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Background Scientists identified vitamin E acetate (VEA) and “Dank Vapes” (a fake brand of tetrahydrocannabinol [THC] vaping products) as contributors to the 2019–2020 outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI). On social media, people who post about vaping or THC discussed the causes of EVALI. We examined whether Reddit conversations may have served as early signals of the outbreak. Methods We collected Reddit posts from March 2018 to February 2020 on vaping- and THC-related subreddits that mentioned VEA or Dank Vapes. We identified peaks in post volume, examined post content, and used natural language processing to identify terms most characteristic of posts. Results There were almost no posts about VEA before EVALI. Subsequently, there were two peaks, both referencing media coverage of scientific findings that linked VEA to EVALI. Discussion regularly referenced concerns about the legitimacy of Dank Vapes before EVALI; peaks in posts were largely unrelated to scientific findings or media coverage of those findings. The terms most characteristic of VEA posts were EVALI-related; those most characteristic of Dank Vapes posts were about quality or legitimacy. Conclusions Although posts about VEA and Dank Vapes did not predict the outbreak, the public health community could use social media to encourage people who vape or use THC to report future health concerns (e.g., through FDA’s Safety Reporting Portal). Researchers and regulators could also use social media to see if potentially problematic products, such as Dank Vapes, have a history of concern among individuals who use those products.

BACKGROUND The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. OBJECTIVE The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. METHODS We developed a named entity recognition model to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. To identify effects that are symptoms of opioid withdrawal and substances that are potential remedies for these symptoms, we deduplicated substances and effects by using clustering and manual review, then built a network of substance and effect co-occurrence. For each of the 16 effects identified as symptoms of opioid withdrawal in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, we identified the 10 most strongly associated substances. We classified these pairs as follows: substance is a Food and Drug Administration–approved or commonly used treatment for the symptom, substance is not often used to treat the symptom but could be potentially useful given its pharmacological profile, substance is a home or natural remedy for the symptom, substance can cause the symptom, or other or unclear. We developed the Withdrawal Remedy Explorer application to facilitate the further exploration of the data. RESULTS Our named entity recognition model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on hold-out data. We identified 458 unique substances and 235 unique effects. Of the 130 potential remedies strongly associated with withdrawal symptoms, 54 (41.5%) were Food and Drug Administration–approved or commonly used treatments for the symptom, 17 (13.1%) were not often used to treat the symptom but could be potentially useful given their pharmacological profile, 13 (10%) were natural or home remedies, 7 (5.4%) were causes of the symptom, and 39 (30%) were other or unclear. We identified both potentially promising remedies (eg, gabapentin for body aches) and potentially common but harmful remedies (eg, antihistamines for restless leg syndrome). CONCLUSIONS Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of a web-based community of people who use opioids. Our Withdrawal Remedy Explorer application provides a platform for using these data for pharmacovigilance, the identification of new treatments, and the better understanding of the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states for which people self-manage their symptoms and discuss their experiences on the web. CLINICALTRIAL

Background The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. Objective The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. Methods We developed a named entity recognition model to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. To identify effects that are symptoms of opioid withdrawal and substances that are potential remedies for these symptoms, we deduplicated substances and effects by using clustering and manual review, then built a network of substance and effect co-occurrence. For each of the 16 effects identified as symptoms of opioid withdrawal in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, we identified the 10 most strongly associated substances. We classified these pairs as follows: substance is a Food and Drug Administration–approved or commonly used treatment for the symptom, substance is not often used to treat the symptom but could be potentially useful given its pharmacological profile, substance is a home or natural remedy for the symptom, substance can cause the symptom, or other or unclear. We developed the Withdrawal Remedy Explorer application to facilitate the further exploration of the data. Results Our named entity recognition model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on hold-out data. We identified 458 unique substances and 235 unique effects. Of the 130 potential remedies strongly associated with withdrawal symptoms, 54 (41.5%) were Food and Drug Administration–approved or commonly used treatments for the symptom, 17 (13.1%) were not often used to treat the symptom but could be potentially useful given their pharmacological profile, 13 (10%) were natural or home remedies, 7 (5.4%) were causes of the symptom, and 39 (30%) were other or unclear. We identified both potentially promising remedies (eg, gabapentin for body aches) and potentially common but harmful remedies (eg, antihistamines for restless leg syndrome). Conclusions Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of a web-based community of people who use opioids. Our Withdrawal Remedy Explorer application provides a platform for using these data for pharmacovigilance, the identification of new treatments, and the better understanding of the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states for which people self-manage their symptoms and discuss their experiences on the web.

MotivationAs the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned.ResultsDeveloped through the National Heart, Lung, and Blood Institute’s (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15,911 study variables from public datasets. On a manually curated search dataset, Dug’s total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch’s total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results.Availability and ImplementationDug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/.Contactawaldrop@rti.org or scox@renci.org

