Last data update: May 28, 2024. (Total: 46864 publications since 2009)
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ADHD prevalence among U.S. Children and adolescents in 2022: Diagnosis, severity, co-occurring disorders, and treatment
Danielson ML , Claussen AH , Bitsko RH , Katz SM , Newsome K , Blumberg SJ , Kogan MD , Ghandour R . J Clin Child Adolesc Psychol 2024 1-18 OBJECTIVE: To provide updated national prevalence estimates of diagnosed attention-deficit/hyperactivity disorder (ADHD), ADHD severity, co-occurring disorders, and receipt of ADHD medication and behavioral treatment among U.S. children and adolescents by demographic and clinical subgroups using data from the 2022 National Survey of Children's Health (NSCH). METHOD: This study used 2022 NSCH data to estimate the prevalence of ever diagnosed and current ADHD among U.S. children aged 3-17 years. Among children with current ADHD, ADHD severity, presence of current co-occurring disorders, and receipt of medication and behavioral treatment were estimated. Weighted estimates were calculated overall and for demographic and clinical subgroups (n = 45,169). RESULTS: Approximately 1 in 9 U.S. children have ever received an ADHD diagnosis (11.4%, 7.1 million children) and 10.5% (6.5 million) had current ADHD. Among children with current ADHD, 58.1% had moderate or severe ADHD, 77.9% had at least one co-occurring disorder, approximately half of children with current ADHD (53.6%) received ADHD medication, and 44.4% had received behavioral treatment for ADHD in the past year; nearly one third (30.1%) did not receive any ADHD-specific treatment. CONCLUSIONS: Pediatric ADHD remains an ongoing and expanding public health concern, as approximately 1 million more children had ever received an ADHD diagnosis in 2022 than in 2016. Estimates from the 2022 NSCH provide information on pediatric ADHD during the last full year of the COVID-19 pandemic and can be used by policymakers, government agencies, health care systems, public health practitioners, and other partners to plan for needs of children with ADHD. |
Challenges of COVID-19 case forecasting in the US, 2020-2021
Lopez VK , Cramer EY , Pagano R , Drake JM , O'Dea EB , Adee M , Ayer T , Chhatwal J , Dalgic OO , Ladd MA , Linas BP , Mueller PP , Xiao J , Bracher J , Castro Rivadeneira AJ , Gerding A , Gneiting T , Huang Y , Jayawardena D , Kanji AH , Le K , Mühlemann A , Niemi J , Ray EL , Stark A , Wang Y , Wattanachit N , Zorn MW , Pei S , Shaman J , Yamana TK , Tarasewicz SR , Wilson DJ , Baccam S , Gurung H , Stage S , Suchoski B , Gao L , Gu Z , Kim M , Li X , Wang G , Wang L , Wang Y , Yu S , Gardner L , Jindal S , Marshall M , Nixon K , Dent J , Hill AL , Kaminsky J , Lee EC , Lemaitre JC , Lessler J , Smith CP , Truelove S , Kinsey M , Mullany LC , Rainwater-Lovett K , Shin L , Tallaksen K , Wilson S , Karlen D , Castro L , Fairchild G , Michaud I , Osthus D , Bian J , Cao W , Gao Z , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Vespignani A , Xiong X , Walraven R , Chen J , Gu Q , Wang L , Xu P , Zhang W , Zou D , Gibson GC , Sheldon D , Srivastava A , Adiga A , Hurt B , Kaur G , Lewis B , Marathe M , Peddireddy AS , Porebski P , Venkatramanan S , Wang L , Prasad PV , Walker JW , Webber AE , Slayton RB , Biggerstaff M , Reich NG , Johansson MA . PLoS Comput Biol 2024 20 (5) e1011200 During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making. |
Sosuga virus detected in Egyptian rousette bats (Rousettus aegyptiacus) in Sierra Leone
Amman BR , Koroma AH , Schuh AJ , Conteh I , Sealy TK , Foday I , Johnny J , Bakarr IA , Whitmer SLM , Wright EA , Gbakima AA , Graziano J , Bangura C , Kamanda E , Osborne A , Saidu E , Musa JA , Bangura DF , Williams SMT , Fefegula GM , Sumaila C , Jabaty J , James FH , Jambai A , Garnett K , Kamara TF , Towner JS , Lebbie A . Viruses 2024 16 (4) Sosuga virus (SOSV), a rare human pathogenic paramyxovirus, was first discovered in 2012 when a person became ill after working in South Sudan and Uganda. During an ecological investigation, several species of bats were sampled and tested for SOSV RNA and only one species, the Egyptian rousette bat (ERBs; Rousettus aegyptiacus), tested positive. Since that time, multiple other species have been sampled and ERBs in Uganda have continued to be the only species of bat positive for SOSV infection. Subsequent studies of ERBs with SOSV demonstrated that ERBs are a competent host for SOSV and shed this infectious virus while exhibiting only minor infection-associated pathology. Following the 2014 Ebola outbreak in West Africa, surveillance efforts focused on discovering reservoirs for zoonotic pathogens resulted in the capture and testing of many bat species. Here, SOSV RNA was detected by qRT-PCR only in ERBs captured in the Moyamba District of Sierra Leone in the central region of the country. These findings represent a substantial range extension from East Africa to West Africa for SOSV, suggesting that this paramyxovirus may occur in ERB populations throughout its sub-Saharan African range. |
Novel antifungals and treatment approaches to tackle resistance and improve outcomes of invasive fungal disease
Hoenigl M , Arastehfar A , Arendrup MC , Brüggemann R , Carvalho A , Chiller T , Chen S , Egger M , Feys S , Gangneux JP , Gold JAW , Groll AH , Heylen J , Jenks JD , Krause R , Lagrou K , Lamoth F , Prattes J , Sedik S , Wauters J , Wiederhold NP , Thompson GR 3rd . Clin Microbiol Rev 2024 e0007423 SUMMARYFungal infections are on the rise, driven by a growing population at risk and climate change. Currently available antifungals include only five classes, and their utility and efficacy in antifungal treatment are limited by one or more of innate or acquired resistance in some fungi, poor penetration into "sequestered" sites, and agent-specific side effect which require frequent patient reassessment and monitoring. Agents with novel mechanisms, favorable pharmacokinetic (PK) profiles including good oral bioavailability, and fungicidal mechanism(s) are urgently needed. Here, we provide a comprehensive review of novel antifungal agents, with both improved known mechanisms of actions and new antifungal classes, currently in clinical development for treating invasive yeast, mold (filamentous fungi), Pneumocystis jirovecii infections, and dimorphic fungi (endemic mycoses). We further focus on inhaled antifungals and the role of immunotherapy in tackling fungal infections, and the specific PK/pharmacodynamic profiles, tissue distributions as well as drug-drug interactions of novel antifungals. Finally, we review antifungal resistance mechanisms, the role of use of antifungal pesticides in agriculture as drivers of drug resistance, and detail detection methods for antifungal resistance. |
Who provides outpatient clinical care for adults with ADHD? Analysis of healthcare claims by types of providers among private insurance and Medicaid enrollees, 2021
