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Trapped ions (TIs) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The quantum charge-coupled device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift toward more efficacious designs, this article introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities, such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.

Background The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)–third quartile (Q3) = 94–108%) of typical usage to 10% (Q1–Q3 = 6–15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859–0.896]) and a decrease in the effective reproduction number (β = −0.058; 95% CI: [−0.068 to −0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of “big data” from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.

ABSTRACTTools to advance antimicrobial stewardship in the primary health care setting, where most antimicrobials are prescribed, are urgently needed. The aim of this study was to evaluate OPEN Stewarship (Online Platform for Expanding aNtibiotic Stewardship), an automated feedback intervention, among a cohort of primary care physicians. We performed a controlled, interrupted time-series study of 32 intervention and 725 control participants, consisting of primary care physicians from Ontario, Canada and Southern Israel, from October 2020 to December 2021. Intervention participants received three personalized feedback reports targeting several aspects of antibiotic prescribing. Study outcomes (overall prescribing rate, prescribing rate for viral respiratory conditions, prescribing rate for acute sinusitis, and mean duration of therapy) were evaluated using multilevel regression models. We observed a decrease in the mean duration of antibiotic therapy (IRR = 0.94; 95% CI: 0.90, 0.99) in intervention participants during the intervention period. We did not observe a significant decline in overall antibiotic prescribing (OR = 1.01; 95% CI: 0.94, 1.07), prescribing for viral respiratory conditions (OR = 0.87; 95% CI: 0.73, 1.03), or prescribing for acute sinusitis (OR = 0.85; 95% CI: 0.67, 1.07). In this antimicrobial stewardship intervention among primary care physicians, we observed shorter durations of therapy per antibiotic prescription during the intervention period. The COVID-19 pandemic may have hampered recruitment; a dramatic reduction in antibiotic prescribing rates in the months before our intervention may have made physicians less amenable to further reductions in prescribing, limiting the generalizability of the estimates obtained.IMPORTANCEAntibiotic overprescribing contributes to antibiotic resistance, a major threat to our ability to treat infections. We developed the OPEN Stewardship (Online Platform for Expanding aNtibiotic Stewardship) platform to provide automated feedback on antibiotic prescribing in primary care, where most antibiotics for human use are prescribed but where the resources to improve antibiotic prescribing are limited. We evaluated the platform among a cohort of primary care physicians from Ontario, Canada and Southern Israel from October 2020 to December 2021. The results showed that physicians who received personalized feedback reports prescribed shorter courses of antibiotics compared to controls, although they did not write fewer antibiotic prescriptions. While the COVID-19 pandemic presented logistical and analytical challenges, our study suggests that our intervention meaningfully improved an important aspect of antibiotic prescribing. The OPEN Stewardship platform stands as an automated, scalable intervention for improving antibiotic prescribing in primary care, where needs are diverse and technical capacity is limited.

The escalating threat of antimicrobial resistance (AMR) necessitates impactful, reproducible, and scalable antimicrobial stewardship strategies. This review addresses the critical need to enhance the quality of antimicrobial stewardship intervention research. We propose five considerations for authors planning and evaluating antimicrobial stewardship initiatives. Antimicrobial stewards should consider the following mnemonic ABCDE: (A) plan Ahead using implementation science; (B) Be clear and thoroughly describe the intervention by using the TidIER checklist; (C) Use a Checklist to comprehensively report study components; (D) Select a study Design carefully; and (E) Assess Effectiveness and implementation by selecting meaningful outcomes. Incorporating these recommendations will help strengthen the evidence base of antimicrobial stewardship literature and support optimal implementation of strategies to mitigate AMR.

Background: Pertussis is a reportable disease in many countries, but ascertainment bias has limited data accuracy. This study aims to validate pertussis data measures using a reference standard that incorporates different suspected case severities, allowing for the impact of case severity on accuracy and detection to be explored. Methods: We evaluated 25 pertussis detection algorithms in a primary care electronic medical record database between January 1, 1986 and December 30, 2016. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We used sensitivity analyses to explore areas of uncertainty and evaluated reasons for lack of detection. Results: The algorithm including all data measures achieved the highest sensitivity at 20.6%. Sensitivity increased to 100% after reclassifying symptom-only cases as non-cases, but the PPV remained low. Age at first episode was significantly associated with detection in half of the tested scenarios, and false negatives often had some history of immunization. Conclusions: Sensitivity improved by reclassifying symptom-only cases but remained low unless multiple data sources were used. Results demonstrate a trade-off between PPV and sensitivity. EMRs can enhance detection through patient history and clinical note data. It is essential to improve case identification of older individuals with vaccination history to reduce ascertainment bias.

