In the coronavirus disease 2019 (COVID-19) pandemic, child and adolescent psychiatry wards face the risk of severe acute respiratory coronavirus 2 (SARS-CoV-2) introduction and spread within the facility. In this setting, mask and vaccine mandates are hard to enforce, especially for younger children. Surveillance testing may detect infection early and enable mitigation measures to prevent viral spread. We conducted a modeling study to determine the optimal method and frequency of surveillance testing and to analyze the effect of weekly team meetings on transmission dynamics.

Simulation with an agent-based model reflecting ward structure, work processes, and contact networks from a real-world child and adolescent psychiatry clinic with 4 wards, 40 patients, and 72 healthcare workers.

We simulated the spread of 2 SARS-CoV-2 variants over 60 days under surveillance testing with polymerase chain reaction (PCR) tests and rapid antigen tests in different scenarios. We measured the size, peak, and the duration of an outbreak. We compared medians and percentage of spillover events to other wards from 1,000 simulations for each setting.

The outbreak size, peak, and duration were dependent on test frequency, test type, SARS-CoV-2 variant, and ward connectivity. Under surveillance conditions, joint staff meetings and therapists shared between wards did not significantly change median outbreak size under surveillance conditions. With daily antigen testing, outbreaks were mostly confined to 1 ward and median outbreak sizes were lower than with twice-weekly PCR testing (1 vs 22; P < .001).

Modeling can help to understand transmission patterns and guide local infection control measures.

To estimate the incidence, duration and risk factors for diagnostic delays associated with pertussis.

We used longitudinal retrospective insurance claims from the Marketscan Commercial Claims and Encounters, Medicare Supplemental (2001–2020), and Multi-State Medicaid (2014–2018) databases.

Inpatient, emergency department, and outpatient visits.

The study included patients diagnosed with pertussis (International Classification of Diseases [ICD] codes) and receipt of macrolide antibiotic treatment.

We estimated the number of visits with pertussis-related symptoms before diagnosis beyond that expected in the absence of diagnostic delays. Using a bootstrapping approach, we estimated the number of visits representing a delay, the number of missed diagnostic opportunities per patient, and the duration of delays. Results were stratified by age groups. We also used a logistic regression model to evaluate potential factors associated with delay.

We identified 20,828 patients meeting inclusion criteria. On average, patients had almost 2 missed opportunities prior to diagnosis, and delay duration was 12 days. Across age groups, the percentage of patients experiencing a delay ranged from 29.7% to 37.6%. The duration of delays increased considerably with age from an average of 5.6 days for patients aged <2 years to 13.8 days for patients aged ≥18 years. Factors associated with increased risk of delays included emergency department visits, telehealth visits, and recent prescriptions for antibiotics not effective against pertussis.

Diagnostic delays for pertussis are frequent. More work is needed to decrease diagnostic delays, especially among adults. Earlier case identification may play an important role in the response to outbreaks by facilitating treatment, isolation, and improved contact tracing.

In response to the 2014–2016 West Africa Ebola virus disease (EVD) epidemic, the Centers for Disease Control and Prevention (CDC) designated 56 US hospitals as Ebola treatment centers (ETCs) with high-level isolation capabilities. We sought to determine the ongoing sustainability of ETCs and to identify how ETC capabilities have affected hospital, local, and regional coronavirus disease 2019 (COVID-19) readiness and response.

An electronic survey included both qualitative and quantitative questions and was structured into 2 sections: operational sustainability and role in the COVID-19 response.

The survey was distributed to site representatives from the 56 originally designated ETCs, and 37 (66%) responded.

Data were coded and analyzed using descriptive statistics.

Of the 37 responding ETCs, 33 (89%) reported that they were still operating, and 4 had decommissioned. ETCs that maintain high-level isolation capabilities incurred a mean of $234,367 in expenses per year. All but 1 ETC reported that existing capabilities (eg, trained staff, infrastructure) before COVID-19 positively affected their hospital, local, and regional COVID-19 readiness and response (eg, ETC trained staff, donated supplies, and shared developed protocols).

Existing high-level isolation capabilities and expertise developed following the 2014–2016 EVD epidemic were leveraged by ETCs to assist hospital-wide readiness for COVID-19 and to support responses by other local and regional hospitals However, ETCs face continued challenges in sustaining those capabilities for high-consequence infectious diseases.

