To evaluate the impact of a multicenter, try automated dashboard on ASP activities and its acceptance among ASP leaders.

Frontline stewards were asked to participate in semi-structured interviews before and after implementation of a web-based ASP information dashboard providing risk-adjusted benchmarking, longitudinal trends, and analysis of antimicrobial usage patterns at each facility.

The study was performed at Iowa City VA Health Care System.

ASP team members from nine medical centers in the VA Midwest Health Care Network (VISN 23).

Semi-structured interviews were conducted pre- and post-implementation, with interview guides informed by clinical experiences and the Consolidated Framework for Implementation Research (CFIR). Participants evaluated the dashboard’s ease of use, applicability to ongoing ASP activities, perceived validity and reliability, and relative advantage over other ASP monitoring systems.

Compared to established stewardship data collection and reporting methods, participants found the dashboard more intuitive and accessible, allowing them to reduce dependence on other systems and staff to obtain and share data. Standardized and risk-adjusted rankings were largely accepted as a valuable benchmarking method; however, participants felt their facility’s characteristics significantly influenced the rankings’ validity. Participants recognized staffing, training, and uncertainty with using the dashboard as an intervention tool as barriers to consistent and comprehensive dashboard implementation.

Participants generally accepted the dashboard’s risk-adjusted metrics and appreciated its usability. While creating automated tools to rigorously benchmark antimicrobial use across hospitals can be helpful, the displayed metrics require further validation, and the longitudinal utility of the dashboard warrants additional study.

To compare the long-term vaccine effectiveness between those receiving viral vector [Oxford-AstraZeneca (ChAdOx1)] or inactivated viral (CoronaVac) primary series (2 doses) and those who received an mRNA booster (Pfizer/BioNTech) (the third dose) among healthcare workers (HCWs).

We conducted a retrospective cohort study among HCWs (aged ≥18 years) in Brazil from January 2021 to July 2022. To assess the variation in the effectiveness of booster dose over time, we estimated the effectiveness rate by taking the log risk ratio as a function of time.

Of 14,532 HCWs, coronavirus disease 2019 (COVID-19) was confirmed in 56.3% of HCWs receiving 2 doses of CoronaVac vaccine versus 23.2% of HCWs receiving 2 doses of CoronaVac vaccine with mRNA booster (P < .001), and 37.1% of HCWs receiving 2 doses of ChAdOx1 vaccine versus 22.7% among HCWs receiving 2 doses of ChAdOx1 vaccine with mRNA booster (P < .001). The highest vaccine effectiveness with mRNA booster was observed 30 days after vaccination: 91% for the CoronaVac vaccine group and 97% for the ChAdOx1 vaccine group. Vacine effectiveness declined to 55% and 67%, respectively, at 180 days. Of 430 samples screened for mutations, 49.5% were SARS-CoV-2 delta variants and 34.2% were SARS-CoV-2 omicron variants.

Heterologous COVID-19 vaccines were effective for up to 180 days in preventing COVID-19 in the SARS-CoV-2 delta and omicron variant eras, which suggests the need for a second booster.

To determine risk factors for the development of long coronavirus disease 2019 (COVID-19) in healthcare personnel (HCP).

We conducted a case–control study among HCP who had confirmed symptomatic COVID-19 working in a Brazilian healthcare system between March 1, 2020, and July 15, 2022. Cases were defined as those having long COVID according to the Centers for Disease Control and Prevention definition. Controls were defined as HCP who had documented COVID-19 but did not develop long COVID. Multiple logistic regression was used to assess the association between exposure variables and long COVID during 180 days of follow-up.

Of 7,051 HCP diagnosed with COVID-19, 1,933 (27.4%) who developed long COVID were compared to 5,118 (72.6%) who did not. The majority of those with long COVID (51.8%) had 3 or more symptoms. Factors associated with the development of long COVID were female sex (OR, 1.21; 95% CI, 1.05–1.39), age (OR, 1.01; 95% CI, 1.00–1.02), and 2 or more SARS-CoV-2 infections (OR, 1.27; 95% CI, 1.07–1.50). Those infected with the SARS-CoV-2 δ (delta) variant (OR, 0.30; 95% CI, 0.17–0.50) or the SARS-CoV-2 o (omicron) variant (OR, 0.49; 95% CI, 0.30–0.78), and those receiving 4 COVID-19 vaccine doses prior to infection (OR, 0.05; 95% CI, 0.01–0.19) were significantly less likely to develop long COVID.

