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Direct oral anticoagulants (DOACs) are increasingly being used over low molecular weight heparin (LMWH) and vitamin K antagonists for the treatment of venous thromboembolism (VTE). The objective of this study was to examine predictors of anticoagulant type (DOAC vs. LMWH) prescribed at discharge from the emergency department (ED) among patients diagnosed with VTE in the ED. We conducted a retrospective chart review of adult (>17 years) patients discharged from an Ontario, Canada ED in a tertiary care centre with an ED diagnosis of deep vein thrombosis or pulmonary embolism from January 2019 to December 2021. A multivariable logistic regression model was used to examine the predictors of the anticoagulant (DOAC vs. LMWH) prescribed at discharge. Covariables included: age, sex, history of major bleeding, history of cancer, and previous anticoagulation. VTE was confirmed in 390 ED visits by 365 unique patients. Among unique patients, 239 (65.5 %) patients were discharged from the ED and included in analysis. Of the 239 patients included, 12.1 % of patients were over the age of 80, 46.4 % were female and 29.7 % had a history of cancer. The majority of patients discharged from the ED were prescribed DOACs (70.7 %,169/239). Cancer history was associated with anticoagulation with LMWH (vs. DOAC) on discharge (adjusted odds ratio [aOR] =12.81, 95 % CI: 6.60–25.90). While most patients diagnosed with VTE in the ED setting were discharged with DOACs, most cancer patients included in our study were treated with LMWH over DOACs, despite increasing evidence around the efficacy and safety of DOACs in most cancer patients. Further research is needed to understand longitudinal trends in anticoagulation.

Background In non-inferiority trials, there is a concern that intention-to-treat (ITT) analysis, by including participants who did not receive the planned interventions, may bias towards making the treatment and control arms look similar and lead to mistaken claims of non-inferiority. In contrast, per protocol (PP) analysis is viewed as less likely to make this mistake and therefore preferable in non-inferiority trials. In a systematic review of antibiotic non-inferiority trials, we compared ITT and PP analyses to determine which analysis was more conservative. Methods In a secondary analysis of a systematic review, we included non-inferiority trials that compared different antibiotic regimens, used absolute risk reduction (ARR) as the main outcome and reported both ITT and PP analyses. All estimates and confidence intervals (CIs) were oriented so that a negative ARR favored the control arm, and a positive ARR favored the treatment arm. We compared ITT to PP analyses results. The more conservative analysis between ITT and PP analyses was defined as the one having a more negative lower CI limit. Results The analysis included 164 comparisons from 154 studies. In terms of the ARR, ITT analysis yielded the more conservative point estimate and lower CI limit in 83 (50.6%) and 92 (56.1%) comparisons respectively. The lower CI limits in ITT analysis favored the control arm more than in PP analysis (median of − 7.5% vs. -6.9%, p  = 0.0402). CIs were slightly wider in ITT analyses than in PP analyses (median of 13.3% vs. 12.4%, p  < 0.0001). The median success rate was 89% (interquartile range IQR 82 to 93%) in the PP population and 44% (IQR 23 to 60%) in the patients who were included in the ITT population but excluded from the PP population ( p  < 0.0001). Conclusions Contrary to common belief, ITT analysis was more conservative than PP analysis in the majority of antibiotic non-inferiority trials. The lower treatment success rate in the ITT analysis led to a larger variance and wider CI, resulting in a more conservative lower CI limit. ITT analysis should be mandatory and considered as either the primary or co-primary analysis for non-inferiority trials. Trial registration PROSPERO registration number CRD42020165040 .

Coronavirus disease 2019 (COVID-19) mortality and morbidity have been shown to increase with deprivation and impact non-White ethnicities more severely. Despite the extra risk Black, Asian and Minority Ethnicity (BAME) groups face in the pandemic, our current medical research system seems to prioritise innovation aimed at people of European descent. We found significant difficulties in assessing baseline demographics in clinical trials for COVID-19 vaccines, displaying a lack of transparency in reporting. Further, we found that most of these trials take place in high-income countries, with only 25 of 219 trials (11.4%) taking place in lower middle- or low-income countries. Trials for the current best vaccine candidates (BNT162b2, ChadOx1, mRNA-173) recruited 80.0% White participants. Underrepresentation of BAME groups in medical research will perpetuate historical distrust in healthcare processes, and poses a risk of unknown differences in efficacy and safety of these vaccines by phenotype. Limiting trial demographics and settings will mean a lack of global applicability of the results of COVID-19 vaccine trials, which will slow progress towards ending the pandemic.

