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IntroductionThe wide range of psychosocial interventions designed to assist people with Autism Spectrum Disorder (ASD) makes it challenging to compile and hierarchize the scientific evidence that supports the efficacy of these interventions. Thus, we performed an umbrella review of published meta-analyses of controlled clinical trials that investigated the efficacy of psychosocial interventions on both core and related ASD symptoms.MethodsEach meta-analysis that was identified was re-estimated using a random-effects model with a restricted maximum likelihood estimator. The methodological quality of included meta-analyses was critically appraised and the credibility of the evidence was assessed algorithmically according to criteria adapted for the purpose of this study.ResultsWe identified a total of 128 meta-analyses derived from 44 reports. More than half of the non-overlapping meta-analyses were nominally statistically significant and/or displayed a moderate-to-large pooled effect size that favored the psychosocial interventions. The assessment of the credibility of evidence pointed out that the efficacy of early intensive behavioral interventions, developmental interventions, naturalistic developmental behavioral interventions, and parent-mediated interventions was supported by suggestive evidence on at least one outcome in preschool children. Possible outcomes included social communication deficits, global cognitive abilities, and adaptive behaviors. Results also revealed highly suggestive indications that parent-mediated interventions improved disruptive behaviors in early school-aged children. The efficacy of social skills groups was supported by suggestive evidence for improving social communication deficits and overall ASD symptoms in school-aged children and adolescents. Only four meta-analyses had a statistically significant pooled effect size in a sensitivity analysis restricted to randomized controlled trials at low risk of detection bias.DiscussionThis umbrella review confirmed that several psychosocial interventions show promise for improving symptoms related to ASD at different stages of life. However, additional well-designed randomized controlled trials are still required to produce a clearer picture of the efficacy of these interventions. To facilitate the dissemination of scientific knowledge about psychosocial interventions for individuals with ASD, we built an open-access and interactive website that shares the information collected and the results generated during this umbrella review.Pre-registrationPROSPERO ID CRD42020212630.

ObjectiveUmbrella reviews are a new form of literature review that summarises the strength and/or quality of the evidence from all systematic reviews and meta-analyses conducted on a broad topic. This type of review thus provides an exhaustive examination of a vast body of information, providing the highest synthesis of knowledge. A critical strength of umbrella reviews is recalculating the meta-analytic estimates within a uniform framework to allow a consistent evidence stratification. To our best knowledge, there is no comprehensive package or software to conduct umbrella reviews.MethodsThe R package metaumbrella accomplishes this aim by building on three core functions that (1) automatically perform all required calculations in an umbrella review (including but not limited to pairwise meta-analyses), (2) stratify evidence according to various classification criteria and (3) generate a visual representation of the results. In addition, this package allows flexible inputs for each review or meta-analysis analysed (eg, means plus SD, or effect size estimate and CI) and customisation (eg, stratification criteria following Ioannidis, algorithmic GRADE or personalised classification).ResultsThe R package metaumbrella thus provides the first comprehensive range of facilities to perform umbrella reviews with stratification of the evidence.ConclusionTo facilitate the use of this package, even for researchers unfamiliar with R, we also provide a JAMOVI module and an open-access, browser-based graphical interface that allow use of the core functions of the package with a few mouse clicks.

•The effects of the site may bias accuracy in machine learning multisite MRI studies.•We provide methods for measuring the removal of the effects of the site.•We provide a benchmarking for methods for different machine-learning algorithms.•ComBat removes the effects of the site that could bias lasso.•ComBat only partially removes the effects of the site for standard support vector machine. Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms.