Long COVID can be prevalent among HCP. Acquiring >1 SARS-CoV-2 infection was a major risk factor for long COVID, while maintenance of immunity via vaccination was highly protective.

Although multiple studies have revealed that coronavirus disease 2019 (COVID-19) vaccines can reduce COVID-19–related outcomes, little is known about their impact on post–COVID-19 conditions. We performed a systematic literature review and meta-analysis on the effectiveness of COVID-19 vaccination against post–COVID-19 conditions (ie, long COVID).

We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 1, 2019, to April 27, 2022, for studies evaluating COVID-19 vaccine effectiveness against post–COVID-19 conditions among individuals who received at least 1 dose of Pfizer/BioNTech, Moderna, AstraZeneca, or Janssen vaccine. A post–COVID-19 condition was defined as any symptom that was present 3 or more weeks after having COVID-19. Editorials, commentaries, reviews, study protocols, and studies in the pediatric population were excluded. We calculated the pooled diagnostic odds ratios (DORs) for post–COVID-19 conditions between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% × (1 − DOR).

In total, 10 studies with 1,600,830 individuals evaluated the effect of vaccination on post–COVID-19 conditions, of which 6 studies were included in the meta-analysis. The pooled DOR for post–COVID-19 conditions among individuals vaccinated with at least 1 dose was 0.708 (95% confidence interval (CI), 0.692–0.725) with an estimated vaccine effectiveness of 29.2% (95% CI, 27.5%–30.8%). The vaccine effectiveness was 35.3% (95% CI, 32.3%–38.1%) among those who received the COVID-19 vaccine before having COVID-19, and 27.4% (95% CI, 25.4%–29.3%) among those who received it after having COVID-19.

COVID-19 vaccination both before and after having COVID-19 significantly decreased post–COVID-19 conditions for the circulating variants during the study period although vaccine effectiveness was low.

We sought to determine whether an electronic hand hygiene (HH) system could monitor HH compliance at similar rates to direct human observation.

This 4-year proof-of-concept study was conducted in an intensive care unit (ICU) of a private tertiary-care hospital in São Paulo, Brazil, where electronic HH systems were installed in 2 rooms. HH compliance was reported respectively using direct observation and electronic counter devices with an infrared system for detecting HH opportunities.

In phase 1, HH compliance by human observers was 56.3% (564 of 1,001 opportunities), while HH compliance detected by the electronic observer was 51.0% (515 of 1,010 opportunities). In phase 2, human observers registered 484 HH opportunities with a HH compliance rate of 64.7% (313 of 484) versus 70.6% (346 of 490) simultaneously detected by the electronic system. In addition, an enhanced HH electronic system monitored activity 24 hours per day and HH compliance without the presence of a human observer was 40.3% (10,642 of 26,421 opportunities), providing evidence for the Hawthorne effect.

The electronic HH monitoring system had good correlation with human HH observation, but compliance was remarkably lower when human observers were not present due to the Hawthorne effect (25%–30% absolute difference). Electronic monitoring systems can replace direct observation and can markedly reduce the Hawthorne effect.

Most hand hygiene (HH) intervention studies use a quasi-experimental design, are primarily uncontrolled before-and-after studies, or are controlled before-and-after studies with a nonequivalent control group. Well-funded studies with improved designs and HH interventions are needed.

To evaluate healthcare worker (HCW) HH compliance with alcohol-based hand rub (ABHR) through direct observation (human observer), 2 electronic technologies, a radio frequency identification (RFID) badge system, and an invasive device sensor.

In our controlled experimental study, 2,269 observations were made over a 6-month period from July 1 to December 30, 2020, in a 4-bed intensive care unit. We compared HH compliance between a basic feedback loop system with RFID badges and an enhanced feedback loop system that utilized sensors on invasive devices.

