COVID-19-related medical research: a meta-research and critical appraisal

Impact of Xpert MTB/RIF implementation in tuberculosis case detection and control in Brazil: a nationwide intervention time-series analysis (2011-2022)

Background

Since 2014, Brazil has gradually implemented the Xpert MTB/RIF (Xpert) test to enhance early tuberculosis (TB) and drug-resistant (DR-TB) detection and control, yet its nationwide impact remains underexplored. Our study conducts an intervention time-series analysis (ITSA) to evaluate how the Xpert's implementation has improved TB and DR-TB detection nationwide.

Methods

1,061,776 cases from Brazil's National TB Registry (2011-2022) were reviewed and ITSA (2011-2019) was used to gauge the impact of the Xpert's adoption on TB and DR-TB notification. Granger Causality and dynamic regression modelling determined if incorporating Xpert testing as an external regressor enhanced forecasting accuracy for Brazil's future TB trends.

Findings

Xpert implementation resulted in a 9.7% increase in TB notification and substantial improvements in DR-TB (63.6%) and drug-susceptible TB (92.1%) detection compared to expected notifications if it had not been implemented. Xpert testing counts also presented a time-dependent relationship with DR-TB detection post-implementation, and improved predictions in forecasting models, which depicted a potential increase in TB and DR-TB detection in the next six years.

Interpretation

This study underscores the critical role of Xpert's adoption in boosting TB and DR-TB detection in Brazil, reinforcing the case for its widespread use in disease control. Improvements in prediction accuracy resulting from integrating Xpert data are crucial for allocating resources and reducing the incidence of TB. By acknowledging Xpert's role in both disease control and improving predictions, we advocate for its expanded use and further research into advanced molecular diagnostics for effective TB and DR-TB control.

Funding

FIOCRUZ.

SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.

Changes in the number of new takeaway food outlets associated with adoption of management zones around schools: A natural experimental evaluation in England

By the end of 2017, 35 local authorities (LAs) across England had adopted takeaway management zones (or "exclusion zones") around schools as a means to curb proliferation of new takeaways. In this nationwide, natural experimental study, we evaluated the impact of management zones on takeaway retail, including unintended displacement of takeaways to areas immediately beyond management zones, and impacts on chain fast-food outlets. We used uncontrolled interrupted time series analyses to estimate changes from up to six years pre- and post-adoption of takeaway management zones around schools. We evaluated three outcomes: mean number of new takeaways within management zones (and by three identified sub-types: full management, town centre exempt and time management zones); mean number on the periphery of management zones (i.e. within an additional 100 m of the edge of zones); and presence of new chain fast-food outlets within management zones. For 26 LAs, we observed an overall decrease in the number of new takeaways opening within management zones. Six years post-intervention, we observed 0.83 (95% CI -0.30, -1.03) fewer new outlets opening per LA than would have been expected in absence of the intervention, equivalent to an 81.0% (95% CI -29.1, -100) reduction in the number of new outlets. Cumulatively, 12 (54%) fewer new takeaways opened than would have been expected over the six-year post-intervention period. When stratified by policy type, effects were most prominent for full management zones and town centre exempt zones. Estimates of intervention effects on numbers of new takeaways on the periphery of management zones, and on the presence of new chain fast-food outlets within management zones, did not meet statistical significance. Our findings suggest that management zone policies were able to demonstrably curb the proliferation of new takeaways. Modelling studies are required to measure the possible population health impacts associated with this change.

Maternal and neonatal outcomes of French prospective multicenter cohort study COVIPREG during the first two COVID-19 waves

BACKGROUND

SARS-CoV-2 infection on pregnant women was the subject of many questions since the COVID-19 pandemic.

METHODS

We aim to assess maternal and neonatal outcomes of SARS-CoV-2 infection contracted during 2nd and 3rd trimesters of pregnancy during the first two COVID-19 waves across a prospective French multicenter cohort study. Patients were included between April 2020 and January 2021 in 10 maternity hospitals in Paris area with two groups (i) pregnant women with a positive SARS-CoV-2 nasopharyngeal RT-PCR between [14WG; 37WG[(symptomatic infection), (ii) pregnant women with a negative serology (or equivocal) at delivery and without a positive SARS-CoV-2 nasopharyngeal RT-PCR at any time during pregnancy (G2 group) MAIN FINDINGS: 2410 pregnant women were included, of whom 310 had a positive SARS-CoV-2 nasopharyngeal RT-PCR and 217 between [14WG; 37WG[. Most infections occurred between 28 and 37 weeks of gestation (56 %). Most patients could be managed as outpatients, while 23 % had to be hospitalized. Among women with a positive RT-PCR, multiparous women were over-represented (OR = 2.45[1.52;3.87]); were more likely to deliver before 37 weeks of gestation (OR = 2.19[1.44;3.24]) and overall cesarean deliveries were significantly increased (OR = 1.53[1.09;2.13]).

CONCLUSIONS

This study highlights the maternal, obstetrical, and neonatal burden associated with SARS-CoV-2 infections during the first two pandemic waves before availability of vaccines.

TRIAL REGISTRATION

NCT04355234 (registration date: 21/04/2020).

The efficacy and safety of the novel combination lenvatinib and pembrolizumab in endometrial cancer: A systematic review and single-arm meta-analysis

Objective

Endometrial carcinoma is the most widespread gynecological cancer, with increasing morbidity and mortality. Pembrolizumab, a monoclonal antibody that targets PD1 receptor tumors, is approved for patients with microsatellite instability-high (MSI-H) solid tumors. Many clinical trials and observational studies have been conducted to assess the safety and efficacy of Lenvatinib and Pembrolizumab combination therapy in the setting of endometrial cancer. However, results have been inconsistent, and current data is based on a heterogeneous population. The primary objective was to assess the safety and efficacy of Lenvatinib plus Pembrolizumab for endometrial cancer.

Data sources

The search was conducted from inception from four databases; PubMed, Google Scholar, the Cochrane Library, and ClinicalTrials.gov. The electronic database search was conducted from inception to August 20, 2023.

Study eligibility criteria

We considered randomized controlled trials and single-arm observational studies, i.e. cohort, case-control and cross-sectional studies.

Methodology

We performed a single-arm meta-analysis, involving 7 studies having a total of 495 patients with endometrial cancer were eventually included which had the following outcomes: Complete response, Partial response, Progression-free survival, stable disease, progressive disease, safety outcomes, Adverse events, and the total number of deaths.

Results

Our results showed that 88.6 % of the patients were positive for non-MSI-H/pMMR tumors (95 % CI = 0.825-0.927) whereas 6.5 % (95 % CI = 3.8-9.8 %) of the patients for MSI-H/dMMR tumors. The pooled objective response of endometrial cancer patients treated with Lenvatinib and Pembrolizumab was 36.5 % (95 % CI = 0.258-0.471), the pooled estimate of complete and partial response was 47 % (95 % CI = 0.024-0.070) and 31.3 % (95 % CI = 0.230-0.396). 38.2 % patients had stable disease (95 % CI = 0.329-0.435) and 24.0 % patients had progressive disease (95 % CI = 0.103-0.378). The pooled median progression-free survival was 5.97 (95 % CI 5.43-7.63) months and, whereas the median overall survival was 17.19 months (95 % CI 15.34-19.31). All grade adverse events occurred in 85 % and Grade 3 or worse adverse events occurred in 39 % of patients during the therapy whereas death occurred in 23.8 % during the treatment.

