer TM , Eisenberg MC , Falb K , Huang Y , Martin ET , McCauley E , Myers RL , Schwarz T , Sheldon D , Gibson GC , Yu R , Gao L , Ma Y , Wu D , Yan X , Jin X , Wang YX , Chen Y , Guo L , Zhao Y , Gu Q , Chen J , Wang L , Xu P , Zhang W , Zou D , Biegel H , Lega J , McConnell S , Nagraj VP , Guertin SL , Hulme-Lowe C , Turner SD , Shi Y , Ban X , Walraven R , Hong QJ , Kong S , van de Walle A , Turtle JA , Ben-Nun M , Riley S , Riley P , Koyluoglu U , DesRoches D , Forli P , Hamory B , Kyriakides C , Leis H , Milliken J , Moloney M , Morgan J , Nirgudkar N , Ozcan G , Piwonka N , Ravi M , Schrader C , Shakhnovich E , Siegel D , Spatz R , Stiefeling C , Wilkinson B , Wong A , Cavany S , España G , Moore S , Oidtman R , Perkins A , Kraus D , Kraus A , Gao Z , Bian J , Cao W , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Vespignani A , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Xiong X , Zheng A , Baek J , Farias V , Georgescu A , Levi R , Sinha D , Wilde J , Perakis G , Bennouna MA , Nze-Ndong D , Singhvi D , Spantidakis I , Thayaparan L , Tsiourvas A , Sarker A , Jadbabaie A , Shah D , Della Penna N , Celi LA , Sundar S , Wolfinger R , Osthus D , Castro L , Fairchild G , Michaud I , Karlen D , Kinsey M , Mullany LC , Rainwater-Lovett K , Shin L , Tallaksen K , Wilson S , Lee EC , Dent J , Grantz KH , Hill AL , Kaminsky J , Kaminsky K , Keegan LT , Lauer SA , Lemaitre JC , Lessler J , Meredith HR , Perez-Saez J , Shah S , Smith CP , Truelove SA , Wills J , Marshall M , Gardner L , Nixon K , Burant JC , Wang L , Gao L , Gu Z , Kim M , Li X , Wang G , Wang Y , Yu S , Reiner RC , Barber R , Gakidou E , Hay SI , Lim S , Murray C , Pigott D , Gurung HL , Baccam P , Stage SA , Suchoski BT , Prakash BA , Adhikari B , Cui J , Rodríguez A , Tabassum A , Xie J , Keskinocak P , Asplund J , Baxter A , Oruc BE , Serban N , Arik SO , Dusenberry M , Epshteyn A , Kanal E , Le LT , Li CL , Pfister T , Sava D , Sinha R , Tsai T , Yoder N , Yoon J , Zhang L , Abbott S , Bosse NI , Funk S , Hellewell J , Meakin SR , Sherratt K , Zhou M , Kalantari R , Yamana TK , Pei S , Shaman J , Li ML , Bertsimas D , Skali Lami O , Soni S , Tazi Bouardi H , Ayer T , Adee M , Chhatwal J , Dalgic OO , Ladd MA , Linas BP , Mueller P , Xiao J , Wang Y , Wang Q , Xie S , Zeng D , Green A , Bien J , Brooks L , Hu AJ , Jahja M , McDonald D , Narasimhan B , Politsch C , Rajanala S , Rumack A , Simon N , Tibshirani RJ , Tibshirani R , Ventura V , Wasserman L , O'Dea EB , Drake JM , Pagano R , Tran QT , Ho LST , Huynh H , Walker JW , Slayton RB , Johansson MA , Biggerstaff M , Reich NG . medRxiv 2021 2021.02.03.21250974 Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44 |
Causes of severe pneumonia requiring hospital admission in children without HIV infection from Africa and Asia: the PERCH multi-country case-control study
Pneumonia Etiology Research for Child Health Study Group , O'Brien Katherine L , Levine Orin S , Knoll Maria Deloria , Feikin Daniel R , DeLuca Andrea N , Driscoll Amanda J , Fancourt Nicholas , Fu Wei , Haddix Meredith , Hammitt Laura L , Higdon Melissa M , Kagucia E Wangeci , Karron Ruth A , Li Mengying , Park Daniel E , Prosperi Christine , Shi Qiyuan , Wu Zhenke , Zeger Scott L , Watson Nora L , Crawley Jane , Murdoch David R , Brooks W Abdullah , Endtz Hubert P , Zaman Khalequ , Goswami Doli , Hossain Lokman , Jahan Yasmin , Chisti Mohammod Jobayer , Howie Stephen R C , Ebruke Bernard E , Antonio Martin , McLellan Jessica L , Machuka Eunice M , Shamsul Arifin , Zaman Syed M A , Mackenzie Grant , Scott J Anthony G , Awori Juliet O , Morpeth Susan C , Kamau Alice , Kazungu Sidi , Ominde Micah Silaba , Kotloff Karen L , Tapia Milagritos D , Sow Samba O , Sylla Mamadou , Tamboura Boubou , Onwuchekwa Uma , Kourouma Nana , Toure Aliou , Sissoko Seydou , Madhi Shabir A , Moore David P , Adrian Peter V , Baillie Vicky L , Kuwanda Locadiah , Mudau Azwifarwi , Groome Michelle J , Mahomed Nasreen , Simões Eric A F , Baggett Henry C , Thamthitiwat Somsak , Maloney Susan A , Bunthi Charatdao , Rhodes Julia , Sawatwong Pongpun , Akarasewi Pasakorn , Thea Donald M , Mwananyanda Lawrence , Chipeta James , Seidenberg Phil , Mwansa James , Somwe Somwe Wa , Kwenda Geoffrey , Anderson Trevor P , Mitchell Joanne L . Lancet 2019 394 (10200) 757-779 BACKGROUND: Pneumonia is the leading cause of death among children younger than 5 years. In this study, we estimated causes of pneumonia in young African and Asian children, using novel analytical methods applied to clinical and microbiological findings. METHODS: We did a multi-site, international case-control study in nine study sites in seven countries: Bangladesh, The Gambia, Kenya, Mali, South Africa, Thailand, and Zambia. All sites enrolled in the study for 24 months. Cases were children aged 1-59 months admitted to hospital with severe pneumonia. Controls were age-group-matched children randomly selected from communities surrounding study sites. Nasopharyngeal and oropharyngeal (NP-OP), urine, blood, induced sputum, lung aspirate, pleural fluid, and gastric aspirates were tested with cultures, multiplex PCR, or both. Primary analyses were restricted to cases without HIV infection and with abnormal chest x-rays and to controls without HIV infection. We applied a Bayesian, partial latent class analysis to estimate probabilities of aetiological agents at the individual and population level, incorporating case and control data. FINDINGS: Between Aug 15, 2011, and Jan 30, 2014, we enrolled 4232 cases and 5119 community controls. The primary analysis group was comprised of 1769 (41·8% of 4232) cases without HIV infection and with positive chest x-rays and 5102 (99·7% of 5119) community controls without HIV infection. Wheezing was present in 555 (31·7%) of 1752 cases (range by site 10·6-97·3%). 