Real-time feedback by wireless technology connected to a patient’s invasive device (enhanced feedback loop) resulted in a significant increase in HH compliance (69.5% in the enhanced group vs 59.1% in the basic group; P = .0001).

An enhanced feedback loop system connected to invasive devices, providing real-time alerts to HCWs, is effective in improving HH compliance.

We investigated real-world vaccine effectiveness for Oxford-AstraZeneca (ChAdOx1) and CoronaVac against laboratory-confirmed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among healthcare workers (HCWs).

We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil between January 1, 2021 and August 3, 2021, to assess vaccine effectiveness. We calculated vaccine effectiveness as 1 − rate ratio (RR), with RR determined by adjusting Poisson models with the occurrence of SARS-CoV-2 infection as the outcome and the vaccination status as the main variable. We used the logarithmic link function and simple models adjusting for sex, age, and job types.

In total, 13,813 HCWs met the inclusion criteria for this analysis. Among them, 6,385 (46.2%) received the CoronaVac vaccine, 5,916 (42.8%) received the ChAdOx1 vaccine, and 1,512 (11.0%) were not vaccinated. Overall, COVID-19 occurred in 6% of unvaccinated HCWs, 3% of HCWs who received 2 doses of CoronaVac vaccine, and 0.7% of HCWs who received 2 doses of ChAdOx1 vaccine (P < .001). In the adjusted analyses, the estimated vaccine effectiveness rates were 51.3% for CoronaVac, and 88.1% for ChAdOx1 vaccine. Both vaccines reduced the number of hospitalizations, the length of hospital stay, and the need for mechanical ventilation. In addition, 19 SARS-CoV-2 samples from 19 HCWs were screened for mutations of interest. Of 19 samples, 18 were the γ (gamma) variant.

Although both COVID-19 vaccines (viral vector and inactivated virus) can significantly prevent COVID-19 among HCWs, CoronaVac was much less effective. The COVID-19 vaccines were also effective against the dominant γ variant.

Although multiple studies revealed high vaccine effectiveness of coronavirus disease 2019 (COVID-19) vaccines within 3 months after the completion of vaccines, long-term vaccine effectiveness has not been well established, especially after the δ (delta) variant became prominent. We performed a systematic literature review and meta-analysis of long-term vaccine effectiveness.

We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 2019 to November 15, 2021, for studies evaluating the long-term vaccine effectiveness against laboratory-confirmed COVID-19 or COVID-19 hospitalization among individuals who received 2 doses of Pfizer/BioNTech, Moderna, or AstraZeneca vaccines, or 1 dose of the Janssen vaccine. Long-term was defined as >5 months after the last dose. We calculated the pooled diagnostic odds ratio (DOR) with 95% confidence interval for COVID-19 between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% × (1 − DOR).

In total, 16 studies including 17,939,172 individuals evaluated long-term vaccine effectiveness and were included in the meta-analysis. The pooled DOR for COVID-19 was 0.158 (95% CI: 0.157-0.160) with an estimated vaccine effectiveness of 84.2% (95% CI, 84.0- 84.3%). Estimated vaccine effectiveness against COVID-19 hospitalization was 88.7% (95% CI, 55.8%–97.1%). Vaccine effectiveness against COVID-19 during the δ variant period was 61.2% (95% CI, 59.0%–63.3%).

COVID-19 vaccines are effective in preventing COVID-19 and COVID-19 hospitalization across a long-term period for the circulating variants during the study period. More observational studies are needed to evaluate the vaccine effectiveness of third dose of a COVID-19 vaccine, the vaccine effectiveness of mixing COVID-19 vaccines, COVID-19 breakthrough infection, and vaccine effectiveness against newly emerging variants.

To identify drugs that were administered off label to hospitalized patients with suspected coronavirus disease 2019 (COVID-19) and to identify adverse drug reactions (ADRs) and drug–drug interactions associated with these therapies.

This case–control study was conducted in a Brazilian hospital from March to April 2020 among patients with suspected COVID-19, comparing those with positive severe acute respiratory coronavirus virus 2 (SARS-CoV-2) reverse-transcriptase polymerase chain reaction (RT-PCR) results and those with negative results.

