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Abstract Background Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. Methods An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. Results The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. Conclusions We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.

Abstract Introduction Universal health coverage (UHC) is an emerging priority of health systems worldwide and central to Sustainable Development Goal 3 (target 3.8). Critical to the achievement of UHC, is quality of care. However, current evidence suggests that quality of care is suboptimal, particularly in low- and middle-income countries. The primary objective of this scoping review was to summarize the existing conceptual and empirical literature on quality of care within the context of UHC and identify knowledge gaps. Methods We conducted a scoping review using the Arksey and O’Malley framework and further elaborated by Levac et al. and applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews reporting guidelines. We systematically searched MEDLINE, EMBASE, CINAHL-Plus, PAIS Index, ProQuest and PsycINFO for reviews published between 1 January 1995 and 27 September 2021. Reviews were eligible for inclusion if the article had a central focus on UHC and discussed quality of care. We did not apply any country-based restrictions. All screening, data extraction and analyses were completed by two reviewers. Results Of the 4128 database results, we included 45 studies that met the eligibility criteria, spanning multiple geographic regions. We synthesized and analysed our findings according to Kruk et al.’s conceptual framework for high-quality systems, including foundations, processes of care and quality impacts. Discussions of governance in relation to quality of care were discussed in a high number of studies. Studies that explored the efficiency of health systems and services were also highly represented in the included reviews. In contrast, we found that limited information was reported on health outcomes in relation to quality of care within the context of UHC. In addition, there was a global lack of evidence on measures of quality of care related to UHC, particularly country-specific measures and measures related to equity. Conclusion There is growing evidence on the relationship between quality of care and UHC, especially related to the governance and efficiency of healthcare services and systems. However, several knowledge gaps remain, particularly related to monitoring and evaluation, including of equity. Further research, evaluation and monitoring frameworks are required to strengthen the existing evidence base to improve UHC.

Abstract Background There is low uptake of the pneumococcal vaccination in eligible older adults, even in high-income countries that offer routine and universal vaccination programs. Objective To systematically characterize interventions aimed at improving pneumococcal vaccine uptake in older adults. Design We conducted a scoping review following PRISMA-SCr guidelines of five interdisciplinary databases: Medline-Ovid, Embase, CINAHL, PsychInfo, and Cochrane Library. Databases were searched from January 2015 until April 2020. The interventions were summarized into three pillars according to the European Union Conceptional Framework for Action: information campaigns, prioritization of vaccination schemes, and primary care interventions. Results Our scoping review included 39 studies that summarized interventions related to pneumococcal vaccine uptake for older adults, encompassing 2,481,887 study participants (945 healthcare providers and 2,480,942 older adults) across seven countries. Examples of interventions that were associated with increased pneumococcal vaccination rate included periodic health examinations, reminders and decision-making tools built into electronic medical records, inpatient vaccination protocols, preventative health checklists, and multimodal educational interventions. When comparing the three pillars, prioiritization of vaccination schemes had the highest evidence for improved rates of vaccination (n = 14 studies), followed by primary care interventions (n = 8 studies), then information campaigns (n = 5 studies). Conclusion Several promising interventions were associated with improved outcomes related to vaccine uptake, although controlled study designs are needed to determine which interventions are most effective.

A key problem in code-division multiple access (CDMA) system is to mitigate the multiple access interference (MAI) from other users while detecting the desired user. The performance of the conventional minimum output energy (MOE) multiuser detector for CDMA system significantly degrades in the presence of signature waveform distortions induced by multipath propagation or timing asynchronism. In this paper, a robust linear programming (ROLP) algorithm for blind multiuser detection is proposed. Different from the existing MOE-based multiuser detection techniques, the proposed ROLP minimizes the ℓ∞-norm of the output to exploit the non-Gaussianity of the communication signals. To achieve robustness against signature waveform mismatch, the proposed method constrains the magnitude response of any signature vector within a specified uncertainty set to exceed unity. The uncertainty set is modeled as a rhombus, which differs from the spherical uncertainty region widely taken in the existing robust multiuser detectors. The resulting optimization problem is reformulated into a linear programming program and hence can be solved efficiently. The proposed ROLP is computationally simpler than its robust counterparts that requires solving a second-order cone programming. Simulation results demonstrate the superiority of the ROLP over several robust detectors, which indicate that its performance approaches the optimal performance bound.

