Search results

[Study of application of Common Data Model of Observational Medical Outcomes Partnership in China].

Objective: To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China. Methods: PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted. Results: A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia. Conclusions: This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Unlocking the potential of electronic blood transfusion systems: Implementation insights from NHS hospitals in England.

Electronic blood transfusion (EBT) systems have the potential to significantly enhance patient safety and healthcare efficiency. Although national guidelines recommend their implementation, widespread adoption requires addressing gaps in evidence regarding cost-effectiveness and clinical impact. This study assessed EBT implementation in English hospitals via a 2023 survey focusing on three key components: (i) electronic blood fridges (EBFs) for traceability and stock management, (ii) electronic blood ordering systems with clinical decision support (CDSS) and (iii) bedside patient identification (PID) systems. The survey also examined EBT integration with electronic health records (EHRs) and inter-hospital record linkages. Among 206 surveyed hospitals, 114 responded, resulting in a 55.6% response rate. Of the responding sites, 76 (67.3%) had implemented at least one EBT component. EBFs were the most adopted technology (65 sites; 57.5%), followed by bedside identification systems (37 sites; 32.7%). Advanced systems like CDSS were implemented in only 16 sites (14.2%). Barriers to adoption included financial constraints, limited senior management engagement and technical challenges. Our results show variability in EBT system adoption across NHS England hospitals. There is a pressing need for cost-effectiveness analyses to support investment decisions, while evidence of clinical effectiveness is needed to justify advanced EBT systems and overcome organisational barriers.

Behaviour change physiotherapy intervention to increase physical activity following hip and knee replacement (PEP-TALK): study protocol for a pragmatic randomised controlled trial.

INTRODUCTION: While total hip replacement (THR) and total knee replacement (TKR) successfully reduce pain associated with chronic joint pathology, this infrequently translates into increased physical activity. This is a challenge given that over 50% of individuals who undergo these operations are physically inactive and have medical comorbidities such as hypertension, heart disease, diabetes and depression. The impact of these diseases can be reduced with physical activity. This trial aims to investigate the effectiveness of a behaviour change physiotherapy intervention to increase physical activity compared with usual rehabilitation after THR or TKR. METHODS AND ANALYSIS: The PEP-TALK trial is a multicentre, open-labelled, pragmatic randomised controlled trial. 260 adults who are scheduled to undergo a primary unilateral THR or TKR and are moderately inactive or inactive, with comorbidities, will be recruited across eight sites in England. They will be randomised post-surgery, prior to hospital discharge, to either six, 30 min weekly group-based exercise sessions (control), or the same six weekly, group-based, exercise sessions each preceded by a 30 min cognitive behaviour approach discussion group. Participants will be followed-up to 12 months by postal questionnaire. The primary outcome is the University of California, Los Angeles (UCLA) Physical Activity Score at 12 months. Secondary outcomes include: physical function, disability, health-related quality of life, kinesiophobia, perceived pain, self-efficacy and health resource utilisation. ETHICS AND DISSEMINATION: Research ethics committee approval was granted by the NRES Committee South Central (Oxford B - 18/SC/0423). Dissemination of results will be through peer-reviewed, scientific journals and conference presentations. TRIAL REGISTRATION NUMBER: ISRCTN29770908.

The role of weaning in brace treatment for developmental dysplasia of the hip : time to define best practice?

