The addictive use of video games is recognized as a problem with clinical relevance and is included in international diagnostic manuals and classifications of diseases. The association between “Internet addiction” and mental health has been well documented across a range of investigations. However, a major drawback of these studies is that no controls have been placed on the type of Internet use investigated. The aim of this study is to review systematically the current literature in order to explore the association between Internet Gaming Disorder (IGD) and psychopathology. An electronic literature search was conducted using PubMed, PsychINFO, ScienceDirect, Web of Science and Google Scholar (r.n. CRD42018082398). The effect sizes for the observed correlations were identified or computed. Twenty-four articles met the eligibility criteria. The studies included comprised 21 cross-sectional and three prospective designs. Most of the research was conducted in Europe. The significant correlations reported comprised: 92% between IGD and anxiety, 89% with depression, 85% with symptoms of attention deficit hyperactivity disorder (ADHD), and 75% with social phobia/anxiety and obsessive-compulsive symptoms. Most of the studies reported higher rates of IGD in males. The lack of longitudinal studies and the contradictory results obtained prevent detection of the directionality of the associations and, furthermore, show the complex relationship between both phenomena.

Internet Gaming Disorder is an increasingly prevalent disorder, which can have severe consequences in affected young people and in their families. There is an urgent need to improve existing treatment programs; these are currently hampered by the lack of research in this area. It is necessary to more carefully define the symptomatic, psychosocial and personality characterization of these patients and the interaction between treatment and relevant variables. The objectives of this study were three: (1) to analyze the symptomatic and personality profiles of young patients with Internet Gaming Disorder in comparison with healthy controls; (2) to analyze the effectiveness of a cognitive behavioral treatment on reducing symptomatology; and (3) to compare the results of that treatment with or without the addition of a psychoeducational group offered to the parents. The final sample consisted of 30 patients consecutively admitted to a specialized mental health unit in Spain, and 30 healthy controls. The experimental group received individual cognitive-behavioral therapy. The experimental group was divided into two subgroups (N = 15), depending on the addition or not of a psychoeducational group for their parents (consecutively admitted). Scores on the Millon Adolescent Personality Inventory (MACI), the Symptom Checklist-Revised (SCL-90-R), the State-Trait Anxiety Index (STAI), and other clinical and psychopathological measures were recorded. The patients were re-assessed post treatment (except for the MACI questionnaire). Compared with healthy controls, patients did not differ in symptomatology at baseline, but scored significantly higher in the personality scales: Introversive and Inhibited, and in the expressed concerns scales: Identity Confusion, Self-Devaluation, and Peer Insecurity and scored significantly lower in the Histrionic and Egotistic scale. In the experimental group, pre-post changes differed statistically on SCL-90-R scales Hostility, Psychoticism, and Global Severity Index and on the diagnostic criteria for Internet Gaming Disorder, regardless of the addition of a psychoeducational group for parents. Pre-post changes did not differ between experimental subgroups. However, the subgroup without psychoeducation for parents presented statistically higher drop-out rates during treatment. The results of this study are based on a sample of patients seeking treatment related to problems with online gaming, therefore, they may be of value for similar patients.

Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Computational statistics and data analysis. URL d'aquest document a UPCommons E-prints:

ObjectivesOur aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates.DesignThe total sample was 72 140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50–64 and 65+ years old).ResultsThree clusters were identified: ‘cardio-metabolic’ (8.93% in participants aged 50–64 years and 27.22% in those aged 65+ years), ‘respiratory-mental-articular’ (3.91% and 5.27%) and ‘healthy’ (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the ‘cardio-metabolic’ group (18.8%) and England the ‘respiratory-mental-articular’ (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the ‘healthy’ class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the ‘respiratory-mental-articular’.ConclusionMultimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity.

No abstract

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports (of 218 countries) with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses are adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM 10, PM 2.5 and O 3 ). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The significant relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets.Electronic supplementary materialThe online version of this article (doi:10.1007/s11336-016-9503-3) contains supplementary material, which is available to authorized users.

No abstract