BackgroundSince the legalization of medical cannabis in Canada in 2013, prescription of cannabis for medical purposes has become commonplace and a multibillion dollar industry has formed. Much of the media coverage surrounding medical cannabis has been positive in nature, leading to Canadians potentially underestimating the adverse effects of medical cannabis use. In recent years, there has been a large increase in clinic websites advertising the use of medical cannabis for health indications. However, little is known about the quality of the evidence used by these clinic websites to describe the effectiveness of cannabis used for medical purposes. ObjectiveWe aimed to identify the indications for medical cannabis reported by cannabis clinics in Ontario, Canada, and the evidence these clinics cited to support cannabis prescription. MethodsWe conducted a cross-sectional web search to identify all cannabis clinic websites within Ontario, Canada, that had physician involvement and identified their primary purpose as cannabis prescription. Two reviewers independently searched these websites to identify all medical indications for which cannabis was promoted and reviewed and critically appraised all studies cited using the Oxford Centre for Evidence-Based Medicine Levels of Evidence rubric. ResultsA total of 29 clinics were identified, promoting cannabis for 20 different medical indications including migraines, insomnia, and fibromyalgia. There were 235 unique studies cited on these websites to support the effectiveness of cannabis for these indications. A high proportion (36/235, 15.3%) of the studies were identified to be at the lowest level of evidence (level 5). Only 4 clinic websites included any mention of harms associated with cannabis. ConclusionsCannabis clinic websites generally promote cannabis use as medically effective but cite low-quality evidence to support these claims and rarely discuss harms. The recommendation of cannabis as a general therapeutic for many indications unsupported by high-quality evidence is potentially misleading for medical practitioners and patients. This disparity should be carefully evaluated in context of the specific medical indication and an individualized patient risk assessment. Our work illustrates the need to increase the quality of research performed on the medical effects of cannabis.

Abstract Background Preliminary evidence suggests that individuals living in lower income neighbourhoods are at higher risk of COVID-19 infection. The relationship between sociodemographic characteristics and COVID-19 risk warrants further study. Methods We explored the association between COVID-19 test positivity and patients’ socio-demographic variables, using neighborhood sociodemographic data collected retrospectively from two COVID-19 Assessment Centres in Toronto, ON. Results Eighty-three thousand four hundred forty three COVID-19 tests completed between April 5–September 30, 2020, were analyzed. Individuals living in neighbourhoods with the lowest income or highest concentration of immigrants were 3.4 (95% CI: 2.7 to 4.9) and 2.5 (95% CI: 1.8 to 3.7) times more likely to test positive for COVID-19 than those in highest income or lowest immigrant neighbourhoods, respectively. Testing was higher among individuals from higher income neighbourhoods, at lowest COVID-19 risk, compared with those from low-income neighbourhoods. Conclusions Targeted efforts are needed to improve testing availability in high-risk regions. These same strategies may also ensure equitable COVID-19 vaccine delivery.

Psychedelics, including ketamine, 3,4-Methyl enedioxy methamphetamine (MDMA), and psilocybin, have gained attention for their potential therapeutic role in mental health treatment. While recreational use is prohibited in Canada, medicinal exemptions can be granted. There are several psychedelic clinics in Ontario, Canada, promoting the use of psychedelics for a variety of medical indications. Our objective was to identify the indications for which psychedelics are being prescribed in Ontario clinics and assess the quality of evidence used to support these claims. Internet searches were conducted using Google and Bing to identify psychedelic clinics in Ontario. Inclusion criteria was as follow: clinics were physically located in Ontario, had a functioning website link, and demonstrated involvement of a licensed physician or nurse practitioner. Identified clinics were evaluated for their claims of effectiveness, the quality of evidence used to support these claims, and statements on psychedelic-related harms. The cited studies were appraised for quality using Oxford Centre for Evidence-Based Medicine Levels of Evidence, "level 5" being the lowest quality and "level 1" being the highest quality. Out of 200 search results, 10 psychedelic clinic websites met our inclusion criteria. These clinics advertised psychedelics for 47 medical conditions, most commonly for depression. Only 2 out of 10 clinics described potential risks associated with psychedelic use. There were 29 studies cited by these websites, majority coming from "level 4" evidence consisting of case-series and case-control studies. Overall, the cited evidence quality was low to moderate. Psychedelic clinics in Ontario promote a wide range of medical indications for psychedelics using primarily low to moderate "level 4" evidence. There is limited information shared on the potential adverse effects of psychedelics. Our study emphasizes the importance of using transparent and high-quality evidence by clinics and clinicians to ensure safe and effective use of psychedelics in mental health treatments. [ABSTRACT FROM AUTHOR]

Spectral congestion necessitates the in-band operation or the spectrum-sharing of legacy radar and communication systems. Since these systems operate in the same band in spectrum-sharing mode, they interfere with one another. To address this problem from the radar’s perspective, this paper considers the coherent detection of target-reflected radar signals in the presence of interference from an in-band cyclostationary digital modulated wireless communication signal. Three different cases of target-reflected radar signals, namely, deterministic signals, signals with random phase, and completely random signals, are considered in this paper. The optimum detection rules are derived for these three cases and the corresponding receiver structures for the equalization of the interfering signal are presented. Sub-optimum detection structures are also derived with the assumption that the in-band interference is a white stationary time-invariant Gaussian process. Further, considering the equalization, modified CFAR receiver structures are also presented. By considering the mathematical models for cyclostationary or periodic in-band interference, the performances of the optimum, sub-optimum detectors, and modified CFAR detectors are quantified analytically in terms of detection probability and false alarm probability, and the resulting receiver operating characteristic (ROC) curves are analyzed as a function of the signal-to-interference ratio. It is demonstrated that improper equalization of the interfering signal significantly affects the performance of the optimum detector and this impact is analyzed in detail. As spectrum-sharing becomes more prevalent due to spectrum congestion, the proposed optimal, sub-optimal, and modified CFAR detection rules and receiver structures can be incorporated into existing systems with substantial savings.