Danielson ML , Claussen AH , Arifkhanova A , Gonzalez MG , Surman C . J Atten Disord 2024 10870547241238899 OBJECTIVE: To characterize provider types delivering outpatient care overall and through telehealth to U.S. adults with ADHD. METHOD: Using employer-sponsored insurance (ESI) and Medicaid claims, we identified enrollees aged 18 to 64 years who received outpatient care for ADHD in 2021. Billing provider codes were used to tabulate the percentage of enrollees receiving ADHD care from 10 provider types overall and through telehealth. RESULTS: Family practice physicians, psychiatrists, and nurse practitioners/psychiatric nurses were the most common providers for adults with ESI, although the distribution of provider types varied across states. Lower percentages of adults with Medicaid received ADHD care from physicians. Approximately half of adults receiving outpatient ADHD care received ADHD care by telehealth. CONCLUSION: Results may inform the development of clinical guidelines for adult ADHD and identify audiences for guideline dissemination and education planning. |
Mental health surveillance among children - United States, 2013-2019
Bitsko RH , Claussen AH , Lichstein J , Black LI , Jones SE , Danielson ML , Hoenig JM , Davis Jack SP , Brody DJ , Gyawali S , Maenner MJ , Warner M , Holland KM , Perou R , Crosby AE , Blumberg SJ , Avenevoli S , Kaminski JW , Ghandour RM . MMWR Suppl 2022 71 (2) 1-42 Mental health encompasses a range of mental, emotional, social, and behavioral functioning and occurs along a continuum from good to poor. Previous research has documented that mental health among children and adolescents is associated with immediate and long-term physical health and chronic disease, health risk behaviors, social relationships, education, and employment. Public health surveillance of children's mental health can be used to monitor trends in prevalence across populations, increase knowledge about demographic and geographic differences, and support decision-making about prevention and intervention. Numerous federal data systems collect data on various indicators of children's mental health, particularly mental disorders. The 2013-2019 data from these data systems show that mental disorders begin in early childhood and affect children with a range of sociodemographic characteristics. During this period, the most prevalent disorders diagnosed among U.S. children and adolescents aged 3-17 years were attention-deficit/hyperactivity disorder and anxiety, each affecting approximately one in 11 (9.4%-9.8%) children. Among children and adolescents aged 12-17 years, one fifth (20.9%) had ever experienced a major depressive episode. Among high school students in 2019, 36.7% reported persistently feeling sad or hopeless in the past year, and 18.8% had seriously considered attempting suicide. Approximately seven in 100,000 persons aged 10-19 years died by suicide in 2018 and 2019. Among children and adolescents aged 3-17 years, 9.6%-10.1% had received mental health services, and 7.8% of all children and adolescents aged 3-17 years had taken medication for mental health problems during the past year, based on parent report. Approximately one in four children and adolescents aged 12-17 years reported having received mental health services during the past year. In federal data systems, data on positive indicators of mental health (e.g., resilience) are limited. Although no comprehensive surveillance system for children's mental health exists and no single indicator can be used to define the mental health of children or to identify the overall number of children with mental disorders, these data confirm that mental disorders among children continue to be a substantial public health concern. These findings can be used by public health professionals, health care providers, state health officials, policymakers, and educators to understand the prevalence of specific mental disorders and other indicators of mental health and the challenges related to mental health surveillance. |
Viral shedding of SARS-CoV-2 in body fluids associated with sexual activity: a systematic review and meta-analysis
Calvet GA , Kara E , Gonsalves L , Seuc AH , de Oliveira RVC , Thwin SS , Gomez Ponce de León R , Gámez MC , Peña GM , Pendás BVR , Alzugaray MG , Carballo GO , Cala DC , Guimarães PMQ , Bonet M , Taylor M , Thorson A , Kim C , Ali M , Broutet N . BMJ Open 2024 14 (2) e073084 OBJECTIVE: To identify and summarise the evidence on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection and persistence in body fluids associated with sexual activity (saliva, semen, vaginal secretion, urine and faeces/rectal secretion). ELIGIBILITY: All studies that reported detection of SARS-CoV-2 in saliva, semen, vaginal secretion, urine and faeces/rectal swabs. INFORMATION SOURCES: The WHO COVID-19 database from inception to 20 April 2022. RISK OF BIAS ASSESSMENT: The National Institutes of Health tools. SYNTHESIS OF RESULTS: The proportion of patients with positive results for SARS-CoV-2 and the proportion of patients with a viral duration/persistence of at least 14 days in each fluid was calculated using fixed or random effects models. INCLUDED STUDIES: A total of 182 studies with 10 023 participants. RESULTS: The combined proportion of individuals with detection of SARS-CoV-2 was 82.6% (95% CI: 68.8% to 91.0%) in saliva, 1.6% (95% CI: 0.9% to 2.6%) in semen, 2.7% (95% CI: 1.8% to 4.0%) in vaginal secretion, 3.8% (95% CI: 1.9% to 7.6%) in urine and 31.8% (95% CI: 26.4% to 37.7%) in faeces/rectal swabs. The maximum viral persistence for faeces/rectal secretions was 210 days, followed by semen 121 days, saliva 112 days, urine 77 days and vaginal secretions 13 days. Culturable SARS-CoV-2 was positive for saliva and faeces. LIMITATIONS: Scarcity of longitudinal studies with follow-up until negative results. INTERPRETATION: SARS-CoV-2 RNA was detected in all fluids associated with sexual activity but was rare in semen and vaginal secretions. Ongoing droplet precautions and awareness of the potential risk of contact with faecal matter/rectal mucosa are needed. PROSPERO REGISTRATION NUMBER: CRD42020204741. |
Ethnic and racial differences in self-reported symptoms, health status, activity level, and missed work at 3 and 6 months following SARS-CoV-2 infection
O'Laughlin KN , Klabbers RE , Ebna Mannan I , Gentile NL , Geyer RE , Zheng Z , Yu H , Li SX , Chan KCG , Spatz ES , Wang RC , L'Hommedieu M , Weinstein RA , Plumb ID , Gottlieb M , Huebinger RM , Hagen M , Elmore JG , Hill MJ , Kelly M , McDonald S , Rising KL , Rodriguez RM , Venkatesh A , Idris AH , Santangelo M , Koo K , Saydah S , Nichol G , Stephens KA . Front Public Health 2023 11 1324636 INTRODUCTION: Data on ethnic and racial differences in symptoms and health-related impacts following SARS-CoV-2 infection are limited. We aimed to estimate the ethnic and racial differences in symptoms and health-related impacts 3 and 6 months after the first SARS-CoV-2 infection. METHODS: Participants included adults with SARS-CoV-2 infection enrolled in a prospective multicenter US study between 12/11/2020 and 7/4/2022 as the primary cohort of interest, as well as a SARS-CoV-2-negative cohort to account for non-SARS-CoV-2-infection impacts, who completed enrollment and 3-month surveys (N = 3,161; 2,402 SARS-CoV-2-positive, 759 SARS-CoV-2-negative). Marginal odds ratios were estimated using GEE logistic regression for individual symptoms, health status, activity level, and missed work 3 and 6 months after COVID-19 illness, comparing each ethnicity or race to