Dehydrative alkynylation of 3-hydroxyisoindolinones with terminal alkynes for the synthesis of 3-alkynylated 3,3-disubstituted isoindolinones.

A brand-new procedure for the synthesis of 3-alkynylated 3,3-disubstituted isoindolinones has been disclosed via a HOTf or Fe(OTf)3-catalyzed dehydrative alkynylation of 3-hydroxyisoindolinones with terminal alkynes. Aryl, alkenyl and alkyl terminal alkynes are suitable to couple with a broad range of 3-hydroxyisoindolinones to afford the desired products in moderate to good yields. This protocol features the use of an inexpensive catalyst, mild reaction conditions, broad substrate scope and easy elaboration of the products.

Tandem phospha-Michael addition/cyclization/dehydration of 2-hydroxychalcones with H-phosphine oxides for the synthesis of 4-phosphorylated 4H-chromenes.

A Hg(OTf)2-catalyzed tandem phospha-Michael addition/cyclization/dehydration of 2-hydroxychalcones with H-phosphine oxides is presented. This protocol provides a new and supplementary approach for the preparation of 4-phosphorylated 4H-chromenes in good yields (up to 99%). In addition, this domino reaction allows the successful construction of two new C-P and C-O bonds in a one-pot operation.

One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.

BACKGROUND: Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. OBJECTIVE: Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. METHODS: We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. RESULTS: Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. CONCLUSIONS: The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises.

Copper(II)-catalyzed direct dehydrative alkynylation of 2H-chromene hemiketals with terminal alkynes to 2,2-disubstituted 2-alkynylated 2H-chromenes.

The first copper-catalyzed direct dehydrative alkynylation of 2H-chromene hemiketals with terminal alkynes has been uncovered. The use of cheap and readily available CuCl2 as the catalyst allowed the preparation of various 2,2-disubstituted 2-alkynylated 2H-chromenes in moderate to good yields, which compensates for the limitation of the current methods only suited for the synthesis of 2-monosubsituted 2-alkynylated 2H-chromenes.

Advantage of Being Multicomponent and Spatial: Multipartite Viruses Colonize Structured Populations with Lower Thresholds.

Multipartite viruses have a genome divided into different disconnected viral particles. A majority of multipartite viruses infect plants; very few target animals. To understand why, we use a simple, network-based susceptible-latent-infectious-recovered model. We show both analytically and numerically that, provided that the average degree of the contact network exceeds a critical value, even in the absence of an explicit microscopic advantage, multipartite viruses have a lower threshold to colonizing network-structured populations compared to a well-mixed population. We further corroborate this finding on two-dimensional lattice networks, which better represent the typical contact structures of plants.

Clerodane diterpenoids with in-vitro anti-neuroinflammatory activity from the tuberous root of Tinospora sagittata (Menispermaceae).

Twenty-six clerodane diterpenoids have been isolated from T. sagittata, a plant species of traditional Chinese medicine Radix Tinosporae, also named as "Jin Guo Lan". Among them, there are eight previously undescribed clerodane diterpenoids (tinotanoids A-H: 1-8), and 18 known diterpenoids (9-26). The absolute configurations of compounds 1, 2, 5, 8, 13, 17 and 20 were determined by single-crystal X-ray diffraction. Compound 1 is the first example of rotameric clerodane diterpenoid with a γ-lactone ring which is constructed between C-11 and C-17; meanwhile, compounds 3 and 4 are two pairs of inseparable epimers. Compounds 2, 12 and 17 demonstrated excellent inhibitory activity on NO production against LPS-stimulated BV-2 cells with IC50 values of 9.56 ± 0.69, 9.11 ± 0.53 and 11.12 ± 0.70 µM, respectively. These activities were significantly higher than that of the positive control minocycline (IC50 = 23.57 ± 0.92 µM). Moreover, compounds 2, 12 and 17 dramatically reduced the LPS-induced upregulation of iNOS and COX-2 expression. Compounds 2 and 12 significantly inhibited the levels of pro-inflammatory cytokines TNF-α, IL-1ß and IL-6 that were increased by LPS stimulation.

Exposure to social bots amplifies perceptual biases and regulation propensity.

Automated accounts on social media that impersonate real users, often called "social bots," have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.

Cortical superficial siderosis, hematoma volume, and outcomes after intracerebral hemorrhage: a mediation analysis.

