We examined parent- and adolescent-reported executive functioning (EF) behaviors following pediatric traumatic brain injury (TBI) in the context of Online Family Problem-Solving Therapy (OFPST) and moderators of change in EF behaviors.

In total, 274 families were randomized to OFPST or an internet resource comparison group. Parents and adolescents completed the Behavior Rating Inventory of Executive Function at four time points. Mixed models were used to examine EF behaviors, assessing the effects of visit, treatment group, rater, TBI severity, age, socioeconomic status, and family functioning.

Parents rated their adolescents’ EF as poorer (F(3,1156) = 220.15, p < .001; M = 58.11, SE = 0.73) than adolescents rated themselves (M = 51.81, SE = 0.73). Across raters, EF behaviors were poorer for adolescents whose parents had less education (F(3,1156) = 8.60, p = .003; M = 56.76, SE = 0.98) than for those with more education (M = 53.16, SE = 0.88). Age at baseline interacted with visit (F(3,1156) = 5.05, p = .002), such that families of older adolescents reported improvement in EF behaviors over time. Family functioning also interacted with visit (F(3, 1156) = 2.61, p = .049), indicating more improvement in EF behaviors over time in higher functioning families. There were no effects of treatment or TBI severity.

We identified a discrepancy between parent- and adolescent-reported EF, suggesting reduced awareness of deficits in adolescents with TBI. We also found that poorer family functioning and younger age were associated with poorer recovery after TBI, whereas adolescents of parents with less education were reported as having greater EF deficits across time points.

This study aimed to examine the predictors of cognitive performance in patients with pediatric mild traumatic brain injury (pmTBI) and to determine whether group differences in cognitive performance on a computerized test battery could be observed between pmTBI patients and healthy controls (HC) in the sub-acute (SA) and the early chronic (EC) phases of injury.

203 pmTBI patients recruited from emergency settings and 159 age- and sex-matched HC aged 8–18 rated their ongoing post-concussive symptoms (PCS) on the Post-Concussion Symptom Inventory and completed the Cogstate brief battery in the SA (1–11 days) phase of injury. A subset (156 pmTBI patients; 144 HC) completed testing in the EC (∼4 months) phase.

Within the SA phase, a group difference was only observed for the visual learning task (One-Card Learning), with pmTBI patients being less accurate relative to HC. Follow-up analyses indicated higher ongoing PCS and higher 5P clinical risk scores were significant predictors of lower One-Card Learning accuracy within SA phase, while premorbid variables (estimates of intellectual functioning, parental education, and presence of learning disabilities or attention-deficit/hyperactivity disorder) were not.

The absence of group differences at EC phase is supportive of cognitive recovery by 4 months post-injury. While the severity of ongoing PCS and the 5P score were better overall predictors of cognitive performance on the Cogstate at SA relative to premorbid variables, the full regression model explained only 4.1% of the variance, highlighting the need for future work on predictors of cognitive outcomes.

Individuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia.

To link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability.

We linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores.

Individuals with schizophrenia in SAIL had increased rates of epilepsy (standardised rate ratio (SRR) = 5.34), intellectual disability (SRR = 3.11), type 2 diabetes (SRR = 2.45), congenital disorders (SRR = 1.77), ischaemic heart disease (SRR = 1.57) and smoking (SRR = 1.44) in comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015–0.017), with carriers being 7.5–7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder.

This study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.

It is not clear to what extent associations between schizophrenia, cannabis use and cigarette use are due to a shared genetic etiology. We, therefore, examined whether schizophrenia genetic risk associates with longitudinal patterns of cigarette and cannabis use in adolescence and mediating pathways for any association to inform potential reduction strategies.

Associations between schizophrenia polygenic scores and longitudinal latent classes of cigarette and cannabis use from ages 14 to 19 years were investigated in up to 3925 individuals in the Avon Longitudinal Study of Parents and Children. Mediation models were estimated to assess the potential mediating effects of a range of cognitive, emotional, and behavioral phenotypes.

The schizophrenia polygenic score, based on single nucleotide polymorphisms meeting a training-set p threshold of 0.05, was associated with late-onset cannabis use (OR = 1.23; 95% CI = 1.08,1.41), but not with cigarette or early-onset cannabis use classes. This association was not mediated through lower IQ, victimization, emotional difficulties, antisocial behavior, impulsivity, or poorer social relationships during childhood. Sensitivity analyses adjusting for genetic liability to cannabis or cigarette use, using polygenic scores excluding the CHRNA5-A3-B4 gene cluster, or basing scores on a 0.5 training-set p threshold, provided results consistent with our main analyses.