the referent group (non-Hispanic or white), adjusting for demographic factors, social determinants of health, substance use, pre-existing health conditions, SARS-CoV-2 infection status, COVID-19 vaccination status, and survey time point, with interactions between ethnicity or race and time point, ethnicity or race and SARS-CoV-2 infection status, and SARS-CoV-2 infection status and time point. RESULTS: Following SARS-CoV-2 infection, the majority of symptoms were similar over time between ethnic and racial groups. At 3 months, Hispanic participants were more likely than non-Hispanic participants to report fair/poor health (OR: 1.94; 95%CI: 1.36-2.78) and reduced activity (somewhat less, OR: 1.47; 95%CI: 1.06-2.02; much less, OR: 2.23; 95%CI: 1.38-3.61). At 6 months, differences by ethnicity were not present. At 3 months, Other/Multiple race participants were more likely than white participants to report fair/poor health (OR: 1.90; 95% CI: 1.25-2.88), reduced activity (somewhat less, OR: 1.72; 95%CI: 1.21-2.46; much less, OR: 2.08; 95%CI: 1.18-3.65). At 6 months, Asian participants were more likely than white participants to report fair/poor health (OR: 1.88; 95%CI: 1.13-3.12); Black participants reported more missed work (OR, 2.83; 95%CI: 1.60-5.00); and Other/Multiple race participants reported more fair/poor health (OR: 1.83; 95%CI: 1.10-3.05), reduced activity (somewhat less, OR: 1.60; 95%CI: 1.02-2.51; much less, OR: 2.49; 95%CI: 1.40-4.44), and more missed work (OR: 2.25; 95%CI: 1.27-3.98). DISCUSSION: Awareness of ethnic and racial differences in outcomes following SARS-CoV-2 infection may inform clinical and public health efforts to advance health equity in long-term outcomes. |
Cross-sectional study of soil-transmitted helminthiases in Black Belt Region of Alabama, USA
Poole C , Barker T , Bradbury R , Capone D , Chatham AH , Handali S , Rodriguez E , Qvarnstrom Y , Brown J . Emerg Infect Dis 2023 29 (12) 2461-2470 We conducted a cross-sectional study to determine the prevalence of soil-transmitted helminthiases (STH) in areas of rural Alabama, USA, that have sanitation deficits. We enrolled 777 children; 704 submitted stool specimens and 227 a dried blood spot sample. We microscopically examined stool specimens from all 704 children by using Mini-FLOTAC for helminth eggs. We tested a subset by using molecular techniques: real-time PCR analysis for 5 STH species, TaqMan Array Cards for enteric helminths, and digital PCR for Necator americanus hookworm. We analyzed dried blood spots for Strongyloides stercoralis and Toxocara spp. roundworms by using serologic testing. Despite 12% of our cohort reporting living in homes that directly discharge untreated domestic wastewater, stool testing for STH was negative; however, 5% of dried blood spots were positive for Toxocara spp. roundworms. Survey data suggests substantial numbers of children in this region may be exposed to raw sewage, which is itself a major public health concern. |
Bias analyses to investigate the impact of differential participation: Application to a birth defects case-control study
Petersen JM , Kahrs JC , Adrien N , Wood ME , Olshan AF , Smith LH , Howley MM , Ailes EC , Romitti PA , Herring AH , Parker SE , Shaw GM , Politis MD . Paediatr Perinat Epidemiol 2023 BACKGROUND: Certain associations observed in the National Birth Defects Prevention Study (NBDPS) contrasted with other research or were from areas with mixed findings, including no decrease in odds of spina bifida with periconceptional folic acid supplementation, moderately increased cleft palate odds with ondansetron use and reduced hypospadias odds with maternal smoking. OBJECTIVES: To investigate the plausibility and extent of differential participation to produce effect estimates observed in NBDPS. METHODS: We searched the literature for factors related to these exposures and participation and conducted deterministic quantitative bias analyses. We estimated case-control participation and expected exposure prevalence based on internal and external reports, respectively. For the folic acid-spina bifida and ondansetron-cleft palate analyses, we hypothesized the true odds ratio (OR) based on prior studies and quantified the degree of exposure over- (or under-) representation to produce the crude OR (cOR) in NBDPS. For the smoking-hypospadias analysis, we estimated the extent of selection bias needed to nullify the association as well as the maximum potential harmful OR. RESULTS: Under our assumptions (participation, exposure prevalence, true OR), there was overrepresentation of folic acid use and underrepresentation of ondansetron use and smoking among participants. Folic acid-exposed spina bifida cases would need to have been ≥1.2× more likely to participate than exposed controls to yield the observed null cOR. Ondansetron-exposed cleft palate cases would need to have been 1.6× more likely to participate than exposed controls if the true OR is null. Smoking-exposed hypospadias cases would need to have been ≥1.2 times less likely to participate than exposed controls for the association to falsely appear protective (upper bound of selection bias adjusted smoking-hypospadias OR = 2.02). CONCLUSIONS: Differential participation could partly explain certain associations observed in NBDPS, but questions remain about why. Potential impacts of other systematic errors (e.g. exposure misclassification) could be informed by additional research. |
Use of supervision data to improve quality of care for malaria in pregnancy: Experience in six African countries
Wolf K , Mostel J , Oseni L , Gomez P , Kibuka T , Emerson C , Gutman JR , Malpass A , Youll S , Mukamba JY , Tchinda E , Achu D , Tjek P , Assa JL , Silue M , Tanoh MA , Kokrasset-Yah C , Babanawo F , Asiedu A , Komey M , Boateng P , Mabiria M , Ngindu A , Njiru P , Omar AH , Sidibe FA , Diallo C , Kamate B , Kone A , Elisha S , Maiga AD , Mayaki AI , Tidjani Issa Gana F , Tetteh G . Am J Trop Med Hyg 2023 Malaria in pregnancy (MiP) intervention coverage, especially intermittent preventive treatment in pregnancy (IPTp), lags behind other global malaria indicators. In 2020, across Africa, only 32% of eligible pregnant women received at least three IPTp doses, despite high antenatal care attendance. We conducted a secondary analysis of data collected during outreach, training, and supportive supervision visits from 2019 to 2020 to assess quality of care and explore factors contributing to providers' competence in providing IPTp, insecticide-treated nets, malaria case management, and respectful maternity care. Data were collected during observations of provider-patient interactions in six countries (Cameroon, Cote d'Ivoire, Ghana, Kenya, Mali, and Niger). Competency scores (i.e., composite scores of supervisory checklist observations) were calculated across three domains: MiP prevention, MiP treatment, and respectful maternity care. Scores are used to understand drivers of competency, rather than to assess individual health worker performance. Country-specific multilinear regressions were used to assess how competency score was influenced by commodity availability, training, provider gender and cadre, job aid availability, and facility type. Average competency scores varied across countries: prevention (44-90%), treatment (78-90%), and respectful maternity care (53-93%). The relative association of each factor with competency score varied. Commodity availability, training, and access to job aids correlated positively with competency in multiple countries. To improve MiP service quality, equitable access to training opportunities for different cadres, targeted training, and access to job aids and guidelines should be available for providers. Collection and analysis of routine supervision data can support tailored actions to improve quality MiP services. |