Background: Previous studies have shown that cortical superficial siderosis (cSS) can increase hematoma volume and predict poor outcomes following primary intracerebral hemorrhage (ICH). Objective: We aimed to determine whether a large hematoma volume was the essential factor contributing to worse outcomes of cSS. Methods: Patients with spontaneous ICH underwent a CT scan within 48 h after ictus. Evaluation of cSS was performed using magnetic resonance imaging (MRI) within 7 days. The 90-day outcome was assessed using the modified Rankin Scale (mRS). In addition, we investigated the correlation between cSS, hematoma volume, and 90-day outcomes using multivariate regression and mediation analyses. Results: Among the 673 patients with ICH [mean (SD) age, 61 (13) years; 237 female subjects (35.2%); median (IQR) hematoma volume, 9.0 (3.0-17.6) ml], 131 (19.5%) had cSS. There was an association between cSS and larger hematoma volume (ß = 4.449, 95% CI 1.890-7.009, p < 0.001) independent of hematoma location and was also related to worse 90-day mRS (ß = 0.333, 95% CI 0.008-0.659, p = 0.045) in multivariable regression. In addition, mediation analyses revealed that hematoma volume was an essential factor mediating the effect of cSS on unfavorable 90-day outcomes (proportion mediated:66.04%, p = 0.01). Conclusion: Large hematoma volume was the major charge of directing cSS to worse outcomes in patients with mild to moderate ICH, and cSS was related to a larger hematoma in both lobar and non-lobar areas. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT04803292, identifier: NCT04803292.

Combined effect of cortical superficial siderosis and cerebral microbleed on short-term and long-term outcomes after intracerebral haemorrhage.

BACKGROUND AND PURPOSE: Cortical superficial siderosis (cSS) and cerebral microbleed (CMB) have distinct effects on intracerebral haemorrhage (ICH). We aim to investigate the combined effect of cSS and CMB on outcomes after ICH. METHODS: Based on a single-centre stroke registry database, patients with spontaneous ICH who had CT scan within 48 hours after ictus and MRI subsequently were identified. Eligible patients were divided into four groups (cSS-CMB-, cSS-CMB+, cSS+CMB-, cSS+CMB+) according to cSS and CMB on susceptibility-weighted image of MRI. Primary outcomes were haematoma volume on admission and unfavourable outcome defined as modified Rankin Scale scores ≥3 at 3 months. Secondary outcomes were all-cause death, recurrence of stroke and ICH during follow-up (median follow-up 2.0 years, IQR 1.0-3.0 years). RESULTS: A total of 673 patients were identified from 1044 patients with spontaneous ICH. 131 (19.5%) had cSS and 468 (69.5%) had CMB. Patients with cSS+CMB+ had the highest rate of poor outcome at 3 months, as well as all-cause death, recurrent stroke and ICH during follow-up. In cSS- patients, CMB was associated with smaller haematoma (ß -0.13; 95% CI -0.22 to -0.03; p=0.009), but it still increased risks of recurrent ICH (OR 4.6; 95% CI 1.3 to 15.6; p=0.015) and stroke (OR 2.0; 95% CI 1.0 to 4.0; p=0.049). These effects of CMB became unremarkable in the context of cSS+. CONCLUSIONS: Patients with different combinations of cSS and CMB have distinct patterns of short-term and long-term outcomes. Although CMB is related to restrained haematoma, it does not improve long-term outcomes. TRIAL REGISTRATION NUMBER: NCT04803292.

Botometer 101: social bot practicum for computational social scientists.

Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.

How Twitter data sampling biases U.S. voter behavior characterizations.

Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.

Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics.

Visiting multiple prescribers is a common method for obtaining prescription opioids for nonmedical use and has played an important role in fueling the United States opioid epidemic, leading to increased drug use disorder and overdose. Recent studies show that centrality of the bipartite network formed by prescription ties between patients and prescribers of opioids is a promising indicator for drug seeking. However, node prominence in bipartite networks is typically estimated with methods that do not fully account for the two-mode topology of the underlying network. Although several algorithms have been proposed recently to address this challenge, it is unclear how these algorithms perform on real-world networks. Here, we compare their performance in the context of identifying opioid drug seeking behaviors by applying them to massive bipartite networks of patients and providers extracted from insurance claims data. We find that two variants of bipartite centrality are significantly better predictors of subsequent opioid overdose than traditional centrality estimates. Moreover, we show that incorporating non-network attributes such as the potency of the opioid prescriptions into the measures can further improve their performance. These findings can be reproduced on different datasets. Our results demonstrate the potential of bipartiteness-aware indices for identifying patterns of high-risk behavior.

