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It remains unclear which individuals with subthreshold depression benefit most from psychological intervention, and what long-term effects this has on symptom deterioration, response and remission.
Aims
To synthesise psychological intervention benefits in adults with subthreshold depression up to 2 years, and explore participant-level effect-modifiers.
Method
Randomised trials comparing psychological intervention with inactive control were identified via systematic search. Authors were contacted to obtain individual participant data (IPD), analysed using Bayesian one-stage meta-analysis. Treatment–covariate interactions were added to examine moderators. Hierarchical-additive models were used to explore treatment benefits conditional on baseline Patient Health Questionnaire 9 (PHQ-9) values.
Results
IPD of 10 671 individuals (50 studies) could be included. We found significant effects on depressive symptom severity up to 12 months (standardised mean-difference [s.m.d.] = −0.48 to −0.27). Effects could not be ascertained up to 24 months (s.m.d. = −0.18). Similar findings emerged for 50% symptom reduction (relative risk = 1.27–2.79), reliable improvement (relative risk = 1.38–3.17), deterioration (relative risk = 0.67–0.54) and close-to-symptom-free status (relative risk = 1.41–2.80). Among participant-level moderators, only initial depression and anxiety severity were highly credible (P > 0.99). Predicted treatment benefits decreased with lower symptom severity but remained minimally important even for very mild symptoms (s.m.d. = −0.33 for PHQ-9 = 5).
Conclusions
Psychological intervention reduces the symptom burden in individuals with subthreshold depression up to 1 year, and protects against symptom deterioration. Benefits up to 2 years are less certain. We find strong support for intervention in subthreshold depression, particularly with PHQ-9 scores ≥ 10. For very mild symptoms, scalable treatments could be an attractive option.
Emerging wildlife pathogens often display geographic variability due to landscape heterogeneity. Modeling approaches capable of learning complex, non-linear spatial dynamics of diseases are needed to rigorously assess and mitigate the effects of pathogens on wildlife health and biodiversity. We propose a novel machine learning (ML)-guided approach that leverages prior physical knowledge of ecological systems, using partial differential equations. We present our approach, taking advantage of the universal function approximation property of neural networks for flexible representation of the underlying dynamics of the geographic spread and growth of wildlife diseases. We demonstrate the benefits of our approach by comparing its forecasting power with commonly used methods and highlighting the obtained insights on disease dynamics. Additionally, we show the theoretical guarantees for the approximation error of our model. We illustrate the implementation of our ML-guided approach using data from white-nose syndrome (WNS) outbreaks in bat populations across the US. WNS is an infectious fungal disease responsible for significant declines in bat populations. Our results on WNS are useful for disease surveillance and bat conservation efforts. Our methods can be broadly used to assess the effects of environmental and anthropogenic drivers impacting wildlife health and biodiversity.
Pubertal development variations have consequences for adolescent internalizing problems, which likely continue into adulthood. Key questions concern the extent of these links between pubertal timing and adult symptoms, as well as the underlying mechanisms.
Methods
Longitudinal data were available for 475 female and 404 male participants. Pubertal timing was indicated by age at mid-puberty for both groups and age at menarche for female participants (both assessed continuously). Adult self-reported outcomes of recent and lifetime depression and anxiety were predicted from pubertal timing, also controlling for adolescent (then childhood) internalizing problems. Emerging adulthood self-esteem, body dissatisfaction, education level, and age at sexual initiation were examined as mediators of the pubertal timing-adult internalizing link. Multilevel models tested hypotheses.
Results
Pubertal timing had persisting and sex-dependent psychological associations. Specifically, in female, but not male, adults, early puberty was associated with all adult internalizing outcomes, and for past year and lifetime depression symptoms, even after controlling for adolescent internalizing problems. Pubertal timing links with past-year depression symptoms were mediated by age at sexual initiation, while all other persisting pubertal timing links with adult symptoms were mediated by body dissatisfaction. Most findings concerning depression held when childhood internalizing problems were also a covariate.
