bluuebunny/crossref_metadata_2025_split huggingface dataset:
HostSeq : A Canadian Whole Genome Sequencing and Clinical Data Resource
Authors: Yoo S , Garg E , Elliott LT , Hung RJ , Halevy AR , Brooks JD , Bull SB , Gagnon F , Greenwood CMT , Lawless JF , Paterson AD , Sun L , Zawati MH , Lerner-Ellis J , Abraham RJS , Birol I , Bourque G , Garant J-M , Gosselin C , Li J , Whitney J , Thiruvahindrapuram B , Herbrick J-A , Lorenti M , Reuter MS , Adeoye NO , Liu S , Allen U , Bernier FP , Biggs CM , Cheung AM , Cowan J , Herridge M , Maslove DM , Modi BP , Mooser V , Morris SK , Ostrowski M , Parekh RS , Pfeffer G , Suchowersky O , Taher J , Upton J , Warren RL , Yeung RSM , Aziz N , Turvey SE , Knoppers BM , Lathrop M , Jones SJM , Scherer SW , Strug LJ 
Published: May 2022

ABSTRACTHostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.
Rapid growth in disposable e-cigarette vaping among young adults in Great Britain from 2021 to 2022: a repeat cross-sectional survey
Authors: Tattan-Birch Harry , Jackson Sarah E , Kock Loren , Dockrell Martin , Brown Jamie 
Published: May 2022

AbstractAimsTo estimate recent trends in the prevalence of disposable e-cigarette vaping in Great Britain, overall and across ages.DesignThe Smoking Toolkit Study, a monthly representative cross-sectional survey. Setting: Great Britain.Participants29,976 adults (≥18 years) completed telephone interviews from January 2021 to January 2022.MeasurementsCurrent e-cigarette vapers were asked which type of device they mainly use. We estimated age-specific time trends in the prevalence of current disposable e-cigarette use among vapers and inhaled nicotine use (vaping/smoking) among adults.FindingsFrom January 2021 to January 2022, there was a 14-fold increase in the percentage of vapers that used disposables, rising from 1.2% to 16.7% (prevalence ratio [PR], 14.4; 95%CI, 6.6-49.0). Growth in disposable e-cigarette vaping was most pronounced in younger adults (interaction p-value, .0.007): for example, the percentage of 18-year-old vapers using of disposables rose from 0.89% to 56.7% (PR, 64; 95%CI, 15-5790) while it rose from 1.3% to 6.2% (PR, 4.7; 95%CI, 1.7-144) among 45-year-old vapers. However, the overall percentage people currently using any inhaled nicotine, vaped or smoked, remained stable over time both among all adults (20.1% vs. 20.6%; PR, 1.03; 95%CI, 0.88-1.20) and among 18-year-olds (29.6% vs. 29.6%; PR, 1.00; 95%CI, 0.79-1.26).ConclusionsUse of disposable e-cigarettes in Great Britain grew rapidly between 2021 and 2022, especially among younger adults, but the overall prevalence of inhaled nicotine use — smoked or vaped — was stable over time. Most young adult vapers in Great Britain now use disposable products.
Meta-analysis methods for risk difference: a comparison of different models
Authors: Guo Juanru , Xiao Mengli , Chu Haitao , Lin Lifeng 
Published: May 2022

AbstractRisk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: 1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and 2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.
Neighborhood distribution of availability of newer tobacco products: A four-site study, 2021
Authors: Rose Shyanika W. , Annabathula Arati , Westneat Susan , van de Venne Judy , Hrywna Mary , Ackerman Christopher , Lee Joseph G. L. , Sesay Mahdi , Giovenco Daniel P. , Spillane Torra , Hudson Shawna V. , Delnevo Cristine D. 
Published: May 2022