Systematic review and meta-analysis of the relationship between exposure to parental substance use and attention-deficit/hyperactivity disorder in children
Maher BS , Bitsko RH , Claussen AH , O'Masta B , Cerles A , Holbrook JR , Mahmooth Z , Chen-Bowers N , Rojo ALA , Kaminski JW , Rush M . Prev Sci 2023 Attention-deficit/hyperactivity disorder (ADHD) is characterized by persistent patterns of inattention, hyperactivity, and impulsiveness. Among US children and adolescents aged 3-17 years, 9.4% have a diagnosis of ADHD. Previous research suggests possible links between parental substance use and ADHD among children. We conducted a systematic review and meta-analysis of 86 longitudinal or retrospective studies of prenatal or postnatal alcohol, tobacco, or other parental substance use and substance use disorders and childhood ADHD and its related behavioral dimensions of inattention and hyperactivity-impulsivity. Meta-analyses were grouped by drug class and pre- and postnatal periods with combined sample sizes ranging from 789 to 135,732. Prenatal exposure to alcohol or tobacco and parent substance use disorders were consistently and significantly associated with ADHD among children. Other parental drug use exposures resulted in inconsistent or non-significant findings. Prevention and treatment of parental substance use may have potential for impacts on childhood ADHD. |
The Human Phenotype Ontology in 2024: phenotypes around the world
Gargano MA , Matentzoglu N , Coleman B , Addo-Lartey EB , Anagnostopoulos AV , Anderton J , Avillach P , Bagley AM , Bakštein E , Balhoff JP , Baynam G , Bello SM , Berk M , Bertram H , Bishop S , Blau H , Bodenstein DF , Botas P , Boztug K , Čady J , Callahan TJ , Cameron R , Carbon SJ , Castellanos F , Caufield JH , Chan LE , Chute CG , Cruz-Rojo J , Dahan-Oliel N , Davids JR , de Dieuleveult M , de Souza V , de Vries BBA , de Vries E , DePaulo JR , Derfalvi B , Dhombres F , Diaz-Byrd C , Dingemans AJM , Donadille B , Duyzend M , Elfeky R , Essaid S , Fabrizzi C , Fico G , Firth HV , Freudenberg-Hua Y , Fullerton JM , Gabriel DL , Gilmour K , Giordano J , Goes FS , Moses RG , Green I , Griese M , Groza T , Gu W , Guthrie J , Gyori B , Hamosh A , Hanauer M , Hanušová K , He YO , Hegde H , Helbig I , Holasová K , Hoyt CT , Huang S , Hurwitz E , Jacobsen JOB , Jiang X , Joseph L , Keramatian K , King B , Knoflach K , Koolen DA , Kraus ML , Kroll C , Kusters M , Ladewig MS , Lagorce D , Lai MC , Lapunzina P , Laraway B , Lewis-Smith D , Li X , Lucano C , Majd M , Marazita ML , Martinez-Glez V , McHenry TH , McInnis MG , McMurry JA , Mihulová M , Millett CE , Mitchell PB , Moslerová V , Narutomi K , Nematollahi S , Nevado J , Nierenberg AA , Čajbiková NN , Nurnberger JI Jr , Ogishima S , Olson D , Ortiz A , Pachajoa H , Perez de Nanclares G , Peters A , Putman T , Rapp CK , Rath A , Reese J , Rekerle L , Roberts AM , Roy S , Sanders SJ , Schuetz C , Schulte EC , Schulze TG , Schwarz M , Scott K , Seelow D , Seitz B , Shen Y , Similuk MN , Simon ES , Singh B , Smedley D , Smith CL , Smolinsky JT , Sperry S , Stafford E , Stefancsik R , Steinhaus R , Strawbridge R , Sundaramurthi JC , Talapova P , Tenorio Castano JA , Tesner P , Thomas RH , Thurm A , Turnovec M , van Gijn ME , Vasilevsky NA , Vlčková M , Walden A , Wang K , Wapner R , Ware JS , Wiafe AA , Wiafe SA , Wiggins LD , Williams AE , Wu C , Wyrwoll MJ , Xiong H , Yalin N , Yamamoto Y , Yatham LN , Yocum AK , Young AH , Yüksel Z , Zandi PP , Zankl A , Zarante I , Zvolský M , Toro S , Carmody LC , Harris NL , Munoz-Torres MC , Danis D , Mungall CJ , Köhler S , Haendel MA , Robinson PN . Nucleic Acids Res 2023 52 D1333-D1346 The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs. |
Previous infection and effectiveness of COVID-19 vaccination in middle- and high-school students
Almendares OM , Ruffin JD , Collingwood AH , Nolen LD , Lanier WA , Dash SR , Ciesla AA , Wiegand R , Tate JE , Kirking HL . Pediatrics 2023 152 (6) BACKGROUND AND OBJECTIVES: Understanding the real-world impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mitigation measures, particularly vaccination, in children and adolescents in congregate settings remains important. We evaluated protection against SARS-CoV-2 infection using school-based testing data. METHODS: Using data from Utah middle- and high-school students participating in school-wide antigen testing in January 2022 during omicron (BA.1) variant predominance, log binomial models were fit to estimate the protection of previous SARS-CoV-2 infection and coronavirus disease 2019 vaccination against SARS-CoV-2 infection. RESULTS: Among 17 910 students, median age was 16 years (range: 12-19), 16.7% had documented previous SARS-CoV-2 infection; 55.6% received 2 vaccine doses with 211 median days since the second dose; and 8.6% of students aged 16 to 19 years received 3 vaccine doses with 21 median days since the third dose. Protection from previous infection alone was 35.9% (95% confidence interval [CI]: 12.9%-52.8%) and 23.8% (95% CI: 2.1%-40.7%) for students aged 12 to 15 and 16 to 19 years, respectively. Protection from 2-dose hybrid immunity (previous SARS-CoV-2 infection and vaccination) with <180 days since the second dose was 58.7% (95% CI: 33.2%-74.4%) for students aged 12 to 15 and 54.7% (95% CI: 31.0%-70.3%) for students aged 16 to 19 years. Protection was highest (70.0%, 95% CI: 42.3%-84.5%) among students with 3-dose hybrid immunity, although confidence intervals overlap with 2-dose vaccination. CONCLUSIONS: The estimated protection against infection was strongest for those with hybrid immunity from previous infection and recent vaccination with a third dose. |
Risk factors for recent HIV infections among adults in 14 countries in Africa identified by population-based HIV impact assessment surveys, 2015-2019
Currie DW , West CA , Patel HK , Favaloro J , Asiimwe F , Ndagije F , Silver R , Mugurungi O , Shang J , Ndongmo CB , Williams DB , Dzinotyiweyi E , Waruru A , Pasipamire M , Nuwagaba-Biribonwoha H , Dlamini S , McLeod N , Kayirangwa E , Rwibasira G , Minchella PA , Auld AF , Nyirenda R , Getaneh Y , Hailemariam AH , Tondoh-Koui I , Kohemun N , Mgomella GS , Njau PF , Kirungi WL , Dalhatu I , Stafford KA , Bodika SM , Ussery F , McCracken S , Stupp P , Brown K , Duong YT , Parekh BS , Voetsch AC . Emerg Infect Dis 2023 29 (11) 2325-2334 Identifying persons who have newly acquired HIV infections is critical for characterizing the HIV epidemic direction. We analyzed pooled data from nationally representative Population-Based HIV Impact Assessment surveys conducted across 14 countries in Africa for recent infection risk factors. We included adults 15-49 years of age who had sex during the previous year and used a recent infection testing algorithm to distinguish recent from long-term infections. We collected risk factor information via participant interviews and assessed correlates of recent infection using multinomial logistic regression, incorporating each survey's complex sampling design. Compared with HIV-negative persons, persons with higher odds of recent HIV infection were women, were divorced/separated/widowed, had multiple recent sex partners, had a recent HIV-positive sex partner or one with unknown status, and lived in communities with higher HIV viremia prevalence. Prevention programs focusing on persons at higher risk for HIV and their sexual partners will contribute to reducing HIV incidence. |