Conclusions
Leveraging data spanning four developmental periods, findings highlight the associations between pubertal variations and adult internalizing symptoms by revealing underlying sex-dependent behavioral pathways. Only for female participants did pubertal timing affect depression and anxiety in established adulthood, with body dissatisfaction and age at sexual initiation as unique developmental mechanisms.
The random lottery incentive system is widely used in experimental economics to motivate subjects. This paper investigates its validity. It reports three experiments which compare responses given to decision tasks which are embedded in random lottery designs with responses in ‘single choice’ designs in which each subject faces just one task for real. The experiments were designed to detect cross-task contamination effects in the random lottery treatment. No significant differences between treatments, and no significant contamination effects, were found. Over the three experiments, observed differences between the treatments are adequately explained as sampling variation.
In this paper, we report an experimental investigation of the effect of framing on social preferences, as revealed in a one-shot linear public goods game. We use two types of indicator to measure social preferences: self-reported emotional responses; and, as a behavioural indicator of disapproval, punishment. Our findings are that, for a given pattern of contributions, neither type of indicator depends on the Give versus Take framing that we manipulate. To this extent, they suggest that the social preferences we observe are robust to framing effects.
The paper considers what can be inferred about experimental subjects’ time preferences for consumption from responses to laboratory tasks involving tradeoffs between sums of money at different dates, if subjects can reschedule consumption spending relative to income in external capital markets. It distinguishes three approaches identifiable in the literature: the straightforward view; the separation view; and the censored data view. It shows that none of these is fully satisfactory and discusses the resulting implications for intertemporal decision-making experiments.
Previous studies identified clusters of first-episode psychosis (FEP) patients based on cognition and premorbid adjustment. This study examined a range of socio-environmental risk factors associated with clusters of FEP, aiming a) to compare clusters of FEP and community controls using the Maudsley Environmental Risk Score for psychosis (ERS), a weighted sum of the following risks: paternal age, childhood adversities, cannabis use, and ethnic minority membership; b) to explore the putative differences in specific environmental risk factors in distinguishing within patient clusters and from controls.
Methods
A univariable general linear model (GLS) compared the ERS between 1,263 community controls and clusters derived from 802 FEP patients, namely, low (n = 223) and high-cognitive-functioning (n = 205), intermediate (n = 224) and deteriorating (n = 150), from the EU-GEI study. A multivariable GLS compared clusters and controls by different exposures included in the ERS.
Results
The ERS was higher in all clusters compared to controls, mostly in the deteriorating (β=2.8, 95% CI 2.3 3.4, η2 = 0.049) and the low-cognitive-functioning cluster (β=2.4, 95% CI 1.9 2.8, η2 = 0.049) and distinguished them from the cluster with high-cognitive-functioning. The deteriorating cluster had higher cannabis exposure (meandifference = 0.48, 95% CI 0.49 0.91) than the intermediate having identical IQ, and more people from an ethnic minority (meandifference = 0.77, 95% CI 0.24 1.29) compared to the high-cognitive-functioning cluster.
Conclusions
High exposure to environmental risk factors might result in cognitive impairment and lower-than-expected functioning in individuals at the onset of psychosis. Some patients’ trajectories involved risk factors that could be modified by tailored interventions.
Cannabis use severely affects the outcome of people with psychotic disorders, yet there is a lack of treatments. To address this, in 2019 the National Health Service (NHS) Cannabis Clinic for Psychosis (CCP) was developed to support adults suffering from psychosis to reduce and/or stop their cannabis use.
Aims
Examine outcome data from the first 46 individuals to complete the CCP's intervention.
Method
The sample (N = 46) consisted of adults (aged ≥ 18) with psychosis under the care of the South London and Maudsley NHS Foundation Trust, referred to the CCP between January 2020 and February 2023, who completed their intervention by September 2023. Clinical and functional measures were collected before (T0) and after (T1) the CCP intervention (one-to-one sessions and peer group attendance). Primary outcomes were changes in the Cannabis Use Disorders Identification Test-Revised (CUDIT-R) score and pattern of cannabis use. Secondary outcomes included T0–T1 changes in measures of delusions, paranoia, depression, anxiety and functioning.