AIMS: In infants aged under six months with developmental dysplasia of the hip (DDH), the use of a removable brace is considered the gold-standard treatment. However, considerable variation exists for brace removal after 'successful' treatment. Some clinicians support immediate cessation, while others prefer weaning of the brace. This study aimed to explore clinicians' understanding of weaning, and to identify current practices and the rationale behind different approaches, in order to inform the design of a future randomized controlled trial (RCT). METHODS: A survey was developed using Google Forms and disseminated via professional networks, social media, and the British Society of Children's Orthopaedic Surgery mailing list. It targeted clinicians involved in DDH care, gathering information on demographics, treatment protocols, criteria for removal, and weaning practices. Quantitative and qualitative data were analyzed to identify patterns and variability. RESULTS: In total, 139 clinicians from 25 countries responded, with 50% from the UK. Most respondents (87.8%) followed a protocol for brace treatment, with considerable variation in definition and implementation of weaning. 'Weaning' was most commonly defined as a gradual reduction in brace wear over time (n = 103, 74.1%). Overall, 47.4% of respondents (n = 65) reported never weaning, 39.4% (n = 54) always wean, and 13.1% (n = 18) varied their approach. Among clinicians who always wean, the most common approach involved gradually reducing the hours per day over several weeks (n = 28, 51.9%). However, for those who sometimes wean, the most frequent practice was night-time only wear (n = 8, 44.4%). Durations of weaning differed, although the majority of clinicians reported weaning periods from two to six weeks. There is broad support for a future RCT, with 75.9% (n = 105) expressing a willingness to participate. CONCLUSION: This survey highlights considerable variability in weaning practices for brace treatment in DDH, and underscores the need for standardized terminology and protocols. These findings provide a foundation for designing a RCT to evaluate weaning compared with immediate brace cessation, ultimately informing evidence-based guidelines.

Development of a multicentre cohort study to understand the role of MRI and ultrasound in the diagnosis of acute haematogenous bone and joint infection in children (the PIC Bone study) : a study protocol.

AIMS: Bone and joint infections (BJI) in children are rare but can be serious. Differentiating BJI from other conditions with similar symptoms is critical. Advanced imaging (ultrasound scans (USS) and MRI) is often required to confirm the diagnosis. The differing merits of imaging type and regional variation in access to advanced imaging can lead to diagnostic uncertainty and treatment variation. The aim of this study is to evaluate the diagnostic accuracy of MRI and USS for the investigation of BJI in children, and develop and validate prediction models to aid the diagnosis of BJI in children. A nested qualitative sub-study will explore acceptability of the imaging to children, parents, and health practitioners. METHODS: A multicentre retrospective cohort of children (aged < 16 years) with suspected diagnosis of BJI will be used to estimate the diagnostic accuracy of the two imaging methods and develop the prediction models. The models will be evaluated in a second cohort of prospectively recruited children. Diagnostic test accuracy will be estimated overall, and separately for children aged under and over five years. The prediction models will be fit using logistic regression, with candidate predictors chosen based on clinical plausibility and from a review of the literature. Continuous predictors will be examined for non-linearity with confirmed BJI using fractional polynomials. Multiple imputation will be used to replace missing values. Internal validation will be carried out using bootstrapping. Model performance will be assessed with discrimination and calibration. DISCUSSION: Ethical approval for this study (registration: ISRCTN15471635) was granted (REC reference 23/WM/0027). Informed consent is being obtained from participants in the prospective cohort and the qualitative sub-study. Study findings will be published in an open access journal and presented at relevant national and international conferences. Relevant charities and associations are being engaged to promote awareness of the project.

Bridging innovation to implementation in artificial intelligence fracture detection : a commentary piece.

The deployment of AI in medical imaging, particularly in areas such as fracture detection, represents a transformative advancement in orthopaedic care. AI-driven systems, leveraging deep-learning algorithms, promise to enhance diagnostic accuracy, reduce variability, and streamline workflows by analyzing radiograph images swiftly and accurately. Despite these potential benefits, the integration of AI into clinical settings faces substantial barriers, including slow adoption across health systems, technical challenges, and a major lag between technology development and clinical implementation. This commentary explores the role of AI in healthcare, highlighting its potential to enhance patient outcomes through more accurate and timely diagnoses. It addresses the necessity of bridging the gap between AI innovation and practical application. It also emphasizes the importance of implementation science in effectively integrating AI technologies into healthcare systems, using frameworks such as the Consolidated Framework for Implementation Research and the Knowledge-to-Action Cycle to guide this process. We call for a structured approach to address the challenges of deploying AI in clinical settings, ensuring that AI's benefits translate into improved healthcare delivery and patient care.