Conclusion

The results of this meta-analysis concludes that although the combined treatment of a Lenvatinib and Pembrolizumab had a PFS and OS that was inferior to the standard therapy used to treat advanced and recurrent endometrial cancer, it is still a novel treatment and shows potential for further research with a greater sample size.

Is musculoskeletal pain associated with increased muscle stiffness? Evidence map and critical appraisal of muscle measurements using shear wave elastography

INTRODUCTION AND AIMS

Approximately 21% of the world's population suffers from musculoskeletal conditions, often associated with sensations of stiff muscles. Targeted therapy requires knowing whether typically involved muscles are objectively stiffer compared to asymptomatic individuals. Muscle stiffness is quantified using ultrasound shear wave elastography (SWE). Publications on SWE-based comparisons of muscle stiffness between individuals with and without musculoskeletal pain are increasing rapidly. This work reviewed and mapped the existing evidence regarding objectively measured muscle stiffness in musculoskeletal pain conditions and surveyed current methods of applying SWE to measure muscle stiffness.

METHODS

A systematic search was conducted in PubMed and CINAHL using the keywords "muscle stiffness", "shear wave elastography", "pain", "asymptomatic controls" and synonyms. The search was supplemented by a hand search using Google Scholar. Included articles were critically appraised with the AXIS tool, supplemented by items related to SWE methods. Results were visually mapped and narratively described.

RESULTS

Thirty of 137 identified articles were included. High-quality evidence was missing. The results comprise studies reporting lower stiffness in symptomatic participants, no differences between groups and higher stiffness in symptomatic individuals. Results differed between pain conditions and muscles, and also between studies that examined the same muscle(s) and pathology. The methods of the application of SWE were inconsistent and the reporting was often incomplete.

CONCLUSIONS

Existing evidence regarding the objective stiffness of muscles in musculoskeletal pain conditions is conflicting. Methodological differences may explain most of the inconsistencies between findings. Methodological standards for SWE measurements of muscles are urgently required.

Contrast-specific propensity scores for causal inference with multiple interventions

Existing methods that use propensity scores for heterogeneous treatment effect estimation on non-experimental data do not readily extend to the case of more than two treatment options. In this work, we develop a new propensity score-based method for heterogeneous treatment effect estimation when there are three or more treatment options, and prove that it generates unbiased estimates. We demonstrate our method on a real patient registry of patients in Singapore with diabetic dyslipidemia. On this dataset, our method generates heterogeneous treatment recommendations for patients among three options: Statins, fibrates, and non-pharmacological treatment to control patients' lipid ratios (total cholesterol divided by high-density lipoprotein level). In our numerical study, our proposed method generated more stable estimates compared to a benchmark method based on a multi-dimensional propensity score.

Joint associations of device-measured physical activity and abdominal obesity with incident cardiovascular disease: a prospective cohort study

OBJECTIVE

To examine the joint associations between physical activity and abdominal obesity with the risk of cardiovascular disease (CVD) events.

METHODS

We included 70 830 UK Biobank participants (mean age±SD=61.6 ± 7.9 years; 56.4% women) with physical activity measured by wrist-worn accelerometers and without major chronic diseases. Participants were jointly categorised into six groups based on their physical activity level (tertiles of total volume and specific intensity levels) and presence or absence of abdominal obesity based on measured waist circumference. Associations with incident CVD (fatal and non-fatal events) were determined using proportional subdistribution hazard models with multivariable adjustment.

RESULTS

After excluding events during the first 2 years of follow-up, participants were followed for a median of 6.8 years, during which 2795 CVD events were recorded. Compared with the low abdominal adiposity and highest tertile of physical activity, abdominal obesity was associated with higher risk of incident CVD, especially in those with low levels of vigorous-intensity physical activity (HR 1.42, 95% CI 1.22 to 1.64). Approximately 500 min per week of moderate-to-vigorous intensity and approximately 30-35 min of vigorous-intensity physical activity offset the association of abdominal obesity and the risk of having a CVD event.

CONCLUSION

Physical activity equivalent to approximately 30-35 min of vigorous intensity per week appears to offset the association between abdominal obesity and incident CVD. About 15 times more physical activity of at least moderate intensity is needed to achieve similar results.

GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models

Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.

Opportunistic screening for coronary artery calcium deposition using chest radiographs - a multi-objective models with multi-modal data fusion

Background

To create an opportunistic screening strategy by multitask deep learning methods to stratify prediction for coronary artery calcium (CAC) and associated cardiovascular risk with frontal chest x-rays (CXR) and minimal data from electronic health records (EHR).

Methods

In this retrospective study, 2,121 patients with available computed tomography (CT) scans and corresponding CXR images were collected internally (Mayo Enterprise) with calculated CAC scores binned into 3 categories (0, 1-99, and 100+) as ground truths for model training. Results from the internal training were tested on multiple external datasets (domestic (EUH) and foreign (VGHTPE)) with significant racial and ethnic differences and classification performance was compared.

Findings

Classification performance between 0, 1-99, and 100+ CAC scores performed moderately on both the internal test and external datasets, reaching average f1-score of 0.66 for Mayo, 0.62 for EUH and 0.61 for VGHTPE. For the clinically relevant binary task of 0 vs 400+ CAC classification, the performance of our model on the internal test and external datasets reached an average AUCROC of 0.84.

Interpretation

The fusion model trained on CXR performed better (0.84 average AUROC on internal and external dataset) than existing state-of-the-art models on predicting CAC scores only on internal (0.73 AUROC), with robust performance on external datasets. Thus, our proposed model may be used as a robust, first-pass opportunistic screening method for cardiovascular risk from regular chest radiographs. For community use, trained model and the inference code can be downloaded with an academic open-source license from https://github.com/jeong-jasonji/MTL_CAC_classification .

Funding

The study was partially supported by National Institute of Health 1R01HL155410-01A1 award.

Is biomedical research self-correcting? Modelling insights on the persistence of spurious science

The reality that volumes of published biomedical research are not reproducible is an increasingly recognized problem. Spurious results reduce trustworthiness of reported science, increasing research waste. While science should be self-correcting from a philosophical perspective, that in insolation yields no information on efforts required to nullify suspect findings or factors shaping how quickly science may be corrected. There is also a paucity of information on how perverse incentives in the publishing ecosystem favouring novel positive findings over null results shape the ability of published science to self-correct. Knowledge of factors shaping self-correction of science remain obscure, limiting our ability to mitigate harms. This modelling study introduces a simple model to capture dynamics of the publication ecosystem, exploring factors influencing research waste, trustworthiness, corrective effort and time to correction. Results from this work indicate that research waste and corrective effort are highly dependent on field-specific false positive rates and time delays to corrective results to spurious findings are propagated. The model also suggests conditions under which biomedical science is self-correcting and those under which publication of correctives alone cannot stem propagation of untrustworthy results. Finally, this work models a variety of potential mitigation strategies, including researcher- and publisher-driven interventions.

Antibacterial activity of medicinal plants on the management of mastitis in dairy cows: A systematic review

BACKGROUND

Mastitis is a disease of economic importance in dairy production systems. The common management regime for mastitis is the use of synthetic antibiotics, giving a new problem of antibiotic resistance. There is, therefore, a need to prospect for alternatives to conventional antibiotics from herbal plants.

OBJECTIVES

This systematic review evaluates the use of plants as alternatives for the control of mastitis in dairy cattle, focussing on the effectiveness of studied plants and plant-based products and possible implications on the use of these products in livestock health.