Spinal fusion surgery (SFS) is one of the most common operations in the United States, >450,000 SFSs are performed annually, incurring annual costs >$10 billion.

We used a nationwide longitudinal database to accurately assess incidence and payments associated with management of postoperative infection following SFS.

We conducted a retrospective, observational cohort analysis of 210,019 patients undergoing SFS from 2014 to 2018 using IBM MarketScan commercial and Medicaid–Medicare databases. We assessed rates of superficial/deep incisional SSIs, from 3 to 180 days after surgery using Cox proportional hazard regression models. To evaluate adjusted payments for patients with/without SSIs, adjusted for inflation to 2019 Consumer Price Index, we used generalized linear regression models with log-link and γ distribution.

Overall, 6.6% of patients experienced an SSI, 1.7% superficial SSIs and 4.9% deep-incisional SSIs, with a median of 44 days to presentation for superficial SSIs and 28 days for deep-incisional SSIs. Selective risk factors included surgical approach, admission type, payer, and higher comorbidity score. Postoperative incremental commercial payments for patients with superficial SSI were $20,800 at 6 months, $26,937 at 12 months, and $32,821 at 24 months; incremental payments for patients with deep-incisional SSI were $59,766 at 6 months, $74,875 at 12 months, and $93,741 at 24 months. Corresponding incremental Medicare payments for patients with superficial incisional at 6, 12, 24-months were $11,044, $17,967, and $24,096; while payments for patients with deep-infection were: $48,662, $53,757, and $73,803 at 6, 12, 24-months.

We identified a 4.9% rate of deep infection following SFS, with substantial payer burden. The findings suggest that the implementation of robust evidence-based surgical-care bundles to mitigate postoperative SFS infection is warranted.

To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on the incidence of central-line–associated bloodstream infections (CLABSIs), Clostridioides difficile infections (CDIs), and methicillin-resistant Staphyloccocus aureus (MRSA) bloodstream infections (BSIs) in California acute-care hospitals.

Retrospective cohort and before-and-after study.

We compared standardized infection ratios (SIRs) for CLABSI, CDI, and MRSA BSI from the second half of 2020 to the second half of 2019. We performed interrupted time-series (ITS) analyses for these infections to assess departures from long-term trends. We also examined the association between the proportion of facility beds that were occupied by COVID-19 patients in May and June of 2020 and the incidence of infections using negative binomial models. In addition, we compared standardized antimicrobial administration ratios (SAARs) for the second halves of 2019 and 2020.

We detected substantial and significant increases in the SIRs for CLABSI and MRSA BSI from 2019 to 2020. For the ITS analysis, CLABSI and had significant positive values for the pandemic onset level-change parameters, and CLABSI and MRSA BSI had significant positive values for the postinterruption slope-change parameters. We also detected a positive association between facility COVID-19 patient occupancy and CLABSI and MRSA BSI incidence. We did not detect associations with the onset of the pandemic or COVID-19 patient occupancy and CDI. The SAAR for all antibacterial drugs decreased slightly, but the SAAR for drugs with a high risk for CDI increased slightly.

This study adds to a body of literature documenting increases in CLABSI and MRSA BSI incidence during the pandemic.

From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation.

We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach.

We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3–1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2–1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1–1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5–1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1–1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms.

We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.

Hospital readmission is unsettling to patients and caregivers, costly to the healthcare system, and may leave patients at additional risk for hospital-acquired infections and other complications. We evaluated the association between comorbidities present during index coronavirus disease 2019 (COVID-19) hospitalization and the risk of 30-day readmission.

We used the Premier Healthcare database to perform a retrospective cohort study of COVID-19 hospitalized patients discharged between April 2020 and March 2021 who were followed for 30 days after discharge to capture readmission to the same hospital.

Among the 331,136 unique patients in the index cohort, 36,827 (11.1%) had at least 1 all-cause readmission within 30 days. Of the readmitted patients, 11,382 (3.4%) were readmitted with COVID-19 as the primary diagnosis. In the multivariable model adjusted for demographics, hospital characteristics, coexisting comorbidities, and COVID-19 severity, each additional comorbidity category was associated with an 18% increase in the odds of all-cause readmission (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.17–1.19) and a 10% increase in the odds of readmission with COVID-19 as the primary readmission diagnosis (aOR, 1.10; 95% CI, 1.09–1.11). Lymphoma (aOR, 1.86; 95% CI, 1.58–2.19), renal failure (aOR, 1.32; 95% CI, 1.25–1.40), and chronic lung disease (aOR, 1.29; 95% CI, 1.24–1.34) were most associated with readmission for COVID-19.

