Search results
Found 16011 matches for
The new teaching package aims to give students greater confidence and experience when consulting with patients who live with psychiatric illness.
The impact of the COVID-19 pandemic on children and adolescent mental health in-patient service use in England: interrupted time-series analysis of national patient records.
BACKGROUND: During the initial phases of the COVID-19 pandemic, children and young people (CYP) faced significant restrictions. The virus and mitigation approaches significantly impacted how health services could function and be safely delivered. AIMS: To investigate the impact of COVID-19 lockdowns on CYP psychiatric admission trends during lockdown 1 (started 23 Mar 2020) and lockdown 2 (started 5 Nov 2020) of the COVID-19 pandemic in England. METHOD: Routinely collected, retrospective English administrative data regarding psychiatric hospital admissions, length of stay and patient demographic factors were analysed using an interrupted time series analysis (ITSA) to estimate the impact of COVID-19 lockdowns 1 and 2 on service use trends. We analysed data of 6250 CYP (up to 18 years of age) using ordinary least squares (OLS) regression analysis with Newey-West standard errors to handle autocorrelation and heteroscedasticity. RESULTS: Psychiatric hospital admissions for CYP significantly fell during lockdown 1, and then fell even further during lockdown 2. A greater proportion of admissions during lockdown were out of area or to independent sector units. During lockdown, the average age of CYP admitted was higher, and a greater proportion were female. There was also a significant increase in the proportion of looked-after children and CYP from the most socioeconomically deprived areas admitted during lockdown 2. CONCLUSIONS: During both lockdowns, fewer CYP had psychiatric admissions. The subsequent rise in admissions for more socioeconomically deprived CYP and looked-after children suggests that these CYP may have been disproportionately affected by the pandemic, or overlooked during earlier phases.
Utility of the 3Di short version in the identification and diagnosis of autism in children at the Kenyan coast.
INTRODUCTION: The precise epidemiological burden of autism is unknown because of the limited capacity to identify and diagnose the disorder in resource-constrained settings, related in part to a lack of appropriate standardised assessment tools and health care experts. We assessed the reliability, validity, and diagnostic accuracy of the Developmental Diagnostic Dimensional Interview (3Di) in a rural setting on the Kenyan coast. METHODS: Using a large community survey of neurodevelopmental disorders (NDDs), we administered the 3Di to 2,110 children aged between 6 years and 9 years who screened positive or negative for any NDD and selected 242 who had specific symptoms suggestive of autism based on parental report and the screening tools for review by a child and adolescent psychiatrist. On the basis of recorded video, a multi-disciplinary team applied the Autism Diagnostic Observation Schedule to establish an autism diagnosis. Internal consistency was used to examine the reliability of the Swahili version of the 3Di, tetrachoric correlations to determine criterion validity, structural equation modelling to evaluate factorial structure and receiver operating characteristic analysis to calculate diagnostic accuracy against Diagnostic Statistical Manual of Mental Disorders (DSM) diagnosis. RESULTS: The reliability coefficients for 3Di were excellent for the entire scale {McDonald's omega (ω) = 0.83 [95% confidence interval (CI) 0.79-0.91]}. A higher-order three-factor DSM-IV-TR model showed an adequate fit with the model, improving greatly after retaining high-loading items and correlated items. A higher-order two-factor DSM-5 model also showed an adequate fit. There were weak to satisfactory criterion validity scores [tetrachoric rho = 0.38 (p = 0.049) and 0.59 (p = 0.014)] and good diagnostic accuracy metrics [area under the curve = 0.75 (95% CI: 0.54-0.96) and 0.61 (95% CI: 0.49-0.73] for 3Di against the DSM criteria. The 3Di had a moderate sensitivity [66.7% (95% CI: 0.22-0.96)] and a good specificity [82.5% (95% CI: 0.74-0.89)], when compared with the DSM-5. However, we observed poor sensitivity [38.9% (95% CI: 0.17-0.64)] and good specificity [83.5% (95% CI: 0.74-0.91)] against DSM-IV-TR. CONCLUSION: The Swahili version of the 3Di provides information on autism traits, which may be helpful for descriptive research of endophenotypes, for instance. However, for accuracy in newly diagnosed autism, it should be complemented by other tools, e.g., observational clinical judgment using the DSM criteria or assessments such as the Autism Diagnostic Observation Schedule. The construct validity of the Swahili 3Di for some domains, e.g., communication, should be explored in future studies.
