Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Anterior temporal lobectomy is a common surgical approach for medication-resistant temporal lobe epilepsy (TLE). Prior studies have shown inconsistent findings regarding the utility of presurgical intracarotid sodium amobarbital testing (IAT; also known as Wada test) and neuroimaging in predicting postoperative seizure control. In the present study, we evaluated the predictive utility of IAT, as well as structural magnetic resonance imaging (MRI) and positron emission tomography (PET), on long-term (3-years) seizure outcome following surgery for TLE.

Patients consisted of 107 adults (mean age=38.6, SD=12.2; mean education=13.3 years, SD=2.0; female=47.7%; White=100%) with TLE (mean epilepsy duration =23.0 years, SD=15.7; left TLE surgery=50.5%). We examined whether demographic, clinical (side of resection, resection type [selective vs. non-selective], hemisphere of language dominance, epilepsy duration), and presurgical studies (normal vs. abnormal MRI, normal vs. abnormal PET, correctly lateralizing vs. incorrectly lateralizing IAT) were associated with absolute (cross-sectional) seizure outcome (i.e., freedom vs. recurrence) with a series of chi-squared and t-tests. Additionally, we determined whether presurgical evaluations predicted time to seizure recurrence (longitudinal outcome) over a three-year period with univariate Cox regression models, and we compared survival curves with Mantel-Cox (log rank) tests.

Demographic and clinical variables (including type [selective vs. whole lobectomy] and side of resection) were not associated with seizure outcome. No associations were found among the presurgical variables. Presurgical MRI was not associated with cross-sectional (OR=1.5, p=.557, 95% CI=0.4-5.7) or longitudinal (HR=1.2, p=.641, 95% CI=0.4-3.9) seizure outcome. Normal PET scan (OR= 4.8, p=.045, 95% CI=1.0-24.3) and IAT incorrectly lateralizing to seizure focus (OR=3.9, p=.018, 95% CI=1.2-12.9) were associated with higher odds of seizure recurrence. Furthermore, normal PET scan (HR=3.6, p=.028, 95% CI =1.0-13.5) and incorrectly lateralized IAT (HR= 2.8, p=.012, 95% CI=1.2-7.0) were presurgical predictors of earlier seizure recurrence within three years of TLE surgery. Log rank tests indicated that survival functions were significantly different between patients with normal vs. abnormal PET and incorrectly vs. correctly lateralizing IAT such that these had seizure relapse five and seven months earlier on average (respectively).

Presurgical normal PET scan and incorrectly lateralizing IAT were associated with increased risk of post-surgical seizure recurrence and shorter time-to-seizure relapse.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Due to the high number of road traffic accidents with acute injuries and fatalities–particularly in Mass Casualty Incidents (MCI) in low-resource urban sub-Saharan African cities–research was undertaken to create an evidence-based algorithm that could be used to assess and geospatially link EMS needs in Kumasi, Ghana, to trauma resources. Our examination showed that non-MCI fatalities was approximately 2.5%, however, MCI fatalities were found to be 1.8 times higher–at 4.3%, indicating significant opportunities in the planning, preparedness, care, and transport among MCI patient management.

Therefore, several studies (funded through Fulbright-Fogarty and Fulbright Specialist programs), supported the development of the A-E-M-S (Assess-EOP-Map-Simulate) Medical Mass Casualty Algorithm that began networking accident ‘hotspots’ to existing trauma-level capabilities and surge capacity competencies in eight specified Kumasi hospitals. This low-cost response model promises to be an innovative alternative to long-term infrastructure development and high-priced resource distributions. Use of GIS and UAV drones allowed response systems to geospatially locate, classify, shift, and/or augment resources as needed in conjunction with hotspots.

Sample sizes were averaged at 295 for all patients' ages, with only a sample size of 292 for adults at 95% confidence intervals, and a standard deviation of 0.5. A total of 300 road-traffic accident victims were collected at KATH A&E in February-May, 2017, utilizing handheld devices by four researchers 24/7 daily.

Our examination showed that non-MCI fatalities were approximately 2.5%, however, MCI fatalities were found to be 1.8 times higher–at 4.3%, indicating significant opportunities in the planning, preparedness, care, and transport among MCI patient management.

To date–and in partnership with Kwame Nkrumah University of Science and Technology, Komfo Anokye Teaching Hospital, Ghana Medical Council, Health Services, National Disaster Management Organization, and others–over 306 Ghanaian healthcare providers from 80 different facilities have been trained in the AEMS program.

The COVID-19 pandemic may disproportionately affect the mental health of healthcare professionals (HCPs), especially patient-facing HCPs.

To longitudinally examine mental health in HCPs versus non-HCPs, and patient-facing HCPs versus non-patient-facing HCPs.

Online surveys were distributed to a cohort at three phases (baseline, July to September 2020; phase 2, 6 weeks post-baseline; phase 3, 4 months post-baseline). Each survey contained validated assessments for depression, anxiety, insomnia, burnout and well-being. For each outcome, we conducted mixed-effects logistic regression models (adjusted for a priori confounders) comparing the risk in different groups at each phase.