Our study provides evidence that genetic risk for schizophrenia is associated with patterns of cannabis use during adolescence. Investigation of pathways other than the cognitive, emotional, and behavioral phenotypes examined here is required to identify modifiable targets to reduce the public health burden of cannabis use in the population.

Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes.

We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as ‘Responders’ (⩾50% improvement in depression severity ratings) or ‘Non-responders’ (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups.

Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively).

These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

Registry-based trials have emerged as a potentially cost-saving study methodology. Early estimates of cost savings, however, conflated the benefits associated with registry utilisation and those associated with other aspects of pragmatic trial designs, which might not all be as broadly applicable. In this study, we sought to build a practical tool that investigators could use across disciplines to estimate the ranges of potential cost differences associated with implementing registry-based trials versus standard clinical trials.

We built simulation Markov models to compare unique costs associated with data acquisition, cleaning, and linkage under a registry-based trial design versus a standard clinical trial. We conducted one-way, two-way, and probabilistic sensitivity analyses, varying study characteristics over broad ranges, to determine thresholds at which investigators might optimally select each trial design.

Registry-based trials were more cost effective than standard clinical trials 98.6% of the time. Data-related cost savings ranged from $4300 to $600,000 with variation in study characteristics. Cost differences were most reactive to the number of patients in a study, the number of data elements per patient available in a registry, and the speed with which research coordinators could manually abstract data. Registry incorporation resulted in cost savings when as few as 3768 independent data elements were available and when manual data abstraction took as little as 3.4 seconds per data field.

Registries offer important resources for investigators. When available, their broad incorporation may help the scientific community reduce the costs of clinical investigation. We offer here a practical tool for investigators to assess potential costs savings.

To evaluate the long-term tolerability and effectiveness of aripiprazole adjunctive to lithium or valproate in bipolar mania.

Completers of a 6-week double-blind comparison of adjunctive aripiprazole versus placebo in bipolar mania partially responsive to monotherapy were followed up over 46-weeks on open-label aripiprazole plus lithium (ARI+LI) or valproate (ARI+VAL).

283 (ARI+LI n=108; ARI+VAL n=175) patients entered and 146 (ARI+LI n=55; ARI+VAL n=91) completed the 46-week extension. Safety results for both combinations were consistent with the known tolerability profile of aripiprazole, lithium and valproate. No clinically significant changes in lipids or glucose were observed with either ARI+LI or ARI+VAL. Mean (SE) weight change from double-blind endpoint to Week 46 (LOCF) was 2.3 (0.6) kg with ARI+LI and 2.0 (0.4) kg with ARI+VAL. Temporal analysis of the time of first onset of adverse events showed that akathisia and insomnia tended to occur early in treatment, with few new cases in patients previously treated with aripiprazole during the 6-week study.

Significant improvements from baseline in YMRS total score and MADRS total score were sustained over the 52 weeks with both ARI+LI and ARI+VAL treatment.

Long-term aripiprazole adjunctive to lithium/valproate in bipolar mania was safe and well-tolerated. Improvements in manic and depressive symptoms observed during the first 6 weeks of treatment were maintained.

Around 30% of individuals with schizophrenia remain symptomatic and significantly impaired despite antipsychotic treatment and are considered to be treatment resistant. Clinicians are currently unable to predict which patients are at higher risk of treatment resistance.

To determine whether genetic liability for schizophrenia and/or clinical characteristics measurable at illness onset can prospectively indicate a higher risk of treatment-resistant psychosis (TRP).

In 1070 individuals with schizophrenia or related psychotic disorders, schizophrenia polygenic risk scores (PRS) and large copy number variations (CNVs) were assessed for enrichment in TRP. Regression and machine-learning approaches were used to investigate the association of phenotypes related to demographics, family history, premorbid factors and illness onset with TRP.

Younger age at onset (odds ratio 0.94, P = 7.79 × 10−13) and poor premorbid social adjustment (odds ratio 1.64, P = 2.41 × 10−4) increased risk of TRP in univariate regression analyses. These factors remained associated in multivariate regression analyses, which also found lower premorbid IQ (odds ratio 0.98, P = 7.76 × 10−3), younger father's age at birth (odds ratio 0.97, P = 0.015) and cannabis use (odds ratio 1.60, P = 0.025) increased the risk of TRP. Machine-learning approaches found age at onset to be the most important predictor and also identified premorbid IQ and poor social adjustment as predictors of TRP, mirroring findings from regression analyses. Genetic liability for schizophrenia was not associated with TRP.