Association between psychosis and substance use in Kenya. Findings from the NeuroGAP-Psychosis study.
BACKGROUND: Substance use is prevalent among people with mental health issues, and patients with psychosis are more likely to use and misuse substances than the general population. Despite extensive research on substance abuse among the general public in Kenya, there is a scarcity of data comparing substance use among people with and without psychosis. This study investigates the association between psychosis and various substances in Kenya. METHODS: This study utilized data from the Neuro-GAP Psychosis Case-Control Study between April 2018 and December 2022. The KEMRI-Wellcome Trust Research Programme recruited participants from various sites in Kenya, including Kilifi County, Malindi Sub-County, Port Reitz and Coast General Provincial Hospitals, and Moi Teaching and Referral Hospital, as well as affiliated sites in Webuye, Kapenguria, Kitale, Kapsabet, and Iten Kakamega. The collected data included sociodemographic information, substance use, and clinical diagnosis. We used the summary measures of frequency (percentages) and median (interquartile range) to describe the categorical and continuous data, respectively. We examined the association between categorical variables related to psychosis using the chi-square test. Logistic regression models were used to assess the factors associated with the odds of substance use, considering all relevant sociodemographic variables. RESULTS: We assessed a total of 4,415 cases and 3,940 controls. Except for alcohol consumption (p-value=0.41), all forms of substance use showed statistically significant differences between the case and control groups. Cases had 16% higher odds of using any substance than controls (aOR: 1.16, 95%CI: 1.05-1.28, p=0.005). Moreover, males were 3.95 times more likely to use any substance than females (aOR:3.95; 95%CI: 3.43-4.56). All the categories of living arrangements were protective against substance use. CONCLUSION: The findings of this study suggest that psychotic illnesses are associated with an increased likelihood of using various substances. These findings are consistent with those of previous studies; however, it is crucial to investigate further the potential for reverse causality between psychosis and substance abuse using genetically informed methods.
Cross-cultural equivalence of the Kessler Psychological Distress Scale (K10) across four African countries in a multi-national study of adults
The Kessler Psychological Distress Scale (K10) has been widely used to screen psychological distress across many countries. However, its performance has not been extensively studied in Africa. The present study sought to evaluate and compare measurement properties of the K10 across four African countries: Ethiopia, Kenya, Uganda, and South Africa. Our hypothesis is that the measure will show equivalence across all. Data are drawn from a neuropsychiatric genetic study among adult participants (N = 9179) from general medical settings in Ethiopia (n = 1928), Kenya (n = 2556), Uganda (n = 2104), and South Africa (n = 2591). A unidimensional model with correlated errors was tested for equivalence across study countries using confirmatory factor analyses and the alignment optimization method. Results displayed 30 % noninvariance (i.e., variation) for both intercepts and factor loadings across all countries. Monte Carlo simulations showed a correlation of 0.998, a good replication of population values, indicating minimal noninvariance, or variation. Items “so nervous,” “lack of energy/effortful tasks,” and “tired” were consistently equivalent for intercepts and factor loadings, respectively. However, items “depressed” and “so depressed” consistently differed across study countries (R2 = 0) for intercepts and factor loadings for both items. The K10 scale likely functions equivalently across the four countries for most items, except “depressed” and “so depressed.” Differences in K10 items were more common in Kenya and Ethiopia, suggesting cultural context may influence the interpretation of some items and the potential need for cultural adaptations in these countries.