METHODOLOGY

The PRISMA model was implemented with searches done in five electronic databases: Scopus, ScienceDirect, PubMed, Ovid and Research4Life. Data were extracted from 45 studies with 112 plant species from plant species belonging to 42 different families. The specific keywords were 'mastitis', 'dairy cows' and 'medicinal plants'.

RESULTS

The most cited plant species included Allium sativum L., Azadirachta indica and Eucalyptus globulus Labill with the latter further exploring its components. Microbial species causing mastitis mainly were Staphylococcus aureus and Escherichia coli. The extraction methods used included maceration approach using ethanol, methanol and water as solvents for phytochemicals and chromatographic techniques for essential oils. A few studies explored the mode of action, and toxicities of the herbal extracts as well as evaluating their efficacy in clinical trials using animal models.

CONCLUSION

Plants with defined levels of phytochemicals were essential sources of antibacterials. Standardisation of analytical methods is required.

Ayurveda research: Emerging trends and mapping to sustainable development goals

Ayurveda is India's prominent traditional medical system. The World Health Organization has stated the need for more evidence and data from conventional medicine methods to inform policymakers, regulatory bodies, healthcare stakeholders, and the public about its safe, effective, and equitable use. This study aims to provide a comprehensive analysis of the emerging trends in Ayurveda research, mapping research to the UN Sustainable Development Goals (SDG) and examining the impact of COVID-19. Using bibliometric methods, the researchers analyzed a total of 11,773 publications between 1993 and 2022 to understand the temporal evolution of publications, open-access publications, patterns of author collaboration, top-performing countries, and co-citation networks. The keyword co-occurrence analysis identifies networks of concentrated studies on Ayurveda research themes relating to the four clusters, Alternative and Traditional Medicine, Bioactive Compounds and Biological Activities, Analytical Techniques and Herbal Standardization, and Herbal Medicines and Immunomodulation, reflecting the diverse research areas within Ayurveda. The last cluster included research related to the SARS-CoV-2 virus, suggesting research on herbal approaches to immune modulation in the context of COVID-19. The most prominent SDG among these research themes was Good Health and Well-being (SDG 3), emphasizing the potential of natural products and traditional medicine in promoting holistic health and combating antibiotic resistance.

The Effect of Unrefined Sugar on Inflammation: A Systematic Review of Intervention Studies

Background

It is well established that unrefined sugarcane products have antioxidant activity due to phytochemicals, polyphenols, and total antioxidant capacity, which may decrease inflammation and oxidative stress. Therefore, we conducted a systematic review to evaluate the association of unrefined sugar consumption with inflammatory biomarkers.

Methods

Google Scholar, ScienceDirect, Scopus, Cochrane Library, and ProQuest databases were searched up to December 2021 for studies that report the effect of unrefined sugar on inflammation according to inflammatory cytokines, chemokine, and adhesion molecules as outcome measures. Results: Thirty-six studies were evaluated. Across all research, five studies (two in vitro and three animal studies) reported the effect of unrefined sugar on levels of cytokines, including IL-6, TNF-α, IL-10, IL-1β, and IFN-γ. Additionally, the quality of the studies was assessed for risk of bias.

Conclusions

it is possible to affirm that unrefined sugarcane products, including jaggery, may have a protective effect on inflammation via regulating some of the inflammatory pathways and a favorable impact on cytokines secretion according to the results of in vitro and animal model studies. However, since the findings are still insufficient, more scientific research, especially well-designed human trials, is highly recommended to conclude the outcomes confidently. Human data may encourage industries and the public to replace purified sugar with unrefined sugarcane in sugar-based food and for further health-care policy decisions.

Impact of the COVID-19 pandemic on the conduct of non-COVID-19 clinical trials: protocol for a scoping review

INTRODUCTION

The COVID-19 pandemic posed a detrimental impact on the conduct of non-COVID-19 related clinical trials, raising concerns about the completeness of these studies and waste of resources. While several measures and strategies have been suggested to address these issues, a thorough and timely summarisation is still lacking. Therefore, our aim is to conduct a scoping review to summarise the negative effects of COVID-19 on non-COVID-19 clinical trials, outline the effective measures for mitigating these impacts, and provide insights for future pandemics.

METHODS AND ANALYSIS

This scoping review will be conducted in line with the Joanna Briggs Institute's scoping review methodological framework, and the results will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Relevant articles will be searched in PubMed, Embase and the Cochrane Library from 1 December 2019 to 1 July 2023. We will also screen the reference lists of the included studies manually to identify more potentially relevant articles. Articles focusing on the adverse impacts of COVID-19 on non-COVID-19 clinical trials and effective measures for mitigating them will be included. Two investigators will perform study selection and data extraction independently. A narrative summary as well as a descriptive analysis of the basic characteristics and key results of the included studies will be performed.

ETHICS AND DISSEMINATION

Ethical approval is not required, as this scoping review will be completed based only on published literature. The findings of this scoping review will be disseminated through a peer-reviewed publication and/or conference presentations.

Prediction of 30-day unplanned hospital readmission through survival analysis

Background and Objective

Unplanned hospital readmissions are a severe and recurrent problem that affects all health systems. Estimating the risk of being readmitted the following days after discharge is difficult since many heterogeneous factors can influence this. The extensive work concerning this problem proposes solutions mostly based on classification machine-learning models. Survival analysis methods could make a better match with the assessment of readmission risk and are yet to become well-established in this field.

Methods

We compare different statistical and machine learning survival analysis models trained with right-censored all-cause hospital admission data with covariates available at the moment of discharge. The main focus is on tree-ensemble regression methods based on the assumption of proportional hazards. These models are more thoroughly evaluated at a 30-day time period after discharge, although the actual prediction could be set to any time up to 90 days.

Results

The mean performance obtained by each of the proposed survival models ranges from 0.707 to 0.716 C-Index and 0.709 to 0.72 ROC-AUC at a 30-day time period after discharge. The model with the lower performance on both metrics was Cox Proportional Hazards, while the model marking the upper end on both ranges is an XGBoost Regression model with a Cox objective function.

Conclusions

Our findings indicate that survival models perform well addressing the hospital readmission problem, machine-learning models getting the edge over statistical methods. There seems to be an improvement over classification models when attempting to predict at a 30-day period since discharge, perhaps due to a better handling of cases nearing the 30-day boundary. Some preprocessing steps, such as limiting the observation period to 90 days after discharge, are also highlighted since they resulted in a performance boost.

The use and acceptability of preprints in health and social care settings: A scoping review

BACKGROUND

Preprints are open and accessible scientific manuscript or report that is shared publicly, through a preprint server, before being submitted to a journal. The value and importance of preprints has grown since its contribution during the public health emergency of the COVID-19 pandemic. Funders and publishers are establishing their position on the use of preprints, in grant applications and publishing models. However, the evidence supporting the use and acceptability of preprints varies across funders, publishers, and researchers. The scoping review explored the current evidence on the use and acceptability of preprints in health and social care settings by publishers, funders, and the research community throughout the research lifecycle.

METHODS

A scoping review was undertaken with no study or language limits. The search strategy was limited to the last five years (2017-2022) to capture changes influenced by COVID-19 (e.g., accelerated use and role of preprints in research). The review included international literature, including grey literature, and two databases were searched: Scopus and Web of Science (24 August 2022).