Do peer reviewers comment on reporting items as instructed by the journal? A secondary analysis of two randomised trials.

OBJECTIVES: Two studies randomising manuscripts submitted to biomedical journals have previously shown that reminding peer reviewers about key reporting items did not improve the reporting quality in published articles. Within this secondary analysis of peer reviewer reports we aimed to assess at what stage the intervention failed. STUDY DESIGN AND SETTING: We exploratively analysed peer reviewer reports from two published randomised controlled trials (RCTs) conducted at biomedical journals. The first RCT (CONSORT-PR) assessed adherence to the Consolidated Standards of Reporting Trials (CONSORT) guideline in manuscripts presenting primary RCT results. The second RCT (SPIRIT-PR) included manuscripts presenting RCT protocols and assessed adherence to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guideline. In both RCTs the control group consisted of peer reviewers receiving no reminder, while all reviewers in the intervention group received a reminder of the 10 most important reporting items. For this secondary analysis two independent, blinded authors extracted from peer reviewer reports which of the ten most important reporting items were mentioned by peer reviewers for clarification. The main outcome of this secondary analysis was the difference in the mean proportion of these ten reporting items for which at least one peer reviewer requested clarification. Furthermore, we assessed how this difference changed if (i) only published manuscripts were considered and (ii) when only considering requested changes that were implemented by authors. RESULTS: We assessed peer reviewer reports from 533 manuscripts (n=265 intervention group; n=268 control group). Among the manuscripts in the intervention group, 21.1% (95% CI, 18.6-23.6%) of the ten reporting items were requested for clarification, compared to 13.1% (95% CI, 18.6-23.6%) in the control group, resulting in a mean difference of 8.0% (95% CI, 4.9-11.1%). However, this difference diminished to 4.2% when assessing solely accepted and published manuscripts and was even further reduced to 2.6% when accounting for changes actually implemented by authors. CONCLUSION: Reminding peer reviewers to check reporting items increased their focus on reporting guidelines, leading to more reporting-related requests in their reviews. However, the effect was strongly diluted during the peer review process due to rejected articles and requests not implemented by authors.

Executive Summary: Treatment of Osteoporosis and Osteoarthritis in the Oldest Old.

This is the executive summary of a work by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) (Nicholas Fuggle et al. in Drugs, 2024).

Collateral effects of the COVID-19 pandemic on endocrine treatments for breast and prostate cancer in the UK: a cohort study.

BACKGROUND: The COVID-19 pandemic affected cancer screening, diagnosis and treatments. Many surgeries were substituted with bridging therapies during the initial lockdown, yet consideration of treatment side effects and their management was not a priority. OBJECTIVES: To examine how the changing social restrictions imposed by the pandemic affected incidence and trends of endocrine treatment prescriptions in newly diagnosed (incident) breast and prostate cancer patients and, secondarily, endocrine treatment-related outcomes (including bisphosphonate prescriptions, osteopenia and osteoporosis), in UK clinical practice from March 2020 to June 2022. DESIGN: Population-based cohort study using UK primary care Clinical Practice Research Datalink GOLD database. METHODS: There were 13,701 newly diagnosed breast cancer patients and 12,221 prostate cancer patients with ⩾1-year data availability since diagnosis between January 2017 and June 2022. Incidence rates (IR) and incidence rate ratios (IRR) were calculated across multiple time periods before and after lockdown to examine the impact of changing social restrictions on endocrine treatments and treatment-related outcomes, including osteopenia, osteoporosis and bisphosphonate prescriptions. RESULTS: In breast cancer patients, aromatase inhibitor (AI) prescriptions increased during lockdown versus pre-pandemic [IRR: 1.22 (95% confidence interval (CI): 1.11-1.34)], followed by a decrease post-first lockdown [IRR: 0.79 (95% CI: 0.69-0.89)]. In prostate cancer patients, first-generation antiandrogen prescriptions increased versus pre-pandemic [IRR: 1.23 (95% CI: 1.08-1.4)]. For breast cancer patients on AIs, diagnoses of osteopenia, osteoporosis and bisphosphonate prescriptions were reduced across all lockdown periods versus pre-pandemic (IRR range: 0.31-0.62). CONCLUSION: During the first 2 years of the pandemic, newly diagnosed breast and prostate cancer patients were prescribed more endocrine treatments compared to pre-pandemic due to restrictions on hospital procedures replacing surgeries with bridging therapies. But breast cancer patients had fewer diagnoses of osteopenia and osteoporosis and bisphosphonate prescriptions. These patients should be followed up in the coming years for signs of bone thinning. Evidence of poorer management of treatment-related side effects will help assess resource allocation for patients at high risk for bone-related complications.