AbstractBackgroundAudits of tobacco retail stores can identify marketing patterns as newer tobacco products are introduced in the US. Our study examined store and neighborhood correlates of availability of nicotine pouches and disposable e-cigarettes in four US sites.MethodsWe conducted standardized store audits of n=242 tobacco retailers in 2021 in different states: New Jersey, Kentucky, North Carolina, and New York. Store audits focused on availability of nicotine pouches and disposable e-cigarettes. We geocoded stores linking them with census tract demographics. We conducted unadjusted and adjusted Poisson regression of availability of each product with correlates of the proportion of Non-Hispanic White residents, households under poverty, proximity to schools, site, and store type.ResultsNicotine pouches and disposable e-cigarettes were each available in around half the stores, but availability differed across sites. In adjusted analyses, nicotine pouches were less likely to be available in each store type vs. chain convenience and more likely in stores in census tracts with more non-Hispanic White residents. In contrast, disposable e-cigarettes were more likely to be available in tobacco store/vape shops than convenience stores and less likely in non-specialty store types like groceries.ConclusionsThe availability of newer tobacco products like nicotine pouches and disposable e-cigarettes were widely available in stores across sites, but retail marketing patterns appear to differ. As these product types become subject to increased regulation as they go through the FDA pre-market authorization process, understanding changes in their retail environment is critical to inform potential policies regulating their sale and marketing.
Socio-economic determinants of SARS-CoV-2 infection: results from a population-based serosurvey in Geneva, Switzerland
Authors: Santa-Ramírez Hugo-Alejandro , Wisniak Ania , Pullen Nick , Zaballa María-Eugenia , Pennacchio Francesco , Lorthe Elsa , Dumont Roxane , Baysson Hélène , Guessous Idris , Stringhini Silvia 
Published: May 2022

AbstractBackgroundSARS-CoV-2 infection and its health consequences have disproportionally affected disadvantaged socio-economic groups globally. This study aimed to analyze the association between socio-economic conditions and having developed anti-SARS-CoV-2 antibodies in a population-based sample in the canton of Geneva, Switzerland.MethodsData was obtained from a population-based serosurvey of adults in Geneva and their household members, between November and December, 2020, towards the end of the second pandemic wave in the canton. Participants were tested for anti-SARS-CoV-2 antibodies. Socio-economic conditions representing different dimensions were self-reported. Mixed effects logistic regressions were conducted for each predictor to test its association with seropositive status as the main outcome.Results2,889 adults completed the study questionnaire and were included in the final analysis. Retired participants and those living in suburban areas had lower odds of a seropositive result when compared to employed participants (OR 0.42, 95% CI - 0.20 – 0.87) and those living in urban areas (OR 0.67, 95% CI - 0.46 – 0.97), respectively. People facing financial hardship for less than a year had higher odds of a seropositive result compared to those who had never faced them (OR 2.23, 95% CI - 1.01 – 4.95). Educational level, occupational position and household income were not associated with being seropositive, nor were ethnicity or country of birth.DiscussionWhile traditional measures of socio-economic position did not seem to be related to the risk of being infected in this sample, this study sheds lights on the importance of examining the broader social determinants of health when evaluating the differential impact of the pandemic within the population.
COVID-19 Vaccine Acceptance and Uptake Among Healthcare Workers in Trinidad & Tobago
Authors: Gopaul Chavin D. , Ventour Dale , Thomas Davlin 
Published: May 2022

AbstractBackgroundCOVID-19 vaccine acceptance is important in ensuring the widespread vaccination of the population to achieve herd immunity. Establishing the acceptance of vaccines among healthcare workers, who play a vital role immunization program success, is important. The aim of this study was to assess the influence of social trust and demographic factors on COVID-19 vaccine acceptance among healthcare workers.MethodsA cross-sectional survey utilizing an electronic questionnaire inquiring about COVID-19 vaccine uptake, preferences, and concerns was distributed via email to 1,351 North Central Regional Health Authority (NCRHA) healthcare workers of the following categories: medical practitioners, nursing personnel, veterinary surgeons, medical interns, dental interns, paramedics, and pharmacists. These professions were selected as they were granted power to administer COVID-19 vaccines during this period of public emergency by the President of Trinidad and Tobago and were therefore likely to be NCRHA healthcare workers directly involved in vaccine administration services. 584 participants returned a completed questionnaire. Bivariate analysis using Chi-square analysis of association was used to determine the association between the respondents’ characteristics and the acceptance of the vaccine and the association between vaccine acceptance among healthcare workers and trust. The association between the acceptance of the COVID-19 vaccines and healthcare workers’ characteristics and trust was established using multinomial logistic regression.ResultsA total of 584 healthcare workers took part in the study and 1.4% showed unwillingness to receive COVID-19 vaccine. The study indicates that age, profession, and the trust in international organizations and other healthcare providers predict the uptake of COVID-19 vaccines among healthcare workers. However, gender of the healthcare workers does not predict vaccine acceptance.Conclusions and RelevanceEfforts towards enhanced vaccine acceptance among healthcare workers should take into consideration age, profession, and the trust in international organizations and other healthcare providers. Sensitization programs should be age-specific as well as occupation-based.
Time series modeling to estimate unrecorded burden of 12 symptomatic medical conditions among United States Medicare beneficiaries during the COVID-19 pandemic
Authors: Melgar Michael , Leung Jessica , Colombe Jeffrey , Dooling Kathleen 
Published: May 2022