RESULTS

379 titles and abstracts and 193 full text articles were assessed for eligibility. Ninety-eight articles met eligibility criteria and were included for full extraction. For barriers and challenges, 26 statements were grouped under four main themes (e.g., volume/growth of publications, quality assurance/trustworthiness, risks associated to credibility, and validation). For benefits and value, 34 statements were grouped under six themes (e.g., openness/transparency, increased visibility/credibility, open review process, open research, democratic process/systems, increased productivity/opportunities).

CONCLUSIONS

Preprints provide opportunities for rapid dissemination but there is a need for clear policies and guidance from journals, publishers, and funders. Cautionary measures are needed to maintain the quality and value of preprints, paying particular attention to how findings are translated to the public. More research is needed to address some of the uncertainties addressed in this review.

Prognostic Factors of COVID-19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology

During the coronavirus disease 2019 (COVID-19) pandemic, the urgency for updated evidence to inform public health and clinical care placed systematic literature reviews (SLRs) at the cornerstone of research. We aimed to summarize evidence on prognostic factors for COVID-19 outcomes through published SLRs and to critically assess quality elements in the findings' interpretation. An umbrella review was conducted via electronic databases from January 2020 to April 2022. All SLRs (and meta-analyses) in English were considered. Data screening and extraction were conducted by two independent reviewers. AMSTAR 2 tool was used to assess SLR quality. The study was registered with PROSPERO (CRD4202232576). Out of 4,564 publications, 171 SLRs were included of which 3 were umbrella reviews. Our primary analysis included 35 SLRs published in 2022, which incorporated studies since the beginning of the pandemic. Consistent findings showed that, for adults, older age, obesity, heart disease, diabetes, and cancer were more strongly predictive of risk of hospitalization, intensive care unit admission, and mortality due to COVID-19. Male sex was associated with higher risk of short-term adverse outcomes, but female sex was associated with higher risk of long COVID. For children, socioeconomic determinants that may unravel COVID-19 disparities were rarely reported. This review highlights key prognostic factors of COVID-19, which can help clinicians and health officers identify high-risk groups for optimal care. Findings can also help optimize confounding adjustment and patient phenotyping in comparative effectiveness research. A living SLR approach may facilitate dissemination of new findings. This paper is endorsed by the International Society for Pharmacoepidemiology.

Completeness and consistency of primary outcome reporting in COVID-19 publications in the early pandemic phase: a descriptive study

BACKGROUND

The COVID-19 pandemic saw a steep increase in the number of rapidly published scientific studies, especially early in the pandemic. Some have suggested COVID-19 trial reporting is of lower quality than typical reports, but there is limited evidence for this in terms of primary outcome reporting. The objective of this study was to assess the prevalence of completely defined primary outcomes reported in registry entries, preprints, and journal articles, and to assess consistent primary outcome reporting between these sources.

METHODS

This is a descriptive study of a cohort of registered interventional clinical trials for the treatment and prevention of COVID-19, drawn from the DIssemination of REgistered COVID-19 Clinical Trials (DIRECCT) study dataset. The main outcomes are: 1) Prevalence of complete primary outcome reporting; 2) Prevalence of consistent primary outcome reporting between registry entry and preprint as well as registry entry and journal article pairs.

RESULTS

We analyzed 87 trials with 116 corresponding publications (87 registry entries, 53 preprints and 63 journal articles). All primary outcomes were completely defined in 47/87 (54%) registry entries, 31/53 (58%) preprints and 44/63 (70%) journal articles. All primary outcomes were consistently reported in 13/53 (25%) registry-preprint pairs and 27/63 (43%) registry-journal article pairs. No primary outcome was specified in 13/53 (25%) preprints and 8/63 (13%) journal articles. In this sample, complete primary outcome reporting occurred more frequently in trials with vs. without involvement of pharmaceutical companies (76% vs. 45%), and in RCTs vs. other study designs (68% vs. 49%). The same pattern was observed for consistent primary outcome reporting (with vs. without pharma: 56% vs. 12%, RCT vs. other: 43% vs. 22%).

CONCLUSIONS

In COVID-19 trials in the early phase of the pandemic, all primary outcomes were completely defined in 54%, 58%, and 70% of registry entries, preprints and journal articles, respectively. Only 25% of preprints and 43% of journal articles reported primary outcomes consistent with registry entries.

Hindsight2020: Characterizing Uncertainty in the COVID-19 Scientific Literature

Following emerging, re-emerging, and endemic pathogen outbreaks, the rush to publish and the risk of data misrepresentation, misinterpretation, and even misinformation puts an even greater onus on methodological rigor, which includes revisiting initial assumptions as new evidence becomes available. This study sought to understand how and when early evidence emerges and evolves when addressing different types of recurring pathogen-related questions. By applying claim-matching by means of deep learning Natural Language Processing (NLP) of coronavirus disease 2019 (COVID-19) scientific literature against a set of expert-curated evidence, patterns in timing across different COVID-19 questions-and-answers were identified, to build a framework for characterizing uncertainty in emerging infectious disease (EID) research over time. COVID-19 was chosen as a use case for this framework given the large and accessible datasets curated for scientists during the beginning of the pandemic. Timing patterns in reliably answering broad COVID-19 questions often do not align with general publication patterns, but early expert-curated evidence was generally stable. Because instability in answers often occurred within the first 2 to 6 mo for specific COVID-19 topics, public health officials could apply more conservative policies at the start of future pandemics, to be revised as evidence stabilizes.

Use of second-generation antipsychotics in autism spectrum disorder: a systematic review and meta-analysis protocol

INTRODUCTION

Atypical antipsychotics have been studied to treat autism spectrum disorder (ASD). However, like little is known about whether these drugs are effective and safe when compared in controlled and non-controlled settings. This study aims to assess the efficacy and safety of second-generation antipsychotics in ASD in randomised controlled trials (RCT) and observational studies.

METHODS AND ANALYSIS

This systematic review will include RCT and prospective cohorts evaluating second-generation antipsychotics in people 5 years and older diagnosed with ASD. Searches will be conducted in Medline, Embase, Cochrane Library, Epistemonikos, Lilacs, CINAHL, PsycINFO, trial registries and grey literature databases without restriction on publication status, year of publication and language. The primary outcomes will be symptoms of aggressive behaviour, quality of life for the individual or their careers, and discontinuation or dropouts/withdrawals of antipsychotics due to adverse events. The secondary outcomes are other not serious adverse events and adherence to pharmacotherapy. Selection, data extraction, and quality assessment will be performed by pairs of reviewers, independently. The Risk of Bias 2 (RoB 2) and Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) tools will be used to assess the risk of bias in the included studies. If appropriate, a meta-analysis and network meta-analysis will be conducted to synthesise the results. The overall quality of the evidence for each outcome will be determined by the Recommendation, Assessment, Development and Evaluation approach.

ETHICS AND DISSEMINATION

This study will systematically summarise the existing evidence evaluating the use of second-generation antipsychotics for treating ASD, in controlled and uncontrolled studies. The results of this review will be disseminated through peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42022353795.

The effect of progressive and individualised sport-specific training on the prevalence of injury in football and handball student athletes: a randomised controlled trial

Objective

To evaluate the effectiveness of communication and coordination combined with designing a progressive and individualised sport-specific training program for reducing injury prevalence in youth female and male football and handball players transitioning to a sports academy high school. An additional aim was to investigate the characteristics of the reported injuries.