The 2022 World Antimicrobial Awareness Week saw the World Health Organization, the Food and Agriculture Organization, the United Nations Environment Programme and the World Organisation for Animal Health focused their campaign on the theme ‘Preventing AMR together’ to improve awareness and understanding of AMR and encourage best practices.2 While a One Health framework is now promoted for conceptualising the complex problem of AMR, the evidence base of interventions designed within this rubric is thin. Infection Prevention and Control (IPC) in human health is considered fundamental for AMR, defined as measures ‘that prevent patients and health workers from being harmed by avoidable infections and as a result of AMR’.4 In animal health, the prevention and control of infections commonly focus on measures to reduce the risk of introduction and/or spread of diseases between animals on farms and from and to farm workers. Measures to prevent and control infections at a community level in animal agricultural settings where humans and animals live in close contact is an overlooked area ripe for a One Health approach, especially when a significant proportion of the global population is involved in small-scale, semi-intensive livestock farming. WASH and on-farm biosecurity as infection prevention and control measures WASH comprises a group of measures to provide or improve drinking water supply (water quantity), as well as to remove or inactivate pathogens and chemicals ‘at source’ and/or ‘at point of use’ (water quality), to provide or improve facilities for the disposal of human waste (sanitation), and to promote or implement changes in hygienic practices (hygiene).8 A recent study9 suggests biosecurity measures in animal health to be defined as ‘the implementation of a segregation, hygiene or management procedure (excluding medically effective feed additives and preventive/curative treatment of animals) that specifically aims at reducing the probability of the introduction, establishment, survival or spread of any potential pathogen to, within or from a farm, a linked processing operation or a geographical area’.

Background Numerous statistical methods can be used to calculate the confidence interval (CI) of risk differences. There is consensus in previous literature that the Wald method should be discouraged. We compared five statistical methods for estimating the CI of risk difference in terms of CI width and study conclusion in antibiotic non-inferiority trials. Methods In a secondary analysis of a systematic review, we included non-inferiority trials that compared different antibiotic regimens, reported risk differences for the primary outcome, and described the number of successes and/or failures as well as patients in each arm. For each study, we re-calculated the risk difference CI using the Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and skewness-corrected asymptotic score (SCAS) methods. The CIs by different statistical methods were compared in terms of CI width and conclusion on non-inferiority. A wider CI was considered to be more conservative. Results The analysis included 224 comparisons from 213 studies. The statistical method used to calculate CI was not reported in 134 (59.8%) cases. The median (interquartile range IQR) for CI width by Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and SCAS methods was 13.0% (10.8%, 17.4%), 13.3% (10.9%, 18.5%), 13.6% (11.1%, 18.9%), 13.6% (11.1% and 19.0%), and 13.4% (11.1%, 18.9%), respectively. In 216 comparisons that reported a non-inferiority margin, the conclusion on non-inferiority was the same across the five statistical methods in 211 (97.7%) cases. The differences in CI width were more in trials with a sample size of 100 or less in each group and treatment success rate above 90%. Of the 18 trials in this subgroup with a specified non-inferiority margin, non-inferiority was shown in 17 (94.4%), 16 (88.9%), 14 (77.8%), 14 (77.8%), and 15 (83.3%) cases based on CI by Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and SCAS methods, respectively. Conclusions The statistical method used to calculate CI was not reported in the majority of antibiotic non-inferiority trials. Different statistical methods for CI resulted in different conclusions on non-inferiority in 2.3% cases. The differences in CI widths were highest in trials with a sample size of 100 or less in each group and a treatment success rate above 90%. Trial registration PROSPERO CRD42020165040 . April 28, 2020.