30-day case-fatality ratio was 6·4% (114 of 1769 cases). Blood cultures were positive in 56 (3·2%) of 1749 cases, and Streptococcus pneumoniae was the most common bacteria isolated (19 [33·9%] of 56). Almost all cases (98·9%) and controls (98·0%) had at least one pathogen detected by PCR in the NP-OP specimen. The detection of respiratory syncytial virus (RSV), parainfluenza virus, human metapneumovirus, influenza virus, S pneumoniae, Haemophilus influenzae type b (Hib), H influenzae non-type b, and Pneumocystis jirovecii in NP-OP specimens was associated with case status. The aetiology analysis estimated that viruses accounted for 61·4% (95% credible interval [CrI] 57·3-65·6) of causes, whereas bacteria accounted for 27·3% (23·3-31·6) and Mycobacterium tuberculosis for 5·9% (3·9-8·3). Viruses were less common (54·5%, 95% CrI 47·4-61·5 vs 68·0%, 62·7-72·7) and bacteria more common (33·7%, 27·2-40·8 vs 22·8%, 18·3-27·6) in very severe pneumonia cases than in severe cases. RSV had the greatest aetiological fraction (31·1%, 95% CrI 28·4-34·2) of all pathogens. Human rhinovirus, human metapneumovirus A or B, human parainfluenza virus, S pneumoniae, M tuberculosis, and H influenzae each accounted for 5% or more of the aetiological distribution. We observed differences in aetiological fraction by age for Bordetella pertussis, parainfluenza types 1 and 3, parechovirus-enterovirus, P jirovecii, RSV, rhinovirus, Staphylococcus aureus, and S pneumoniae, and differences by severity for RSV, S aureus, S pneumoniae, and parainfluenza type 3. The leading ten pathogens of each site accounted for 79% or more of the site's aetiological fraction. INTERPRETATION: In our study, a small set of pathogens accounted for most cases of pneumonia requiring hospital admission. Preventing and treating a subset of pathogens could substantially affect childhood pneumonia outcomes. FUNDING: Bill & Melinda Gates Foundation. |
A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference.
Calleja N , AbdAllah A , Abad N , Ahmed N , Albarracin D , Altieri E , Anoko JN , Arcos R , Azlan AA , Bayer J , Bechmann A , Bezbaruah S , Briand SC , Brooks I , Bucci LM , Burzo S , Czerniak C , De Domenico M , Dunn AG , Ecker UKH , Espinosa L , Francois C , Gradon K , Gruzd A , Gülgün BS , Haydarov R , Hurley C , Astuti SI , Ishizumi A , Johnson N , Johnson Restrepo D , Kajimoto M , Koyuncu A , Kulkarni S , Lamichhane J , Lewis R , Mahajan A , Mandil A , McAweeney E , Messer M , Moy W , Ndumbi Ngamala P , Nguyen T , Nunn M , Omer SB , Pagliari C , Patel P , Phuong L , Prybylski D , Rashidian A , Rempel E , Rubinelli S , Sacco P , Schneider A , Shu K , Smith M , Sufehmi H , Tangcharoensathien V , Terry R , Thacker N , Trewinnard T , Turner S , Tworek H , Uakkas S , Vraga E , Wardle C , Wasserman H , Wilhelm E , Würz A , Yau B , Zhou L , Purnat TD . JMIR Infodemiology 2021 1 (1) e30979 BACKGROUND: An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders. OBJECTIVE: The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics. METHODS: As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions. RESULTS: The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions. CONCLUSIONS: Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider. |
Ebola Virus Disease Outbreak - Democratic Republic of the Congo, August 2018-November 2019.
Aruna A , Mbala P , Minikulu L , Mukadi D , Bulemfu D , Edidi F , Bulabula J , Tshapenda G , Nsio J , Kitenge R , Mbuyi G , Mwanzembe C , Kombe J , Lubula L , Shako JC , Mossoko M , Mulangu F , Mutombo A , Sana E , Tutu Y , Kabange L , Makengo J , Tshibinkufua F , Ahuka-Mundeke S , Muyembe JJ , Ebola Response CDC , Alarcon Walter , Bonwitt Jesse , Bugli Dante , Bustamante Nirma D , Choi Mary , Dahl Benjamin A , DeCock Kevin , Dismer Amber , Doshi Reena , Dubray Christine , Fitter David , Ghiselli Margherita , Hall Noemi , Hamida Amen Ben , McCollum Andrea M , Neatherlin John , Raghunathan Pratima L , Ravat Fatima , Reynolds Mary G , Rico Adriana , Smith Nailah , Soke Gnakub Norbert , Trudeau Aimee T , Victory Kerton R , Worrell Mary Claire . MMWR Morb Mortal Wkly Rep 2019 68 (50) 1162-1165 On August 1, 2018, the Democratic Republic of the Congo Ministry of Health (DRC MoH) declared the tenth outbreak of Ebola virus disease (Ebola) in DRC, in the North Kivu province in eastern DRC on the border with Uganda, 8 days after another Ebola outbreak was declared over in northwest Équateur province. During mid- to late-July 2018, a cluster of 26 cases of acute hemorrhagic fever, including 20 deaths, was reported in North Kivu province.* Blood specimens from six patients hospitalized in the Mabalako health zone and sent to the Institut National de Recherche Biomédicale (National Biomedical Research Institute) in Kinshasa tested positive for Ebola virus. Genetic sequencing confirmed that the outbreaks in North Kivu and Équateur provinces were unrelated. From North Kivu province, the outbreak spread north to Ituri province, and south to South Kivu province (1). On July 17, 2019, the World Health Organization designated the North Kivu and Ituri outbreak a public health emergency of international concern, based on the geographic spread of the disease to Goma, the capital of North Kivu province, and to Uganda and the challenges to implementing prevention and control measures specific to this region (2). This report describes the outbreak in the North Kivu and Ituri provinces. As of November 17, 2019, a total of 3,296 Ebola cases and 2,196 (67%) deaths were reported, making this the second largest documented outbreak after the 2014-2016 epidemic in West Africa, which resulted in 28,600 cases and 11,325 deaths.(†) Since August 2018, DRC MoH has been collaborating with partners, including the World Health Organization, the United Nations Children's Fund, the United Nations Office for the Coordination of Humanitarian Affairs, the International Organization of Migration, The Alliance for International Medical Action (ALIMA), Médecins Sans Frontières, DRC Red Cross National Society, and CDC, to control the outbreak. Enhanced communication and effective community engagement, timing of interventions during periods of relative stability, and intensive training of local residents to manage response activities with periodic supervision by national and international personnel are needed to end the outbreak. |
Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing.