Burden of neurodevelopmental disorders in low and middle-income countries: A systematic review and meta-analysis
Background: Childhood mortality from infectious diseases has declined steadily in many low and middle-income (LAMIC) countries, with increased recognition of non-communicable diseases such as neurodevelopmental disorders (NDD). There is lack of data on the burden of NDD in LAMIC. Current global burden of these disorders are largely extrapolated from high-income countries. The main objective of the study was therefore to estimate the burden of NDD in LAMIC using meta-analytic techniques. Methods: We systematically searched online databases including Medline/PubMed, PsychoInfo, and Embase for studies that reported prevalence or incidence of NDD. Pooled prevalence, heterogeneity and risk factors for prevalence were determined using meta-analytic techniques. Results: We identified 4,802 records, but only 51 studies met the eligibility criteria. Most studies were from Asia-Pacific (52.2%) and most were on neurological disorders (63.1%). The median pooled prevalence per 1,000 for any NDD was 7.6 (95%CI 7.5-7.7), being 11.3 (11.7-12.0) for neurological disorders and 3.2 (95%CI 3.1-3.3) for mental conditions such as attention-deficit hyperactivity disorder (ADHD). The type of NDD was significantly associated with the greatest prevalence ratio in the multivariable model (PR=2.6(95%CI 0.6-11.6) (P>0.05). Incidence was only reported for epilepsy (mean of 447.7 (95%CI 415.3-481.9) per 100,000). Perinatal complications were the commonest risk factor for NDD. Conclusion: The burden of NDD in LAMIC is considerable. Epidemiological surveys on NDD should screen all types of NDD to provide reliable estimates.
Impact of genetic, sociodemographic, and clinical features on antidepressant treatment trajectories in the perinatal period.
Pregnant women on antidepressants must balance potential fetal harm with the relapse risk. While various clinical and sociodemographic factors are known to influence treatment decisions, the impact of genetic factors remains unexplored. We conducted a cohort study among 2,316 women with diagnosed affective disorders who had redeemed antidepressant prescriptions six months before pregnancy, identified from the Danish Integrated Psychiatric Research study. We calculated polygenic risk scores (PGSs) for major depression (MDD), bipolar disorder (BD), and schizophrenia (SCZ) using individual-level genetic data and summary statistics from genome-wide association studies. We retrieved data on sociodemographic and clinical features from national registers. Applying group-based trajectory modeling, we identified four treatment trajectories across pregnancy and postpartum: Continuers (38.2 %), early discontinuers (22.7 %), late discontinuers (23.8 %), and interrupters (15.3 %). All three PGSs were not associated with treatment trajectories; for instance, the relative risk ratio for continuers versus early discontinuers was 0.93 (95 % CI: 0.81-1.06), 0.98 (0.84-1.13), 1.09 (0.95-1.27) for per 1-SD increase in PGS for MDD, BD, and SCZ, respectively. Sociodemographic factors were generally not associated with treatment trajectories, except for the association between primiparity and continuing antidepressant use. Women who received ≥2 classes or a higher dose of antidepressants had a higher probability of being late discontinuers, interrupters, and continuers. The likelihood of continuing antidepressants or restarting antidepressants postpartum increased with the previous antidepressant treatment duration. Our findings indicate that continued antidepressant use during pregnancy is influenced by the severity of the disease rather than genetic predisposition as measured by PGSs.
Online peer-led intervention to improve adolescent wellbeing during the COVID-19 pandemic: a randomised controlled trial.
BACKGROUND: The COVID-19 pandemic and associated lockdown measures have posed a major risk to young people's wellbeing, which might be ameliorated by peer-led programmes. Using a randomised controlled trial (ISRCTN registry, number ISRCTN77941736 https://doi.org/10.1186/ISRCTN77941736 ), we tested the short-term efficacy of an online peer-led intervention designed to equip young people with skills to support their mental health and wellbeing during the COVID-19 pandemic. METHODS: Through schools and social media ads, we recruited one hundred young people (aged 16-18) in the UK, focusing on areas with the highest incidence of COVID cases. In December 2020, participants were randomly allocated (1:1) to immediate 5 day Coping during COVID course (n = 49) or a wait-list (n = 51) through a survey software automated randomisation tool. Our primary outcome was self-reported mental wellbeing, and secondary outcomes included self-reported social connectedness, coping skills, sense of purpose, self-esteem, and self-compassion. We also collected qualitative reports of participants' perceived impact of the course and intentions to use what they have learnt from the course in their life moving forward. Assessments were completed at baseline, 1 week post randomisation (primary endpoint), and 2-weeks post-randomisation. RESULTS: Young people allocated to the peer-led intervention reported significantly greater wellbeing, social connectedness, coping skills, sense of purpose, self-esteem, and self-compassion 1 week and 2 weeks post-randomisation (medium-large effect sizes). Specific benefits to mental health, sense of purpose and connectedness were also emphasised in qualitative reports. CONCLUSIONS: An online, peer-led intervention targeting youth wellbeing during the context of the COVID-19 pandemic brought benefits across a range of outcomes, suggesting that structured programmes that incorporate peer-to-peer support can be a valuable approach to promote young people's wellbeing and foster psychological resources during a health crisis.
