This study explores the impact of heatwaves on emergency calls for assistance resulting in service attendance in the Australian state of Queensland for the period from January 1, 2010 through December 31, 2019. The study uses data from the Queensland Ambulance Service (QAS), a state-wide prehospital health system for emergency health care.

A retrospective case series using de-identified data from QAS explored spatial and demographic characteristics of patients attended by ambulance and the reason for attendance. All individuals for which there was an emergency call to “000” that resulted in ambulance attendance in Queensland across the ten years were captured. Demand for ambulance services during heatwave and non-heatwave periods were compared. Incidence rate ratio (IRR) and 95% confidence intervals (CI) were constructed exploring ambulance usage patterns during heatwaves and by rurality, climate zone, age groups, sex, and reasons for attendance.

Compared with non-heatwave days, ambulance attendance across Queensland increased by 9.3% during heatwave days. The impact of heatwaves on ambulance demand differed by climate zone (high humidity summer with warm winter; hot dry summer with warm winter; warm humid summer with mild winter). Attendances related to heat exposure, dehydration, alcohol/drug use, and sepsis increased substantially during heatwaves.

Heatwaves are a driver of increased ambulance demand in Queensland. The data raise questions about climatic conditions and heat tolerance, and how future cascading and compounding heat disasters may influence work practices and demands on the ambulance service. Understanding the implications of heatwaves in the prehospital setting is important to inform community, service, and system preparedness.

In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety.

A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites.

We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites.

The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is a complex, yet treatable autoimmune disorder characterized by a fairly abrupt onset of a constellation of symptoms attributable to diffuse brain dysfunction (Tarantino et al., 2021). Despite the potential for a severe disease course, most patients have a favorable outcome with substantial recovery (Dalmau et al., 2011; Titulaer et al., 2013). Nevertheless, there is limited literature discussing the long-term outcomes in patients with anti-NMDARE, particularly in pediatric patients. The primary objective of this study is to examine and describe behavioral, emotional, adaptive, and executive functioning outcomes in pediatric and young adult patients with this disease. This study also sought to provide information on the perceived health-related quality of life (HRQoL) of patients and their parents and investigate the impact of anti-NMDARE on parents and family functioning.

All individuals known to have been diagnosed and treated for anti-NMDARE at The Children’s Hospital of Philadelphia (CHOP) between January 1, 2005, and October 1, 2020, were contacted with both patients and their parents/guardians invited to participate. Eighteen pediatric patients between the ages of 6 and 26 and/or their parents/caregivers participated in the study. Of the 18 patients represented in the sample, 50% were white/Caucasian, and 67% were female. The mean duration of time since symptom onset was 7.1 years. Primary outcomes were measured through standardized questionnaires of emotional, behavioral, and adaptive functioning (BASC-3) and executive functioning (BRIEF2 or BRIEF-A). Secondary outcomes related to family functioning and HRQoL were measured through (PedsQL™ and PedsQL™ Family Impact Module.)

All aggregate T-scores for the BASC and BRIEF placed children with anti-NMDARE within an age-appropriate range regarding behavioral, emotional, adaptive, and executive functioning outcomes. Children with anti-NMDARE were not found to have lower HRQoL compared to their healthy same-age peers. Moreover, parents of children with anti-NMDARE did not endorse a prolonged impact of this illness on family functioning and adjustment.

This study aimed to better understand the neurobehavioral profile and the long-term outcomes of children diagnosed with anti-NMDARE, with the ultimate goal of advancing understanding of this encephalitis. Consistent with findings from several reviewed studies on long-term follow-up, the present study suggests that most children with a history of anti-NMDARE show good functional recovery over time. However, data on the neurobehavioral sequelae, quality of life, and adaptive behavior in patients diagnosed with anti-NMDARE are still sparse, especially at pediatric age. In order to understand and learn to manage the needs of patients with anti-NMDARE, particularly regarding the impact this disease can have on daily life and school performance, additional neuropsychological research involving larger samples, longitudinal studies, and increased methodological consistency is required.

