The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

EQ-5D in Central and Eastern Europe : 2000-2015

Objective: Cost per quality-adjusted life year data are required for reimbursement decisions in many Central and Eastern European (CEE) countries. EQ-5D is by far the most commonly used instrument to generate utility values in CEE. This study aims to systematically review the literature on EQ-5D from eight CEE countries. Methods: An electronic database search was performed up to July 1, 2015 to identify original EQ-5D studies from the countries of interest. We analysed the use of EQ-5D with respect to clinical areas, methodological rigor, population norms and value sets. Results: We identified 143 studies providing 152 country-specific results with a total sample size of 81,619: Austria (n=11), Bulgaria (n=6), Czech Republic (n=18), Hungary (n=47), Poland (n=51), Romania (n=2), Slovakia (n=3) and Slovenia (n=14). Cardiovascular (20%), neurologic (16%), musculoskeletal (15%) and endocrine/nutritional/metabolic diseases (14%) were the most frequently studied clinical areas. Overall 112 (78%) of the studies reported EQ VAS results and 86 (60%) EQ-5D index scores, of which 27 (31%) did not specify the applied tariff. Hungary, Poland and Slovenia have population norms. Poland and Slovenia also have a national value set. Conclusions: Increasing use of EQ-5D is observed throughout CEE. The spread of health technology assessment activities in countries seems to be reflected in the number of EQ-5D studies. However, improvement in informed use and methodological quality of reporting is needed. In jurisdictions where no national value set is available, in order to ensure comparability we recommend to apply the most frequently used UK tariff. Regional collaboration between CEE countries should be strengthened

Prevalence and determinants of substance use among a sample of Iranian adolescents with ease of access to drugs: an application of Social Development Model

The aim of this study was to investigate the prevalence rate and determinants of SU in adolescents based on the social development model (SDM). In 2018, applying a cross-sectional design, cluster multistage random sampling was employed to recruit 600 adolescents in Bam County, Iran, to participate in the study. A valid and reliable SDM-based instrument was used to collect data. The prevalence rate of using at least one substance was 42 (in girls 33.6 and in boys 50.3). Adjusted for covariates, having close friends with SU was found as the factor with the highest risk. Higher scores in involvement in prosocial activities and interactions (OR: 0.47; 95 Confidence interval (CI): 0.33�0.66, p < 0.001), attachment and commitment to prosocial others (family and school) (OR: 0.73; 95 CI: 0.58�0.93, p < 0.05), and skills for interaction/involvement (OR: 0.51; 95CI: 0.39�0.67, p < 0.001) reduced the odds of ever use of SU among adolescents. Also, higher levels of perceived rewards for antisocial interaction/involvement (OR: 2.22; 95 Confidence interval (CI): 1.53�3.22, p < 0.001) and belief in antisocial values (OR: 2.24; 95 CI: 1.67�2.94, p < 0.001) increased the odds of ever use SU among the respondents. In community-based interventions to prevent SU among adolescents, a great focus should be firstly on identifying the probability of SU in close friends. Moreover, the involvement of adolescents in prosocial activities and interactions, attachment and commitment to prosocial others (family and school), and skills for interaction/involvement should be core categories while designing community-based interventional studies. © 2020 Institute of Health Promotion and Education