Allix-Béguec C , Arandjelovic I , Bi L , Beckert P , Bonnet M , Bradley P , Cabibbe AM , Cancino-Muñoz I , Caulfield MJ , Chaiprasert A , Cirillo DM , Clifton DA , Comas I , Crook DW , De Filippo MR , de Neeling H , Diel R , Drobniewski FA , Faksri K , Farhat MR , Fleming J , Fowler P , Fowler TA , Gao Q , Gardy J , Gascoyne-Binzi D , Gibertoni-Cruz AL , Gil-Brusola A , Golubchik T , Gonzalo X , Grandjean L , He G , Guthrie JL , Hoosdally S , Hunt M , Iqbal Z , Ismail N , Johnston J , Khanzada FM , Khor CC , Kohl TA , Kong C , Lipworth S , Liu Q , Maphalala G , Martinez E , Mathys V , Merker M , Miotto P , Mistry N , Moore DAJ , Murray M , Niemann S , Omar SV , Ong RT , Peto TEA , Posey JE , Prammananan T , Pym A , Rodrigues C , Rodrigues M , Rodwell T , Rossolini GM , Sánchez Padilla E , Schito M , Shen X , Shendure J , Sintchenko V , Sloutsky A , Smith EG , Snyder M , Soetaert K , Starks AM , Supply P , Suriyapol P , Tahseen S , Tang P , Teo YY , Thuong TNT , Thwaites G , Tortoli E , van Soolingen D , Walker AS , Walker TM , Wilcox M , Wilson DJ , Wyllie D , Yang Y , Zhang H , Zhao Y , Zhu B . N Engl J Med 2018 379 (15) 1403-1415 BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.). |
COVID-19-Associated Hospitalizations Among U.S. Infants Aged <6 Months - COVID-NET, 13 States, June 2021-August 2022.
Hamid Sarah, Woodworth Kate, Pham Huong, Milucky Jennifer, Chai Shua J, Kawasaki Breanna, Yousey-Hindes Kimberly, Anderson Evan J, Henderson Justin, Lynfield Ruth, Pacheco Francesca, Barney Grant, Bennett Nancy M, Shiltz Eli, Sutton Melissa, Talbot H Keipp, Price Andrea, Havers Fiona P, Taylor Christopher A, . MMWR. Morbidity and mortality weekly report 2022 71(45) 1442-1448 . MMWR. Morbidity and mortality weekly report 2022 71(45) 1442-1448 Hamid Sarah, Woodworth Kate, Pham Huong, Milucky Jennifer, Chai Shua J, Kawasaki Breanna, Yousey-Hindes Kimberly, Anderson Evan J, Henderson Justin, Lynfield Ruth, Pacheco Francesca, Barney Grant, Bennett Nancy M, Shiltz Eli, Sutton Melissa, Talbot H Keipp, Price Andrea, Havers Fiona P, Taylor Christopher A, . MMWR. Morbidity and mortality weekly report 2022 71(45) 1442-1448 |
Antibodies to SARS-CoV-2 in All of Us Research Program Participants, January 2-March 18, 2020.
Althoff KN , Schlueter DJ , Anton-Culver H , Cherry J , Denny JC , Thomsen I , Karlson EW , Havers FP , Cicek MS , Thibodeau SN , Pinto LA , Lowy D , Malin BA , Ohno-Machado L , Williams C , Goldstein D , Kouame A , Ramirez A , Roman A , Sharpless NE , Gebo KA , Schully SD . Clin Infect Dis 2021 74 (4) 584-590 BACKGROUND: With limited SARS-CoV-2 testing capacity in the US at the start of the epidemic (January - March), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic. METHODS: All of Us study participants in all 50 US states provided blood specimens during study visits from January 2 to March 18, 2020. A participant was considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies on the Abbott Architect SARS-CoV-2 IgG ELISA and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. Sensitivity and specificity of the Abbott and EUROIMMUNE ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated with 95% confidence intervals. RESULTS: The estimated sensitivity of Abbott and EUROIMMUN was 100% (107/107 [96.6%, 100%]) and 90.7% (97/107 [83.5%, 95.4%]), respectively. The estimated specificity of Abbott and EUROIMMUN was 99.5% (995/1,000 [98.8%, 99.8%]) and 99.7% (997/1,000 [99.1%, 99.9%), respectively. The net sensitivity and specificity of our sequential testing algorithm was 90.7% (97/107 [83.5%, 95.4%]) and 100.0% (1,000/1,000 [99.6%, 100%]), respectively. Of the 24,079 study participants with blood specimens from January 2 to March 18, 2020, 9 were seropositive, 7 of whom were seropositive prior to the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi. CONCLUSIONS: Our findings indicate SARS-CoV-2 infections weeks prior to the first recognized cases in 5 US states. |
Investigating the Impact of Job Loss and Decreased Work Hours on Physical and Mental Health Outcomes Among US Adults During the COVID-19 Pandemic.
Guerin RJ , Barile JP , Thompson WW , McKnight-Eily L , Okun AH . J Occup Environ Med 2021 63 (9) e571-e579 OBJECTIVE: To investigate associations between adverse changes in employment status and physical and mental health among US adults (more than or equal to 18 years) during the COVID-19 pandemic. METHODS: Data from participants (N = 2565) of a national Internet panel (June 2020) were assessed using path analyses to test associations between changes in self-reported employment status and hours worked and physical and mental health outcomes. RESULTS: Respondents who lost a job after March 1, 2020 (vs those who did not) reported more than twice the number of mentally unhealthy days. Women and those lacking social support had significantly worse physical and mental health outcomes. Participants in the lowest, pre-pandemic household income groups reported experiencing worse mental health. CONCLUSIONS: Results demonstrate the importance of providing social and economic support services to US adults experiencing poor mental and physical health during the COVID-19 pandemic. |
Hospitalization of Adolescents Aged 12-17 Years with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, March 1, 2020-April 24, 2021.