A conceptual framework for nomenclatural stability and validity of medically important fungi: a proposed global consensus guideline for fungal name changes supported by ABP, ASM, CLSI, ECMM, ESCMID-EFISG, EUCAST-AFST, FDLC, IDSA, ISHAM, MMSA, and MSGERC
de Hoog S , Walsh TJ , Ahmed SA , Alastruey-Izquierdo A , Alexander BD , Arendrup MC , Babady E , Bai FY , Balada-Llasat JM , Borman A , Chowdhary A , Clark A , Colgrove RC , Cornely OA , Dingle TC , Dufresne PJ , Fuller J , Gangneux JP , Gibas C , Glasgow H , Gräser Y , Guillot J , Groll AH , Haase G , Hanson K , Harrington A , Hawksworth DL , Hayden RT , Hoenigl M , Hubka V , Johnson K , Kus JV , Li R , Meis JF , Lackner M , Lanternier F , Leal SM Jr , Lee F , Lockhart SR , Luethy P , Martin I , Kwon-Chung KJ , Meyer W , Nguyen MH , Ostrosky-Zeichner L , Palavecino E , Pancholi P , Pappas PG , Procop GW , Redhead SA , Rhoads DD , Riedel S , Stevens B , Sullivan KO , Vergidis P , Roilides E , Seyedmousavi A , Tao L , Vicente VA , Vitale RG , Wang QM , Wengenack NL , Westblade L , Wiederhold N , White L , Wojewoda CM , Zhang SX . J Clin Microbiol 2023 61 (11) e0087323 The rapid pace of name changes of medically important fungi is creating challenges for clinical laboratories and clinicians involved in patient care. We describe two sources of name change which have different drivers, at the species versus the genus level. Some suggestions are made here to reduce the number of name changes. We urge taxonomists to provide diagnostic markers of taxonomic novelties. Given the instability of phylogenetic trees due to variable taxon sampling, we advocate to maintain genera at the largest possible size. Reporting of identified species in complexes or series should where possible comprise both the name of the overarching species and that of the molecular sibling, often cryptic species. Because the use of different names for the same species will be unavoidable for many years to come, an open access online database of the names of all medically important fungi, with proper nomenclatural designation and synonymy, is essential. We further recommend that while taxonomic discovery continues, the adaptation of new name changes by clinical laboratories and clinicians be reviewed routinely by a standing committee for validation and stability over time, with reference to an open access database, wherein reasons for changes are listed in a transparent way. |
Vaginal microbiome, antiretroviral concentrations, and HIV genital shedding in the setting of hormonal contraception initiation in Malawi
Lantz AM , Cottrell ML , Corbett AH , Chinula L , Kourtis AP , Nelson JAE , Tegha G , Hurst S , Gajer P , Ravel J , Haddad LB , Tang JH , Nicol MR . AIDS 2023 37 (14) 2185-2190 OBJECTIVE: The aim of this study was to understand how vaginal microbiota composition affects antiretroviral concentrations in the setting of hormonal contraception initiation. METHODS: Cervicovaginal fluid (CVF) concentrations of tenofovir, lamivudine, and efavirenz from 73 Malawian women with HIV were compared before and after initiation of depot-medroxyprogesterone acetate (DMPA) or levonorgestrel implant. We evaluated antiretroviral concentrations and vaginal microbiota composition/structure in the context of contraception initiation and predicted genital shedding using multivariable repeated measurements models fit by generalized estimating equations. RESULTS: Mean lamivudine CVF concentrations decreased 37% 1 month after contraception initiation. Subgroup analyses revealed a 41% decrease in women 1 month after initiating levonorgestrel implant, but no significant difference was observed in DMPA group alone. Tenofovir, lamivudine, and efavirenz CVF concentrations were positively correlated with anaerobic bacteria associated with nonoptimal vaginal microbiota. Risk of genital HIV shedding was not significantly associated with tenofovir or lamivudine CVF concentrations [tenofovir relative risk (RR): 0.098, P = 0.75; lamivudine RR: 0.142, P = 0.54]. Lack of association between genital HIV shedding and efavirenz CVF concentrations did not change when adjusting for vaginal microbiota composition and lamivudine/tenofovir CVF concentrations (RR: 1.33, P = 0.531). CONCLUSION: No effect of hormone initiation on genital shedding provides confidence that women with HIV on either DMPA or levonorgestrel implant contraception will not have compromised ART efficacy. The unexpected positive correlation between antiretroviral CVF concentrations and certain bacterial taxa relative abundance requires further work to understand the mechanism and clinical relevance. |
Inferring time of infection from field data using dynamic models of antibody decay
Borremans B , Mummah RO , Guglielmino AH , Galloway RL , Hens N , Prager KC , Lloyd-Smith JO . Methods Ecol Evol 2023 Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody-level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times. The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross-sectional data once the model is trained. © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. |
Effect of Test and Treat on clinical outcomes in Nigeria: A national retrospective study
Lavoie MC , Ehoche A , Blanco N , Ahmed El-Imam I , Oladipo A , Dalhatu I , Odafe S , Adebajo S , Ng AH , Rapoport L , Lawton JG , Obanubi C , Onotu D , Patel S , Ikpeazu A , Ashefor G , Adebobola B , Adetinuke Boyd M , Aliyu G , Stafford KA . PLoS One 2023 18 (8) e0284847 BACKGROUND: In Nigeria, results from the pilot of the Test and Treat strategy showed higher loss to follow up (LTFU) among people living with HIV compared to before its implementation. The aim of this evaluation was to assess the effects of antiretroviral therapy (ART) initiation within 14 days on LTFU at 12 months and viral suppression. METHODS: We conducted a retrospective cohort study using routinely collected de-identified patient-level data hosted on the Nigeria National Data Repository from 1,007 facilities. The study population included people living with HIV age ≥15. We used multivariable Cox proportional frailty hazard models to assess time to LTFU comparing ART initiation strategy and multivariable log-binomial regression for viral suppression. RESULTS: Overall, 26,937 (38.13%) were LTFU at 12 months. Among individuals initiated within 14 days, 38.4% were LTFU by 12 months compared to 35.4% for individuals initiated >14 days (p<0.001). In the adjusted analysis, individuals who were initiated ≤14 days after HIV diagnosis had a higher hazard of being LTFU (aHR 1.15, 95% CI 1.10-1.20) than individuals initiated after 14 days of HIV diagnosis. Among individuals with viral load results, 86.2% were virally suppressed. The adjusted risk ratio for viral suppression among individuals who were initiated ≤14 days compared to >14 days was not statistically significant. CONCLUSION: LTFU was higher among individuals who were initiated within 14 days compared to greater than 14 days after HIV diagnosis. There was no difference for viral suppression. The provision of early tailored interventions to support newly diagnosed people living may contribute to reducing LTFU. |