A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates
Background Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education. Methods The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation. Results The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation. Conclusions The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies.
Psilocybin therapy for treatment resistant depression: prediction of clinical outcome by natural language processing.
RATIONALE: Therapeutic administration of psychedelics has shown significant potential in historical accounts and recent clinical trials in the treatment of depression and other mood disorders. A recent randomized double-blind phase-IIb study demonstrated the safety and efficacy of COMP360, COMPASS Pathways' proprietary synthetic formulation of psilocybin, in participants with treatment-resistant depression. OBJECTIVE: While the phase-IIb results are promising, the treatment works for a portion of the population and early prediction of outcome is a key objective as it would allow early identification of those likely to require alternative treatment. METHODS: Transcripts were made from audio recordings of the psychological support session between participant and therapist 1 day post COMP360 administration. A zero-shot machine learning classifier based on the BART large language model was used to compute two-dimensional sentiment (valence and arousal) for the participant and therapist from the transcript. These scores, combined with the Emotional Breakthrough Index (EBI) and treatment arm were used to predict treatment outcome as measured by MADRS scores. (Code and data are available at https://github.com/compasspathways/Sentiment2D .) RESULTS: Two multinomial logistic regression models were fit to predict responder status at week 3 and through week 12. Cross-validation of these models resulted in 85% and 88% accuracy and AUC values of 88% and 85%. CONCLUSIONS: A machine learning algorithm using NLP and EBI accurately predicts long-term patient response, allowing rapid prognostication of personalized response to psilocybin treatment and insight into therapeutic model optimization. Further research is required to understand if language data from earlier stages in the therapeutic process hold similar predictive power.
Reducing intrusive memories after trauma via an imagery-competing task intervention in COVID-19 intensive care staff: a randomised controlled trial.
Intrusive memories (IMs) after traumatic events can be distressing and disrupt mental health and functioning. We evaluated the impact of a brief remotely-delivered digital imagery-competing task intervention on the number of IMs for intensive care unit (ICU) staff who faced repeated trauma exposure during the COVID-19 pandemic using a two-arm, parallel-group, single-blind randomised controlled trial, with the comparator arm receiving delayed access to active treatment (crossover). Eligible participants worked clinically in a UK NHS ICU during the pandemic and had at least 3 IMs of work-related traumatic events in the week before recruitment. Participants were randomly assigned (1:1) to immediate (weeks 1-4) or delayed (weeks 5-8) intervention access. Sequential Bayesian analyses to optimise the intervention and increase trial efficiency are reported elsewhere [1]. The primary endpoint for the pre-specified frequentist analysis of the final study population compared the number of IMs experienced in week 4 between the immediate and delayed access arms. Secondary outcomes included clinical symptoms, work functioning and wellbeing. Safety was assessed throughout the trial by scheduled questions and free report. All analyses were undertaken on an intention-to-treat basis (86 randomised participants). There were significantly fewer intrusive memories during week 4 in the immediate (median = 1, IQR = 0-3, n = 43), compared to the comparator delayed arm (median = 10, IQR = 6-17, n = 43), IRR 0.31, 95% CI: 0.20-0.48, p
Combining antidepressants and attention bias modification in primary health care (DEPTREAT): Protocol for a pragmatic randomized controlled trial.