Collateral effects of the COVID-19 pandemic on endocrine treatments for breast and prostate cancer in the UK: implications for bone health

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model.

PURPOSE: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM). MATERIALS AND METHODS: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases. RESULTS: IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic. CONCLUSION: For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Incident Use of Hydroxychloroquine for the Treatment of Rheumatoid Arthritis and Systemic Lupus Erythematosus During the COVID-19 Pandemic.

OBJECTIVE: We studied whether the use of hydroxychloroquine (HCQ) for COVID-19 resulted in supply shortages for patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). METHODS: We used US claims data (IQVIA PHARMETRICS® Plus for Academics [PHARMETRICS]) and hospital electronic records from Spain (Institut Municipal d'Assistència Sanitària Information System [IMASIS]) to estimate monthly rates of HCQ use between January 2019 and March 2022, in the general population and in patients with RA and SLE. Methotrexate (MTX) use was estimated as a control. RESULTS: More than 13.5 million individuals (13,311,811 PHARMETRICS, 207,646 IMASIS) were included in the general population cohort. RA and SLE cohorts enrolled 135,259 and 39,295 patients, respectively, in PHARMETRICS. Incidence of MTX and HCQ were stable before March 2020. On March 2020, the incidence of HCQ increased by 9- and 67-fold in PHARMETRICS and IMASIS, respectively, and decreased in May 2020. Usage rates of HCQ went back to prepandemic trends in Spain but remained high in the United States, mimicking waves of COVID-19. No significant changes in HCQ use were noted among patients with RA and SLE. MTX use rates decreased during HCQ approval period for COVID-19 treatment. CONCLUSION: Use of HCQ increased dramatically in the general population in both Spain and the United States during March and April 2020. Whereas Spain returned to prepandemic rates after the first wave, use of HCQ remained high and followed waves of COVID-19 in the United States. However, we found no evidence of general shortages in the use of HCQ for both RA and SLE in the United States.

Early prediction of bronchopulmonary dysplasia: comparison of modelling methods, development and validation studies.

BACKGROUND: Machine-learning methods are gaining in popularity to predict medical events but their added value to other methods is still to be determined. We compared performances of clinical prediction models for bronchopulmonary dysplasia (BPD) or death in very preterm infants using logistic regression and random forests methods. METHODS: Two population-based cohorts of very preterm infants were used: EPIPAGE-2 (France, 2011) for development and internal validation and EPICE (Europe, 2011) for external validation. Eligible infants were born before 30 weeks' gestation and admitted in neonatal units. BPD was defined as any respiratory support at 36 weeks postmenstrual age. Candidate predictors were available shortly after birth or at day 3. Logistic regression and random forest models performance was assessed in terms of discrimination (c-statistic) and calibration plots. RESULTS: Prevalence of BPD/death was 32.1% (668/1923) in EPIPAGE-2 and 41.0% (1368/3335) in EPICE. At both time points, logistic regression and random forest models showed similar performance during internal validation. At birth, external validation in EPICE showed good discrimination (logistic regression model: c-statistics 0.81, 95% CI 0.80-0.83; random forest: 0.80, 95% CI 0.79-0.81) but both models underestimated the probability of BPD/death. Model performances were heterogeneous throughout European regions. CONCLUSIONS: Both modelling methods performed similarly to predict BPD/death shortly after birth in very preterm children. IMPACT: Whether machine-learning methods predict better short-term respiratory outcomes in very preterm infants than logistic regression models is debated. Random forest-based prediction models did not perform better than logistic regression to predict bronchopulmonary dysplasia or death shortly after birth in very preterm infants. Calibration performances varied among European countries. While offering the same performance, regression models are easier to understand, to disseminate and to apply to different populations.