Havers FP , Whitaker M , Self JL , Chai SJ , Kirley PD , Alden NB , Kawasaki B , Meek J , Yousey-Hindes K , Anderson EJ , Openo KP , Weigel A , Teno K , Monroe ML , Ryan PA , Reeg L , Kohrman A , Lynfield R , Como-Sabetti K , Poblete M , McMullen C , Muse A , Spina N , Bennett NM , Gaitán M , Billing LM , Shiltz J , Sutton M , Abdullah N , Schaffner W , Talbot HK , Crossland M , George A , Patel K , Pham H , Milucky J , Anglin O , Ujamaa D , Hall AJ , Garg S , Taylor CA . MMWR Morb Mortal Wkly Rep 2021 70 (23) 851-857 Most COVID-19-associated hospitalizations occur in older adults, but severe disease that requires hospitalization occurs in all age groups, including adolescents aged 12-17 years (1). On May 10, 2021, the Food and Drug Administration expanded the Emergency Use Authorization for Pfizer-BioNTech COVID-19 vaccine to include persons aged 12-15 years, and CDC's Advisory Committee on Immunization Practices recommended it for this age group on May 12, 2021.* Before that time, COVID-19 vaccines had been available only to persons aged ≥16 years. Understanding and describing the epidemiology of COVID-19-associated hospitalizations in adolescents and comparing it with adolescent hospitalizations associated with other vaccine-preventable respiratory viruses, such as influenza, offers evidence of the benefits of expanding the recommended age range for vaccination and provides a baseline and context from which to assess vaccination impact. Using the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET), CDC examined COVID-19-associated hospitalizations among adolescents aged 12-17 years, including demographic and clinical characteristics of adolescents admitted during January 1-March 31, 2021, and hospitalization rates (hospitalizations per 100,000 persons) among adolescents during March 1, 2020-April 24, 2021. Among 204 adolescents who were likely hospitalized primarily for COVID-19 during January 1-March 31, 2021, 31.4% were admitted to an intensive care unit (ICU), and 4.9% required invasive mechanical ventilation; there were no associated deaths. During March 1, 2020-April 24, 2021, weekly adolescent hospitalization rates peaked at 2.1 per 100,000 in early January 2021, declined to 0.6 in mid-March, and then rose to 1.3 in April. Cumulative COVID-19-associated hospitalization rates during October 1, 2020-April 24, 2021, were 2.5-3.0 times higher than were influenza-associated hospitalization rates from three recent influenza seasons (2017-18, 2018-19, and 2019-20) obtained from the Influenza Hospitalization Surveillance Network (FluSurv-NET). Recent increased COVID-19-associated hospitalization rates in March and April 2021 and the potential for severe disease in adolescents reinforce the importance of continued COVID-19 prevention measures, including vaccination and correct and consistent wearing of masks by persons not yet fully vaccinated or when required by laws, rules, or regulations.(†). |
Changes in the Number of Intensive Care Unit Beds in U.S. Hospitals During the Early Months of the COVID-19 Pandemic, as reported to the National Healthcare Safety Network's COVID-19 Module.
Weiner-Lastinger LM , Dudeck MA , Allen-Bridson K , Dantes R , Gross C , Nkwata A , Tejedor SC , Pollock D , Benin A . Infect Control Hosp Epidemiol 2021 43 (10) 1-12 Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic. |
Characterization of Reference Materials for CYP2C9, CYP2C19, VKORC1, CYP2C Cluster Variant, GGCX, and Other Pharmacogenetic Alleles with an Association for Molecular Pathology (AMP) Pharmacogenetics Working Group Tier 2 Status - A GeT-RM Collaborative Project.
Pratt VM , Turner A , Broeckel U , Dawson DB , Gaedigk A , Lynnes TC , Medeiros EB , Moyer AM , Requesens D , Ventrini F , Kalman LV . J Mol Diagn 2021 23 (8) 952-958 Pharmacogenetic (PGx) testing is increasingly available from clinical and research laboratories. However, only a limited number of quality control and other reference materials (RMs) are currently available for many of the variants that are tested. The Association for Molecular Pathology PGx Work Group has published a series of papers recommending alleles for inclusion in clinical testing. Several of the alleles were not considered for Tier 1 due to a lack of reference materials. To address this need, the Division of Laboratory Systems, Centers for Disease Control and Prevention (CDC) based Genetic Testing Reference Material Coordination Program (GeT-RM), in collaboration with members of the pharmacogenetic testing and research communities and the Coriell Institute for Medical Research, has characterized 18 DNA samples derived from Coriell cell lines. DNA samples were distributed to five volunteer testing laboratories for genotyping using three commercially available and laboratory developed tests. Several Tier 2 variants including CYP2C9*13, CYP2C19*35, the CYP2C cluster variant (rs12777823), two variants in VKORC1 (rs61742245 and rs72547529) related to warfarin resistance and two variants in GGCX (rs12714145 and rs11676382) related to clotting factor activation were identified among these samples. These publicly available materials complement the pharmacogenetic reference materials previously characterized by GeT-RM and will support the quality assurance and quality control programs of clinical laboratories performing pharmacogenetic testing. |
Intersecting Paths of Emerging and Reemerging Infectious Diseases.
Wilson TM , Paddock CD , Reagan-Steiner S , Bhatnagar J , Martines RB , Wiens AL , Madsen M , Komatsu KK , Venkat H , Zaki SR . Emerg Infect Dis 2021 27 (5) 1517-1519 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shares common clinicopathologic features with other severe pulmonary illnesses. Hantavirus pulmonary syndrome was diagnosed in 2 patients in Arizona, USA, suspected of dying from infection with SARS-CoV-2. Differential diagnoses and possible co-infections should be considered for cases of respiratory distress during the SARS-CoV-2 pandemic. |
Disease surveillance for the COVID-19 era: time for bold changes.
Morgan OW , Aguilera X , Ammon A , Amuasi J , Fall IS , Frieden T , Heymann D , Ihekweazu C , Jeong EK , Leung GM , Mahon B , Nkengasong J , Qamar FN , Schuchat A , Wieler LH , Dowell SF . Lancet 2021 397 (10292) 2317-2319 The COVID-19 pandemic has exposed weaknesses in disease surveillance in nearly all countries. Early identification of COVID-19 cases and clusters for rapid containment was hampered by inadequate diagnostic capacity, insufficient contact tracing, fragmented data systems, incomplete data insights for public health responders, and suboptimal governance of all these elements. Once SARS-CoV-2 became widespread, interventions to control community transmission were undermined by weak surveillance of cases and insufficient national capacity to integrate data for timely adjustment of public health measures.1, 2 Although some countries had little or no reliable data, others did not share data consistently with their own populations and with WHO and other multilateral agencies. The emergence of SARS-CoV-2 variants has highlighted inadequate national pathogen genomic sequencing capacities in many countries and led to calls for expanded virus sequencing. However, sequencing without epidemiological and clinical surveillance data is insufficient to show whether new SARS-CoV-2 variants are more transmissible, more lethal, or more capable of evading immunity, including vaccine-induced immunity.3, 4 |
Patterns in COVID-19 Vaccination Coverage, by Social Vulnerability and Urbanicity - United States, December 14, 2020-May 1, 2021.
