Guideline-based tobacco treatment is infrequently offered. Electronic health record-enabled patient-generated health data (PGHD) has the potential to increase patient treatment engagement and satisfaction.

We evaluated outcomes of a strategy to enable PGHD in a medical oncology clinic from July 1, 2021 to December 31, 2022. Among 12,777 patients, 82.1% received a tobacco screener about use and interest in treatment as part of eCheck-in via the patient portal.

We attained a broad reach (82.1%) and moderate response rate (30.9%) for this low-burden PGHD strategy. Patients reporting current smoking (n = 240) expressed interest in smoking cessation medication (47.9%) and counseling (35.8%). As a result of patient requests via PGHD, most tobacco treatment requests by patients were addressed by their providers (40.6–80.3%). Among patients with active smoking, those who received/answered the screener (n = 309 ) were more likely to receive tobacco treatment compared with usual care patients who did not have the patient portal (n = 323) (OR = 2.72, 95% CI = 1.93–3.82, P < 0.0001) using propensity scores to adjust for the effect of age, sex, race, insurance, and comorbidity. Patients who received yet ignored the screener (n = 1024) compared with usual care were also more likely to receive tobacco treatment, but to a lesser extent (OR = 2.20, 95% CI = 1.68–2.86, P < 0.0001). We mapped observed and potential benefits to the Translational Science Benefits Model (TSBM).

PGHD via patient portal appears to be a feasible, acceptable, scalable, and cost-effective approach to promote patient-centered care and tobacco treatment in cancer patients. Importantly, the PGHD approach serves as a real world example of cancer prevention leveraging the TSBM.

Patients discharged from emergency departments (ED) with antibiotics for common infections often receive unnecessarily prolonged durations, representing a target for transition of care (TOC) antimicrobial stewardship intervention.

This study aimed to evaluate the effectiveness of TOC pharmacists’ review on decreasing the duration of discharge oral antibiotics in patients discharged from the ED at an academic medical center. Pharmacist interventions were guided by an antibiotic duration of therapy guidance focused on respiratory, urinary, and skin infections developed and implemented by the antimicrobial stewardship program. Pharmacist interventions from January 27, 2023, to December 29, 2023, were analyzed to quantify the total number of antibiotic days saved and the percentage of provider acceptance.

The ED TOC pharmacists reviewed a total of 157 oral antibiotic prescriptions. 86.6% percent of the reviews required pharmacist interventions. The most common indications for the discharge antibiotics were urinary tract infections (50.0%) and skin infections (23.4%). The total number of antibiotic days saved was 155 days with the provider acceptance rate of 76.5%. In 21% of cases, providers did not count the antibiotic doses administered in the ED, contributing to unnecessarily prolonged duration. 10.2% of patients re-presented to the ED while 6.4% of patients were hospitalized within 30 days of index ED discharge.

The transitions of care pharmacist-led intervention was successful in optimizing the duration of discharge oral antibiotics in the ED utilizing prospective audit and feedback based on institutional guidance. The ED represents a high-yield setting for TOC-directed antimicrobial stewardship.

To analyze antimicrobial prescribing practices in Australian emergency departments (ED), identifying prescribing areas requiring improvement. This aims to inform antimicrobial stewardship (AMS) strategies to enhance antimicrobial prescribing quality.

Retrospective analysis of the Hospital National Antimicrobial Prescribing Survey (NAPS) data set.

EDs in public and private Australian hospitals (n = 652).

Hospitals (n = 652) that participated in the Hospital NAPS from 2013 to 2022.

Data were collected by trained auditors from participating hospitals with the use of a standardized auditing tool, the Hospital NAPS. Data from 2013 to 2022 were analyzed descriptively. Variables assessed included guideline compliance and appropriateness by antimicrobial and indication, and reasons for inappropriateness.

There were 3,098 antimicrobial prescriptions from EDs included for analysis. Guideline compliance (63.5%) and appropriateness (70.4%) in EDs were lower compared to overall prescribing practices from all departments. The most commonly prescribed antimicrobial was ceftriaxone (16.9%, n = 523), and the most common indication was empiric prescribing for community-acquired pneumonia (16.0%, n = 497). Amoxicillin-clavulanic acid (53.2%, n = 99), and acute exacerbation of chronic obstructive pulmonary disease (54.3%, n = 57), were the antimicrobial and indication with the lowest rates of appropriateness respectively. Ceftriaxone prescribing also had a low rate of appropriateness (62.3%, n = 326). Selection of antimicrobials with too broad of a spectrum was the most common reason for inappropriateness (40.2%).

Antimicrobial prescribing quality in EDs warrants improvement. Recommended targets for AMS interventions are the excessive and inappropriate use of broad-spectrum antimicrobials such as ceftriaxone and amoxicillin-clavulanic acid in common respiratory and urinary tract infections.

A significant proportion of people with clozapine-treated schizophrenia develop ‘checking’ compulsions, a phenomenon yet to be understood.

To use habit formation models developed in cognitive neuroscience to investigate the dynamic interplay between psychosis, clozapine dose and obsessive–compulsive symptoms (OCS).

Using the anonymised electronic records of a cohort of clozapine-treated patients, including longitudinal assessments of OCS and psychosis, we performed longitudinal multi-level mediation and multi-level moderation analyses to explore associations of psychosis with obsessiveness and excessive checking. Classic bivariate correlation tests were used to assess clozapine load and checking compulsions. The influence of specific genetic variants was tested in a subsample.

A total of 196 clozapine-treated individuals and 459 face-to-face assessments were included. We found significant OCS to be common (37.9%), with checking being the most prevalent symptom. In mediation models, psychosis severity mediated checking behaviour indirectly by inducing obsessions (r = 0.07, 95% CI 0.04–0.09; P < 0.001). No direct effect of psychosis on checking was identified (r = −0.28, 95% CI −0.09 to 0.03; P = 0.340). After psychosis remission (n = 65), checking compulsions correlated with both clozapine plasma levels (r = 0.35; P = 0.004) and dose (r = 0.38; P = 0.002). None of the glutamatergic and serotonergic genetic variants were found to moderate the effect of psychosis on obsession and compulsion (SLC6A4, SLC1A1 and HTR2C) survived the multiple comparisons correction.

We elucidated different phases of the complex interplay of psychosis and compulsions, which may inform clinicians’ therapeutic decisions.

Conduct secondary analyses on longitudinal data to determine if caregiver-reported sleep quantity and sleep problems across early childhood (ages 2 - 5 years) predict their child’s attention and executive functioning at age 8 years.

This study utilized data from the Health Outcomes and Measures of the Environment (HOME) Study. The HOME Study recruited pregnant women from 20032006 within a nine-county area surrounding Cincinnati, OH. Caregivers reported on their child’s sleep patterns when children were roughly 2, 2.5, 3, 4, and 5 years of age. Our analysis included 410 participants from the HOME Study where caregivers reported sleep measures on at least 1 occasion or their child completed an assessment of attention and executive functioning at age 8. At each time point, caregiver report on an adapted version of the Child Sleep Habits Questionnaire (CSHQ) was used to determine: (1) total sleep time (TST; “your child’s usual amount of sleep each day, combining nighttime sleep and naps”) and (2) overall sleep problems (23 items related to difficulties with sleep onset, sleep maintenance, and nocturnal events). Our outcome variables, collected at age 8, included caregiver-report forms and measures of attention and executive functioning. Caregiver report measures included normed scores on the Behavior Rating Inventory of Executive Function, from which we focused on the Behavior Regulation Index (BRIEF BRI) and Metacognition Index (BRIEF MI). Performance based measures included T-scores for Omission and Commission errors on the Conner’s Continuous Performance Test, Second Edition (CPT-2) and Standard Scores on the WISC-IV; Working Memory Index (WMI). We used longitudinal growth curve models of early childhood sleep patterns to predict attention and executive functioning at age 8. Predictive analyses were run with and without key covariates: annual household income, child sex and race. To account for general intellectual functioning, we also included covariates children’s WISC-IV Verbal Comprehension and Perceptual Reasoning Indexes.