BACKGROUND: Major depressive disorder (MDD) is a highly prevalent psychiatric condition associated with significant disability, mortality and economic burden. A large proportion of MDD patients are treated in primary health care in the local community. Attentional Bias Modification (ABM) training in combination with antidepressants could be an effective treatment. Here we test the hypothesis that adding an ABM procedure to regular treatment with antidepressants in primary health care will result in further improvement of symptoms compared to treatment with antidepressants alone (treatment as usual, TAU) and as compared to an active comparison condition. METHODS: A total of 246 patients with a diagnosis of MDD will be included in this study. The study is a three-armed pragmatic randomized controlled trial comparing the efficacy of ABM as add-on to treatment with antidepressants in primary care (ABM condition) compared to standard antidepressant treatment (TAU condition). In a third group participants will complete the same schedule of intermediate assessments as the ABM condition in addition to TAU, but no ABM, thus controlling for the non-training-specific aspects of the ABM condition (Antidepressant active comparison group). DISCUSSION: The clinical outcome of this study may help develop easily accessible, low-cost treatment of depression in primary health care. Moreover, the study aims to broaden our knowledge of optimal treatment for patients with a MDD by providing adjunct treatment to facilitate recovery and long-term gain.
USING ELECTRONIC HEALTH RECORDS TO FACILITATE PRECISION PSYCHIATRY.
The use of clinical prediction models to produce individualised risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implementing them in routine clinical care. The present review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number-needed-to-test). We review four externally validated clinical prediction models designed to predict, respectively: psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models, and the potential added value of integrating data from evidence syntheses, standardised psychometric assessments, and biological data into EHRs. Clinical prediction models can utilise routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g. meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve performance of clinical prediction models.
Suicide risk assessment tools and prediction models: new evidence, methodological innovations, outdated criticisms
The number of prediction models for suicide-related outcomes has grown substantially in recent years. These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine approach to the prevention of suicidal behaviour. However, there are contrasting views as to whether prediction models have potential to inform and improve assessment of suicide risk. In this perspective, we discuss common misconceptions that characterise criticisms of suicide risk prediction research. First, we discuss the limitations of a classification approach to risk assessment (eg, categorising individuals as low-risk vs high-risk), and highlight the benefits of probability estimation. Second, we argue that the preoccupation with classification measures (such as positive predictive value) when assessing a model’s predictive performance is inappropriate, and discuss the importance of clinical context in determining the most appropriate risk threshold for a given model. Third, we highlight that adequate discriminative ability for a prediction model depends on the clinical area, and emphasise the importance of calibration, which is almost entirely overlooked in the suicide risk prediction literature. Finally, we point out that conclusions about the clinical utility and health-economic value of suicide prediction models should be based on appropriate measures (such as net benefit and decision-analytic modelling), and highlight the role of impact assessment studies. We conclude that the discussion around using suicide prediction models and risk assessment tools requires more nuance and statistical expertise, and that guidelines and suicide prevention strategies should be informed by the new and higher quality evidence in the field.
Tracing Tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology.
BACKGROUND: Use of personal sensing to predict mental health risk has sparked interest in adolescent psychiatry, offering a potential tool for targeted early intervention. OBJECTIVES: We investigated the preferences and values of UK adolescents with regard to use of digital sensing information, including social media and internet searching behaviour. We also investigated the impact of risk information on adolescents' self-understanding. METHODS: Following a Design Bioethics approach, we created and disseminated a purpose-built digital game (www.tracingtomorrow.org) that immersed the player-character in a fictional scenario in which they received a risk assessment for depression Data were collected through game choices across relevant scenarios, with decision-making supported through clickable information points. FINDINGS: The game was played by 7337 UK adolescents aged 16-18 years. Most participants were willing to personally communicate mental health risk information to their parents or best friend. The acceptability of school involvement in risk predictions based on digital traces was mixed, due mainly to privacy concerns. Most participants indicated that risk information could negatively impact their academic self-understanding. Participants overwhelmingly preferred individual face-to-face over digital options for support. CONCLUSIONS: The potential of digital phenotyping in supporting early intervention in mental health can only be fulfilled if data are collected, communicated and actioned in ways that are trustworthy, relevant and acceptable to young people. CLINICAL IMPLICATIONS: To minimise the risk of ethical harms in real-world applications of preventive psychiatric technologies, it is essential to investigate young people's values and preferences as part of design and implementation processes.