Barry V , Dasgupta S , Weller DL , Kriss JL , Cadwell BL , Rose C , Pingali C , Musial T , Sharpe JD , Flores SA , Greenlund KJ , Patel A , Stewart A , Qualters JR , Harris L , Barbour KE , Black CL . MMWR Morb Mortal Wkly Rep 2021 70 (22) 818-824 Disparities in vaccination coverage by social vulnerability, defined as social and structural factors associated with adverse health outcomes, were noted during the first 2.5 months of the U.S. COVID-19 vaccination campaign, which began during mid-December 2020 (1). As vaccine eligibility and availability continue to expand, assuring equitable coverage for disproportionately affected communities remains a priority. CDC examined COVID-19 vaccine administration and 2018 CDC social vulnerability index (SVI) data to ascertain whether inequities in COVID-19 vaccination coverage with respect to county-level SVI have persisted, overall and by urbanicity. Vaccination coverage was defined as the number of persons aged ≥18 years (adults) who had received ≥1 dose of any Food and Drug Administration (FDA)-authorized COVID-19 vaccine divided by the total adult population in a specified SVI category.(†) SVI was examined overall and by its four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation). Counties were categorized into SVI quartiles, in which quartile 1 (Q1) represented the lowest level of vulnerability and quartile 4 (Q4), the highest. Trends in vaccination coverage were assessed by SVI quartile and urbanicity, which was categorized as large central metropolitan, large fringe metropolitan (areas surrounding large cities, e.g., suburban), medium and small metropolitan, and nonmetropolitan counties.(§) During December 14, 2020-May 1, 2021, disparities in vaccination coverage by SVI increased, especially in large fringe metropolitan (e.g., suburban) and nonmetropolitan counties. By May 1, 2021, vaccination coverage was lower among adults living in counties with the highest overall SVI; differences were most pronounced in large fringe metropolitan (Q4 coverage = 45.0% versus Q1 coverage = 61.7%) and nonmetropolitan (Q4 = 40.6% versus Q1 = 52.9%) counties. Vaccination coverage disparities were largest for two SVI themes: socioeconomic status (Q4 = 44.3% versus Q1 = 61.0%) and household composition and disability (Q4 = 42.0% versus Q1 = 60.1%). Outreach efforts, including expanding public health messaging tailored to local populations and increasing vaccination access, could help increase vaccination coverage in high-SVI counties. |
Seasonal Influenza Prevention and Control Progress in Latin America and the Caribbean in the Context of the Global Influenza Strategy and the COVID-19 Pandemic.
Vicari AS , Olson D , Vilajeliu A , Andrus JK , Ropero AM , Morens DM , Santos IJ , Azziz-Baumgartner E , Berman S . Am J Trop Med Hyg 2021 105 (1) 93-101 Each year in Latin America and the Caribbean, seasonal influenza is associated with an estimated 36,500 respiratory deaths and 400,000 hospitalizations. Since the 2009 influenza A(H1N1) pandemic, the Region has made significant advances in the prevention and control of seasonal influenza, including improved surveillance systems, burden estimates, and vaccination of at-risk groups. The Global Influenza Strategy 2019-2030 provides a framework to strengthen these advances. Against the backdrop of this new framework, the University of Colorado convened in October 2020 its Immunization Advisory Group of Experts to review and discuss current surveillance, prevention, and control strategies for seasonal influenza in Latin America and the Caribbean, also in the context of the COVID-19 pandemic. This review identified five areas for action and made recommendations specific to each area. The Region should continue its efforts to strengthen surveillance and impact evaluations. Existing data on disease burden, seasonality patterns, and vaccination effectiveness should be used to inform decision-making at the country level as well as advocacy efforts for programmatic resources. Regional and country strategic plans should be prepared and include specific targets for 2030. Existing investments in influenza prevention and control, including for immunization programs, should be optimized. Finally, regional partnerships, such as the regional networks for syndromic surveillance and vaccine effectiveness evaluation (SARInet and REVELAC-i), should continue to play a critical role in continuous learning and standardization by sharing experiences and best practices among countries. |
Cruise ship travel in the era of COVID-19: A summary of outbreaks and a model of public health interventions.
Guagliardo SAJ , Prasad PV , Rodriguez A , Fukunaga R , Novak RT , Ahart L , Reynolds J , Griffin I , Wiegand R , Quilter LAS , Morrison S , Jenkins K , Wall HK , Treffiletti A , White SB , Regan J , Tardivel K , Freeland A , Brown C , Wolford H , Johansson MA , Cetron MS , Slayton RB , Friedman CR . Clin Infect Dis 2021 74 (3) 490-497 BACKGROUND: Cruise travel contributed to SARS-CoV-2 transmission when there were relatively few cases in the United States. By March 14, 2020, the Centers for Disease Control and Prevention (CDC) issued a No Sail Order suspending U.S. cruise operations; the last U.S. passenger ship docked on April 16. METHODS: We analyzed SARS-CoV-2 outbreaks on cruises in U.S. waters or carrying U.S. citizens and used regression models to compare voyage characteristics. We used compartmental models to simulate the potential impact of four interventions (screening for COVID-19 symptoms; viral testing on two days and isolation of positive persons; reduction of passengers by 40%, crew by 20%, and port visits to one) for 7-day and 14-day voyages. RESULTS: During January 19-April 16, 2020, 89 voyages on 70 ships had known SARS-CoV-2 outbreaks; 16 ships had recurrent outbreaks. There were 1,669 RT-PCR-confirmed SARS-CoV-2 infections and 29 confirmed deaths. Longer voyages were associated with more cases (adjusted incidence rate ratio, 1.10, 95% CI: 1.03-1.17, p < 0.0001). Mathematical models showed that 7-day voyages had about 70% fewer cases than 14-day voyages. On 7-day voyages, the most effective interventions were reducing the number of individuals onboard (43-49% reduction in total infections) and testing passengers and crew (42-43% reduction in total infections). All four interventions reduced transmission by 80%, but no single intervention or combination eliminated transmission. Results were similar for 14-day voyages. CONCLUSIONS: SARS-CoV-2 outbreaks on cruises were common during January-April 2020. Despite all interventions modeled, cruise travel still poses a significant SARS-CoV-2 transmission risk. |
CATMoS: Collaborative Acute Toxicity Modeling Suite.