Summary: Background: COVID-19 and antimicrobial resistance (AMR) are two intersecting public health crises. Antimicrobial overuse in patients with COVID-19 threatens to worsen AMR. Guidelines are fundamental in encouraging antimicrobial stewardship. We sought to assess the quality of antibiotic prescribing guidelines and recommendations in the context of COVID-19, and whether they incorporate principles of antimicrobial stewardship. Methods: We performed a systematic survey which included a search using the concepts “antibiotic/antimicrobial” up to November 15, 2022 of the eCOVID-19 living map of recommendations (RecMap) which aggregates guidelines across a range of international sources and all languages. Guidelines providing explicit recommendations regarding antibacterial use in COVID-19 were eligible for inclusion. Guideline and recommendation quality were assessed using the AGREE II and AGREE-REX instruments, respectively. We extracted guideline characteristics including panel representation and the presence or absence of explicit statements related to antimicrobial stewardship (i.e., judicious antibiotic use, antimicrobial resistance or adverse effects as a consequence of antibiotic use). We used logistic regression to evaluate the relationship between guideline characteristics including quality and incorporation of antimicrobial stewardship principles. Protocol registration (OSF): https://osf.io/4pgtc. Findings: Twenty-eight guidelines with 63 antibiotic prescribing recommendations were included. Recommendations focused on antibiotic initiation (n = 52, 83%) and less commonly antibiotic selection (n = 13, 21%), and duration of therapy (n = 15, 24%). Guideline and recommendation quality varied widely. Twenty (71%) guidelines incorporated at least one concept relating to antimicrobial stewardship. Including infectious diseases expertise on the guideline panel (OR 9.44, 97.5% CI: 1.09–81.59) and AGREE-REX score (OR 3.26, 97.5% CI: 1.14–9.31 per 10% increase in overall score) were associated with a higher odds of guidelines addressing antimicrobial stewardship. Interpretation: There is an opportunity to improve antibiotic prescribing guidelines in terms of both quality and incorporation of antimicrobial stewardship principles. These findings can help guideline developers better address antibiotic stewardship in future recommendations beyond COVID-19. Funding: This project was funded by Michael G. DeGroote Cochrane Canada and McMaster GRADE centres.

Abstract We estimated the effectiveness of booster doses of monovalent mRNA COVID-19 vaccines against Omicron-associated severe outcomes among adults in Ontario, Canada. We used a test-negative design to estimate vaccine effectiveness (VE) against hospitalization or death among SARS-CoV-2-tested adults aged ≥50 years from January 2 to October 1, 2022, stratified by age and time since vaccination. We also compared VE during BA.1/BA.2 and BA.4/BA.5 sublineage predominance. We included 11,160 cases and 62,880 tests for test-negative controls. Depending on the age group, compared to unvaccinated adults, VE was 91–98% 7–59 days after a third dose, waned to 76–87% after ≥240 days, was restored to 92–97% 7–59 days after a fourth dose, and waned to 86–89% after ≥120 days. VE was lower and declined faster during BA.4/BA.5 versus BA.1/BA.2 predominance, particularly after ≥120 days. Here we show that booster doses of monovalent mRNA COVID-19 vaccines restored strong protection against severe outcomes for at least 3 months after vaccination. Across the entire study period, protection declined slightly over time, but waned more during BA.4/BA.5 predominance.

