Background: This systematic review aims to summarize latent classes of polysubstance use in adolescents (10-19 years), and to describe predictors of polysubstance use.
Methods: A systematic literature review was conducted in three databases (PUBMED, PsycINFO, PsycARTICLES) to identify peer-reviewed articles on latent classes of adolescent polysubstance use (published through June 30, 2015), and to assess the comparability of their results.
Results: 23 studies (N=450-N=419,698) met the inclusion criteria. The studies showed predominantly (18 studies) average to low risk of bias. 17 studies (74%) identified between three or four latent classes, with "no use" or "low use" classes being the largest and "polysubstance use" being the smallest ones. Intermediate classes included extensive single substance use, such as "alcohol only" classes. Polysubstance use classes were unanimously predicted by higher age, higher parental and peer substance use, and poor academic performance, other predictors were highly heterogeneous.
Conclusions: Latent classes deliver solid information on polysubstance use in adolescence. Despite their sample sensitivity, the studies possess manifold similarities, hence, modeling latent classes seems to be an ecologically valid approach to further research, e.g., for subgroup analyses or on substance use trajectories. Finally, latent classes may help to illustrate differential effects and special groups in prevention and treatment that depend on the actual consumption pattern. However, there are certain methodological recommendations to be considered in order to obtain reliable results.
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http://dx.doi.org/10.1016/j.drugalcdep.2015.11.035 | DOI Listing |
Transportmetr A Transp Sci
February 2023
Department of Transport and Planning, Delft University of Technology, Delft, Netherlands.
On-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations.
View Article and Find Full Text PDFSci Rep
January 2025
School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result.
View Article and Find Full Text PDFHand, foot and mouth disease (HFMD) is a major public health issue in Hubei Province; however, research on the direct and indirect effects of factors affecting HFMD is limited. This study employed structural equation modeling (SEM) and geographically weighted regression (GWR) to investigate the various impacts and spatial variations in the factors influencing the HFMD epidemic in Hubei Province from 2016 to 2018. The results indicated that (1) with respect to the direct effects, the number of primary school students had the greatest positive direct effect on the number of HFMD cases, with a coefficient of 0.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia.
Importance: Multisystem inflammatory syndrome in children (MIS-C) is an uncommon but severe hyperinflammatory illness that occurs 2 to 6 weeks after SARS-CoV-2 infection. Presentation overlaps with other conditions, and risk factors for severity differ by patient. Characterizing patterns of MIS-C presentation can guide efforts to reduce misclassification, categorize phenotypes, and identify patients at risk for severe outcomes.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Health Policy and Management, Fielding School of Public Health, UCLA, Los Angeles, CA 90095, United States.
Objective: To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups.
Materials And Methods: Data from the Rock Health Digital Health Consumer Adoption Survey from 2018, 2019, 2020, and 2022 were analyzed. This study comprised a Census-matched representative sample of U.
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