Mansouri K , Karmaus AL , Fitzpatrick J , Patlewicz G , Pradeep P , Alberga D , Alepee N , Allen TEH , Allen D , Alves VM , Andrade CH , Auernhammer TR , Ballabio D , Bell S , Benfenati E , Bhattacharya S , Bastos JV , Boyd S , Brown JB , Capuzzi SJ , Chushak Y , Ciallella H , Clark AM , Consonni V , Daga PR , Ekins S , Farag S , Fedorov M , Fourches D , Gadaleta D , Gao F , Gearhart JM , Goh G , Goodman JM , Grisoni F , Grulke CM , Hartung T , Hirn M , Karpov P , Korotcov A , Lavado GJ , Lawless M , Li X , Luechtefeld T , Lunghini F , Mangiatordi GF , Marcou G , Marsh D , Martin T , Mauri A , Muratov EN , Myatt GJ , Nguyen DT , Nicolotti O , Note R , Pande P , Parks AK , Peryea T , Polash AH , Rallo R , Roncaglioni A , Rowlands C , Ruiz P , Russo DP , Sayed A , Sayre R , Sheils T , Siegel C , Silva AC , Simeonov A , Sosnin S , Southall N , Strickland J , Tang Y , Teppen B , Tetko IV , Thomas D , Tkachenko V , Todeschini R , Toma C , Tripodi I , Trisciuzzi D , Tropsha A , Varnek A , Vukovic K , Wang Z , Wang L , Waters KM , Wedlake AJ , Wijeyesakere SJ , Wilson D , Xiao Z , Yang H , Zahoranszky-Kohalmi G , Zakharov AV , Zhang FF , Zhang Z , Zhao T , Zhu H , Zorn KM , Casey W , Kleinstreuer NC . Environ Health Perspect 2021 129 (4) 47013 BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495. |
COVID-19 Mitigation Efforts and Testing During an In-Person Training Event - Uganda, October 12-29, 2020.
Laws RL , Biraro S , Kirungi W , Gianetti B , Aibo D , Awor AC , West C , Sachathep KK , Kiyingi H , Ward J , Mwangi C , Nkurunziza P , Okimait D , Currie D , Ajiboye A , Moore CS , Patel H , Sendagala S , Naluguza M , Mugisha V , Low A , Delgado S , Hoos D , Brown K , Galbraith JS , Hladik W , Nelson L , El-Sadr W , Musinguzi J , Voetsch AC . Clin Infect Dis 2021 73 S42-S44 Large public-health training events may result in SARS-CoV-2 transmission. Universal SARS-CoV-2 testing during trainings for the Uganda Population-based HIV Impact Assessment identified 28/475 (5.9%) individuals with COVID-19 among attendees; most (89.3%) were asymptomatic. Effective COVID-19 mitigation measures, along with SARS-CoV-2 testing, are recommended for in-person trainings, particularly when trainees will have subsequent contact with survey participants. |
Community-Associated Outbreak of COVID-19 in a Correctional Facility - Utah, September 2020-January 2021.
Lewis NM , Salmanson AP , Price A , Risk I , Guymon C , Wisner M , Gardner K , Fukunaga R , Schwitters A , Lambert L , Baggett HC , Ewetola R , Dunn AC . MMWR Morb Mortal Wkly Rep 2021 70 (13) 467-472 Transmission of SARS-CoV-2, the virus that causes COVID-19, is common in congregate settings such as correctional and detention facilities (1-3). On September 17, 2020, a Utah correctional facility (facility A) received a report of laboratory-confirmed SARS-CoV-2 infection in a dental health care provider (DHCP) who had treated incarcerated persons at facility A on September 14, 2020 while asymptomatic. On September 21, 2020, the roommate of an incarcerated person who had received dental treatment experienced COVID-19-compatible symptoms*; both were housed in block 1 of facility A (one of 16 occupied blocks across eight residential units). Two days later, the roommate received a positive SARS-CoV-2 test result, becoming the first person with a known-associated case of COVID-19 at facility A. During September 23-24, 2020, screening of 10 incarcerated persons who had received treatment from the DHCP identified another two persons with COVID-19, prompting isolation of all three patients in an unoccupied block at the facility. Within block 1, group activities were stopped to limit interaction among staff members and incarcerated persons and prevent further spread. During September 14-24, 2020, six facility A staff members, one of whom had previous close contact(†) with one of the patients, also reported symptoms. On September 27, 2020, an outbreak was confirmed after specimens from all remaining incarcerated persons in block 1 were tested; an additional 46 cases of COVID-19 were identified, which were reported to the Salt Lake County Health Department and the Utah Department of Health. On September 30, 2020, CDC, in collaboration with both health departments and the correctional facility, initiated an investigation to identify factors associated with the outbreak and implement control measures. As of January 31, 2021, a total of 1,368 cases among 2,632 incarcerated persons (attack rate = 52%) and 88 cases among 550 staff members (attack rate = 16%) were reported in facility A. Among 33 hospitalized incarcerated persons, 11 died. Quarantine and monitoring of potentially exposed persons and implementation of available prevention measures, including vaccination, are important in preventing introduction and spread of SARS-CoV-2 in correctional facilities and other congregate settings (4). |
Impact of COVID-19 Pandemic on Central Line-Associated Bloodstream Infections During the Early Months of 2020, National Healthcare Safety Network.
Patel PR , Weiner-Lastinger LM , Dudeck MA , Fike LV , Kuhar DT , Edwards JR , Pollock D , Benin A . Infect Control Hosp Epidemiol 2021 43 (6) 1-8 Data reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN) were analyzed to understand the potential impact of the COVID-19 pandemic on central line-associated bloodstream infections (CLABSIs) in acute care hospitals. Descriptive analysis of the Standardized Infection Ratio (SIR) was conducted by locations, location type, geographic area, and bed size. |
Use of US Public Health Travel Restrictions during COVID-19 Outbreak on Diamond Princess Ship, Japan, February-April 2020.
Medley AM , Marston BJ , Toda M , Kobayashi M , Weinberg M , Moriarty LF , Jungerman MR , Surpris ACA , Knust B , Acosta AM , Shockey CE , Daigle D , Schneider ZD , Charles J , Ishizumi A , Stewart A , Vonnahme LA , Brown C , White S , Cohen NJ , Cetron M . Emerg Infect Dis 2021 27 (3) 710-718 Public health travel restrictions (PHTR) are crucial measures during communicable disease outbreaks to prevent transmission during commercial airline travel and mitigate cross-border importation and spread. We evaluated PHTR implementation for US citizens on the Diamond Princess during its coronavirus disease (COVID-19) outbreak in Japan in February 2020 to explore how PHTR reduced importation of COVID-19 to the United States during the early phase of disease containment. Using PHTR required substantial collaboration among the US Centers for Disease Control and Prevention, other US government agencies, the cruise line, and public health authorities in Japan. Original US PHTR removal criteria were modified to reflect international testing protocols and enable removal of PHTR for persons who recovered from illness. The impact of PHTR on epidemic trajectory depends on the risk for transmission during travel and geographic spread of disease. Lessons learned from the Diamond Princess outbreak provide critical information for future PHTR use. |
Impact of coronavirus disease 2019 (COVID-19) on US Hospitals and Patients, April-July 2020.