A total of 1574 HCPs and 147 non-HCPs completed the baseline survey. Although there were generally higher rates of various probable mental health issues among HCPs versus non-HCPs at each phase, there was no significant difference, except that HCPs had 2.5-fold increased risk of burnout at phase 2 (emotional exhaustion: odds ratio 2.50, 95% CI 1.15–5.46, P = 0.021), which increased at phase 3 (emotional exhaustion: odds ratio 3.32, 95% CI 1.40–7.87, P = 0.006; depersonalisation: odds ratio 3.29, 95% CI 1.12–9.71, P = 0.031). At baseline, patient-facing HCPs (versus non-patient-facing HCPs) had a five-fold increased risk of depersonalisation (odds ratio 5.02, 95% CI 1.65–15.26, P = 0.004), with no significant difference in the risk for other outcomes. The difference in depersonalisation reduced over time, but patient-facing HCPs still had a 2.7-fold increased risk of emotional exhaustion (odds ratio 2.74, 95% CI 1.28–5.85, P = 0.009) by phase 3.

The COVID-19 pandemic had a huge impact on the mental health and well-being of both HCPs and non-HCPs, but there is disproportionately higher burnout among HCPs, particularly patient-facing HCPs.

Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them.

Literature and expert opinions were integrated to formulate relevant guiding principles.

Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it.

The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.

To examine the relationship between self-reported level of workplace support (WS) and various mental health outcomes in HCPs and non-HCPs at different time-points during the COVID-19 pandemic, and to examine whether improved WS is associated with improved mental health outcomes over time. Lastly, to identify what support healthcare professionals (HCPs) perceive to be most helpful.

Cohort survey study at baseline (July-September 2020) and follow-up (approximately four months later).

Setting

HCPs working in primary or secondary care, from UK and other countries, and non-HCP controls from primarily London-based universities.

Participants

1574 HCPs and 147 non-HCPs (academic and research staff at London-based universities). The inclusion criteria for the study were: 1) aged 18 or older, 2) electronic consent given, and 3) identified as HCP or non-healthcare academic staff or self-declared non-HCPs.

Main outcome measures

Presence of generalized anxiety disorder (assessed using the GAD-7), clinical insomnia (ISI), major depressive disorder (PHQ-9), well-being (SWEMWBS), and burnout (emotional exhaustion and depersonalization; EEDP2Q). Qualitative data exploring what support HCPs perceive as most useful was gathered using free-text inputs.

At baseline and follow-up, consistently, compared to those who felt unsupported, those who felt supported had significantly reduced risk (odds) of generalized anxiety disorder (baseline: 59% [95% CI of OR, 0.29 to 0.57], follow-up: 41% [0.38 to 0.92]), clinical insomnia (51% [0.34 to 0.69], 66% [0.20 to 0.55]), major depressive disorder (58% [0.31 to 0.58], 54% [0.31 to 0.74]), emotional exhaustion (65% [0.26 to 0.46], 61% [0.27 to 0.56]) and depersonalization (58% [0.28 to 0.61], 68% [0.21 to 0.50]).

At follow-up, self-reported improved WS (vs. baseline) was associated with significantly improved GAD-7 (adjusted difference. −1.73 [-2.54 to −0.91]), ISI (-0.96 [-1.88 to −0.04]), PHQ−9 (-1.32 [-2.16 to −0.49]), SWEMWBS (0.97 [0.37 to 1.57]) and EEDP2Q (burnout) (-1.30 [-1.82 to −0.79]) scores, independent of baseline level of support.

Five themes were identified constituting WS: ‘managerial support’ was the largest sub-theme.

A consistent association was observed between level of WS and the mental health of HCPs and non-HCPs. Improved WS was associated with improved mental health scores over a four-month period during the pandemic.

Racial disparities in colorectal cancer (CRC) can be addressed through increased adherence to screening guidelines. In real-life encounters, patients may be more willing to follow screening recommendations delivered by a race concordant clinician. The growth of telehealth to deliver care provides an opportunity to explore whether these effects translate to a virtual setting. The primary purpose of this pilot study is to explore the relationships between virtual clinician (VC) characteristics and CRC screening intentions after engagement with a telehealth intervention leveraging technology to deliver tailored CRC prevention messaging.

Using a posttest-only design with three factors (VC race-matching, VC gender, intervention type), participants (N = 2267) were randomised to one of eight intervention treatments. Participants self-reported perceptions and behavioral intentions.

The benefits of matching participants with a racially similar VC trended positive but did not reach statistical significance. Specifically, race-matching positively influenced screening intentions for Black participants but not for Whites (b = 0.29, p = 0.10). Importantly, perceptions of credibility, attractiveness, and message relevance significantly influenced screening intentions and the relationship with race-matching.

To reduce racial CRC screening disparities, investments are needed to identify patient-focused interventions to address structural barriers to screening. This study suggests that telehealth interventions that match Black patients with a Black VC can enhance perceptions of credibility and message relevance, which may then improve screening intentions. Future research is needed to examine how to increase VC credibility and attractiveness, as well as message relevance without race-matching.