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  1. Abstract Background Opioid-related overdose death is a public health epidemic in much of the USA, yet little is known about how people who use opioids (PWUO) experience overdose deaths in their social networks. We explore these experiences through a qualitative study of opioid-related overdose death bereavement among PWUO. Methods We recruited 30 adults who inject opioids from a syringe service program in the Midwestern USA and interviewed them using a semi-structured guide that addressed experiences of opioid use, opioid-related overdose, and overdose reversal via the medication naloxone. Interviews were transcribed verbatim and analyzed thematically. Findings Participants described overdose death as ever-present in their social worlds. Most (approximately 75%) reported at least one overdose death in their social network, and many came to consider death an inevitable end of opioid use. Participants described grief shaped by complex social relations and mourning that was interrupted due to involvement with social services and criminal legal systems. They also reported several ways that overdose deaths influenced their drug use, with some increasing their use and others adopting safer drug use practices. Despite the high prevalence of overdose deaths in their social networks, only one participant reported receiving grief support services. Discussion Findings underscore the need for interventions that not only maintain life, such as naloxone distribution, but also improve quality of life by attending to grief related to overdose death bereavement. We discuss policies and practices with the potential to address the unique psychological, social, and structural challenges of grief for this population. 
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  2. Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.

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  3. The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an N th-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models. 
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