Mathematical modeling of SARS-CoV-2 variant substitutions in European countries: transmission dynamics and epidemiological insights.

BACKGROUND: Countries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants. MATERIALS AND METHODS: We used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility  ( Δ β )   between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants. RESULTS: Our model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated Δ β between variants, revealing that: ( i ) Δ β increased progressively from the Alpha to the Omicron variant; ( i i ) Δ β showed a high degree of variability within Omicron variants; ( i i i ) a higher Δ β was associated with a later emergence of the variant within a country; ( i v ) a higher degree of immunization of the population against previous variants was associated with a higher Δ β for the Delta variant; ( v ) larger countries exhibited smaller Δ β ,  suggesting regionally diverse outbreaks within the same country; and finally ( v i ) the model reliably captures the dynamics of competing variants, even in complex scenarios. CONCLUSION: The use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying Δ β ,  we have tracked the spread of the different variants across Europe, highlighting a robust increase in transmissibility trend from Alpha to Omicron. Additionally, we have shown that the geographical characteristics of a country, as well as the timing of new variant entrances, can explain some of the observed differences in variant substitution dynamics across countries.

Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.

Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.

A semi-empirical risk panel to monitor epidemics: multi-faceted tool to assist healthcare and public health professionals.

INTRODUCTION: Bronchiolitis, mostly caused by Respiratory Syncytial Virus (RSV), and influenza among other respiratory infections, lead to seasonal saturation at healthcare centers in temperate areas. There is no gold standard to characterize the stages of epidemics, nor the risk of respiratory infections growing. We aimed to define a set of indicators to assess the risk level of respiratory viral epidemics, based on both incidence and their short-term dynamics, and considering epidemical thresholds. METHODS: We used publicly available data on daily cases of influenza for the whole population and bronchiolitis in children <2 years from the Information System for Infection Surveillance in Catalonia (SIVIC). We included a Moving Epidemic Method (MEM) variation to define epidemic threshold and levels. We pre-processed the data with two different nowcasting approaches and performed a 7-day moving average. Weekly incidences (cases per 105 population) were computed and the 5-day growth rate was defined to create the effective potential growth (EPG) indicator. We performed a correlation analysis to define the forecasting ability of this index. RESULTS: Our adaptation of the MEM method allowed us to define epidemic weekly incidence levels and epidemic thresholds for bronchiolitis and influenza. EPG was able to anticipate daily 7-day cumulative incidence by 4-5 (bronchiolitis) or 6-7 (influenza) days. DISCUSSION: We developed a semi-empirical risk panel incorporating the EPG index, which effectively anticipates surpassing epidemic thresholds for bronchiolitis and influenza. This panel could serve as a robust surveillance tool, applicable to respiratory infectious diseases characterized by seasonal epidemics, easy to handle for individuals lacking a mathematical background.

SARS-CoV-2 transmission in teenagers and young adults in Fútbol Club Barcelona's Multidisciplinary Sports Training Academy.