AbstractObjectiveU.S. healthcare utilization declined during the COVID-19 pandemic, potentially leading to spurious drops in disease incidence recorded in administrative healthcare datasets used for public health surveillance. We used time series modeling to characterize the magnitude and duration of the COVID-19 pandemic’s impact on claims-based monthly incidence of 12 symptomatic conditions among Medicare beneficiaries aged ≥65 years.MethodsTime series of observed monthly incidence of each condition were generated using Medicare claims data from January 2016–May 2021. Incidence time series were decomposed through seasonal and trend decomposition using Loess, resulting in seasonal, trend, and remainder components. We fit a non-linear mixed effects model to remainder time series components and used it to estimate underlying incidence and number of unrecorded cases of each condition during the pandemic period.ResultsObserved incidence of all 12 conditions declined steeply in March 2020 with nadirs in April 2020, generally followed by return to pre-pandemic trends. The relative magnitude of the decrease varied by condition, but month of onset and duration did not. Estimated unrecorded cases during March 2020–May 2021 ranged from 9,543 (95% confidence interval [CI]: 854–15,703) for herpes zoster to 236,244 (95% CI: 188,583–292,369) for cataracts.ConclusionsDue to reduced healthcare utilization during the COVID-19 pandemic, claims-based data underestimate incidence of non-COVID-19 conditions. Time series modeling can be used to quantify this underestimation, facilitating longitudinal analyses of disease incidence pre- and post-pandemic.
Artificial Intelligence Model for Analyzing Colonic Endoscopy Images to Detect Changes Associated with Irritable Bowel Syndrome
Authors: Tabata Kazuhisa , Mihara Hiroshi , Nanjo Sohachi , Motoo Iori , Ando Takayuki , Teramoto Akira , Fujinami Haruka , Yasuda Ichiro 
Published: May 2022

AbstractBackground/AimsIBS is not an organic disease, and the patients typically show no abnormalities in lower gastrointestinal endoscopy. Recently, biofilm formation has been visualized by endoscopy, and the ability of endoscopy to detect microscopic changes due to dysbiosis and microinflammation has been reported. In this study, we investigated whether an Artificial Intelligence (AI) colon image model can detect IBS without biofilsm.MethodsStudy subjects were identified based on electronic medical records and categorized as IBS (group I; n=11), IBS with predominant constipation (IBS-C; group C; n=12), and IBS with predominant diarrhea (IBS-D; group D; n=12). Colonoscopy images from IBS patients and from asymptomatic healthy subjects (group N; n=88) were obtained. Google Cloud Platform AutoML Vision (single-label classification) was used to construct AI image models to calculate sensitivity, specificity, predictive value, and AUC. A total of 2479, 382, 538, and 484 images were randomly selected for groups N, IBS, IBC-C and IBS-D groups, respectively.ResultsThe AUC of the model discriminating between group N and group I was 0.95. Sensitivity, specificity, positive predictive value, and negative predictive value of group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The overall AUC of the model discriminating between groups N, C, and D was 0.83; sensitivity, specificity, and positive predictive value of group N were 87.5%, 46.2%, and 79.9%, respectively.ConclusionsUsing the image AI model, colonoscopy images of IBS could be discriminated from healthy subjects at AUC 0.95. Prospective studies are needed to further validate whether this externally validated model has similar diagnostic capabilities at other facilities and whether it can be used to determine treatment efficacy.
A study protocol for a retrospective cohort study and interrupted time series analysis to assess the effect of the COVID-19 pandemic on major trauma presentations and patient outcomes in English hospitals
Authors: Marincowitz Carl , Bouamra Omar , Coates Tim , Kumar Dhushy , Lockey David , Mason Lyndon , Newcombe Virginia , Thompson Julian , Yates David , Lecky Fiona 
Published: May 2022

AbstractA protocol for a retrospective cohort study and interrupted time series analysis to investigate the effect of successive COVID related “lockdown” restrictions on major trauma presentations and patient outcomes in English hospitals. The study specifically aims to assess: 1) The impact of successive “lockdowns” on the volume, demographics, injury mechanism, severity, treatment and outcomes of major trauma in England. 2) If the implementation of “lockdowns” affected major trauma related mortality.A patient cohort will be derived from the Trauma and Audit Research Network (TARN) database, for all trauma receiving hospitals in England, between 1stof January 2017 to 1stof September 2021. This period encompasses two national “lockdown” periods (23rdMarch 2020 to 29thJune 2020 and 2ndNov 2020 to 16thMay 2021) in England. A time series will be used to illustrate changes in the volume and mechanism of injury associated with successive “lockdowns”. Demographic characteristics and features of the clinical care pathways will be compared during the “lockdown” and equivalent pre-COVID periods. To specifically assess if there were any changes in risk adjusted mortality associated with the “lockdowns” interrupted time series analysis will be conducted.
Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA
Authors: Richardson Reese , Jorgensen Emile , Arevalo Philip , Holden Tobias M. , Gostic Katelyn M. , Pacilli Massimo , Ghinai Isaac , Lightner Shannon , Cobey Sarah , Gerardin Jaline 
Published: May 2022

