Variability in changes in physician outpatient antibiotic prescribing from 2019 to 2021 during the COVID-19 pandemic in Ontario, Canada.

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.

Biosecurity and water, sanitation, and hygiene (WASH) interventions in animal agricultural settings for reducing infection burden, antibiotic use, and antibiotic resistance: a One Health systematic review.

Prevention and control of infections across the One Health spectrum is essential for improving antibiotic use and addressing the emergence and spread of antibiotic resistance. Evidence for how best to manage these risks in agricultural communities-45% of households globally-has not been systematically assembled. This systematic review identifies and summarises evidence from on-farm biosecurity and water, sanitation, and hygiene (WASH) interventions with the potential to directly or indirectly reduce infections and antibiotic resistance in animal agricultural settings. We searched 17 scientific databases (including Web of Science, PubMed, and regional databases) and grey literature from database inception to Dec 31, 2019 for articles that assessed biosecurity or WASH interventions measuring our outcomes of interest; namely, infection burden, microbial loads, antibiotic use, and antibiotic resistance in animals, humans, or the environment. Risk of bias was assessed with the Systematic Review Centre for Laboratory Animal Experimentation tool, Risk of Bias in Non-Randomized Studies of Interventions, and the Appraisal tool for Cross-Sectional Studies, although no studies were excluded as a result. Due to the heterogeneity of interventions found, we conducted a narrative synthesis. The protocol was pre-registered with PROSPERO (CRD42020162345). Of the 20 672 publications screened, 104 were included in this systematic review. 64 studies were conducted in high-income countries, 24 studies in upper-middle-income countries, 13 studies in lower-middle-income countries, two in low-income countries, and one included both upper-middle-income countries and lower-middle-income countries. 48 interventions focused on livestock (mainly pigs), 43 poultry (mainly chickens), one on livestock and poultry, and 12 on aquaculture farms. 68 of 104 interventions took place on intensive farms, 22 in experimental settings, and ten in smallholder or subsistence farms. Positive outcomes were reported for ten of 23 water studies, 17 of 35 hygiene studies, 15 of 24 sanitation studies, all three air-quality studies, and 11 of 17 other biosecurity-related interventions. In total, 18 of 26 studies reported reduced infection or diseases, 37 of 71 studies reported reduced microbial loads, four of five studies reported reduced antibiotic use, and seven of 20 studies reported reduced antibiotic resistance. Overall, risk of bias was high in 28 of 57 studies with positive interventions and 17 of 30 studies with negative or neutral interventions. Farm-management interventions successfully reduced antibiotic use by up to 57%. Manure-oriented interventions reduced antibiotic resistance genes or antibiotic-resistant bacteria in animal waste by up to 99%. This systematic review highlights the challenges of preventing and controlling infections and antimicrobial resistance, even in well resourced agricultural settings. Most of the evidence emerges from studies that focus on the farm itself, rather than targeting agricultural communities or the broader social, economic, and policy environment that could affect their outcomes. WASH and biosecurity interventions could complement each other when addressing antimicrobial resistance in the human, animal, and environmental interface.

Staphylococcus aureus bacteremia mortality across country income groups: A secondary analysis of a systematic review.

OBJECTIVES: Staphylococcus aureus bacteremia (SAB) is a common infection worldwide. We compared SAB mortality in low- and middle-income countries (LMIC) versus high-income countries (HIC) in a meta-analysis. METHODS: We searched MEDLINE, Embase, and Cochrane Database of Systematic Reviews from 1991-2021 and included observational, single-country studies on patients with positive blood cultures for S. aureus. The main outcome was the proportion of patients with SAB who died in the hospital. A generalized linear mixed random-effects model was used to pool estimates, and a meta-regression was used to adjust for study-level characteristics. RESULTS: A total of 332 studies involving 517,671 patients in 39 countries were included. No study was conducted in a low-income country. Only 33 (10%) studies were performed in middle-income countries (MIC), which described 6,216 patients. The pooled in-hospital mortality was 32.4% (95% confidence interval [CI] 27.2%-38.2%, T2 = 0.3063) in MIC and 22.3% (95% CI 20.1%-24.6%, T2 = 0.3257) in HIC. In a meta-regression model, MIC had higher in-hospital mortality (adjusted odds ratio 1.37, 95% CI 1.11-1.71; P = 0.0042) than HIC. CONCLUSION: In SAB studies, LMIC are poorly represented. In-hospital mortality was significantly higher in MIC than in HIC. Research should be conducted in LMIC to characterize differences in care processes driving the mortality gap.

What Is the Optimal Follow-up Length for Mortality in Staphylococcus aureus Bacteremia? Observations From a Systematic Review of Attributable Mortality.

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.

How Generalizable Are Randomized Controlled Trials (RCTs) in Staphylococcus aureus Bacteremia? A Description of the Mortality Gap Between RCTs and Observational Studies.