Results
A reduction in the mean CUDIT-R score was observed between T0 (mean difference = 17.10, 95% CI = 15.54–18.67) and T1, with 73.91% of participants achieving abstinence and 26.09% reducing the frequency and potency of their use. Significant improvements in all clinical and functional outcomes were observed, with 90.70% being in work or education at T1 compared with 8.70% at T0. The variance in CUDIT-R scores explained between 34 and 64% of the variance in our secondary measures.
Conclusions
The CCP intervention is a feasible strategy to support cannabis use cessation/reduction and improve clinical and functional outcomes of people with psychotic disorders.
The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.
Methods
Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank. Both datasets had information on cannabis use.
Results
In both samples, schizophrenia PRS and cannabis use independently increased risk of psychosis. Schizophrenia PRS was not associated with patterns of cannabis use in the EU-GEI cases or controls or UK Biobank cases. It was associated with lifetime and daily cannabis use among UK Biobank participants without psychosis, but the effect was substantially reduced when CUD PRS was included in the model. In the EU-GEI sample, regular users of high-potency cannabis had the highest odds of being a case independently of schizophrenia PRS (OR daily use high-potency cannabis adjusted for PRS = 5.09, 95% CI 3.08–8.43, p = 3.21 × 10−10). We found no evidence of interaction between schizophrenia PRS and patterns of cannabis use.
Conclusions
Regular use of high-potency cannabis remains a strong predictor of psychotic disorder independently of schizophrenia PRS, which does not seem to be associated with heavy cannabis use. These are important findings at a time of increasing use and potency of cannabis worldwide.
Research suggests that most mental health conditions have their onset in the critically social period of adolescence. Yet, we lack understanding of the potential social processes underlying early psychopathological development. We propose a conceptual model where daily-life social interactions and social skills form an intermediate link between known risk and protective factors (adverse childhood experiences, bullying, social support, maladaptive parenting) and psychopathology in adolescents – that is explored using cross-sectional data.
Methods
N = 1913 Flemish adolescent participants (Mean age = 13.8; 63% girls) were assessed as part of the SIGMA study, a large-scale, accelerated longitudinal study of adolescent mental health and development. Self-report questionnaires (on risk/protective factors, social skills, and psychopathology) were completed during class time; daily-life social interactions were measured during a subsequent six-day experience-sampling period.
Results
Registered uncorrected multilevel linear regression results revealed significant associations between all risk/protective factors and psychopathology, between all risk/protective factors and social processes, and between all social processes and psychopathology. Social processes (social skills, quantity/quality of daily social interactions) were uniquely predicted by each risk/protective factor and were uniquely associated with both general and specific types of psychopathology. For older participants, some relationships between social processes and psychopathology were stronger.
Conclusions
Unique associations between risk/protective factors and psychopathology signify the distinct relevance of these factors for youth mental health, whereas the broad associations with social processes support these processes as broad correlates. Results align with the idea of a social pathway toward early psychopathology, although follow-up longitudinal research is required to verify any mediation effect.
We present a new explicit formula for the determinant that contains superexponentially fewer terms than the usual Leibniz formula. As an immediate corollary of our formula, we show that the tensor rank of the $n \times n$ determinant tensor is no larger than the $n$-th Bell number, which is much smaller than the previously best-known upper bounds when $n \geq 4$. Over fields of non-zero characteristic we obtain even tighter upper bounds, and we also slightly improve the known lower bounds. In particular, we show that the $4 \times 4$ determinant over ${\mathbb{F}}_2$ has tensor rank exactly equal to $12$. Our results also improve upon the best-known upper bound for the Waring rank of the determinant when $n \geq 17$, and lead to a new family of axis-aligned polytopes that tile ${\mathbb{R}}^n$.