Methods

Forty-two Norwegian athletes were randomised into an intervention or control group. Mean age, height, weight and BMI was 15.5 ± 0.5 years, 178.6 cm ± 6.3 cm, 71.3 ± 9.8 kg, 22.3 ± 2.7 BMI for the intervention group (IG) (n = 23), and 15.4 ± 0.5 years, 175.6 cm ± 6.6 cm, 67.1 ± 9.8 kg, 21.7 ± 2.4 BMI for the control group (CG) (n = 19). During the summer holiday, the intervention group received weekly progressive, individualised sport-specific training programs and weekly follow-up telephone calls from the researchers. All athletes completed a baseline questionnaire and a physical test battery. Training data and injuries were recorded prospectively for 22 weeks using the Oslo Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H2). A two-way chi-square (χ²) test of independence was conducted to examine the relationship between groups and injury.

Results

Average weekly prevalence of all injuries was 11% (95% CI: 8%-14%) in IG and 19% (95% CI: 13%-26%) in CG. Average weekly prevalence of substantial injuries was 7% (95% CI: 3%-10%) in IG and 10% (95% CI: 6%-13%) in CG. The between-group difference in injuries was significant: χ² (1, N = 375) = 4.865, p = .031, φ = .114, with 1.8 times higher injury risk in CG vs. IG during the first 12 weeks after enrolment.

Conclusions

For student athletes transitioning to a sports academy high school, progressive individualised, sport-specific training programs reduced the prevalence of all-complaint injuries following enrolment. Clubs and schools should prioritise time and resources to implement similar interventions in periods where student athletes have less supervision, such as the summer holidays, to facilitate an optimal transition to a sports academy high school.

Randomizing a clinical trial in neuro-degenerative disease

The paper studies randomization rules for a sequential two-treatment, two-site clinical trial in Parkinson's disease. An important feature is that we have values of responses and five potential prognostic factors from a sample of 144 patients similar to those to be enrolled in the trial. Analysis of this sample provides a model for trial analysis. The comparison of allocation rules is made by simulation yielding measures of loss due to imbalance and of potential bias. A major novelty of the paper is the use of this sample, via a two-stage algorithm, to provide an empirical distribution of covariates for the simulation; sampling of a correlated multivariate normal distribution is followed by transformation to variables following the empirical marginal distributions. Six allocation rules are evaluated. The paper concludes with some comments on general aspects of the evaluation of such rules and provides a recommendation for two allocation rules, one for each site, depending on the target number of patients to be enrolled.

Urban resilience to socioeconomic disruptions during the COVID-19 pandemic: Evidence from China

The COVID-19 pandemic and the associated restrictions have raised the awareness of building pandemic-resilient cities. Prior studies often evaluated the resilience of one type of urban system while lacking a comparison across various urban subsystems. This study fills this gap by measuring and comparing the adaptive resilience to the pandemic of various urban subsystems in Chinese cities. We propose a novel outcome measurement of the pandemic's socioeconomic impacts on cities, i.e., the citizens' complaints data, and use its temporal changes to measure cities' adaptive resilience to the pandemic. We find a wide range of urban subsystems were severely shocked by the pandemic, including the urban economy, construction-and-housing sector, welfare system, and education system. Different urban subsystems exhibit divergent degrees of adaptive resilience to the pandemic. Using cluster analysis, we also identify three types of cities with different patterns of adaptive resilience: cities whose general economies were the least resilient, cities whose construction-and-housing system was the least resilient, and cities that were mostly affected by restriction measures. Our findings contribute to the understanding of the pandemic's socioeconomic costs and help identify the divergent resilience of different urban subsystems so as to develop targeted policy interventions to improve cities' resilience to the pandemic.

The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review

OBJECTIVE

Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceeds the capacity of human-only analysis. In this context, artificial intelligence (AI) and its branches such as machine learning (ML) can be used to facilitate human efforts in analyzing the literature to foster EBM. The present scoping review aimed to examine the use of AI in the automation of biomedical literature survey and analysis with a view to establishing the state-of-the-art and identifying knowledge gaps.

MATERIALS AND METHODS

Comprehensive searches of the main databases were performed for articles published up to June 2022 and studies were selected according to inclusion and exclusion criteria. Data were extracted from the included articles and the findings categorized.

RESULTS

The total number of records retrieved from the databases was 12,145, of which 273 were included in the review. Classification of the studies according to the use of AI in evaluating the biomedical literature revealed three main application groups, namely assembly of scientific evidence (n=127; 47%), mining the biomedical literature (n=112; 41%) and quality analysis (n=34; 12%). Most studies addressed the preparation of systematic reviews, while articles focusing on the development of guidelines and evidence synthesis were the least frequent. The biggest knowledge gap was identified within the quality analysis group, particularly regarding methods and tools that assess the strength of recommendation and consistency of evidence.

CONCLUSION

Our review shows that, despite significant progress in the automation of biomedical literature surveys and analyses in recent years, intense research is needed to fill knowledge gaps on more difficult aspects of ML, deep learning and natural language processing, and to consolidate the use of automation by end-users (biomedical researchers and healthcare professionals).

Burden of disease of spinal muscular atrophy linked to chromosome 5q (5q-SMA) in Colombia

OBJECTIVE

This article estimates the disease burden of 5q-SMA in Colombia by using the Disability-Adjusted Life Years (DALYs) metric.

METHODS

Epidemiological data were obtained from local databases and medical literature and were adjusted in the DisMod II tool. DALYs were obtained by adding years of life lost due to premature death (YLL) and years lived with disability (YLD).

RESULTS

The modeled prevalence of 5q-SMA in Colombia was 0.74 per 100,000 population. The fatality rate for all types was 14.1%. The disease burden of 5q-SMA was estimated at 4,421 DALYs (8.6 DALYs/100,000), corresponding to 4,214 (95.3%) YLLs and 207 (4.7%) YLDs. Most of the DALYs were accounted in the 2-17 age group. Of the total burden, 78% correspond to SMA type 1, 18% to type 2, and 4% to type 3.

CONCLUSIONS

Although 5q-SMA is a rare disease, it is linked to a significant disease burden due to premature mortality and severe sequelae. The estimates shown in this article are important inputs to inform public policy decisions on how to ensure adequate health service provision for patients with 5q-SMA.

Risk factors and outcomes of COVID-19 in adult patients with hematological malignancies: A single-center study showing lower than expected rates of hospitalization and mortality

BACKGROUND

Studies addressing coronavirus disease 2019 (COVID-19) in patients with hematological malignancies have reported mortality rates of up to 40%; however, included predominantly hospitalized patients.

METHODS

During the first year of the pandemic, we followed adult patients with hematological malignancies treated at a tertiary center in Jerusalem, Israel, who contracted COVID-19, with the aim of studying risk factors for adverse COVID-19-related outcomes. We used remote communication to track patients managed at home-isolation, and patient questioning to assess the source of COVID-19 infection, community versus nosocomial.

RESULTS

Our series included 183 patients, median age was 62.5 years, 72% had at least one comorbidity and 39% were receiving active antineoplastic treatment. Hospitalization, critical COVID-19, and mortality rates were 32%, 12.6%, and 9.8%, respectively, remarkably lower than previously reported. Age, multiple comorbidities, and active antineoplastic treatment were significantly associated with hospitalization due to COVID-19. Treatment with monoclonal antibodies was strongly associated with both hospitalization and critical COVID-19. In older (≥60) patients not receiving active antineoplastic treatment, mortality, and severe COVID-19 rates were comparable to those of the general Israeli population. We did not detect patients that contracted COVID-19 within the Hematology Division.