Prevalence of symptoms 12 months after acute illness, by COVID-19 testing status among adults - United States, December 2020-March 2023
Montoy JCC , Ford J , Yu H , Gottlieb M , Morse D , Santangelo M , O'Laughlin KN , Schaeffer K , Logan P , Rising K , Hill MJ , Wisk LE , Salah W , Idris AH , Huebinger RM , Spatz ES , Rodriguez RM , Klabbers RE , Gatling K , Wang RC , Elmore JG , McDonald SA , Stephens KA , Weinstein RA , Venkatesh AK , Saydah S . MMWR Morb Mortal Wkly Rep 2023 72 (32) 859-865 To further the understanding of post-COVID conditions, and provide a more nuanced description of symptom progression, resolution, emergence, and reemergence after SARS-CoV-2 infection or COVID-like illness, analysts examined data from the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE), a prospective multicenter cohort study. This report includes analysis of data on self-reported symptoms collected from 1,296 adults with COVID-like illness who were tested for SARS-CoV-2 using a Food and Drug Administration-approved polymerase chain reaction or antigen test at the time of enrollment and reported symptoms at 3-month intervals for 12 months. Prevalence of any symptom decreased substantially between baseline and the 3-month follow-up, from 98.4% to 48.2% for persons who received a positive SARS-CoV-2 test results (COVID test-positive participants) and from 88.2% to 36.6% for persons who received negative SARS-CoV-2 test results (COVID test-negative participants). Persistent symptoms decreased through 12 months; no difference between the groups was observed at 12 months (prevalence among COVID test-positive and COVID test-negative participants = 18.3% and 16.1%, respectively; p>0.05). Both groups reported symptoms that emerged or reemerged at 6, 9, and 12 months. Thus, these symptoms are not unique to COVID-19 or to post-COVID conditions. Awareness that symptoms might persist for up to 12 months, and that many symptoms might emerge or reemerge in the year after COVID-like illness, can assist health care providers in understanding the clinical signs and symptoms associated with post-COVID-like conditions. |
Use of Public Data to Describe COVID-19 Contact Tracing in China during January 20–February 29, 2020 (preprint)
Dirlikov E , Zhou S , Han L , Li Z , Hao L , Millman AJ , Marston B . medRxiv 2020 2020.12.04.20243972 Objective Although contact tracing is generally not used to control influenza pandemics, China and several countries in the Western Pacific Region employed contact tracing as part of COVID-19 response activities. To improve understanding on the use of contact tracing for COVID-19 emergency public health response activities, we describe reported COVID-19 contacts traced and quarantined in China and a proxy for number of reported contacts traced per reported case.Methods We abstracted publicly available online aggregate data reported from China’s National Health Commission and provincial health commissions’ COVID-19 daily situational reports for January 20–February 29, 2020. The number of new contacts traced by report date was computed as the difference between total contacts traced on consecutive reports. A proxy for the number of contacts traced per case was computed as the number of new contacts traced divided by the number of new cases.Results During January 20–February 29, 2020, China reported 80,968 new COVID-19 cases (Hubei Province = 67,608 [83%]), and 659,899 contacts traced (Hubei Province = 265,617 [40%]). Non-Hubei provinces reported more contacts traced per case than Hubei Province; this difference increased over time.Discussion Along with other NPI used in China, contact tracing likely contributed to reducing SARS-CoV-2 transmission by quarantining a large number of potentially infected contacts. Despite reporting only 15% of total cases, non-Hubei provinces had 1.5 times more reported contacts traced compared to Hubei Province. Contract tracing may have been more complete in areas and periods with lower case counts.Competing Interest StatementThe authors have declared no competing interest.Funding StatementData collection and analysis were conducted as part of COVID-19 emergency response. No external funds were used.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:This activity was reviewed by CDC and was determined to be non-research, public health emergency response, consistent with applicable U.S. federal law and CDC policy.All 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.YesData were compiled from Provincial-Level Health Commission Websites Containing Publicly Available Reported Data on COVID-19 National Health Commission http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Anhui http://wjw.ah.gov.cn/ Beijing http://wjw.beijing.gov.cn/xwzx_20031/xwfb/ Chongqing http://wsjkw.cq.gov.cn/ Fujian http://wjw.fujian.gov.cn/ Gansu http://wsjk.gansu.gov.cn/ Guangdong http://wsjkw.gd.gov.cn/zwyw_yqxx/index.html Guangxi http://wsjkw.gxzf.gov.cn/gzdt/bt/ Guizhou http://www.gzhfpc.gov.cn/ Hainan http://wst.hainan.gov.cn/swjw/index.html Hebei http://wsjkw.hebei.gov.cn/ Heilongjiang http://wsjkw.hlj.gov.cn/ Henan http://www.hnwsjsw.gov.cn/ Hubei http://wjw.hubei.gov.cn/fbjd/dtyw/ Hunan http://wjw.hunan.gov.cn/ Inner Mongolia http://wjw.nmg.gov.cn/ Jiangsu http://wjw.jiangsu.gov.cn/ Jiangxi http://hc.jiangxi.gov.cn/ Jilin http://wsjkw.jl.gov.cn/ Liaoning http://wsjk.ln.gov.cn/ Ningxia http://wsjkw.nx.gov.cn/ Qinghai https://wsjkw.qinghai.gov.cn/ Shaanxi http://sx jw.shaanxi.gov.cn/ Shandong http://wsjkw.shandong.gov.cn Shanghai http://wsjkw.sh.gov.cn/xwfb/index.html Shanxi http://wjw.shanxi.gov.cn/ Sichuan http://wsjkw.sc.gov.cn/scwsjkw/szyw/tygl.shtml Tianjin http://wsjs.tj.gov.cn/ Tibet http://wjw.xizang.gov.cn Xinjiang http://xjhfpc.gov.cn Yunnan http://ynswsjkw.yn.gov.cn/wjwWebsite/web/index Zhejiang http://www.zjwjw.gov.cn/col/col1202101/index.html |
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 |
Escaping the Fate of Sisyphus: Assessing Resistome Hybridization Baits for Antimicrobial Resistance Gene Capture (preprint)