Sapiano MRP , Dudeck MA , Soe M , Edwards JR , O'Leary EN , Wu H , Allen-Bridson K , Amor A , Arcement R , Chernetsky Tejedor S , Dantes R , Gross C , Haass K , Konnor R , Kroop SR , Leaptrot D , Lemoine K , Nkwata A , Peterson K , Wattenmaker L , Weiner-Lastinger LM , Pollock D , Benin AL . Infect Control Hosp Epidemiol 2021 43 (1) 1-28 OBJECTIVE: The rapid spread of SARS-CoV-2 throughout key regions of the United States (U.S.) in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN), the nation's largest hospital surveillance system, launched a module for collecting hospital COVID-19 data. This paper presents time series estimates of the critical hospital capacity indicators during April 1-July 14, 2020. DESIGN: From March 27-July 14, 2020, NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and availability/use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near real-time daily national and state estimates to be computed. RESULTS: During the pandemic's April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased during April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July. CONCLUSIONS: The NHSN hospital capacity estimates served as important, near-real time indicators of the pandemic's magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after declining from a peak in April. Patient outcomes appeared to improve from early April to mid-July. |
Reactive astrocyte nomenclature, definitions, and future directions.
Escartin C , Galea E , Lakatos A , O'Callaghan JP , Petzold GC , Serrano-Pozo A , Steinhäuser C , Volterra A , Carmignoto G , Agarwal A , Allen NJ , Araque A , Barbeito L , Barzilai A , Bergles DE , Bonvento G , Butt AM , Chen WT , Cohen-Salmon M , Cunningham C , Deneen B , De Strooper B , Díaz-Castro B , Farina C , Freeman M , Gallo V , Goldman JE , Goldman SA , Götz M , Gutiérrez A , Haydon PG , Heiland DH , Hol EM , Holt MG , Iino M , Kastanenka KV , Kettenmann H , Khakh BS , Koizumi S , Lee CJ , Liddelow SA , MacVicar BA , Magistretti P , Messing A , Mishra A , Molofsky AV , Murai KK , Norris CM , Okada S , Oliet SHR , Oliveira JF , Panatier A , Parpura V , Pekna M , Pekny M , Pellerin L , Perea G , Pérez-Nievas BG , Pfrieger FW , Poskanzer KE , Quintana FJ , Ransohoff RM , Riquelme-Perez M , Robel S , Rose CR , Rothstein JD , Rouach N , Rowitch DH , Semyanov A , Sirko S , Sontheimer H , Swanson RA , Vitorica J , Wanner IB , Wood LB , Wu J , Zheng B , Zimmer ER , Zorec R , Sofroniew MV , Verkhratsky A . Nat Neurosci 2021 24 (3) 312-325 Reactive astrocytes are astrocytes undergoing morphological, molecular, and functional remodeling in response to injury, disease, or infection of the CNS. Although this remodeling was first described over a century ago, uncertainties and controversies remain regarding the contribution of reactive astrocytes to CNS diseases, repair, and aging. It is also unclear whether fixed categories of reactive astrocytes exist and, if so, how to identify them. We point out the shortcomings of binary divisions of reactive astrocytes into good-vs-bad, neurotoxic-vs-neuroprotective or A1-vs-A2. We advocate, instead, that research on reactive astrocytes include assessment of multiple molecular and functional parameters-preferably in vivo-plus multivariate statistics and determination of impact on pathological hallmarks in relevant models. These guidelines may spur the discovery of astrocyte-based biomarkers as well as astrocyte-targeting therapies that abrogate detrimental actions of reactive astrocytes, potentiate their neuro- and glioprotective actions, and restore or augment their homeostatic, modulatory, and defensive functions. |
Randomized Trial of a Vaccine Regimen to Prevent Chronic HCV Infection.
Page K , Melia MT , Veenhuis RT , Winter M , Rousseau KE , Massaccesi G , Osburn WO , Forman M , Thomas E , Thornton K , Wagner K , Vassilev V , Lin L , Lum PJ , Giudice LC , Stein E , Asher A , Chang S , Gorman R , Ghany MG , Liang TJ , Wierzbicki MR , Scarselli E , Nicosia A , Folgori A , Capone S , Cox AL . N Engl J Med 2021 384 (6) 541-549 BACKGROUND: A safe and effective vaccine to prevent chronic hepatitis C virus (HCV) infection is a critical component of efforts to eliminate the disease. METHODS: In this phase 1-2 randomized, double-blind, placebo-controlled trial, we evaluated a recombinant chimpanzee adenovirus 3 vector priming vaccination followed by a recombinant modified vaccinia Ankara boost; both vaccines encode HCV nonstructural proteins. Adults who were considered to be at risk for HCV infection on the basis of a history of recent injection drug use were randomly assigned (in a 1:1 ratio) to receive vaccine or placebo on days 0 and 56. Vaccine-related serious adverse events, severe local or systemic adverse events, and laboratory adverse events were the primary safety end points. The primary efficacy end point was chronic HCV infection, defined as persistent viremia for 6 months. RESULTS: A total of 548 participants underwent randomization, with 274 assigned to each group. There was no significant difference in the incidence of chronic HCV infection between the groups. In the per-protocol population, chronic HCV infection developed in 14 participants in each group (hazard ratio [vaccine vs. placebo], 1.53; 95% confidence interval [CI], 0.66 to 3.55; vaccine efficacy, -53%; 95% CI, -255 to 34). In the modified intention-to-treat population, chronic HCV infection developed in 19 participants in the vaccine group and 17 in placebo group (hazard ratio, 1.66; 95% CI, 0.79 to 3.50; vaccine efficacy, -66%; 95% CI, -250 to 21). The geometric mean peak HCV RNA level after infection differed between the vaccine group and the placebo group (152.51×10(3) IU per milliliter and 1804.93×10(3) IU per milliliter, respectively). T-cell responses to HCV were detected in 78% of the participants in the vaccine group. The percentages of participants with serious adverse events were similar in the two groups. CONCLUSIONS: In this trial, the HCV vaccine regimen did not cause serious adverse events, produced HCV-specific T-cell responses, and lowered the peak HCV RNA level, but it did not prevent chronic HCV infection. (Funded by the National Institute of Allergy and Infectious Diseases; ClinicalTrials.gov number, NCT01436357.). |
Mitigating SARS-CoV-2 Transmission in Hispanic and Latino Communities-Prince William Health District, Virginia, June 2020.