Most studies, aimed at determining the incidence and transmission of SARS-CoV-2 in children and teenagers, have been developed in school settings. Our study conducted surveillance and inferred attack rates focusing on the practice of sports. Prospective and observational study of those attending the sports facilities of Fútbol Club Barcelona (FCB), in Barcelona, Spain, throughout the 2020-2021 season. Participants were young players (from five different sports) and adult workers, who belonged to stable teams (shared routines and were involved in same quarantine rules). Biweekly health questionnaires and SARS-CoV-2 screening were conducted. From the 234 participants included, 70 (30%) both lived and trained in the FCB facilities (Recruitment Pathway 1;RP1) and 164 (70%) lived at their own household and just came to the facilities to train (RP2). During the study, 38 positive cases were identified; none had severe symptoms or needed hospitalization. The overall weekly incidence in the cohorts did not differ compared to the one expected in the community, except for 2 weeks when an outbreak occurred. The attack rate (AR) was three times higher for the participants from RP1, in comparison to those from RP2 (p 

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

BACKGROUND: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. METHODS: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. RESULTS: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. CONCLUSIONS: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. FUNDING: AA, BH, BL, LWa, MMa, PP, SV funded by 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 respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by 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 NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Unravelling the role of the mandatory use of face covering masks for the control of SARS-CoV-2 in schools: a quasi-experimental study nested in a population-based cohort in Catalonia (Spain).

OBJECTIVE: To assess the effectiveness of mandatory use of face covering masks (FCMs) in schools during the first term of the 2021-2022 academic year. DESIGN: A retrospective population-based study. SETTING: Schools in Catalonia (Spain). POPULATION: 599 314 children aged 3-11 years attending preschool (3-5 years, without FCM mandate) and primary education (6-11 years, with FCM mandate). STUDY PERIOD: From 13 September to 22 December 2021 (before Omicron variant). INTERVENTIONS: A quasi-experimental comparison between children in the last grade of preschool (5 years old), as a control group, and children in year 1 of primary education (6 years old), as an interventional group. MAIN OUTCOME MEASURES: Incidence of SARS-CoV-2, secondary attack rates (SARs) and effective reproductive number (R*). RESULTS: SARS-CoV-2 incidence was significantly lower in preschool than in primary education, and an increasing trend with age was observed. Six-year-old children showed higher incidence than 5 year olds (3.54% vs 3.1%; OR 1.15 (95% CI 1.08 to 1.22)) and slightly lower but not statistically significant SAR (4.36% vs 4.59%; incidence risk ratio 0.96 (95% CI 0.82 to 1.11)) and R* (0.9 vs 0.93; OR 0.96 (95% CI 0.87 to 1.09)). Results remained consistent using a regression discontinuity design and linear regression extrapolation approaches. CONCLUSIONS: We found no significant differences in SARS-CoV-2 transmission due to FCM mandates in Catalonian schools. Instead, age was the most important factor in explaining the transmission risk for children attending school.

Immunoregulatory Biomarkers of the Remission Phase in Type 1 Diabetes: miR-30d-5p Modulates PD-1 Expression and Regulatory T Cell Expansion.

The partial remission (PR) phase of type 1 diabetes (T1D) is an underexplored period characterized by endogenous insulin production and downmodulated autoimmunity. To comprehend the mechanisms behind this transitory phase and develop precision medicine strategies, biomarker discovery and patient stratification are unmet needs. MicroRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression and modulate several biological processes, functioning as biomarkers for many diseases. Here, we identify and validate a unique miRNA signature during PR in pediatric patients with T1D by employing small RNA sequencing and RT-qPCR. These miRNAs were mainly related to the immune system, metabolism, stress, and apoptosis pathways. The implication in autoimmunity of the most dysregulated miRNA, miR-30d-5p, was evaluated in vivo in the non-obese diabetic mouse. MiR-30d-5p inhibition resulted in increased regulatory T cell percentages in the pancreatic lymph nodes together with a higher expression of CD200. In the spleen, a decrease in PD-1+ T lymphocytes and reduced PDCD1 expression were observed. Moreover, miR-30d-5p inhibition led to an increased islet leukocytic infiltrate and changes in both effector and memory T lymphocytes. In conclusion, the miRNA signature found during PR shows new putative biomarkers and highlights the immunomodulatory role of miR-30d-5p, elucidating the processes driving this phase.