The development and validation of the pandemic medication-assisted treatment questionnaire for the assessment of pandemic crises impact on medication management and administration for patients with opioid use disorders.

Pandemic and the globally applied restriction measures mainly affect vulnerable population groups, such as patients with opioid use disorders. Towards inhibiting SARS-Cov-2 spread, the medication-assisted treatment (MAT) programs follow strategies targeting the reduction of in-person psychosocial interventions and an increase of take-home doses. However, there is no available instrument to examine the impact of such modifications on diverse health aspects of patients under MAT. The aim of this study was to develop and validate the PANdemic Medication-Assisted Treatment Questionnaire (PANMAT/Q) to address the pandemic effect on the management and administration of MAT. In total, 463 patients under ΜΑΤ participated. Our findings indicate that PANMAT/Q has been successfully validated exerting reliability and validity. It can be completed within approximately 5 min, and its implementation in research settings is advocated. PANMAT/Q could serve as a useful tool to identify the needs of patients under MAT being at high risk of relapse and overdose.

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project.

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP). Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-022-00171-1.