Summary: Background: Global estimates suggest millions of deaths annually are associated with antimicrobial resistance (AMR) but these are generated from scarce data on the relative risk of death attributable to drug-resistant versus drug-sensitive infections. Methods: We examined all episodes of E. coli bloodstream infection in Ontario, Canada between 2017 and 2020, and measured 90 day mortality among those with resistant versus sensitive isolates for each of 8 commonly used antibiotic classes and a category of difficult to treat resistance (DTTR). We used multivariable logistic regression to calculate an adjusted odds of mortality associated with AMR, after accounting for patient demographics, comorbidities, and prior healthcare exposure. Findings: Among 14,548 eligible episodes of E. coli bloodstream infection, resistance was most common to aminopenicillins (46.8%), followed by first generation cephalosporins (38.8%), fluoroquinolones (26.5%), sulfonamides (24.1%), third generation cephalosporins (13.8%), aminoglycosides (11.7%), beta-lactam-beta-lactamase-inhibitors (9.1%) and carbapenems (0.2%). Only 18 (0.1%) episodes exhibited DTTR. For each antibiotic class, the unadjusted odds of mortality (OR) were higher among resistant isolates, but after accounting for patient characteristics the adjusted odds (aOR) of mortality were attenuated: aminopenicillins (OR 1.22, 95% CI 1.12–1.33; aOR 1.09, 95% CI 0.99–1.20), first generation cephalosporins (OR 1.24, 95% CI 1.14–1.35; aOR 1.07, 95% CI 0.97–1.18), third generation cephalosporins (OR 1.64, 95% CI 1.47–1.82; aOR 1.29, 95% CI 1.15–1.46), beta-lactam-beta-lactamase-inhibitors (OR 1.69, 95% CI 1.52–1.89, aOR 1.28, 95% CI 1.13–1.45), carbapenems (OR 3.11, 95% CI 1.52–6.34; aOR 2.06, 95% CI 0.91–4.66), sulfonamides (OR 1.19, 95% CI 1.07–1.31, aOR 1.06, 95% CI 0.95–1.18), fluoroquinolones (OR 1.49, 95% CI 1.36–1.64, aOR 1.16, 95% CI 1.05–1.29), aminoglycosides (OR 1.43, 95% CI 1.27–1.62; aOR 1.27, 95% CI 1.11–1.46), and DTTR (OR 3.71, 95% CI 1.46–9.41; aOR 2.58, 95% CI 0.87–7.66). Interpretation: AMR is associated with substantial increased mortality among patients with E. coli bloodstream infection, particularly for resistance to classes commonly used as empiric treatment. Surveillance for AMR-associated mortality should incorporate adjustment for patient characteristics and prior healthcare utilization. Funding: This work was supported by a project grant from CIHR (grant number 159503). This study was also supported by ICES, which is funded by an annual grant from Ontario Ministry of Health and Long-Term Care (MOHLTC).

Abstract 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.

Objectives We aimed to estimate associations between COVID-19 incidence and mortality with neighbourhood-level immigration, race, housing, and socio-economic characteristics. Methods We conducted a population-based study of 28,808 COVID-19 cases in the provincial reportable infectious disease surveillance systems (Public Health Case and Contact Management System) which includes all known COVID-19 infections and deaths from Ontario, Canada reported between January 23, 2020 and July 28, 2020. Residents of congregate settings, Indigenous communities living on reserves or small neighbourhoods with populations Results Neighbourhoods with the highest proportion of immigrants (relative risk (RR): 4.0, 95%CI:3.5–4.5) and visible minority residents (RR: 3.3, 95%CI:2.9–3.7) showed the strongest association with COVID-19 incidence in adjusted models. Among individual race groups, COVID-19 incidence was highest among neighbourhoods with the high proportions of Black (RR: 2.4, 95%CI:2.2–2.6), South Asian (RR: 1.9, 95%CI:1.8–2.1), Latin American (RR: 1.8, 95%CI:1.6–2.0) and Middle Eastern (RR: 1.2, 95%CI:1.1–1.3) residents. Neighbourhoods with the highest average household size (RR: 1.9, 95%CI:1.7–2.1), proportion of multigenerational families (RR: 1.8, 95%CI:1.7–2.0) and unsuitably crowded housing (RR: 2.1, 95%CI:2.0–2.3) were associated with COVID-19 incidence. Neighbourhoods with the highest proportion of residents with less than high school education (RR: 1.6, 95%CI:1.4–1.8), low income (RR: 1.4, 95%CI:1.2–1.5) and unaffordable housing (RR: 1.6, 95%CI:1.4–1.8) were associated with COVID-19 incidence. Similar inequities were observed across neighbourhood-level sociodemographic characteristics and COVID-19 mortality. Conclusions Neighbourhood-level inequities in COVID-19 incidence and mortality were observed in Ontario, with excess burden experienced in neighbourhoods with a higher proportion of immigrants, racialized populations, large households and low socio-economic status.

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of response measures. Symptom onset date is commonly used to date incident cases on the epidemic curve in public health reports and dashboards; however, third-party trackers date cases by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, 2020, the epidemic curve based on public reporting date for Ontario, Canada showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as the symptom onset date. Delays between the onset of symptoms and the detection of a case by the public health authority mean that recent days will always have incomplete case data, creating a downward bias. Public reporting date is not subject to this bias and can be used to visualize real-time epidemic curves meant to inform the public and decision makers.

scPipeline is a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports.