To model cognitive reserve (CR) longitudinally in a neurodiverse pediatric sample using a residual index approach, and to test the criterion and construct validity of this index.

Participants were N = 115 children aged 9.5–13 years at baseline (MAge = 10.48 years, SDAge = 0.61), and n = 43 (37.4%) met criteria for ADHD. The CR index represented variance in Matrix Reasoning scores from the WASI that was unexplained by MRI-based brain variables (bilateral hippocampal volumes, total gray matter volumes, and total white matter hypointensity volumes) or demographics (age and sex).

At baseline, the CR index predicted math computation ability (estimate = 0.50, SE = 0.07, p < .001), and word reading ability (estimate = 0.26, SE = 0.10, p = .012). Longitudinally, change in CR over time was not associated with change in math computation ability (estimate = −0.02, SE = 0.03, p < .513), but did predict change in word reading ability (estimate = 0.10, SE = 0.03, p < .001). Change in CR was also found to moderate the relationship between change in word reading ability and white matter hypointensity volume (estimate = 0.10, SE = 0.05, p = .045).

Evidence for the criterion validity of this CR index is encouraging, but somewhat mixed, while construct validity was evidenced through interaction between CR, brain, and word reading ability. Future research would benefit from optimization of the CR index through careful selection of brain variables for a pediatric sample.

The National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC) lacks a rigorous enrollment audit process, unlike other collaborative networks. Most centers require individual families to consent to participate. It is unknown whether there is variation across centers or biases in enrollment.

We used the Pediatric Cardiac Critical Care Consortium (PC4) registry to assess enrollment rates in NPC-QIC for those centers participating in both registries using indirect identifiers (date of birth, date of admission, gender, and center) to match patient records. All infants born 1/1/2018–12/31/2020 and admitted 30 days of life were eligible. In PC4, all infants with a fundamental diagnosis of hypoplastic left heart or variant or who underwent a surgical or hybrid Norwood or variant were eligible. Standard descriptive statistics were used to describe the cohort and center match rates were plotted on a funnel chart.

Of 898 eligible NPC-QIC patients, 841 were linked to 1,114 eligible PC4 patients (match rate 75.5%) in 32 centers. Match rates were lower in patients of Hispanic/Latino ethnicity (66.1%, p = 0.005), and those with any specified chromosomal abnormality (57.4%, p = 0.002), noncardiac abnormality (67.8%, p = 0.005), or any specified syndrome (66.5%, p = 0.001). Match rates were lower for patients who transferred to another hospital or died prior to discharge. Match rates varied from 0 to 100% across centers.

It is feasible to match patients between the NPC-QIC and PC4 registries. Variation in match rates suggests opportunities for improvement in NPC-QIC patient enrollment.

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.

While cannabis use is a well-established risk factor for psychosis, little is known about any association between reasons for first using cannabis (RFUC) and later patterns of use and risk of psychosis.

We used data from 11 sites of the multicentre European Gene-Environment Interaction (EU-GEI) case–control study. 558 first-episode psychosis patients (FEPp) and 567 population controls who had used cannabis and reported their RFUC.

We ran logistic regressions to examine whether RFUC were associated with first-episode psychosis (FEP) case–control status. Path analysis then examined the relationship between RFUC, subsequent patterns of cannabis use, and case–control status.

Controls (86.1%) and FEPp (75.63%) were most likely to report ‘because of friends’ as their most common RFUC. However, 20.1% of FEPp compared to 5.8% of controls reported: ‘to feel better’ as their RFUC (χ2 = 50.97; p < 0.001). RFUC ‘to feel better’ was associated with being a FEPp (OR 1.74; 95% CI 1.03–2.95) while RFUC ‘with friends’ was associated with being a control (OR 0.56; 95% CI 0.37–0.83). The path model indicated an association between RFUC ‘to feel better’ with heavy cannabis use and with FEPp-control status.

Both FEPp and controls usually started using cannabis with their friends, but more patients than controls had begun to use ‘to feel better’. People who reported their reason for first using cannabis to ‘feel better’ were more likely to progress to heavy use and develop a psychotic disorder than those reporting ‘because of friends’.

In a climate where natural disasters are becoming progressively more frequent and severe, there is a greater need for healthcare resilience. Hospital pharmacists are important healthcare responders during disasters, but little is known about how prepared pharmacists are to fill roles in disasters or how prepared pharmacy departments are to support their response. The aim of this study was to determine the disaster preparedness of pharmacists and pharmacy departments in a Metropolitan Health Service in Australia and investigate any relationship between the two.

This research utilized two surveys to determine the individual preparedness of registered pharmacists within the eligible hospitals and the preparedness of pharmacy departments (this information was obtained through the Directors of Pharmacy).

In total, 68 individual pharmacists participated in the study. It was found that individuals were moderately prepared (preparedness score 19.98). Interventions, such as education, improved individual preparedness scores, though these had poor uptake, where only 17.4% (n=12/68) of participants had received disaster education or training. Individual preparedness was unaffected by facility preparedness and provision of comprehensive resources.

The preparedness of hospital pharmacy departments was generally low, where two hospitals were rated as ‘somewhat prepared’, due to the presence of a mostly comprehensive plan and a moderate engagement in activities that contributed to preparedness. The third hospital was ‘poorly prepared’, as it did not have a disaster plan and had low engagement in preparedness activities.

This study shows that a substantial improvement in pharmacy preparedness is required to achieve healthcare resilience and quality patient outcomes in disaster aftermath–further reinforcing the need for national and pharmacy-specific guidance, complemented by standardized preparedness interventions such as education and training. There is also a glaring disconnect between the preparedness of pharmacy facilities and their workforce, which demonstrates a culture of disaster preparedness.

Cognitive symptoms are associated with return to work, healthcare use and quality of life after mild traumatic brain injury (mTBI). Additionally, while overall ‘post-concussion’ symptoms are often present at similar levels in mTBI and control groups, cognitive complaints may be specifically elevated in mTBI. A systematic review and meta-analysis was conducted to investigate the frequency and extent of cognitive complaints following adult civilian mTBI, and compare it to the frequency and extent of complaints in control populations (PROSPERO: CRD42020151284).

This review included studies published up to March 2022. Thirteen studies were included in the systematic review, and six were included in the meta-analysis. Data extraction and quality assessment were conducted by two independent reviewers.

Cognitive complaints are common after mTBI, although reported rates differed greatly across studies. Results suggested that mTBI groups report cognitive complaints to a significantly greater extent than control groups (Hedges’ g = 0.85, 95% CI 0.31–1.40, p = .0102). Heterogeneity between studies was high (τ2 = 0.20, 95% CI 0.04–1.58; I2 = 75.0%, 95% CI 43.4%–89.0%). Between-group differences in symptom reporting were most often found when healthy rather than injured controls were employed.

Cognitive complaints are consistently reported after mTBI, and are present at greater levels in mTBI patients than in controls. Despite the importance of these complaints, including in regards to return to work, healthcare use and quality of life, there has been limited research in this area, and heterogeneity in research methodology is common.

To evaluate the pattern of blood-culture utilization among a cohort of 6 hospitals to identify potential opportunities for diagnostic stewardship.

We completed a retrospective analysis of blood-culture utilization during adult inpatient or emergency department (ED) encounters in 6 hospitals from May 2019 to April 2020. We investigated 2 measures of blood-culture utilization rates (BCURs): the total number of blood cultures, defined as a unique accession number per 1,000 patient days (BCX) and a new metric of blood-culture events per 1,000 patient days to account for paired culture practices. We defined a blood-culture event as an initial blood culture and all subsequent samples for culture drawn within 12 hours for patients with an inpatient or ED encounter. Cultures were evaluated by unit type, positivity and contamination rates, and other markers evaluating the quality of blood-culture collection.

In total, 111,520 blood cultures, 52,550 blood culture events, 165,456 inpatient admissions, and 568,928 patient days were analyzed. Overall, the mean BCUR was 196 blood cultures per 1,000 patient days, with 92 blood culture events per 1,000 patient days (range, 64–155 among hospitals). Furthermore, 7% of blood-culture events were single culture events, 55% began in the ED, and 77% occurred in the first 3 hospital days. Among all blood cultures, 7.7% grew a likely pathogen, 2.1% were contaminated, and 5.9% of first blood cultures were collected after the initiation of antibiotics.

Blood-culture utilization varied by hospital and was heavily influenced by ED culture volumes. Hospital comparisons of blood-culture metrics can assist in identifying opportunities to optimize blood-culture collection practices.

The typical 5-day work week affects healthcare outcomes. Structured work hours have also been implicated in antimicrobial prescribing choice. We developed a visualization tool to aid in evaluating breadth of antibiotic use in various time (day of week and hour of day) and space (patient location) combinations.

We evaluated antibiotic administration data from a tertiary-care academic medical center between July 1, 2018, and July 1, 2020. We calculated a cumulative empiric antibiotic spectrum score by adapting a previously validated antibiotic spectrum index (ASI) and applying that score to empiric antibiotic use. We visualized these data as a heat map based on various day-of-week–time combinations and then compared the distribution of scores between weekday nights, weekend days, and weekend nights to the typical workweek hours (weekday days, weekday days) using the Mann-Whitney U nonparametric test with a Bonferroni correction.

The analysis included 76,535 antibiotic starts across 53,900 unique patient admissions over 2 years. The mean cumulative ASI was higher in all 3 night and weekend combinations (weekday nights, 7.3; weekend days, 7.6; weekend nights, 7.5) compared to the weekday daytime hours (weekday days, 7.1) and the distribution of scores was different in all groups compared to the weekday daytime reference. The cumulative ASI was also higher in intensive care units.

Empiric antibiotic prescribing patterns differed across space and time; broader antibiotic choices occurred in the intensive care units and on nights and weekends. Visualization of these patterns aids in antimicrobial prescribing pattern recognition and may assist in finding opportunities for additional antimicrobial stewardship interventions.