Readmission within 30 days was common among COVID-19 survivors. A better understanding of comorbidities associated with readmission will aid hospital care teams in improving postdischarge care. Additionally, it will assist hospital epidemiologists and quality administrators in planning resources, allocating staff, and managing bed-flow issues to improve patient care and safety.

Mycobacterium lentiflavum is a slow-growing nontuberculous mycobacterium that is widely distributed in soil and water systems, but it is sometimes pathogenic to humans. Although cases of M. lentiflavum infections are rare, 22 isolates of M. lentiflavum were identified at a single hospital in Japan. We suspected a nosocomial outbreak; thus, we conducted transmission pattern and genotype analyses.

Cases of M. lentiflavum isolated at Kushiro City General Hospital in Japan between May 2020 and April 2021 were analyzed. The patient samples and environmental culture specimens underwent whole-genome sequencing (WGS). Additionally, we retrospectively collected clinical data from patient medical records.

Altogether, 22 isolates of M. lentiflavum were identified from sputum and bronchoalveolar lavage samples. Clinically, the instances with M. lentiflavum isolates were considered contaminants. In the WGS analysis, 19 specimens, including 18 patient samples and 1 environmental culture from the hospital’s faucet, showed genetic similarity. The frequency of M. lentiflavum isolation decreased after we prohibited the use of taps where M. lentiflavum was isolated.

WGS analysis identified that the cause of M. lentiflavum pseudo-outbreak was the water used for patient examinations, including bronchoscopy.

We sought to determine incidence of common hospital-acquired bacteria among coronavirus disease 2019 (COVID-19) patients in Israeli general hospitals during the first year of the pandemic.

We analyzed routinely collected incidence data to determine hospital acquisition of the following sentinel bacteria: Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, Enterococcus faecalis, Enterococcus faecium, Pseudomonas aeruginosa, Acinetobacter baumannii, and Clostridioides difficile. We examined 3 acquisition measures: (1) sentinel bacteria, (2) sentinel bacteremia, and (3) antimicrobial-resistant sentinel bacteremia. The study period was March 1, 2020, through January 31, 2021.

Analysis of pooled data from the 26 hospitals surveyed revealed that rates were higher for all 3 acquisition measures among COVID-19 patients than they were among patients on general medical wards in 2019, but lower than those among patients in intensive care units in 2019. The incidence rate was highest during the first COVID-19 wave, despite a lower proportion of severe COVID-19 cases among total hospitalized during this wave. Wide variation in incidence was evident between hospitals.

Hospitalized COVID-19 patients experienced nosocomial bacterial infection at rates higher than those of patients on pre-pandemic general medical wards, adding to the complexity of their care. Lower rates of nosocomial infection after the first wave, despite higher proportions of severely ill patients, suggest that healthcare worker practices, rather than patient-related factors, were responsible for most of these infections.

The burden of healthcare-associated infections (HAIs) is higher in low- and middle-income countries, but HAIs are often missed because surveillance is not conducted. Here, we describe the identification of and response to a cluster of Burkholderia cepacia complex (BCC) bloodstream infections (BSIs) associated with high mortality in a surgical ICU (SICU) that joined an HAI surveillance network.

A 780-bed, tertiary-level, public teaching hospital in northern India.

After detecting a cluster of BCC in the SICU, cases were identified by reviewing laboratory registers and automated identification and susceptibility testing outputs. Sociodemographic details, clinical records, and potential exposure histories were collected, and a self-appraisal of infection prevention and control (IPC) practices using assessment tools from the World Health Organization and the US Centers for Disease Control and Prevention was conducted. Training and feedback were provided to hospital staff. Environmental samples were collected from high-touch surfaces, intravenous medications, saline, and mouthwash.

Between October 2017 and October 2018, 183 BCC BSI cases were identified. Case records were available for 121 case patients. Of these 121 cases, 91 (75%) were male, the median age was 35 years, and 57 (47%) died. IPC scores were low in the areas of technical guidelines, human resources, and monitoring and evaluation. Of the 30 environmental samples, 4 grew BCC. A single source of the outbreak was not identified.