AbstractPublic health indicators typically used for COVID-19 surveillance can be biased or lag changing community transmission patterns. The United States city of Chicago opportunistically investigated whether sentinel surveillance of recently symptomatic individuals receiving outpatient diagnostic testing for SARS-CoV-2 could accurately assess the instantaneous reproductive number R(t) and provide early warning of changes in transmission. Patients tested at community-based diagnostic testing sites between September 2020 and June 2021, and reporting symptom onset within four days preceding their test, formed the sentinel population. R(t) calculated from sentinel cases agreed well with R(t) from other indicators. Retrospectively, trends in sentinel cases did not precede trends in COVID-19 hospital admissions by any identifiable lead time. In deployment, sentinel surveillance held an operational recency advantage of nine days over hospital admissions. The promising performance of opportunistic sentinel surveillance suggests that deliberately designed outpatient sentinel surveillance would provide robust early warning of increasing transmission.
A Discovery and Verification Approach for Pharmacovigilance using Electronic Health Care Data
Authors: Dijkstra LJ , Schink T , Linder R , Schwaninger M , Pigeot I , Wright MN , Foraita R 
Published: May 2022

AbstractIntroductionPharmacovigilance shifted its focus from spontaneous reporting systems to electronic health care (EHC) data. Usually, a single statistical method is used to detect signals, i.e., potential adverse drug reactions (ADRs).Objective and MethodWe present a novel approach to detect ADRs in EHC databases. It aggregates the results of multiple statistical signal detection methods applying Borda count ranking, a preference voting system, which results are used by an expert committee to select plausible signals. The obtained signals are afterwards investigated in tailored pharmacoepidemiological studies to provide support of plausibility or spuriousness of the signal.We showcase the approach using data from the German Pharmacoepidemiological Research Database on drug reactions of the direct oral anticoagulant rivaroxaban. Results of four statistical methods are aggregated into Borda count rankings: longitudinal Gamma Poisson shrinker, Bayesian confidence propagation neural network, random forests and LASSO. A verification study designed as nested active comparator case-control study was conducted. We included patients diagnosed with atrial fibrillation who initiated anticoagulant treatment with rivaroxaban or with phenprocoumon as active comparator between 2011 and 2017.ResultsThe case study highlights that our Borda ranking approach (https://borda.bips.eu) is fast, able to retrieve known ADRs and find other interesting signals. Hasty false conclusions are avoided by a verification study, which is, however, time-consuming.ConclusionPost-market signal detection in EHC data is useful to identify and validate safety signals, particularly a few years after first admission to the market, when spontaneous reports are less frequent and more EHC data are available.
Biopsychosocial response to the COVID-19 lockdown in people with major depressive disorder and multiple sclerosis
Authors: Siddi S , Giné-Vázquez I , Bailon R , Matcham F , Lamers F , Kontaxis S , Laporta E , Garcia E , Arranz B , Dalla Costa G , Guerrero Pérez A.I , Zabalza A. , Buron M , Comi G , Leocani L. , Annas P , Hotopf M , Penninx BWJH , Magyari M , Sørensen P. S. , Montalban X , Lavelle G , Ivan A , Oetzmann C , White M K , Difrancesco S , Locatelli P , Mohr DC , Aguiló J , Narayan V , Folarin A , Dobson R , Dineley J , Leightley D , Cummins N , Vairavan S , Ranjan Y , Rashid Z , Rintala A , De Girolamo G , Preti A , Simblett S , Wykes T ,   PAB members, Myin-Germeys I , Haro JM 
Published: May 2022

ABSTRACTBackgroundChanges in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDD) and Multiple Sclerosis (MS).MethodsData were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse – Central Nervous System) programme. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender.ResultsParticipants with MDD (N=255) and MS (N=214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. Lower mean HR and HR variation were observed between pre and during lockdown during the day for MDD and during the night for MS. HR variation during rest periods also decreased between pre-and post-lockdown in both clinical conditions. We observed a reduction of physical activity for MDD and MS upon the introduction of lockdowns. The group with MDD exhibited a net increase in social interaction via social network apps over the three periods.ConclusionsBehavioral response to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDD and MS.