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal.

Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest across Democratic rather than Republican counties. Granger causality analysis shows evidence for a directional relationship from online misinformation to vaccine hesitancy. Our results support a need for interventions that address misbeliefs, allowing individuals to make better-informed health decisions.

New means, new measures: assessing prescription drug-seeking indicators over 10 years of the opioid epidemic.

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.

Systematic comparison of graph embedding methods in practical tasks.

Network embedding techniques aim to represent structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the nontrivial choice of selecting the proper approach for a given application. The present work attempts to close this gap of knowledge through a systematic comparison of 11 different methods for graph embedding. We consider methods for embedding networks in the hyperbolic and Euclidean metric spaces, as well as nonmetric community-based embedding methods. We apply these methods to embed more than 100 real-world and synthetic networks. Three common downstream tasks - mapping accuracy, greedy routing, and link prediction - are considered to evaluate the quality of the various embedding methods. Our results show that some Euclidean embedding methods excel in greedy routing. As for link prediction, community-based and hyperbolic embedding methods yield an overall performance that is superior to that of Euclidean-space-based approaches. We compare the running time for different methods and further analyze the impact of different network characteristics such as degree distribution, modularity, and clustering coefficients on the quality of the embedding results. We release our evaluation framework to provide a standardized benchmark for arbitrary embedding methods.

Model-free hidden geometry of complex networks.

The fundamental idea of embedding a network in a metric space is rooted in the principle of proximity preservation. Nodes are mapped into points of the space with pairwise distance that reflects their proximity in the network. Popular methods employed in network embedding either rely on implicit approximations of the principle of proximity preservation or implement it by enforcing the geometry of the embedding space, thus hindering geometric properties that networks may spontaneously exhibit. Here we take advantage of a model-free embedding method explicitly devised for preserving pairwise proximity and characterize the geometry emerging from the mapping of several networks, both real and synthetic. We show that the learned embedding has simple and intuitive interpretations: the distance of a node from the geometric center is representative for its closeness centrality, and the relative positions of nodes reflect the community structure of the network. Proximity can be preserved in relatively low-dimensional embedding spaces, and the hidden geometry displays optimal performance in guiding greedy navigation regardless of the specific network topology. We finally show that the mapping provides a natural description of contagion processes on networks, with complex spatiotemporal patterns represented by waves propagating from the geometric center to the periphery. The findings deepen our understanding of the model-free hidden geometry of complex networks.

Persona2vec: a flexible multi-role representations learning framework for graphs.

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

Neutral bots probe political bias on social media.

Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal accounts are exposed to moderate content shifting their experience toward the political center. Partisan accounts, especially conservative ones, tend to receive more followers and follow more automated accounts. Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.

Systematic Evaluation of State Policy Interventions Targeting the US Opioid Epidemic, 2007-2018.