Implementing standardized HAI surveillance in a low-resource setting detected an ongoing Burkholderia cepacia outbreak. The outbreak investigation and use of a multimodal approach reduced incident cases and informed changes in IPC practices.

Many studies have described nosocomial outbreaks of influenza in specialized wards. We evaluated nosocomial transmission of influenza in a pediatric general ward.

Retrospective observational study.

Single secondary hospital.

The study included 814 hospitalized children with influenza between September 2015 and August 2020.

The medical records of the included children were retrospectively reviewed, and clinical characteristics of children with community-acquired (CA) influenza and hospital-acquired (HA) influenza were determined. The room of each included child during hospitalization was traced to identify the children exposed to them.

CA influenza and HA influenza were diagnosed in 789 (96.9%) and 25 (3.1%) children, respectively. Among children with CA influenza, 691 (87.6%) were isolated or place in a cohort on admission. In total, 98 children (12.4%) admitted to multibed rooms exposed 307 children with noninfluenza diseases to influenza during 772 patient days; 3 exposed children (1.0%) were diagnosed with HA influenza. Including these 3 children, 25 children (19 without definite in-hospital exposure to influenza and 3 exposed to other children with HA influenza) were diagnosed with HA influenza, and 11 (44.0%) exposed 31 children with noninfluenza diseases to influenza for 85 patient days. Also, 3 exposed children (9.7%) were diagnosed with HA influenza, a significantly higher rate than that for CA influenza (P = .005). The clinical characteristics were comparable between children with HA influenza and those with CA influenza.

Cohort placement of children with influenza in a pediatric general ward can be effective in controlling nosocomial transmission of influenza. However, control measures for children with HA influenza should be emphasized.

To assess the impact of nationwide outpatient antimicrobial stewardship interventions in the form of financial incentives for providers and provider education when antimicrobials are deemed unnecessary for uncomplicated respiratory infections and acute diarrhea.

We collected data from a large claims database from April 2013 through March 2020 and performed a quasi-experimental, interrupted time-series analysis. The outcome of interest was oral antimicrobial prescription rate defined as the number of monthly antimicrobial prescriptions divided by the number of outpatient visits each month. We examined the effects of financial incentive to providers (ie, targeted prescriptions for those aged ≤2 years) and provider education (ie, targeted prescriptions for those aged ≥6 years) on the overall antimicrobial prescription rates and how these interventions affected different age groups before and after their implementation.

In total, 21,647,080 oral antimicrobials were prescribed to 2,920,381 unique outpatients during the study period. At baseline, prescription rates for all age groups followed a downward trend throughout the study period. Immediately after the financial incentive implementation, substantial reductions in prescription rates were observed among only those aged 0–2 years (−47.5 prescriptions per 1,000 clinic visits each month; 95% confidence interval, −77.3 to −17.6; P = .003), whereas provider education immediately reduced prescription rates in all age groups uniformly. These interventions did not affect the long-term trend for any age group.

These results suggest that the nationwide implementation of financial incentives and provider education had an immediate effect on the antimicrobial prescription but no long-term effect.

To examine the perspectives of caregivers that are not part of the antibiotic stewardship program (ASP) leadership team (eg, physicians, nurses, and clinical pharmacists), but who interact with ASPs in their role as frontline healthcare workers.

Qualitative semistructured interviews.

The study was conducted in 2 large national healthcare systems including 7 hospitals in the Veterans’ Health Administration and 4 hospitals in Intermountain Healthcare.

We interviewed 157 participants. The current analysis includes 123 nonsteward clinicians: 47 physicians, 26 pharmacists, 29 nurses, and 21 hospital leaders.

Interviewers utilized a semistructured interview guide based on the Consolidated Framework for Implementation Research (CFIR), which was tailored to the participant’s role in the hospital as it related to ASPs. Qualitative analysis was conducted using a codebook based on the CFIR.

We identified 4 primary perspectives regarding ASPs. (1) Non-ASP pharmacists considered antibiotic stewardship activities to be a high priority despite the added burden to work duties: (2) Nurses acknowledged limited understanding of ASP activities or involvement with these programs; (3) Physicians criticized ASPs for their restrictions on clinical autonomy and questioned the ability of antibiotic stewards to make recommendations without the full clinical picture; And (4) hospital leaders expressed support for ASPs and recognized the unique challenges faced by non-ASP clinical staff.

Further understanding these differing perspectives of ASP implementation will inform possible ways to improve ASP implementation across clinical roles.