Machine learning studies of PTSD show promise for identifying neurobiological signatures of this disorder, but studies to date have largely excluded Black American women, who experience disproportionately greater trauma and have relatively higher rates of PTSD. PTSD is characterized by four symptom clusters: trauma reexperiencing, trauma avoidance, hyperarousal, and anhedonia. A prior machine learning study reported successful PTSD symptom cluster severity prediction using functional MRI data but did not examine white matter predictors. White matter microstructural integrity has been related to PTSD presence and symptoms, and unexplored metrics such as estimates of tract shape may provide unique predictive utility. Therefore, this study examines the relationship between white matter tract shape and PTSD symptom cluster severity amongst trauma-exposed Black American women using multiple machine learning models.

Participants included 45 Black American women with PTSD (Mage=40.4(12.9)) and 89 trauma-exposed controls (Mage=39.8(11.6)). Shape and diffusion metrics for the cingulum, corpus callosum, fornix, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus were calculated using deterministic tractography. Current symptom severity was calculated using the PTSD Symptom Scales. Input features included tract metrics, questionnaire responses, and age. The following regression models were generated: least absolute shrinkage and selection operator (LASSO), ridge, elastic net, and gaussian process (GPR). Additionally, two forms of latent-scale GPR, one without (lsGPR) and with (sp-lsGPR) node selection via spike and slab priors, were calculated. The performance of regression models was estimated using mean square error (MSE) and R2.

sp-lsGPR performed at or above other models across all symptom clusters. LASSO models were comparable to sp-lsGPR for avoidance and hyperarousal clusters. Ridge regression and GPR had the weakest performance across clusters. Scores for sp-lsGPR by cluster are as follows: reexperiencing Mmse =0.70(0.17), Mr2=0.56(0.13); avoidance Mmse =0.75(0.17), Mr2= 0.51(0.13); hyperarousal Mmse =0.57(0.18), Mr2=0.66(0.12); anhedonia Mmse =0.74(0.27), Mr2=0.57(0.13). The top three ranked posterior inclusion probabilities for white matter tracts across sp-lsGPR models include four sections of the cingulum, three sections of the corpus callosum, the right fornix, the left inferior longitudinal fasciculus, the first segment of the right superior longitudinal fasciculus, and the right uncincate fasciculus. The greatest posterior inclusion probability value for the sp-lsGPR models was the left frontal parahippocampal cingulum for the hyperarousal cluster.

Results support the combined predictive utility of white matter metrics for brain imaging regression models of PTSD. Results also support the use of sp-lsGPR models, which are designed to balance interpretable linear models and highly-flexible non-linear models. The sp-lsGPR model performance was similar across clusters but was relatively better for the hyperarousal cluster. This finding contrasts with prior machine learning work using functional data which was unable to predict hyperarousal scores above chance (MR2=0.06). These diverging findings highlight the importance of examining both functional and structural data in PTSD populations. Differing findings may also be related to sample characteristics as the prior study was conducted in China. Black American women and Chinese individuals have unique lived experiences that may differentially impact brain structure and function. Future work should continue to include diverse research samples to account for such experiences.

Little is known about when youth may be at greatest risk for attempting suicide, which is critically important information for the parents, caregivers, and professionals who care for youth at risk. This study used adolescent and parent reports, and a case-crossover, within-subject design to identify 24-hour warning signs (WS) for suicide attempts.

Adolescents (N = 1094, ages 13 to 18) with one or more suicide risk factors were enrolled and invited to complete bi-weekly, 8–10 item text message surveys for 18 months. Adolescents who reported a suicide attempt (survey item) were invited to participate in an interview regarding their thoughts, feelings/emotions, and behaviors/events during the 24-hours prior to their attempt (case period) and a prior 24-hour period (control period). Their parents participated in an interview regarding the adolescents’ behaviors/events during these same periods. Adolescent or adolescent and parent interviews were completed for 105 adolescents (81.9% female; 66.7% White, 19.0% Black, 14.3% other).

Both parent and adolescent reports of suicidal communications and withdrawal from social and other activities differentiated case and control periods. Adolescent reports also identified feelings (self-hate, emotional pain, rush of feelings, lower levels of rage toward others), cognitions (suicidal rumination, perceived burdensomeness, anger/hostility), and serious conflict with parents as WS in multi-variable models.

This study identified 24-hour WS in the domains of cognitions, feelings, and behaviors/events, providing an evidence base for the dissemination of information about signs of proximal risk for adolescent suicide attempts.