Children in our sample were evenly divided by sex; 60% were White. Sleep problems did not show linear or quadratic change over time, so an intercept-only model was used. Sleep problems did not predict any of our outcome measures at age 8 in unadjusted or covariate-adjusted models. As expected, sleep duration was shorter as children matured, so predictive models examined both intercept and slope. Slope was negatively associated with CPT-2 Commissions (unadjusted p=.047; adjusted p=.013); children who showed the least decline in sleep over time had fewer impulsive errors at age 8. The sleep duration intercept was negatively associated with BRIEF BRI (unadjusted p=.002; adjusted p=.043); children who slept less across early childhood had worse parent-reported behavioral regulation at age 8. Neither sleep duration slope nor intercept significantly predicted any other outcomes at age 8 in unadjusted or covariate-adjusted analyses.

Total sleep time across early childhood predicts behavior regulation difficulties in later childhood. Inadequate sleep during early childhood may be a marker for or contribute to poor development of a child’s self-regulatory skills.

Major depressive disorder (MDD) contributes to suicide risk. Treating MDD effectively is considered a key suicide prevention intervention. Yet many patients with MDD do not respond to their initial medication and require a ‘next-step’. The relationship between next-step treatments and suicidal thoughts and behaviors is uncharted.

The VA Augmentation and Switching Treatments for Depression trial randomized 1522 participants to one of three next-step treatments: Switching to Bupropion, combining with Bupropion, and augmenting with Aripiprazole. In this secondary analysis, features associated with lifetime suicidal ideation (SI) and attempts (SA) at baseline and current SI during treatment were explored.

Compared to those with SI only, those with lifetime SI + SA were more likely to be female, divorced, or separated, unemployed; and to have experienced more childhood adversity. They had a more severe depressive episode and were more likely to respond to ‘next-step’ treatment. The prevalence of SI decreased from 46.5% (694/1492) at baseline to 21.1% (315/1492) at end-of-treatment. SI during treatment was associated with baseline SI; low positive mental health, more anxiety, greater severity and longer duration of current MDD episode; being male and White; and treatment with S-BUP or C-BUP as compared to A-ARI.

SI declines for most patients during next-step medication treatments. But about 1 in 5 experienced emergent or worsening SI during treatment, so vigilance for suicide risk through the entire 12-week acute treatment period is necessary. Treatment selection may affect the risk of SI.

Face masks reduce disease transmission by protecting the wearer from inhaled pathogens and reducing the emission of infectious aerosols. Although methods quantifying efficiency for wearer protection are established, current methods for assessing face mask containment efficiency rely on measurement of a low concentration of aerosols emitted from an infected or noninfected individual.

A small port enabled the introduction of 0.05 µm sodium chloride particles at a constant rate behind the mask worn by a study participant. A condensation particle counter monitored ambient particle numbers 60 cm in front of the participant over 3-minute periods of rest, speaking, and coughing. The containment efficiency (%) for each mask and procedure was calculated as follows: 100 × (1 − average ambient concentration with face covering worn/average ambient concentration with a sham face covering in place). The protection efficiency (%) was also measured using previously published methods. The probability of transmission (%) from infected to uninfected (a function of both the containment efficiency and the protection efficiency) was calculated as follows: {1 − (containment efficiency/100)}×{1 − (protection efficiency/100)}×100.

The average containment efficiencies for each mask over all procedures and repeated measures were 94.6%, 60.9%, 38.8%, and 43.2%, respectively, for the N95 mask, the KN95 mask, the procedure face mask, and the gaiter. The corresponding protection efficiencies for each mask were 99.0%, 63.7%, 45.3%, and 24.2%, respectively. For example, the transmission probability for 1 infected and 1 uninfected individual in close proximity was ∼14.2% for KN95 masks, compared to 36%–39% when only 1 individual wore a KN95 mask.

Overall, we detected a good correlation between the protection and containment that a face covering afforded to a wearer.

Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.

We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.

Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.

WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.