The COVID-19 has had major direct (e.g., deaths) and indirect (e.g., social inequities) effects in the United States. While the public health response to the epidemic featured some important successes (e.g., universal masking ,and rapid development and approval of vaccines and therapeutics), there were systemic failures (e.g., inadequate public health infrastructure) that overshadowed these successes. Key deficiency in the U.S. response were shortages of personal protective equipment (PPE) and supply chain deficiencies. Recommendations are provided for mitigating supply shortages and supply chain failures in healthcare settings in future pandemics. Some key recommendations for preventing shortages of essential components of infection control and prevention include increasing the stockpile of PPE in the U.S. National Strategic Stockpile, increased transparency of the Stockpile, invoking the Defense Production Act at an early stage, and rapid review and authorization by FDA/EPA/OSHA of non-U.S. approved products. Recommendations are also provided for mitigating shortages of diagnostic testing, medications and medical equipment.
Throughout the COVID-19 pandemic, many areas in the United States experienced healthcare personnel (HCP) shortages tied to a variety of factors. Infection prevention programs, in particular, faced increasing workload demands with little opportunity to delegate tasks to others without specific infectious diseases or infection control expertise. Shortages of clinicians providing inpatient care to critically ill patients during the early phase of the pandemic were multifactorial, largely attributed to increasing demands on hospitals to provide care to patients hospitalized with COVID-19 and furloughs.1 HCP shortages and challenges during later surges, including the Omicron variant-associated surges, were largely attributed to HCP infections and associated work restrictions during isolation periods and the need to care for family members, particularly children, with COVID-19. Additionally, the detrimental physical and mental health impact of COVID-19 on HCP has led to attrition, which further exacerbates shortages.2 Demands increased in post-acute and long-term care (PALTC) settings, which already faced critical staffing challenges difficulty with recruitment, and high rates of turnover. Although individual healthcare organizations and state and federal governments have taken actions to mitigate recurring shortages, additional work and innovation are needed to develop longer-term solutions to improve healthcare workforce resiliency. The critical role of those with specialized training in infection prevention, including healthcare epidemiologists, was well-demonstrated in pandemic preparedness and response. The COVID-19 pandemic underscored the need to support growth in these fields.3 This commentary outlines the need to develop the US healthcare workforce in preparation for future pandemics.
Throughout history, pandemics and their aftereffects have spurred society to make substantial improvements in healthcare. After the Black Death in 14th century Europe, changes were made to elevate standards of care and nutrition that resulted in improved life expectancy.1 The 1918 influenza pandemic spurred a movement that emphasized public health surveillance and detection of future outbreaks and eventually led to the creation of the World Health Organization Global Influenza Surveillance Network.2 In the present, the COVID-19 pandemic exposed many of the pre-existing problems within the US healthcare system, which included (1) a lack of capacity to manage a large influx of contagious patients while simultaneously maintaining routine and emergency care to non-COVID patients; (2) a “just in time” supply network that led to shortages and competition among hospitals, nursing homes, and other care sites for essential supplies; and (3) longstanding inequities in the distribution of healthcare and the healthcare workforce. The decades-long shift from domestic manufacturing to a reliance on global supply chains has compounded ongoing gaps in preparedness for supplies such as personal protective equipment and ventilators. Inequities in racial and socioeconomic outcomes highlighted during the pandemic have accelerated the call to focus on diversity, equity, and inclusion (DEI) within our communities. The pandemic accelerated cooperation between government entities and the healthcare system, resulting in swift implementation of mitigation measures, new therapies and vaccinations at unprecedented speeds, despite our fragmented healthcare delivery system and political divisions. Still, widespread misinformation or disinformation and political divisions contributed to eroded trust in the public health system and prevented an even uptake of mitigation measures, vaccines and therapeutics, impeding our ability to contain the spread of the virus in this country.3 Ultimately, the lessons of COVID-19 illustrate the need to better prepare for the next pandemic. Rising microbial resistance, emerging and re-emerging pathogens, increased globalization, an aging population, and climate change are all factors that increase the likelihood of another pandemic.4
The Society for Healthcare Epidemiology in America (SHEA) strongly supports modernization of data collection processes and the creation of publicly available data repositories that include a wide variety of data elements and mechanisms for securely storing both cleaned and uncleaned data sets that can be curated as clinical and research needs arise. These elements can be used for clinical research and quality monitoring and to evaluate the impacts of different policies on different outcomes. Achieving these goals will require dedicated, sustained and long-term funding to support data science teams and the creation of central data repositories that include data sets that can be “linked” via a variety of different mechanisms and also data sets that include institutional and state and local policies and procedures. A team-based approach to data science is strongly encouraged and supported to achieve the goal of a sustainable, adaptable national shared data resource.