Objective:To evaluate inter-physician variability and predictors of changes in antibiotic prescribing before (2019) and during (2020/2021) the coronavirus disease 2019 (COVID-19) pandemic.Methods:We conducted a retrospective cohort analysis of physicians in Ontario, Canada prescribing oral antibiotics in the outpatient setting between January 1, 2019 and December 31, 2021 using the IQVIA Xponent data set. The primary outcome was the change in the number of antibiotic prescriptions between the prepandemic and pandemic period. Secondary outcomes were changes in the selection of broad-spectrum agents and long-duration (>7 d) antibiotic use. We used multivariable linear regression models to evaluate predictors of change.Results:There were 17,288 physicians included in the study with substantial inter-physician variability in changes in antibiotic prescribing (median change of −43.5 antibiotics per physician, interquartile range −136.5 to −5.0). In the multivariable model, later career stage (adjusted mean difference [aMD] −45.3, 95% confidence interval [CI] −52.9 to −37.8, p < .001), family medicine (aMD −46.0, 95% CI −62.5 to −29.4, p < .001), male patient sex (aMD −52.4, 95% CI −71.1 to −33.7, p < .001), low patient comorbidity (aMD −42.5, 95% CI −50.3 to −34.8, p < .001), and high prescribing to new patients (aMD −216.5, 95% CI −223.5 to −209.5, p < .001) were associated with decreases in antibiotic initiation. Family medicine and high prescribing to new patients were associated with a decrease in selection of broad-spectrum agents and prolonged antibiotic use.Conclusions:Antibiotic prescribing changed throughout the COVID-19 pandemic with overall decreases in antibiotic initiation, broad-spectrum agents, and prolonged antibiotic courses with inter-physician variability. These findings present opportunities for community antibiotic stewardship interventions.

Abstract Background Deaths following Staphylococcus aureus bacteremia (SAB) may be related or unrelated to the infection. In SAB therapeutics research, the length of follow-up should be optimized to capture most attributable deaths and minimize nonattributable deaths. We performed a secondary analysis of a systematic review to describe attributable mortality in SAB over time. Methods We systematically searched Medline, Embase, and Cochrane Database of Systematic Reviews from 1 January 1991 to 7 May 2021 for human observational studies of SAB. To be included in this secondary analysis, the study must have reported attributable mortality. Two reviewers extracted study data and assessed risk of bias independently. Pooling of study estimates was not performed due to heterogeneity in the definition of attributable deaths. Results Twenty-four observational cohort studies were included. The median proportion of all-cause deaths that were attributable to SAB was 77% (interquartile range [IQR], 72%–89%) at 1 month and 62% (IQR, 58%–75%) at 3 months. At 1 year, this proportion was 57% in 1 study. In 2 studies that described the rate of increase in mortality over time, 2-week follow-up captured 68 of 79 (86%) and 48 of 57 (84%) attributable deaths that occurred by 3 months. By comparison, 1-month follow-up captured 54 of 57 (95%) and 56 of 60 (93%) attributable deaths that occurred by 3 months in 2 studies. Conclusions The proportion of deaths that are attributable to SAB decreases as follow-up lengthens. Follow-up duration between 1 and 3 months seems optimal if evaluating processes of care that impact SAB mortality. Clinical Trials Registration PROSPERO CRD42021253891. In Staphylococcus aureusbacteremia (SAB), the proportion of deaths attributable to SAB decreases as follow-up lengthens. The ideal follow-up for mortality in SAB should be between 1 and 3 months.

Recent advances in vision-language foundational models have enabled development of systems that can perform visual understanding and reasoning tasks. However, it is unclear if these models are robust to distribution shifts, and how their performance and generalization capabilities vary under changes in data distribution. In this project we strive to answer the question \"Are vision-language foundational models robust to distribution shifts like human perception?\" Specifically, we consider a diverse range of vision-language models and compare how the performance of these systems is affected by corruption based distribution shifts (such as \\textit{motion blur, fog, snow, gaussian noise}) commonly found in practical real-world scenarios. We analyse the generalization capabilities qualitatively and quantitatively on zero-shot image classification task under aforementioned distribution shifts. Our code will be avaible at \\url{https://github.com/shivam-chandhok/CPSC-540-Project}

The auditors also cautioned Gammon Infra over its current liabilities exceeding the current assets, casting a shadow over the company's debt servicing ability.