CONCLUSION

These findings are relevant for the future management of patients with hematological malignancies in COVID-19-affected regions.

A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands

The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand prediction-Network optimization-Decision adjustment framework is proposed for the emergency logistic network design. We first present an improved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, we construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimization model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher accuracy and the accuracy increases with the amount of observed data; (ii) considering the demand uncertainty, the proposed robust optimization model combined with uncertainty budget adjustment strategy can improve the performance of the emergency logistic network; (iii) the proposed solution method is more efficient than its benchmark, especially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented.

The prevalence of probable mental health disorders among hospital healthcare workers during COVID-19: A systematic review and meta-analysis

OBJECTIVES

The mental health impacts of the COVID-19 pandemic continue to be documented worldwide with systematic reviews playing a pivotal role. Here we present updated findings from our systematic review and meta-analysis on the mental health impacts among hospital healthcare workers during COVID-19.

METHODS

We searched MEDLINE, CINAHL, PsycINFO, Embase and Web Of Science Core Collection between 1st January 2000 to 17th February 2022 for studies using validated methods and reporting on the prevalence of diagnosed or probable mental health disorders in hospital healthcare workers during the COVID-19 pandemic. A meta-analysis of proportions and odds ratio was performed using a random effects model. Heterogeneity was investigated using test of subgroup differences and 95 % prediction intervals.

RESULTS

The meta-analysis included 401 studies, representing 458,754 participants across 58 countries. Pooled prevalence of depression was 28.5 % (95 % CI: 26.3-30.7), anxiety was 28.7 % (95 % CI: 26.5-31.0), PTSD was 25.5 % (95 % CI: 22.5-28.5), alcohol and substance use disorder was 25.3 % (95 % CI: 13.3-39.6) and insomnia was 24.4 % (95 % CI: 19.4-29.9). Prevalence rates were stratified by physicians, nurses, allied health, support staff and healthcare students, which varied considerably. There were significantly higher odds of probable mental health disorders in women, those working in high-risk units and those providing direct care.

LIMITATIONS

Majority of studies used self-report measures which reflected probable mental health disorders rather than actual diagnosis.

CONCLUSIONS

These updated findings have enhanced our understanding of at-risk groups working in hospitals. Targeted support and research towards these differences in mental health risks are recommended to mitigate any long-term consequences.

Effects of process changes on emergency department crowding in a changing world: an interrupted time-series analysis

BACKGROUND

During a 6-year period, several process changes were introduced at the emergency department (ED) to decrease crowding, such as the implementation of a general practitioner cooperative (GPC) and additional medical staff during peak hours. In this study, we assessed the effects of these process changes on three crowding measures: patients' length of stay (LOS), the modified National ED OverCrowding Score (mNEDOCS), and exit block while taking into account changing external circumstances, such as the COVID-19 pandemic and centralization of acute care.

METHODS

We determined time points of the various interventions and external circumstances and built an interrupted time-series (ITS) model per outcome measure. We analyzed changes in level and trend before and after the selected time points using ARIMA modeling, to account for autocorrelation in the outcome measures.

RESULTS

Longer patients' ED LOS was associated with more inpatient admissions and more urgent patients. The mNEDOCS decreased with the integration of the GPC and the expansion of the ED to 34 beds and increased with the closure of a neighboring ED and ICU. More exit blocks occurred when more patients with shortness of breath and more patients > 70 years of age presented to the ED. During the severe influenza wave of 2018-2019, patients' ED LOS and the number of exit blocks increased.

CONCLUSIONS

In the ongoing battle against ED crowding, it is pivotal to understand the effect of interventions, corrected for changing circumstances and patient and visit characteristics. In our ED, interventions which were associated with decreased crowding measures included the expansion of the ED with more beds and the integration of the GPC on the ED.

Exploring the impact of selection bias in observational studies of COVID-19: a simulation study

BACKGROUND

Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis.

METHODS

We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19.

RESULTS

In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios.

CONCLUSIONS

Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.

Fair admission risk prediction with proportional multicalibration

Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to measure and achieve fair calibration is with multicalibration. Multicalibration constrains calibration error among flexibly-defined subpopulations while maintaining overall calibration. However, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it is possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose proportional multicalibration, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its differential calibration, a fairness criteria that directly measures how closely a model approximates sufficiency. Therefore, proportionally calibrated models limit the ability of decision makers to distinguish between model performance on different patient groups, which may make the models more trustworthy in practice. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.

Minimal important difference in childhood interstitial lung diseases

Background

Monitoring disease progression in childhood interstitial lung diseases (chILD) is essential. No information for the minimal important difference (MID), which is defined as the smallest change in a parameter that is perceived as important prompting a clinician to change the treatment, is available. We calculated MIDs for vital signs (respiratory rate, peripheral oxygen saturation in room air, Fan severity score) and health-related quality of life (HrQoL) scores.

Methods

This study used data from the Kids Lung Register, which is a web-based management platform that collects data of rare paediatric lung disorders with a focus on chILD. Data of vital signs and HrQoL scores (Health Status Questionnaire, chILD-specific questionnaire and PedsQL V.4.0) were collected. MIDs were calculated according to distribution-based (one-third SD) and anchor-based methods (using forced expiratory volume in 1 s and forced vital capacity) as anchors.

Results

Baseline data of 774 children were used to calculate the following MIDs: respiratory rate 1.3 (z-score), O2saturation in room air 3.0%, Fan severity score 0.2–0.4, Health Status Questionnaire 0.4–0.8, chILD-specific questionnaire 4.4%–8.2%, physical health summary score 7.8%–8.9%, psychosocial health summary score 3.4%–6.9% and total score 5.1%–7.4%. Results of the responsiveness analysis generally agreed with the MIDs calculated.

Conclusions

For the first time, we provide estimates of MIDs for vital signs and HrQoL scores in a large cohort of chILD using different methods.

The effects of long COVID-19, its severity, and the need for immediate attention: Analysis of clinical trials and Twitter data

Background

The coronavirus disease 2019 (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization; identifying the disease progression, predicting patient outcomes early, the possibility of long-term adverse events through effective modeling, and the use of real-world data are of immense importance to effective treatment, resource allocation, and prevention of severe adverse events of grade 4 or 5.

Methods

First, we raise awareness about the different clinical trials on long COVID-19. The trials were selected with the search term "long COVID-19" available in ClinicalTrials.gov. Second, we curated the recent tweets on long-haul COVID-19 and gave an overview of the sentiments of the people. The tweets obtained with the query term #long COVID-19 consisted of 8,436 tweets between 28 August 2022 and 06 September 2022. We utilized the National Research Council (NRC) Emotion Lexicon method for sentiment analysis. Finally, we analyze the retweet and favorite counts are associated with the sentiments of the tweeters via a negative binomial regression model.

Results

Our results find that there are two types of clinical trials being conducted: observational and interventional. The retweet counts and favorite counts are associated with the sentiments and emotions, such as disgust, joy, sadness, surprise, trust, negative, and positive.

Conclusion

We need resources and further research in the area of long COVID-19.

Irish Media Coverage of COVID-19 Evidence-Based Research Reports From One National Agency

BACKGROUND

How research findings are presented through domestic news can influence behaviour and risk perceptions, particularly during emergencies such as the coronavirus disease 2019 (COVID-19) pandemic. Monitoring media communications to track misinformation and find information gaps is an important component of emergency risk communication. Therefore, this study investigated the traditional media coverage of nine selected COVID-19 evidence-based research reports and associated press releases (PRs) published during the initial phases of the pandemic (April to July 2020) by one national agency.