Marine litter poses a complex challenge in Indonesia, necessitating a well-informed and coordinated strategy for effective mitigation. This study investigates the seasonality of plastic concentrations around Sulawesi Island in central Indonesia during monsoon-driven wet and dry seasons. By using open data and methodologies including the HYCOM and Parcels models, we simulated the dispersal of plastic waste over 3 months during both the southwest and northeast monsoons. Our research extended beyond data analysis, as we actively engaged with local communities, researchers and policymakers through a range of outreach initiatives, including the development of a web application to visualize model results. Our findings underscore the substantial influence of monsoon-driven currents on surface plastic concentrations, highlighting the seasonal variation in the risk to different regional seas. This study adds to the evidence provided by coarser resolution regional ocean modelling studies, emphasizing that seasonality is a key driver of plastic pollution within the Indonesian archipelago. Inclusive international collaboration and a community-oriented approach were integral to our project, and we recommend that future initiatives similarly engage researchers, local communities and decision-makers in marine litter modelling results. This study aims to support the application of model results in solutions to the marine litter problem.
Edited by
David Kingdon, University of Southampton,Paul Rowlands, Derbyshire Healthcare NHS foundation Trust,George Stein, Emeritus of the Princess Royal University Hospital
Psychosis is characterized by distortions in thinking (e.g. fixed, false beliefs), in perception (e.g. hearing voices or less commonly seeing things that are not there), emotions, language, sense of self and behaviour. Although it used to be thought that schizophrenia was a discrete entity, much recent evidence has shown that this is not so. Schizophrenia does not have clear boundaries; rather, it merges into schizoaffective disorder and bipolar disorder on the one hand and into schizotypal and paranoid personality on the other. It is best considered as the severe form of psychosis. The different psychotic disorders share some of the same risk factors and are sometimes associated with cognitive impairments, co-existing mental health conditions, substance misuse and physical health problems; the latter often develop over the course of the illness.
In this chapter, we review genetic and then environmental risk factors for psychosis. Much knowledge has accumulated regarding both in the last two decades. We now know that the aetiology of psychosis is multifactorial. Genetic and environmental factors occasionally act alone but usually in combination as well as operate at a number of levels and over time to influence an individual’s likelihood of developing psychotic symptoms.
Under enrollment of participants in clinical research is costly and delays study completion to impact public health. Given that research personnel make decisions about which strategies to pursue and participants are the recipients of these efforts, we surveyed research staff (n = 52) and participants (n = 4,144) affiliated with SPARK (Simons Foundation Powering Autism for Knowledge) – the largest study of autism in the U.S. – to understand their perceptions of effective recruitment strategies.
Methods:
In Study 1, research personnel were asked to report recruitment strategies that they tried for SPARK and to indicate which ones they would and would not repeat/recommend. In Study 2, SPARK participants were asked to indicate all the ways they heard about the study prior to enrollment and which one was most influential in their decisions to enroll.
Results:
Staff rated speaking with a SPARK-study-team member (36.5%), speaking with a medical provider (19.2%), word of mouth (11.5%), and a live TV news story (11.5%) as the most successful strategies. Participants most often heard about SPARK via social media (47.0%), speaking with a medical provider (23.1%), and an online search (20.1%). Research personnel’s and participants’ views on effective recruitment strategies often differed, with the exception of speaking with a medical provider.
Conclusion:
Results suggest that a combination of strategies is likely to be most effective in reaching diverse audiences. Findings have implications for the selection of strategies that meet a study’s specific needs, as well as recruitment-strategy “combinations” that may enhance the influence of outreach efforts.