Systematic reviews and meta-analyses have become crucial for evidence-based decision-making in recent decades. However, it is common for the results of multiple reviews on the same topic to be inconsistent, and it is widely recognised that the results of the reviews are not always effectively communicated to healthcare professionals and the lay public. This manuscript proposes a strategy to summarise and communicate the findings of previous systematic reviews and meta-analyses to wider audiences. The proposed approach couples the findings of umbrella reviews with the creation of open-access online platforms that present the results of these umbrella reviews in an accessible way to various stakeholders. The key potential methodological avenues of this approach are presented, and specific examples from the author’s own works and those from other teams are provided. An accompanying website (https://u-reach.org/) has been designed to present this Umbrella-Review, Evaluation, Analysis, and Communication Hub (U-REACH) approach and to overcome the technical challenges associated with this type of project (by sharing the code used to build existing U-REACH projects). The present document is intended to serve as a methodological and technical guide for the creation of large-scale projects designed to synthesise and disseminate scientific information to a broad audience.

IntroductionAttention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with symptoms, especially the hyperactive ones, that tend to decrease in severity with age. Interestingly, children born just before the school-entry cut-off date (ie, the youngest pupils of a classroom) are at higher risk of being diagnosed with ADHD compared with children born just after the cut-off date. Noteworthy, this month-of-birth effect tends to disappear with increasing absolute age. Therefore, it is possible that young children erroneously diagnosed with ADHD due to their month of birth present a lower chance to have their diagnosis confirmed at a later age, artificially reinforcing the low persistence of ADHD across the lifespan. This protocol outlines an individual patient data (IPD) meta-analysis of prospective observational studies to explore the role of the month of birth in the low persistence of ADHD across the lifespan.Methods and analysisFive databases will be systematically searched in order to find prospective observational studies where the presence of ADHD is assessed both at baseline and at a follow-up of at least 4 years. We will use a two-stage IPD meta-analytic approach to estimate the role of the month of birth in the persistence of ADHD. Various sensitivity analyses will be performed to assess the robustness of the results.Ethics and disseminationNo additional data will be collected and no de-identified raw data will be used. Ethics approval is thus not required for the present study. Results of this IPD meta-analysis will be submitted for publication in a peer-reviewed journal.PROSPERO registration numberCRD42020212650

Despite the growing interest in the prevalence and consequences of loneliness, the way it is measured still raises a number of questions. In particular, few studies have directly compared the psychometric properties of very short measures of loneliness to standard measures. We conducted a large epidemiological study of midwife students ( = 1742) and performed a head-to-head comparison of the psychometric properties of the standard (20 items) and short version (3 items) of the UCLA Loneliness Scales (UCLA-LS). All participants completed the UCLA-LS-20, UCLA-LS-3, as well as other measures of mental health, including anxiety and depression. First, as predicted, we found that the two loneliness scales were strongly associated with each other. Second, when using the dimensional scores of the scales, we showed that the internal reliability, convergent-, discriminant-, and known-groups validities were high and of similar magnitude between the UCLA-LS-20 and the UCLA-LS-3. Third, when the scales were dichotomized, the results were more mixed. The sensitivity and/or specificity of the UCLA-LS-3 against the UCLA-LS-20 were systematically below acceptable thresholds, regardless of the dichotomizing process used. In addition, the prevalence of loneliness was strikingly variable as a function of the cut-offs used. Overall, we showed that the UCLA-LS-3 provided an adequate dimensional measure of loneliness that is very similar to the UCLA-LS-20. On the other hand, we were able to highlight more marked differences between the scales when their scores were dichotomized, which has important consequences for studies estimating, for example, the prevalence of loneliness.

Risk-aversion and rationality have both been highlighted as core features of decision making in individuals with Autism Spectrum Disorders (ASD). This study tested whether risk-aversion is related to rational decision-making in ASD individuals. ASD and matched control adults completed a decision-making task that discriminated between the use of risk-averse and rational strategies. Results showed that overall, ASD participants were more risk-averse than control participants. Specifically, both groups made similar choices when risk-aversion was the less rational strategy but ASD participants chose more rational options than control participants when risk-aversion was the most rational strategy. This study confirmed that risk-aversion is a core feature of ASD and revealed that ASD individuals can switch their decision-making strategy adaptively to avoid negative consequences.