METHODS

NVivo was used for summative content analysis. 'Key messages' from each research report were proposed and 488 broadcast, print, and online media sources were coded at the phrase level. Manifest content was coded and counted to locate patterns in the data (what and how many) while latent content was analysed to further investigate these patterns (why and how). This included the coding of the presence of political and public health actors in coverage.

RESULTS

Coverage largely did not misrepresent the results of the reports, however, selective reporting and the variability in the use of quotes from governmental and public health stakeholders changed and contextualised results in different manners than perhaps originally intended in the PR. Reports received varying levels of media attention. Coverage focused on more 'human-interest' stories (eg, spread of COVID-19 by children and excess mortality) as opposed to more technical reports (eg, focusing on viral load, antibodies, testing, etc).

CONCLUSION

Our findings provide a case-study of European media coverage of evidence reports produced by a national agency. Results highlighted several strengths and weaknesses of current communication efforts.

Abortion decision-making process trajectories and determinants in low- and middle-income countries: A mixed-methods systematic review and meta-analysis

BACKGROUND

About 45.1% of all induced abortions are unsafe and 97% of these occur in low- and middle-income countries (LMICs). Women's abortion decisions may be complex and are influenced by various factors. We aimed to delineate women's abortion decision-making trajectories and their determinants in LMICs.

METHODS

We searched Medline, EMBASE, PsychInfo, Global Health, Web of Science, Scopus, IBSS, CINAHL, WHO Global Index Medicus, the Cochrane Library, WHO website, ProQuest, and Google Scholar for primary studies and reports published between January 1, 2000, and February 16, 2021 (updated on June 06, 2022), on induced abortion decision-making trajectories and/or their determinants in LMICs. We excluded studies on spontaneous abortion. Two independent reviewers extracted and assessed quality of each paper. We used "best fit" framework synthesis to synthesise abortion decision-making trajectories and thematic synthesis to synthesise their determinants. We analysed quantitative findings using random effects model. The study protocol is registered with PROSPERO number CRD42021224719.

FINDINGS

Of the 6960 articles identified, we included 79 in the systematic review and 14 in the meta-analysis. We identified nine abortion decision-making trajectories: pregnancy awareness, self-reflection, initial abortion decision, disclosure and seeking support, negotiations, final decision, access and information, abortion procedure, and post-abortion experience and care. Determinants of trajectories included three major themes of autonomy in decision-making, access and choice. A meta-analysis of data from 7737 women showed that the proportion of the overall women's involvement in abortion decision-making was 0.86 (95% CI:0.73-0.95, I² = 99.5%) and overall partner involvement was 0.48 (95% CI:0.29-0.68, I² = 99.6%).

INTERPRETATION

Policies and strategies should address women's perceptions of safe abortion socially, legally, and economically, and where appropriate, involvement of male partners in abortion decision-making processes to facilitate safe abortion. Clinical heterogeneity, in which various studies defined "the final decision-maker" differentially, was a limitation of our study.

FUNDING

Nuffield Department of Population Health DPhil Scholarship for PL, University of Oxford, and the Medical Research Council Career Development Award for MN (Grant Ref: MR/P022030/1).

Three out of four published systematic reviews on COVID-19 treatments were not registered and one-third of those registered were published: a meta-research study

OBJECTIVES

The aim of this study is to describe (1) registered and (2) published systematic reviews (SRs) on COVID-19 treatments, and to analyze (3) the proportion of publications among registered SRs and (4) the proportion of registrations among published SRs.

STUDY DESIGN AND SETTING

This meta-research study (CRD42021240423) is part of CEOsys (http://www.covid-evidenz.de/). Two reviewers identified protocols in PROSPERO (registered January 2020 to September 2020) and SRs published as preprint or peer-reviewed article in L·OVE (Living OVerview of the Evidence) COVID-19 (by May 2021). SRs of all types assessing COVID-19 treatments in humans were included.

RESULTS

We included 239 PROSPERO protocols and 346 SRs published in L·OVE. In both samples, the affiliation of the corresponding author with an Asian institution, standard SR as review type, and meta-analysis as synthesis method were the most frequent characteristics. Living SRs made up ≤10%. A total of 71 of 239 (29.7%) PROSPERO protocols were published as SR by February 2022, that is, after at least 17 months of follow-up (25 of 71 as preprints, 35.2%). In L·OVE, 261 of 346 (75.4%) SRs published by May 2021 were not registered in PROSPERO.

CONCLUSION

Overall, one-third PROSPERO protocols were published and three-fourth published SRs were not registered. We strongly encourage authors to register and publish their SRs promptly to reduce research waste and to allocate resources efficiently during the pandemic and beyond.

The Impact of Diabetes in Intermittent Claudication: A Prospective Cohort Study

The aim of this study was to determine the lower-limb outcome in patients with intermittent claudication (IC) and to identify predictors for deterioration. This study employed a prospective observational cohort single-centre design. One hundred fifty patients with IC attending a vascular surgery unit for the first time were recruited. Lower limb perfusion was assessed utilising ankle brachial index (ABI) measures, toe-brachial index (TBI) measures, Doppler waveform analysis and the walking impairment questionnaire. Follow-up was conducted after 1 year and 2 years following recruitment to assess haemodynamic parameters, symptom severity and outcome. Recruited participants had a mean age of 69.7 (±9.3) years, BMI 27.8(±4.2) and 79.3% were men. Significant haemodynamic decline (decline in ABPI by ≥0.15 and/or decline in TBPI by ≥0.1) occurred in 50.6% of the cohort within 2 years of whom 23.3% developed chronic limb threatening ischaemia (CLTI) with rest pain and/or tissue loss. Baseline ABPI, ABPI ≤ 0.5, TBPI ≤ 0.39, infrapopliteal artery (IPA) disease and high Haemoglobin A1c were identified as significant predictors for deterioration to CLI. (P < .05, binomial logistic regression). Patients with IC are at a high risk of developing CLTI within 2 years. Risk of lower limb adverse events is tripled in patients with IPA disease, low ankle and toe pressures and poorly controlled diabetes. Early identification of those at high risk for early deterioration may justify a paradigm shift in the management of this subgroup.

Transversus Abdominis Plane Block as a Strategy for Effective Pain Management in Patients with Pain during Laparoscopic Cholecystectomy: A Systematic Review

Laparoscopic cholecystectomy (LC), unlike laparotomy, is an invasive surgical procedure, and some patients report mild to moderate pain after surgery. Transversus abdominis plane (TAP) block has been shown to be an appropriate method for postoperative analgesia in patients undergoing abdominal surgery. However, there have been few studies on the efficacy of TAP block after LC surgery, with unclear information on the optimal dose, long-term effects, and clinical significance, and the analgesic efficacy of various procedures, hence the need for this review. Five electronic databases (PubMed, Academic Search Premier, Web of Science, CINAHL, and Cochrane Library) were searched for eligible studies published from inception to the present. Post-mean and standard deviation values for pain assessed were extracted, and mean changes per group were calculated. Clinical significance was determined using the distribution-based approach. Four different local anesthetics (Bupivacaine, Ropivacaine, Lidocaine, and Levobupivacaine) were used at varying concentrations from 0.2% to 0.375%. Ten different drug solutions (i.e., esmolol, Dexamethasone, Magnesium Sulfate, Ketorolac, Oxycodone, Epinephrine, Sufentanil, Tropisetron, normal saline, and Dexmedetomidine) were used as adjuvants. The optimal dose of local anesthetics for LC could be 20 mL with 0.4 mL/kg for port infiltration. Various TAP procedures such as ultrasound-guided transversus abdominis plane (US-TAP) block and other strategies have been shown to be used for pain management in LC; however, TAP blockade procedures were reported to be the most effective method for analgesia compared with general anesthesia and port infiltration. Instead of 0.25% Bupivacaine, 1% Pethidine could be used for the TAP block procedures. Multimodal analgesia could be another strategy for pain management. Analgesia with TAP blockade decreases opioid consumption significantly and provides effective analgesia. Further studies should identify the long-term effects of different TAP block procedures.