Davlantes E , Tippins A , Espinosa C , Lofgren H , Leonard S , Solis M , Young A , Sockwell D , Ansher A . J Racial Ethn Health Disparities 2021 9 (2) 1-9 OBJECTIVES: To identify factors contributing to disproportionate rates of COVID-19 among Hispanic or Latino persons in Prince William Health District, Virginia, and to identify measures to better engage Hispanic and Latino communities in COVID-19 mitigation. METHODS: Data collection proceeded via three methods in June 2020: a quantitative survey of Hispanic or Latino residents, key informant interviews with local leaders familiar with this population, and focus group discussions with Hispanic or Latino community members. RESULTS: Those who worked outside the home, lived in larger households, or lived with someone who had tested positive were more likely to report testing positive for SARS-CoV-2 (unadjusted odds ratios of 2.5, 1.2, and 12.9, respectively). Difficulty implementing COVID-19 prevention practices (reported by 46% of survey respondents), immigration-related fears (repeatedly identified in qualitative data), and limited awareness of local COVID-19 resources (57% of survey respondents spoke little or no English) were identified. Survey respondents also reported declines in their food security (25%) and mental health (25%). CONCLUSIONS: Specific attention to the needs of Hispanic or Latino communities could help reduce the burden of COVID-19. The investigation methods can also be used by other jurisdictions to evaluate the needs of and services provided to diverse underserved populations. |
Observed Face Mask Use at Six Universities - United States, September-November 2020.
Barrios LC , Riggs MA , Green RF , Czarnik M , Nett RJ , Staples JE , Welton MD , Muilenburg JL , Zullig KJ , Gibson-Young L , Perkins AV , Prins C , Lauzardo M , Shapiro J , Asimellis G , Kilgore-Bowling G , Ortiz-Jurado K , Gutilla MJ . MMWR Morb Mortal Wkly Rep 2021 70 (6) 208-211 Approximately 41% of adults aged 18-24 years in the United States are enrolled in a college or university (1). Wearing a face mask can reduce transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (2), and many colleges and universities mandate mask use in public locations and outdoors when within six feet of others. Studies based on self-report have described mask use ranging from 69.1% to 86.1% among adults aged 18-29 years (3); however, more objective measures are needed. Direct observation by trained observers is the accepted standard for monitoring behaviors such as hand hygiene (4). In this investigation, direct observation was used to estimate the proportion of persons wearing masks and the proportion of persons wearing masks correctly (i.e., covering the nose and mouth and secured under the chin*) on campus and at nearby off-campus locations at six rural and suburban universities with mask mandates in the southern and western United States. Trained student observers recorded mask use for up to 8 weeks from fixed sites on campus and nearby. Among 17,200 observed persons, 85.5% wore masks, with 89.7% of those persons wearing the mask correctly (overall correct mask use: 76.7%). Among persons observed indoors, 91.7% wore masks correctly. The proportion correctly wearing masks indoors varied by mask type, from 96.8% for N95-type masks and 92.2% for cloth masks to 78.9% for bandanas, scarves, and similar face coverings. Observed indoor mask use was high at these six universities with mask mandates. Colleges and universities can use direct observation findings to tailor training and messaging toward increasing correct mask use. |
Rates of COVID-19 Among Residents and Staff Members in Nursing Homes - United States, May 25-November 22, 2020.
Bagchi S , Mak J , Li Q , Sheriff E , Mungai E , Anttila A , Soe MM , Edwards JR , Benin AL , Pollock DA , Shulman E , Ling S , Moody-Williams J , Fleisher LA , Srinivasan A , Bell JM . MMWR Morb Mortal Wkly Rep 2021 70 (2) 52-55 During the beginning of the coronavirus disease 2019 (COVID-19) pandemic, nursing homes were identified as congregate settings at high risk for outbreaks of COVID-19 (1,2). Their residents also are at higher risk than the general population for morbidity and mortality associated with infection with SARS-CoV-2, the virus that causes COVID-19, in light of the association of severe outcomes with older age and certain underlying medical conditions (1,3). CDC's National Healthcare Safety Network (NHSN) launched nationwide, facility-level COVID-19 nursing home surveillance on April 26, 2020. A federal mandate issued by the Centers for Medicare & Medicaid Services (CMS), required nursing homes to commence enrollment and routine reporting of COVID-19 cases among residents and staff members by May 25, 2020. This report uses the NHSN nursing home COVID-19 data reported during May 25-November 22, 2020, to describe COVID-19 rates among nursing home residents and staff members and compares these with rates in surrounding communities by corresponding U.S. Department of Health and Human Services (HHS) region.* COVID-19 cases among nursing home residents increased during June and July 2020, reaching 11.5 cases per 1,000 resident-weeks (calculated as the total number of occupied beds on the day that weekly data were reported) (week of July 26). By mid-September, rates had declined to 6.3 per 1,000 resident-weeks (week of September 13) before increasing again, reaching 23.2 cases per 1,000 resident-weeks by late November (week of November 22). COVID-19 cases among nursing home staff members also increased during June and July (week of July 26 = 10.9 cases per 1,000 resident-weeks) before declining during August-September (week of September 13 = 6.3 per 1,000 resident-weeks); rates increased by late November (week of November 22 = 21.3 cases per 1,000 resident-weeks). Rates of COVID-19 in the surrounding communities followed similar trends. Increases in community rates might be associated with increases in nursing home COVID-19 incidence, and nursing home mitigation strategies need to include a comprehensive plan to monitor local SARS-CoV-2 transmission and minimize high-risk exposures within facilities. |
Characterization of Pyrethroid Resistance Mechanisms in Aedes aegypti from the Florida Keys.
Scott ML , Hribar LJ , Leal AL , McAllister JC . Am J Trop Med Hyg 2021 104 (3) 1111-1122 The status of insecticide resistance in Aedes aegypti is of concern in areas where Aedes-borne arboviruses like chikungunya, dengue, and Zika occur. In recent years, outbreaks involving these arboviruses have occurred, for which vaccines do not exist; therefore, disease prevention is only through vector control and personal protection. Aedes aegypti are present on every inhabited island within the Florida Keys. The resistance status of Ae. aegypti in the Florida Keys was assessed to guide knowledge of the best choice of chemical for use during an outbreak. Mosquito eggs were collected using ovitraps placed on Key West, Stock Island, Vaca Key, Upper Matecumbe Key, Plantation Key, and Key Largo. Bottle bioassays were conducted at the Florida Keys Mosquito Control District using Bifenthrin(®) 30+30. Further bottle testing using malathion and permethrin occurred at the CDC, Fort Collins, CO, in addition to molecular and biochemical assays. Levels of resistance varied between islands with different underlying mechanisms present. Resistance was seen to bifenthrin 30+30 but not to permethrin, indicating that piperonyl butoxide (PBO) or the inert ingredients may be involved in resistance. No study has been conducted to date examining the role of PBO in resistance. Key Largo was treated the most with adulticides and expressed the highest levels of alpha and beta esterases, oxidases, glutathione-S-transferases, and frequency of the V1016I knockdown mutation from all sites tested. Knowledge of localized resistance and underlying mechanisms helps in making rational decisions in selection of appropriate and effective insecticides. |
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