The Transition of Sociodemographic and Substance Abuse Characteristics, Pairwise Co-occurrences and Factors Associated with Polysubstance Use Among US Adolescents and Young Adults.

Background: Substance abuse by adolescents and young adults is a major public health issue. This study aimed to (i) show the transition of sociodemographic and substance abuse characteristics from 1992 to 2017 among US adolescents and young adults, (ii) evaluate the likelihood of co-occurrence of substances, and (iii) identify significant sociodemographic characteristics in association with polysubstance abuse. Methods: This study extracted data for adolescents and young adults from 1992 and 2017 Treatment Episode Data Set-Admission (TEDS-A) datasets. The extracted sample included 337858 admissions in 1992 and 333322 in 2017. Findings: Both years experienced significant admissions. A significant transition in 2017 compared to 1992 was evident in education, living status, and ethnicity. Substance-specific transition showed alcohol was dominant in 1992, while marijuana/ hashish was dominant in 2017. Also, heroin, other opiates/synthetics, and methamphetamine experienced an increase, while cocaine/crack decreased. The pairwise co-occurrences exhibited a considerable variation in the likelihood of using one substance given another one. The odds ratios (ORs) obtained from generalized ordered logit models showed significantly higher odds of one or more substances with age, while education showed the opposite scenario. A mixed effect of gender was evident in 1992, whereas females were significantly less likely with one or more substances than males in 2017. Other significant vulnerable groups were those not in the labor force, homeless, white, and Mexican Americans. Conclusion: The findings may help to understand the overall changes between 1992 and 2017 and take necessary measures to reduce the burden of this public health problem.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic.

OBJECTIVES: COVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk. METHODS: For analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality. CONCLUSION: Data monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.