Beaudry MS , Thomas JC , Baptista RP , Sullivan AH , Norfolk W , Devault A , Enk J , Kieran TJ , Rhodes OEJr , Perry KA , Rose LJ , Bayona-Vásquez NJ , Oladeinde A , Lipp EK , Sanchez S , Glenn TC . bioRxiv 2021 2021.07.20.452950 Finding, characterizing, and monitoring reservoirs for antimicrobial resistance (AMR) is vital to protecting public health. Hybridization capture baits are an accurate, sensitive, and cost-effective technique used to enrich and characterize DNA sequences of interest, including antimicrobial resistance genes (ARGs), in complex environmental samples. We demonstrate the continued utility of a set of 19,933 hybridization capture baits designed from the Comprehensive Antibiotic Resistance Database (CARD)v1.1.2 and Pathogenicity Island Database (PAIDB)v2.0, targeting 3,565 unique nucleotide sequences that confer resistance. We demonstrate the efficiency of our bait set on a custom-made resistance mock community and complex environmental samples to increase the proportion of on-target reads as much as >200-fold. However, keeping pace with newly discovered ARGs poses a challenge when studying AMR, because novel ARGs are continually being identified and would not be included in bait sets designed prior to discovery. We provide imperative information on how our bait set performs against CARDv3.3.1, as well as a generalizable approach for deciding when and how to update hybridization capture bait sets. This research encapsulates the full life cycle of baits for hybridization capture of the resistome from design and validation (both in silico and in vitro) to utilization and forecasting updates and retirement.Originality-Significance Statement This work is applicable to a wide range of research. It helps to define conditions under which hybridization capture is useful regarding not only antimicrobial resistance specifically, but also more generally how to assess the ongoing utility of existing bait sets - giving objective criteria for when and by what strategies baits should be updated. We also provide a method for quantifying and comparing antimicrobial resistance genes (ARGs) similar to what is used for RNAseq experiments. This approach improves comparison of ARGs across environments. Thus, the work provides an improved foundation for ARG future studies, while cutting across traditional areas of microbiology and extending beyond.Competing Interest StatementThe EHS DNA lab provides oligonucleotide aliquots and library preparation services at cost, including some oligonucleotides and services used in this manuscript (baddna.uga.edu). JE and AD were employed by, and thereby have financial interest in, Daicel Arbor Biosciences, who provided the in-solution capture reagents used in this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. |
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 |
An assessment of adult mosquito collection techniques for studying species abundance and diversity in Maferinyah, Guinea (preprint)
Cansado-Utrilla C , Jeffries CL , Kristan M , Brugman VA , Heard P , Camara G , Sylla M , Beavogui AH , Messenger LA , Walker T . bioRxiv 2019 772822 Background Guinea is a West African country with a high prevalence of vector-borne diseases where few entomological studies have been undertaken. Although several mosquito collection methods are routinely used for surveillance in vector control programmes, they target different behaviours causing bias in species diversity and abundance. Given the paucity of mosquito trap data in West Africa, we compared the performance of five trap-lure combinations and Human Landing Catches (HLCs) in Guinea.Methods Five mosquito traps were compared in a 5×5 Latin Square design for 15 days in three villages in Guinea between June and July 2018. CDC light traps, BG sentinel 2 traps (with BG and MB5 lures), gravid traps and Stealth traps were deployed for 24-hour intervals with mosquitoes collected every 12 hours (day and night collections). HLCs were also performed for 15 nights. A Generalised Linear Mixed Model was applied to compare the effect of the traps, sites and collection times on the mosquito abundance. Species identification was confirmed using PCR-based analysis and Sanger sequencing.Results In total, 10,610 mosquitoes were captured across all five traps. Significantly more mosquitoes (P<0.005) were collected by Stealth traps (7,096) compared to the rest of the traps. Stealth traps and BG sentinel 2 traps were the best at capturing An. gambiae and Ae. aegypti mosquitoes respectively. HLCs captured predominantly An. coluzzii (41%) and hybrids of An. gambiae s.s. / An. coluzzii (36%) in contrast to the five adult traps, which captured predominantly An. melas (83%). Senguelen (rural) presented the highest abundance of mosquitoes and overall diversity in comparison with Fandie (semi-rural) and Maferinyah Centre One (semi-urban). To our knowledge, four species are reported for the first time in Guinea.Conclusions Stealth traps presented the best performance overall, suggesting that this trap may play an important role for mosquito surveillance in Guinea and similar sites in West Africa. We recommend the incorporation of molecular tools in entomological studies since it has helped to reveal, together with morphological identification, the presence of 25 mosquito species in this area.BG2BG sentinel 2 trapBG2-BGBG sentinel 2 trap with BG lureBG2-MB5BG sentinel 2 trap with MB5 lureGLMMgeneralized linear mixed modelGTGravid trapHLCHuman Landing CatchLTCDC light trapSTStealth trap |
Investigating the relationship between insecticide resistance, underlying molecular mechanisms and malaria prevalence in Anopheles gambiae s.l. from Guinea (preprint)
Collins E , Vaselli NM , Sylla M , Beavogui AH , Orsborne J , Walker T , Messenger LA . bioRxiv 2018 434688 The threat of insecticide resistance across sub-Saharan Africa is anticipated to severely impact the continued effectiveness of malaria vector control. We investigated the effect of carbamate and pyrethroid resistance on Anopheles gambiae s.l age, Plasmodium falciparum infection and characterized molecular resistance mechanisms in Guinea. Pyrethroid resistance was intense, with survivors of ten times the insecticidal concentration required to kill susceptible individuals. The L1014F kdr allele was significantly associated with mosquito survival following deltamethrin or permethrin treatment (p=0.003 and p=0.04, respectively). N1575Y and I1527T mutations were identified in 13% and 10% of individuals, respectively, but neither conferred increased pyrethroid tolerance. Partial restoration of pyrethroid susceptibility following synergist pre-exposure suggest a role for mixed-function oxidases. Carbamate resistance was lower and significantly associated with the G119S Ace-1 mutation (p=0.001). Oocyst rates were 6.8% and 4.2% among resistant and susceptible mosquitoes, respectively; survivors of bendiocarb exposure were significantly more likely to be infected (p=0.03). Resistant mosquitoes had significantly lower parity rates; however, a subset of intensely pyrethroid-resistant vectors were more likely to be parous (p=0.042 and p=0.045, for survivors of five and ten times the diagnostic dose of insecticides, respectively). Our findings emphasize the need for additional studies directly assessing the influence of insecticide resistance on mosquito fitness. |
Self-Reported Mask Use among Persons with or without SARS CoV-2 Vaccination -United States, December 2020-August 2021 (preprint)