Data on incidence, prevalence and burden of ADHD are crucial for clinicians, patients, and stakeholders. We present the incidence, prevalence, and burden of ADHD globally and across countries from 1990 to 2019 from the Global Burden of Disease (GBD) study. We also: (1) calculated the ADHD prevalence based on data actually collected as opposed to the prevalence estimated by the GBD with data imputation for countries without prevalence data; (2) discussed the GBD estimated ADHD burden in the light of recent meta-analytic evidence on ADHD-related mortality. In 2019, GBD estimated global age-standardized incidence and prevalence of ADHD across the lifespan at 0.061% (95%UI = 0.040–0.087) and 1.13% (95%UI = 0.831–1.494), respectively. ADHD accounted for 0.8% of the global mental disorder DALYs, with mortality set at zero by the GBD. From 1990 to 2019 there was a decrease of −8.75% in the global age-standardized prevalence and of −4.77% in the global age-standardized incidence. The largest increase in incidence, prevalence, and burden from 1990 to 2019 was observed in the USA; the largest decrease occurred in Finland. Incidence, prevalence, and DALYs remained approximately 2.5 times higher in males than females from 1990 to 2019. Incidence peaked at age 5–9 years, and prevalence and DALYs at age 10–14 years. Our re-analysis of data prior to 2013 showed a prevalence in children/adolescents two-fold higher (5.41%, 95% CI: 4.67–6.15%) compared to the corresponding GBD estimated prevalence (2.68%, 1.83–3.72%), with no significant differences between low- and middle- and high-income countries. We also found meta-analytic evidence of significantly increased ADHD-related mortality due to unnatural causes. While it provides the most detailed evidence on temporal trends, as well as on geographic and sex variations in incidence, prevalence, and burden of ADHD, the GBD may have underestimated the ADHD prevalence and burden. Given the influence of the GBD on research and policies, methodological issues should be addressed in its future editions.

Recent research has shown that sleep is associated with moral judgment. Most of these studies have focused on moral awareness and unethical behaviors but far fewer have examined the impact of sleep on moral utilitarianism. We report a set of six preregistered cross-sectional studies which explore the association between moral utilitarianism and sleep quantity and quality at both the acute and chronic levels. A total of 582 participants drawn from diverse populations (USA, UK and France) addressed various measures of sleep quantity, sleep quality, and moral utilitarianism. We report a meta-analysis which showed only a weak association between sleep and moral utilitarianism. Despite the heterogeneity in the samples and methods employed, equivalence tests ruled out the possibility that we missed medium to strong effect sizes. We discuss the implication of these findings in the light of the moral judgment literature.

Autism spectrum disorder (ASD) substantially contributes to the burden of mental disorders. Improved awareness and changes in diagnostic criteria of ASD may have influenced the diagnostic rates of ASD. However, while data on trends in diagnostic rates in some individual countries have been published, updated estimates of diagnostic rate trends and ASD-related disability at the global level are lacking. Here, we used the Global Burden of Diseases, Injuries, and Risk Factors Study data to address this gap, focusing on changes in prevalence, incidence, and disability-adjusted life years (DALYs) of ASD across the world. From 1990 to 2019, overall age-standardized estimates remained stable globally. Both prevalence and DALYs increased in countries with high socio-demographic index (SDI). However, the age-standardized incidence decreased in some low SDI countries, indicating a need to improve awareness. The male/female ratio decreased between 1990 and 2019, possibly accounted for by increasing clinical attention to ASD in females. Our results suggest that ASD detection in low SDI countries is suboptimal, and that ASD prevention/treatment in countries with high SDI should be improved, considering the increasing prevalence of the disorder. Additionally, growing attention is being paid to ASD diagnosis in females, who might have been left behind by ASD epidemiologic and clinical research previously. ASD burden estimates are underestimated as GBD does not account for mortality in ASD.