Motivation As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. Results Developed through the National Heart, Lung and Blood Institute’s (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug’s total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch’s total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. Availability and implementation Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND The opioid crisis in the United States may be exacerbated by the COVID-19 pandemic. Increases in opioid use, emergency medical services (EMS) runs for opioid-related overdoses, and opioid overdose deaths have been reported. No study has examined changes in multiple naloxone administrations, an indicator of overdose severity, during the COVID-19 pandemic. OBJECTIVE This study examines changes in the occurrence of naloxone administrations and multiple naloxone administrations during EMS runs for opioid-related overdoses during the COVID-19 pandemic in Guilford County, North Carolina (NC). METHODS Using a period-over-period approach, we compared the occurrence of opioid-related EMS runs, naloxone administrations, and multiple naloxone administrations during the 29-week period before (September 1, 2019, to March 9, 2020) and after NC’s COVID-19 state of emergency declaration (ie, the COVID-19 period of March 10 to September 30, 2020). Furthermore, historical data were used to generate a quasi-control distribution of period-over-period changes to compare the occurrence of each outcome during the COVID-19 period to each 29-week period back to January 1, 2014. RESULTS All outcomes increased during the COVID-19 period. Compared to the previous 29 weeks, the COVID-19 period experienced increases in the weekly mean number of opioid-related EMS runs (25.6, SD 5.6 vs 18.6, SD 6.6; PPP CONCLUSIONS The occurrence of EMS runs for opioid-related overdoses, naloxone administrations, and multiple naloxone administrations during EMS runs increased during the COVID-19 pandemic in Guilford County, NC. For a host of reasons that need to be explored, the COVID-19 pandemic appears to have exacerbated the opioid crisis.

The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19’s impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.

Background: During the COVID-19 pandemic, public-health decision makers have increasingly relied on hospitalization forecasts that are routinely provided, accurate, and based on timely input data to inform pandemic planning. In North Carolina, we adapted an existing agent-based model (ABM) to produce 30-day hospitalization forecasts of COVID-19 and non–COVID-19 hospitalizations for use by public-health decision makers. We sought to continually improve model speed and accuracy during forecasting. Methods: The geospatially explicit ABM included movement of agents (ie, patients) among 104 short-term acute-care hospitals, 10 long-term acute-care hospitals, 421 licensed nursing homes, and the community in North Carolina. Agents were based on a synthetic population of North Carolina residents (ie, >10.4 million agents). We assigned SARS-CoV-2 infections to agents according to county-level susceptible, exposed, infectious, recovered (SEIR) models informed by reported COVID-19 cases by county. Agents’ COVID-19 severity and probability of hospitalization were determined using agent-specific characteristics (eg, age, comorbidities). During May 2020–December 2020, we produced weekly 30-day forecasts of intensive care unit (ICU) and non-ICU bed occupancy for COVID-19 agents and non–COVID-19 agents statewide and by region under a range of SARS-CoV-2 effective reproduction numbers. During the reporting period, we identified optimizations for faster results turnaround. We evaluated the incorporation of real-time hospital-level occupancy data at model initialization on forecast accuracy using mean absolute percent error (MAPE). Results: During May 2020–December 2020, we provided 31 weekly reports of 30-day hospitalization forecasts with a 1-day turnaround time. Reports included (1) raw and smoothed 7-day average values for 42 model output variables; (2) static visuals of ICU and non-ICU bed demand and capacity; and (3) an interactive Tableau workbook of hospital demand variables. Identifying code efficiencies reduced a single model runtime from ~100 seconds to 28 seconds. The use of cloud computing reduced simulation runtime from ~20 hours to 15 minutes. Across forecasts, the average MAPEs were 21.6% and 7.1% for ICU and non-ICU bed demand, respectively. By incorporating hospital-level occupancy data, we reduced the average MAPE to 6.5% for ICU bed demand and 3.9% for non-ICU bed demand, indicating improved accuracy. Conclusions: We adapted an ABM and continually improved it during COVID-19 forecasting by optimizing code and computing resources and including real-time hospital-level occupancy data. Planned SEIR model updates for enhanced forecasts include the addition of compartments for undocumented infections and recoveries as well as permission of reinfection from recovered compartments.Funding: NoDisclosures: None

Background The opioid crisis in the United States may be exacerbated by the COVID-19 pandemic. Increases in opioid use, emergency medical services (EMS) runs for opioid-related overdoses, and opioid overdose deaths have been reported. No study has examined changes in multiple naloxone administrations, an indicator of overdose severity, during the COVID-19 pandemic. Objective This study examines changes in the occurrence of naloxone administrations and multiple naloxone administrations during EMS runs for opioid-related overdoses during the COVID-19 pandemic in Guilford County, North Carolina (NC). Methods Using a period-over-period approach, we compared the occurrence of opioid-related EMS runs, naloxone administrations, and multiple naloxone administrations during the 29-week period before (September 1, 2019, to March 9, 2020) and after NC’s COVID-19 state of emergency declaration (ie, the COVID-19 period of March 10 to September 30, 2020). Furthermore, historical data were used to generate a quasi-control distribution of period-over-period changes to compare the occurrence of each outcome during the COVID-19 period to each 29-week period back to January 1, 2014. Results All outcomes increased during the COVID-19 period. Compared to the previous 29 weeks, the COVID-19 period experienced increases in the weekly mean number of opioid-related EMS runs (25.6, SD 5.6 vs 18.6, SD 6.6; P Conclusions The occurrence of EMS runs for opioid-related overdoses, naloxone administrations, and multiple naloxone administrations during EMS runs increased during the COVID-19 pandemic in Guilford County, NC. For a host of reasons that need to be explored, the COVID-19 pandemic appears to have exacerbated the opioid crisis.

Renewable energy resources like hydro power plants play a more important role in the upcoming future of power grids. Thereby, a full flexibility of utilizing the grid capabilities in terms of active and reactive power production in generating mode as well as power consumption in pumping mode in conjunction with the best available efficiency of both electric and hydraulic devices gains in significance. Nowadays, this can be done by utilizing variable-speed pump-turbine units with either synchronous or doubly-fed asynchronous motor-generators. In order to achieve accurate data about steady-state as well as transient behaviour, simulation models of both arrangements are developed. In particular, the low voltage ride through capabilities according to various grid code requirements have to be analyzed in detail.