Systematic reviews of convalescent plasma in COVID-19 continue to be poorly conducted and reported: a systematic review

OBJECTIVES

To suggest possible approaches to combatting the impact of the COVID-19 infodemic to prevent research waste in future health emergencies and in everyday research and practice.

STUDY DESIGN AND SETTING

Systematic review. The Epistemonikos database was searched in June 2021 for systematic reviews on the effectiveness of convalescent plasma for COVID-19. Two reviewers independently screened the retrieved references with disagreements resolved by discussion. Data extraction was completed by one reviewer with a proportion checked by a second. We used the Assessment of Multiple Systematic Reviews to assess the quality of conduct and reporting of included reviews.

RESULTS

Fifty one systematic reviews are included with 193 individual studies included within the systematic reviews. There was considerable duplication of effort; multiple reviews were conducted at the same time with inconsistencies in the evidence included. The reviews were of low methodological quality, poorly reported, and did not adhere to preferred reporting items for systematic reviews and meta-analysis guidance.

CONCLUSION

Researchers need to conduct, appraise, interpret, and disseminate systematic reviews better. All in the research community (researchers, peer-reviewers, journal editors, funders, decision makers, clinicians, journalists, and the public) need to work together to facilitate the conduct of robust systematic reviews that are published and communicated in a timely manner, reducing research duplication and waste, increasing transparency and accessibility of all systematic reviews.

Studies of prevalence: how a basic epidemiology concept has gained recognition in the COVID-19 pandemic

BACKGROUND

Prevalence measures the occurrence of any health condition, exposure or other factors related to health. The experience of COVID-19, a new disease caused by SARS-CoV-2, has highlighted the importance of prevalence studies, for which issues of reporting and methodology have traditionally been neglected.

OBJECTIVE

This communication highlights key issues about risks of bias in the design and conduct of prevalence studies and in reporting them, using examples about SARS-CoV-2 and COVID-19.

SUMMARY

The two main domains of bias in prevalence studies are those related to the study population (selection bias) and the condition or risk factor being assessed (information bias). Sources of selection bias should be considered both at the time of the invitation to take part in a study and when assessing who participates and provides valid data (respondents and non-respondents). Information bias appears when there are systematic errors affecting the accuracy and reproducibility of the measurement of the condition or risk factor. Types of information bias include misclassification, observer and recall bias. When reporting prevalence studies, clear descriptions of the target population, study population, study setting and context, and clear definitions of the condition or risk factor and its measurement are essential. Without clear reporting, the risks of bias cannot be assessed properly. Bias in the findings of prevalence studies can, however, impact decision-making and the spread of disease. The concepts discussed here can be applied to the assessment of prevalence for many other conditions.

CONCLUSIONS

Efforts to strengthen methodological research and improve assessment of the risk of bias and the quality of reporting of studies of prevalence in all fields of research should continue beyond this pandemic.

Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for a diagnostic test accuracy study

INTRODUCTION

Systemic inflammatory response syndrome (SIRS), sepsis and associated organ dysfunctions are life-threating conditions occurring at paediatric intensive care units (PICUs). Early recognition and treatment within the first hours of onset are critical. However, time pressure, lack of personnel resources, and the need for complex age-dependent diagnoses impede an accurate and timely diagnosis by PICU physicians. Data-driven prediction models integrated in clinical decision support systems (CDSS) could facilitate early recognition of disease onset.

OBJECTIVES

To estimate the sensitivity and specificity of previously developed prediction models (index tests) for the detection of SIRS, sepsis and associated organ dysfunctions in critically ill children up to 12 hours before reference standard diagnosis is possible.

METHODS AND ANALYSIS

We conduct a monocentre, prospective diagnostic test accuracy study. Clinicians in the PICU of the tertiary care centre Hannover Medical School, Germany, continuously screen and recruit patients until the adaptive sample size (originally intended sample size of 500 patients) is enrolled. Eligible are children (0-17 years, all sexes) who stay in the PICU for ≥12 hours and for whom an informed consent is given. All eligible patients are independently assessed for SIRS, sepsis and organ dysfunctions using corresponding predictive and knowledge-based CDSS models. The knowledge-based CDSS models serve as imperfect reference standards. The assessments are used to estimate the sensitivities and specificities of each predictive model using a clustered nonparametric approach (main analysis). Subgroup analyses ('age groups', 'sex' and 'age groups by sex') are predefined.

ETHICS AND DISSEMINATION

This study obtained ethics approval from the Hannover Medical School Ethics Committee (No. 10188_BO_SK_2022). Results will be disseminated as peer-reviewed publications, at scientific conferences, and to patients in an appropriate dissemination approach.

TRIAL REGISTRATION NUMBER

This study was registered with the German Clinical Trial Register (DRKS00029071) on 2022-05-23.

PROTOCOL VERSION

10188_BO_SK_2022_V.2.0-20220330_4_Studienprotokoll.

Algorithmic fairness in computational medicine

Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, which can lead to detrimental effects on the health and well-being of specific demographic groups such as vulnerable ethnic minorities. This problem, termed algorithmic bias, has been extensively studied in theoretical machine learning recently. However, the impact of algorithmic bias on medicine and methods to mitigate this bias remain topics of active discussion. This paper presents a comprehensive review of algorithmic fairness in the context of computational medicine, which aims at improving medicine with computational approaches. Specifically, we overview the different types of algorithmic bias, fairness quantification metrics, and bias mitigation methods, and summarize popular software libraries and tools for bias evaluation and mitigation, with the goal of providing reference and insights to researchers and practitioners in computational medicine.

Randomized controlled trials in patients with COVID-19: a systematic review and critical appraisal

OBJECTIVES

This study aimed to describe the prevalence of risks of bias in randomized trials of therapeutic interventions for COVID-19.

METHODS

Systematic review and risk of bias assessment performed by two independent reviewers of a random sample of 40 randomized trials of therapeutic interventions for moderate-severe COVID-19. We used the RoB 2.0 tool to assess the risk of bias, which evaluates bias under five domains as well as an overall assessment of each trial as high or low risk of bias.

RESULTS

Of the 40 included trials, 19 (47%) were at high risk of bias, and this was particularly frequent in trials from low-middle income countries (11/14, 79%). Potential deviations to intended interventions (i.e., control participants accessing experimental treatments) were considered a potential source of bias in some studies (14, 35%), as was the risk due to selective reporting of results (6, 15%). The randomization process was considered at low risk of bias in most studies (34, 95%), as were missing data (36, 90%) and measurement of the outcome (35, 87%).

CONCLUSION

Many randomized trials evaluating COVID-19 interventions are at risk of bias, particularly those conducted in low-middle income countries. Biases are mostly due to deviations from intended interventions and partly due to the selection of reported results. The use of placebo control and publicly available protocol can mitigate many of these risks.