IMPORTANCEDuring the pandemic, access to medical care unrelated to COVID-19 was limited because of concerns about viral spread and corresponding policies. It is critical to assess how these conditions affected modes of pain treatment, given the addiction risks of prescription opioids. OBJECTIVE To assess the trends in opioid prescription and nonpharmacologic therapy (ie, physical therapy and complementary medicine) for pain management during the COVID-19 pandemic in 2020 compared with the patterns in 2019. DESIGN, SETTING, AND PARTICIPANTS This retrospective, cross-sectional study used weekly claims data from 24 million US patients in a nationwide commercial insurance database (Optum's deidentified Clinformatics Data Mart Database) from January 1, 2019, to September 31, 2020. Among patients with diagnoses of limb, extremity, or joint pain, back pain, and neck pain for each week, patterns of treatment use were identified and evaluated. Data analysis was performed from April 1, 2021, to September 31, 2021. MAIN OUTCOMES AND MEASURESThe main outcomes of interest were weekly rates of opioid prescriptions, the strength and duration of related opioid prescriptions, and the use of nonpharmacologic therapy. Transition rates between different treatment options before the outbreak and during the early months of the pandemic were also assessed.

This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient’s prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics.

Background and aims Prescription drug‐seeking (PDS) from multiple prescribers is a primary means of obtaining prescription opioids; however, PDS behavior has probably evolved in response to policy shifts, and there is little agreement about how to operationalize it. We systematically compared the performance of traditional and novel PDS indicators. Design Longitudinal study using a de‐identified commercial claims database. Setting United States, 2009–18. Participants A total of 318 million provider visits from 21.5 million opioid‐prescribed patients. Measurements We applied binary classification and generalized linear models to compare predictive accuracy and average marginal effect size predicting future opioid use disorder (OUD), overdose and high morphine milligram equivalents (MME). We compared traditional indicators of PDS to a network centrality measure, PageRank, that reflects the prominence of patients in a co‐prescribing network. Analyses used the same data and adjusted for patient demographics, region, SES, diagnoses and health services. Findings The predictive accuracy of a widely used traditional measure (N + unique doctors and N + unique pharmacies in 90 days) on OUD, overdose and MME decreased between 2009 and 2018, and performed no better than chance (50% accuracy) after 2015. Binarized PageRank measures however exhibited higher predictive accuracy than the traditional binary measures throughout 2009‐2018. Continuous indicators of PDS performed better than binary thresholds, with days of Rx performing best overall with 77–93% predictive accuracy. For example, days of Rx had the highest average marginal effects on overdose and OUD: a 1 standard deviation increase in days of Rx was associated with a 6–8% [confidence intervals (CIs) = 0.058–0.061 and 0.078–0.082] increase in the probability of overdose and a 4–5% (CIs = 0.038–0.043 and 0.047–0.053) increase in the probability of OUD. PageRank performed nearly as well or better than traditional indicators of PDS, with predictive performance increasing after 2016. Conclusions In the United States, network‐based measures appear to have increasing promise for identifying prescription opioid drug‐seeking behavior, while indicators based on quantity of providers or pharmacies appear to have decreasing utility.

In the past two decades, researchers have examined the practices of online forums operating markets for the sale of stolen credit card data. Participants cannot rely on traditional legal system regulations in the event of disputes between buyers and sellers. Thus, this analysis focuses on two forms of monitoring within these forums: one based on an emergent social network of transactions among community members (second-party monitoring) and the other consisting of regulatory (third-party) monitoring by forum administrators. Using social network analyses of a series of posts from a data market forum, the findings demonstrate that governance of these forums is enabled by their functioning as a particular kind of market that economists characterize as a platform, or two-sided market. Specifically, second- and third-party trust-creating mechanisms are vital in establishing sustainability in illicit markets by increasing perceived market trustworthiness, which in turn leads to increased market demand.

We examine how social media may facilitate protest mobilization in response to violent state repression. Prior research demonstrates that violent repression can either decrease protest participation through raising the costs of participation, or can generate outrage, resulting in “backfire” and an increase in mobilization. Many recent mass mobilizations have garnered attention from scholars and journalists alike due to the instances of repression backfiring as well as the widespread use of social media in these protest movements. We examine why these two trends may be related using logistic regression analysis on data on participants in the Gezi Park Protests in summer 2013. Controlling for confounding factors, we find a statistically significant relationship between being recruited to participate in the protests through social media and joining the mass mobilization as a reaction to police repression. We argue that in the case of Gezi Park, communication through social media was a key factor in facilitating social movement mobilization in response to repression.