In Staphylococcus aureus bacteremia, mortality rates in randomized controlled trials (RCTs) are consistently lower than observational studies. Stringent eligibility criteria and omission of early deaths in RCTs contribute to this mortality gap. Clinicians should acknowledge the possibility of a lower treatment effect when applying RCT results to bedside care.

Staphylococcus aureus bacteraemia mortality: a systematic review and meta-analysis.

BACKGROUND: Precise estimates of mortality in Staphylococcus aureus bacteraemia (SAB) are important to convey prognosis and guide the design of interventional studies. OBJECTIVES: We performed a systematic review and meta-analysis to estimate all-cause mortality in SAB and explore mortality change over time. DATA SOURCES: The MEDLINE and Embase databases, as well as the Cochrane Database of Systematic Reviews, were searched from January 1, 1991 to May 7, 2021. STUDY ELIGIBILITY CRITERIA: Human observational studies on patients with S. aureus bloodstream infection were included. PARTICIPANTS: The study analyzed data of patients with a positive blood culture for S. aureus. METHODS: Two independent reviewers extracted study data and assessed risk of bias using the Newcastle-Ottawa Scale. A generalized, linear, mixed random effects model was used to pool estimates. RESULTS: A total of 341 studies were included, describing a total of 536,791 patients. From 2011 onward, the estimated mortality was 10.4% (95% CI, 9.0%-12.1%) at 7 days, 13.3% (95% CI, 11.1%-15.8%) at 2 weeks, 18.1% (95% CI, 16.3%-20.0%) at 1 month, 27.0% (95% CI, 21.5%-33.3%) at 3 months, and 30.2% (95% CI, 22.4%-39.3%) at 1 year. In a meta-regression model of 1-month mortality, methicillin-resistant S. aureus had a higher mortality rate (adjusted OR (aOR): 1.04; 95% CI, 1.02-1.06 per 10% increase in methicillin-resistant S. aureus proportion). Compared with prior to 2001, more recent time periods had a lower mortality rate (aOR: 0.88; 95% CI, 0.75-1.03 for 2001-2010; aOR: 0.82; 95% CI, 0.69-0.97 for 2011 onward). CONCLUSIONS: SAB mortality has decreased over the last 3 decades. However, more than one in four patients will die within 3 months, and continuous improvement in care remains necessary.

Confidence interval of risk difference by different statistical methods and its impact on the study conclusion in antibiotic non-inferiority trials.

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.

Intention-to-treat analysis may be more conservative than per protocol analysis in antibiotic non-inferiority trials: a systematic review.

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 .

Making a COVID-19 vaccine that works for everyone: ensuring equity and inclusivity in clinical trials.

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.

Methodological and Reporting Quality of Noninferiority Randomized Controlled Trials Comparing Antibiotic Therapies: A Systematic Review.

BACKGROUND: Antibiotic noninferiority randomized controlled trials (RCTs) are used for approval of new antibiotics and making changes to antibiotic prescribing in clinical practice. We conducted a systematic review to assess the methodological and reporting quality of antibiotic noninferiority RCTs. METHODS: We searched MEDLINE, Embase, the Cochrane Database of Systematic Reviews, and the Food and Drug Administration drug database from inception until November 22, 2019, for noninferiority RCTs comparing different systemic antibiotic therapies. Comparisons between antibiotic types, doses, administration routes, or durations were included. Methodological and reporting quality indicators were based on the Consolidated Standards of Reporting Trials reporting guidelines. Two independent reviewers extracted the data. RESULTS: The systematic review included 227 studies. Of these, 135 (59.5%) studies were supported by pharmaceutical industry. Only 83 (36.6%) studies provided a justification for the noninferiority margin. Reporting of both intention-to-treat (ITT) and per-protocol (PP) analyses were done in 165 (72.7%) studies. The conclusion was misleading in 34 (15.0%) studies. The studies funded by pharmaceutical industry were less likely to be stopped early because of logistical reasons (3.0% vs 19.1%; odds ratio [OR] = 0.13; 95% confidence interval [CI], .04-.37) and to show inconclusive results (11.1% vs 42.9%; OR = 0.17; 95% CI, .08-.33). The quality of studies decreased over time with respect to blinding, early stopping, reporting of ITT with PP analysis, and having misleading conclusions. CONCLUSIONS: There is room for improvement in the methodology and reporting of antibiotic noninferiority trials. Quality can be improved across the entire spectrum from investigators, funding agencies, as well as during the peer-review process.There is room for improvement in the methodology and reporting of antibiotic noninferiority trials including justification of noninferiority margin, reporting of intention-to-treat analysis with per-protocol analysis, and having conclusions that are concordant with study results.Clinical Trials Registration PROSPERO registration number CRD42020165040.

Novel Antibiotics May Be Noninferior but Are They Becoming Less Effective?: a Systematic Review.

Novel antibiotics approved by noninferiority trials may become less effective over time in two scenarios: (i) the treatment effect in studies of novel antibiotics may be consistently worse than studies of older antibiotics; (ii) when a decreasingly effective control arm is used in a series of noninferiority trials. Our systematic review of 175 noninferiority antibiotic trials found these scenarios to be rare.