The most commonly used medications in both groups were azithromycin and hydroxychloroquine. There was a significantly higher prevalence of reactions among patients with positive RT-PCR for SARS-CoV-2 (48.5% vs 28.8%; P = .008) in the propensity score–matched cohort, and the most commonly reported ADRs among these patients were diarrhea (43.8%), elevated liver enzymes (31.3%), and nausea and vomiting (29.7%).

Our data demonstrate that ADRs and drug–drug interactions are common with off-label treatments for COVID-19.

Healthcare workers (HCWs) are at risk of COVID-19 due to high levels of SARS-CoV-2 exposure. Thus, effective vaccines are needed. We performed a systematic literature review and meta-analysis on COVID-19 short-term vaccine effectiveness among HCWs.

We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 2019 to June 11, 2021, for studies evaluating vaccine effectiveness against symptomatic COVID-19 among HCWs. To meta-analyze the extracted data, we calculated the pooled diagnostic odds ratio (DOR) for COVID-19 between vaccinated and unvaccinated HCWs. Vaccine effectiveness was estimated as 100% × (1 − DOR). We also performed a stratified analysis for vaccine effectiveness by vaccination status: 1 dose and 2 doses of the vaccine.

We included 13 studies, including 173,742 HCWs evaluated for vaccine effectiveness in the meta-analysis. The vast majority (99.9%) of HCWs were vaccinated with the Pfizer/BioNTech COVID-19 mRNA vaccine. The pooled DOR for symptomatic COVID-19 among vaccinated HCWs was 0.072 (95% confidence interval [CI], 0.028–0.184) with an estimated vaccine effectiveness of 92.8% (95% CI, 81.6%–97.2%). In stratified analyses, the estimated vaccine effectiveness against symptomatic COVID-19 among HCWs who had received 1 dose of vaccine was 82.1% (95% CI, 46.1%–94.1%) and the vaccine effectiveness among HCWs who had received 2 doses was 93.5% (95% CI, 82.5%–97.6%).

The COVID-19 mRNA vaccines are highly effective against symptomatic COVID-19, even with 1 dose. More observational studies are needed to evaluate the vaccine effectiveness of other COVID-19 vaccines, COVID-19 breakthrough after vaccination, and vaccine efficacy against new variants.

To evaluate the frequency of antibiotic prescribing for common infections via telemedicine compared to face-to-face visits.

Systematic literature review and meta-analysis.

We searched PubMed, CINAHL, Embase (Elsevier platform) and Cochrane CENTRAL to identify studies comparing frequency of antibiotic prescribing via telemedicine and face-to-face visits without restrictions by publish dates or language used. We conducted meta-analyses of 5 infections: sinusitis, pharyngitis, otitis media, upper respiratory infection (URI) and urinary tract infection (UTI). Random-effect models were used to obtain pooled odds ratios (ORs). Heterogeneity was evaluated with I2 estimation and the Cochran Q statistic test.

Among 3,106 studies screened, 23 studies (1 randomized control study, 22 observational studies) were included in the systematic literature review. Most of the studies (21 of 23) were conducted in the United States. Studies were substantially heterogenous, but stratified analyses revealed that providers prescribed antibiotics more frequently via telemedicine for otitis media (pooled odds ratio [OR], 1.26; 95% confidence interval [CI], 1.04–1.52; I2 = 31%) and pharyngitis (pooled OR, 1.16; 95% CI, 1.01–1.33; I2 = 0%). We detected no significant difference in the frequencies of antibiotic prescribing for sinusitis (pooled OR, 0.86; 95% CI, 0.70–1.06; I2 = 91%), URI (pooled OR, 1.18; 95% CI, 0.59–2.39; I2 = 100%), or UTI (pooled OR, 2.57; 95% CI, 0.88–7.46; I2 = 91%).

Telemedicine visits for otitis media and pharyngitis were associated with higher rates of antibiotic prescribing. The interpretation of these findings requires caution due to substantial heterogeneity among available studies. Large-scale, well-designed studies with comprehensive assessment of antibiotic prescribing for common outpatient infections comparing telemedicine and face-to-face visits are needed to validate our findings.