Importance: In response to the increase in opioid overdose deaths in the United States, many states recently have implemented supply-controlling and harm-reduction policy measures. To date, an updated policy evaluation that considers the full policy landscape has not been conducted. Objective: To evaluate 6 US state-level drug policies to ascertain whether they are associated with a reduction in indicators of prescription opioid abuse, the prevalence of opioid use disorder and overdose, the prescription of medication-assisted treatment (MAT), and drug overdose deaths. Design, Setting, and Participants: This cross-sectional study used drug overdose mortality data from 50 states obtained from the National Vital Statistics System and claims data from 23 million commercially insured patients in the US between 2007 and 2018. Difference-in-differences analysis using panel matching was conducted to evaluate the prevalence of indicators of prescription opioid abuse, opioid use disorder and overdose diagnosis, the prescription of MAT, and drug overdose deaths before and after implementation of 6 state-level policies targeting the opioid epidemic. A random-effects meta-analysis model was used to summarize associations over time for each policy and outcome pair. The data analysis was conducted July 12, 2020. Exposures: State-level drug policy changes to address the increase of opioid-related overdose deaths included prescription drug monitoring program (PDMP) access, mandatory PDMPs, pain clinic laws, prescription limit laws, naloxone access laws, and Good Samaritan laws. Main Outcomes and Measures: The outcomes of interests were quarterly state-level mortality from drug overdoses, known indicators for prescription opioid abuse and doctor shopping, MAT, and prevalence of drug overdose and opioid use disorder. Results: This cross-sectional study of drug overdose mortality data and insurance claims data from 23 million commercially insured patients (12 582 378 female patients [55.1%]; mean [SD] age, 45.9 [19.9] years) in the US between 2007 and 2018 found that mandatory PDMPs were associated with decreases in the proportion of patients taking opioids (-0.729%; 95% CI, -1.011% to -0.447%), with overlapping opioid claims (-0.027%; 95% CI, -0.038% to -0.017%), with daily morphine milligram equivalent greater than 90 (-0.095%; 95% CI, -0.150% to -0.041%), and who engaged in drug seeking (-0.002%; 95% CI, -0.003% to -0.001%). The proportion of patients receiving MAT increased after the enactment of mandatory PDMPs (0.015%; 95% CI, 0.002% to 0.028%), pain clinic laws (0.013%, 95% CI, 0.005%-0.021%), and prescription limit laws (0.034%, 95% CI, 0.020% to 0.049%). Mandatory PDMPs were associated with a decrease in the number of overdose deaths due to natural opioids (-518.5 [95% CI, -728.5 to -308.5] per 300 million people) and methadone (-122.7 [95% CI, -207.5 to -37.8] per 300 million people). Prescription drug monitoring program access policies showed similar results, although these policies were also associated with increases in overdose deaths due to synthetic opioids (380.3 [95% CI, 149.6-610.8] per 300 million people) and cocaine (103.7 [95% CI, 28.0-179.5] per 300 million people). Except for the negative association between prescription limit laws and synthetic opioid deaths (-723.9 [95% CI, -1419.7 to -28.1] per 300 million people), other policies were associated with increasing overdose deaths, especially those attributed to non-prescription opioids such as synthetic opioids and heroin. This includes a positive association between naloxone access laws and the number of deaths attributed to synthetic opioids (1338.2 [95% CI, 662.5 to 2014.0] per 300 million people). Conclusions and Relevance: Although this study found that existing state policies were associated with reduced misuse of prescription opioids, they may have the unintended consequence of motivating those with opioid use disorders to access the illicit drug market, potentially increasing overdose mortality. This finding suggests that there is no easy policy solution to reverse the epidemic of opioid dependence and mortality in the US.

Substitution of Nonpharmacologic Therapy With Opioid Prescribing for Pain During the COVID-19 Pandemic.

Importance: During 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 Measures: The 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. Results: A total of 21 430 339 patients (mean [SD] age, 48.6 [24.0] years; 10 960 507 [51.1%] female; 909 061 [4.2%] Asian, 1 688 690 [7.9%] Black, 2 276 075 [10.6%] Hispanic, 11 192 789 [52.2%] White, and 5 363 724 [25.0%] unknown) were enrolled during the first 3 quarters in 2019 and 20 759 788 (mean [SD] age, 47.0 [23.8] years; 10 695 690 [51.5%] female; 798 037 [3.8%] Asian; 1 508 023 [7.3%] Black, 1 976 248 [9.5%] Hispanic, 10 059 597 [48.5%] White, and 6 417 883 [30.9%] unknown) in the first 3 quarters of 2020. During the COVID-19 pandemic, the proportion of patients receiving a pain diagnosis was smaller than that for the same period in 2019 (mean difference, -15.9%; 95% CI, -16.1% to -15.8%). Patients with pain were more likely to receive opioids (mean difference, 3.5%; 95% CI, 3.3%-3.7%) and less likely to receive nonpharmacologic therapy (mean difference, -6.0%; 95% CI, -6.3% to -5.8%), and opioid prescriptions were longer and more potent during the early pandemic in 2020 relative to 2019 (mean difference, 1.07 days; 95% CI, 1.02-1.17 days; mean difference, 0.96 morphine milligram equivalents; 95% CI, 0.76-1.20). Analysis of individuals' transitions between treatment options for pain found that patients were more likely to transition out of nonpharmacologic therapy, replacing it with opioid prescriptions for pain management during the COVID-19 pandemic than in the year before. Conclusions and Relevance: Nonpharmacologic therapy is a benign treatment for pain often recommended instead of opioid therapy. The decrease in nonpharmacologic therapy and increase in opioid prescription during the COVID-19 pandemic found in this cross-sectional study, especially given longer days of prescription and more potent doses, may exacerbate the US opioid epidemic. These findings suggest that it is imperative to investigate the implications of limited medical access on treatment substitution, which may increase patient risk, and implement policies and guidelines to prevent those substitutions.