Calamari LE , Weintraub WS , Santos R , Gibbs M , Bertoni AG , Ward LM , Saydah S , Plumb ID , Runyon MS , Wierzba TF , Sanders JW , Herrington D , Espeland MA , Williamson J , Mongraw-Chaffin M , Bertoni A , Alexander-Miller MA , Castri P , Mathews A , Munawar I , Seals AL , Ostasiewski B , Ballard CAP , Gurcan M , Ivanov A , Zapata GM , Westcott M , Blinson K , Blinson L , Mistysyn M , Davis D , Doomy L , Henderson P , Jessup A , Lane K , Levine B , McCanless J , McDaniel S , Melius K , O'Neill C , Pack A , Rathee R , Rushing S , Sheets J , Soots S , Wall M , Wheeler S , White J , Wilkerson L , Wilson R , Wilson K , Burcombe D , Saylor G , Lunn M , Ordonez K , O'Steen A , Wagner L , McCurdy LH , Gibbs MA , Taylor YJ , Calamari L , Tapp H , Ahmed A , Brennan M , Munn L , Dantuluri KL , Hetherington T , Lu LC , Dunn C , Hogg M , Price A , Leonidas M , Manning M , Rossman W , Gohs FX , Harris A , Priem JS , Tochiki P , Wellinsky N , Silva C , Ludden T , Hernandez J , Spencer K , McAlister L , Weintraub W , Miller K , Washington C , Moses A , Dolman S , Zelaya-Portillo J , Erkus J , Blumenthal J , Romero Barrientos RE , Bennett S , Shah S , Mathur S , Boxley C , Kolm P , Franklin E , Ahmed N , Larsen M , Oberhelman R , Keating J , Kissinger P , Schieffelin J , Yukich J , Beron A , Teigen J , Kotloff K , Chen WH , Friedman-Klabanoff D , Berry AA , Powell H , Roane L , Datar R , Correa A , Navalkele B , Min YI , Castillo A , Ward L , Santos RP , Anugu P , Gao Y , Green J , Sandlin R , Moore D , Drake L , Horton D , Johnson KL , Stover M , Lagarde WH , Daniel L , Maguire PD , Hanlon CL , McFayden L , Rigo I , Hines K , Smith L , Harris M , Lissor B , Cook V , Eversole M , Herrin T , Murphy D , Kinney L , Diehl P , Abromitis N , Pierre TSt , Heckman B , Evans D , March J , Whitlock B , Moore W , Arthur S , Conway J , Gallaher TR , Johanson M , Brown S , Dixon T , Reavis M , Henderson S , Zimmer M , Oliver D , Jackson K , Menon M , Bishop B , Roeth R , King-Thiele R , Hamrick TS , Ihmeidan A , Hinkelman A , Okafor C , Bray Brown RB , Brewster A , Bouyi D , Lamont K , Yoshinaga K , Vinod P , Peela AS , Denbel G , Lo J , Mayet-Khan M , Mittal A , Motwani R , Raafat M , Schultz E , Joseph A , Parkeh A , Patel D , Afridi B , Uschner D , Edelstein SL , Santacatterina M , Strylewicz G , Burke B , Gunaratne M , Turney M , Zhou SQ , Tjaden AH , Fette L , Buahin A , Bott M , Graziani S , Soni A , Mores C , Porzucek A , Laborde R , Acharya P , Guill L , Lamphier D , Schaefer A , Satterwhite WM , McKeague A , Ward J , Naranjo DP , Darko N , Castellon K , Brink R , Shehzad H , Kuprianov D , McGlasson D , Hayes D , Edwards S , Daphnis S , Todd B , Goodwin A , Berkelman R , Hanson K , Zeger S , Hopkins J , Reilly C , Edwards K , Gayle H , Redd S . medRxiv 2022 10 Wearing a facemask can help to decrease the transmission of COVID-19. We investigated self-reported mask use among subjects aged 18 years and older participating in the COVID-19 Community Research Partnership (CRP), a prospective longitudinal COVID-19 surveillance study in the mid-Atlantic and southeastern United States. We included those participants who completed >=5 daily surveys each month from December 1, 2020 through August 31, 2021. Mask use was defined as self-reported use of a face mask or face covering on every interaction with others outside the household within a distance of less than 6 feet. Participants were considered vaccinated if they reported receiving >=1 COVID-19 vaccine dose. Participants (n=17,522) were 91% non-Hispanic White, 68% female, median age 57 years, 26% healthcare workers, with 95% self-reported receiving >=1 COVID-19 vaccine dose through August; mean daily survey response was 85%. Mask use was higher among vaccinated than unvaccinated participants across the study period, regardless of the month of the first dose. Mask use remained relatively stable from December 2020 through April (range 71-80% unvaccinated; 86-93% vaccinated) and declined in both groups beginning in mid-May 2021 to 34% and 42% respectively in June 2021; mask use has increased again since July 2021. Mask use by all was lower during weekends and on Christmas and Easter, regardless of vaccination status. Independent predictors of higher mask use were vaccination, age >=65 years, female sex, racial or ethnic minority group, and healthcare worker occupation, whereas a history of self-reported prior COVID-19 illness was associated with lower use. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. |
Multiple lineages of Monkeypox virus detected in the United States, 2021-2022 (preprint)
Gigante CM , Korber B , Seabolt MH , Wilkins K , Davidson W , Rao AK , Zhao H , Hughes CM , Minhaj F , Waltenburg MA , Theiler J , Smole S , Gallagher GR , Blythe D , Myers R , Schulte J , Stringer J , Lee P , Mendoza RM , Griffin-Thomas LA , Crain J , Murray J , Atkinson A , Gonzalez AH , Nash J , Batra D , Damon I , McQuiston J , Hutson CL , McCollum AM , Li Y . bioRxiv 2022 11 (6619) 560-565 Monkeypox is a viral zoonotic disease endemic in Central and West Africa. In May 2022, dozens of non-endemic countries reported hundreds of monkeypox cases, most with no epidemiological link to Africa. We identified two lineages of Monkeypox virus (MPXV) among nine 2021 and 2022 U.S. monkeypox cases. A 2021 case was highly similar to the 2022 MPXV outbreak variant, suggesting a common ancestor. Analysis of mutations among these two lineages revealed an extreme preference for GA-to-AA mutations indicative of APOBEC3 cytosine deaminase activity that was shared among West African MPXV since 2017 but absent from Congo Basin lineages. Poxviruses are not thought to be subject to APOBEC3 editing; however, these findings suggest APOBEC3 activity has been recurrent and dominant in recent West African MPXV evolution. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. |
Inferring time of infection from field data using dynamic models of antibody decay (preprint)
Borremans B , Mummah RO , Guglielmino AH , Galloway RL , Hens N , Prager KC , Lloyd-Smith JO . bioRxiv 2022 07 Studies of infectious disease ecology often rely heavily on knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. Here, we present a way to do this in a Bayesian framework that facilitates the incorporation of all available information about potential infection times. We apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans, leading to reductions of 51-92% in the window of possible infection times. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements of infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. The method substantially simplifies the challenge of modeling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology, and can even be applied to cross-sectional data once the model is trained. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. |
Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): a longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection (preprint)
O'Laughlin KN , Thompson M , Hota B , Gottlieb M , Plumb ID , Chang AM , Wisk LE , Hall AJ , Wang RC , Spatz ES , Stephens KA , Huebinger RM , McDonald SA , Venkatesh A , Gentile N , Slovis BH , Hill M , Saydah S , Idris AH , Rodriguez R , Krumholz HM , Elmore JG , Weinstein RA , Nichol G . medRxiv 2021 05 BACKGROUND: Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. METHOD(S): INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. RESULT(S): Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. CONCLUSION(S): This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. |
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