People with an earlier age at onset of psychosis and poor premorbid functioning are more likely to be treatment resistant. The genetic architecture of susceptibility to schizophrenia may be distinct from that of treatment outcomes.

Young people with 22q11.2 deletion syndrome (22q11.2DS) are at high risk for neurodevelopmental disorders. Sleep problems may play a role in this risk but their prevalence, nature and links to psychopathology and cognitive function remain undescribed in this population.

Sleep problems, psychopathology, developmental coordination and cognitive function were assessed in 140 young people with 22q11.2DS (mean age = 10.1, s.d. = 2.46) and 65 unaffected sibling controls (mean age = 10.8, s.d.SD = 2.26). Primary carers completed questionnaires screening for the children's developmental coordination and autism spectrum disorder.

Sleep problems were identified in 60% of young people with 22q11.2DS compared to 23% of sibling controls (OR 5.00, p < 0.001). Two patterns best-described sleep problems in 22q11.2DS: restless sleep and insomnia. Restless sleep was linked to increased ADHD symptoms (OR 1.16, p < 0.001) and impaired executive function (OR 0.975, p = 0.013). Both patterns were associated with elevated symptoms of anxiety disorder (restless sleep: OR 1.10, p = 0.006 and insomnia: OR 1.07, p = 0.045) and developmental coordination disorder (OR 0.968, p = 0.0023, and OR 0.955, p = 0.009). The insomnia pattern was also linked to elevated conduct disorder symptoms (OR 1.53, p = 0.020).

Clinicians and carers should be aware that sleep problems are common in 22q11.2DS and index psychiatric risk, cognitive deficits and motor coordination problems. Future studies should explore the physiology of sleep and the links with the neurodevelopment in these young people.

All patients undergoing tympanomastoid surgery should be assessed post-operatively for a ‘dead ear’; however, tuning forks are frequently inaccessible.

To demonstrate that smartphone-based vibration applications provide equivalent accuracy to tuning forks when performing Weber's test.

Data were collected on lay participants with no underlying hearing loss. Earplugs were used to simulate conductive hearing loss. Both the right and left ears were tested with the iBrateMe vibration application on an iPhone and using a 512 Hz tuning fork.

Occluding the left ear, the tuning fork lateralised to the left in 18 out of 20 cases. In 20 out of 20 cases, sound lateralised to the left with the iPhone (chi-square test, p = 0.147). Occluding the right ear, the tuning fork lateralised to the right in 19 out of 20 cases. In 19 out of 20 cases, sound lateralised to the right with the iPhone (chi-square test, p > 0.999).

Smartphone-based vibration applications represent a viable, more accessible alternative to tuning forks when assessing for conductive hearing loss. They can therefore be utilised on the ward round, in patients following tympanomastoid surgery, for example.

Integrating mental health care into HIV services is critical to addressing the high unmet treatment needs for people living with HIV and comorbid major depressive disorder. Introducing routine mental health screening at the primary health care level is a much needed diagonal approach to enhancing HIV care. In low-resource settings with a shortage of mental health care providers, eMental Health may provide a novel opportunity to attenuate this treatment gap and strengthen the health system.

To conduct formative health systems research on the implementation of routine depression screening using a digital tool – Mood in Retroviral Positive Individuals Application Monitoring (MIR  +  IAM) – in an HIV primary care setting in South Africa.

A Theory of Change (ToC) approach was utilised through individual and group session interviews to design an intervention that is embedded in the local context. Ten experts and local stakeholders were selected from the UK and South Africa. Data were analysed thematically using Atlas.ti to identify interventions, assumptions, barriers and facilitators of implementation.

The participants considered digital depression screening in HIV care services relevant for the improvement of mental health in this population. The six main themes identified from the ToC process were: (1) user experience including acceptability by patients, issues of patient privacy and digital literacy, and the need for a patient-centred tool; (2) benefits of the digital tool for data collection and health promotion; (3) availability of treatment after diagnosis; (4) human and physical resource capacity of primary health care; (5) training for lay health care workers; and (6) demonstration of the intervention's usefulness to generate interest from decision-makers.

Digital depression screening coupled with routine mental health data collection and analysis in HIV care is an applicable service that could improve the mental and physical health outcomes of this population. Careful consideration of the local health system capacity, including both workers and patients, is required. Future research to refine this intervention should focus on service users, government stakeholders and funders.