STUDY OBJECTIVE: To understand trends in nonfatal firearm injuries by examining rates of firearm injury emergency department (ED) visits stratified by individual- and county-level characteristics. METHODS: Data from participating EDs within 10 jurisdictions in the United States funded through the Centers for Disease Control and Prevention's Firearm Injury Surveillance Through Emergency Rooms program, including the District of Columbia, Florida, Georgia, New Mexico, North Carolina, Oregon, Utah, Virginia, Washington, and West Virginia, were analyzed. We examined trends in firearm injury ED visits by sex, age group, jurisdiction, county-level urbanicity, and county-level social vulnerability from January 2019 to August 2023. Mean weekly rates of firearm injury ED visits and visit ratios (or the proportion of firearm injury-related ED visits of all visits during the surveillance periods with the same period in 2019) were calculated. RESULTS: Compared with 2019, the proportion of ED visits for firearm injury was elevated each year during 2020 to 2023 overall, with the largest observed increase in 2020 (visit ratio=1.59). All 10 Firearm Injury Surveillance Through Emergency Rooms jurisdictions experienced an increase in the proportion of firearm injury ED visits in 2020 (visit ratios ranging from 1.26 in West Virginia and 2.31 in Washington, DC) when compared with 2019. By county-level social vulnerability, the mean weekly rate of firearm injury ED visits was highest in counties with the highest social vulnerability over the entire study period. CONCLUSION: Results highlight the continued burden of firearm injuries in communities with higher social vulnerability. Timely ED data by community social vulnerability can inform public health interventions and resource allocation at local, state, and national levels.

BACKGROUND: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent's gender. OBJECTIVE: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide. METHODS: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning-based large language model. RESULTS: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause. CONCLUSIONS: Linguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities.

BACKGROUND: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, "Sanctioned Suicide," which is a primary source of information on the use and procurement of SN. OBJECTIVE: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN. METHODS: We collected all publicly available from the site's inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN. RESULTS: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere. CONCLUSIONS: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

BACKGROUND: Delta-8 tetrahydrocannabinol (THC) is a psychoactive cannabinoid found in small amounts naturally in the cannabis plant; it can also be synthetically produced in larger quantities from hemp-derived cannabidiol. Most states permit the sale of hemp and hemp-derived cannabidiol products; thus, hemp-derived delta-8 THC products have become widely available in many state hemp marketplaces, even where delta-9 THC, the most prominently occurring THC isomer in cannabis, is not currently legal. Health concerns related to the processing of delta-8 THC products and their psychoactive effects remain understudied. OBJECTIVE: The goal of this study is to implement a novel topic modeling approach based on transformers, a state-of-the-art natural language processing architecture, to identify and describe emerging trends and topics of discussion about delta-8 THC from social media discourse, including potential symptoms and adverse health outcomes experienced by people using delta-8 THC products. METHODS: Posts from January 2008 to December 2021 discussing delta-8 THC were isolated from cannabis-related drug forums on Reddit (Reddit Inc), a social media platform that hosts the largest web-based drug forums worldwide. Unsupervised topic modeling with state-of-the-art transformer-based models was used to cluster posts into topics and assign labels describing the kinds of issues being discussed with respect to delta-8 THC. Results were then validated by human subject matter experts. RESULTS: There were 41,191 delta-8 THC posts identified and 81 topics isolated, the most prevalent being (1) discussion of specific brands or products, (2) comparison of delta-8 THC to other hemp-derived cannabinoids, and (3) safety warnings. About 5% (n=1220) of posts from the resulting topics included content discussing health-related symptoms such as anxiety, sleep disturbance, and breathing problems. Until 2020, Reddit posts contained fewer than 10 mentions of delta-8-THC for every 100,000 cannabis posts annually. However, in 2020, these rates increased by 13 times the 2019 rate (to 99.2 mentions per 100,000 cannabis posts) and continued to increase into 2021 (349.5 mentions per 100,000 cannabis posts). CONCLUSIONS: Our study provides insights into emerging public health concerns around delta-8 THC, a novel substance about which little is known. Furthermore, we demonstrate the use of transformer-based unsupervised learning approaches to derive intelligible topics from highly unstructured discussions of delta-8 THC, which may help improve the timeliness of identification of emerging health concerns related to new substances.

Suicide, including firearm suicide, remains a substantial public health concern in the United States. During the previous 2 decades, overall suicide rates and firearm suicide rates have risen by approximately one third, approaching 50,000 overall suicides during 2022, including approximately 27,000 firearm suicides (1). Firearm suicides account for approximately one half of all suicides, and this proportion has been increasing (2,3). This analysis includes national firearm suicide data from 2019 through the end of 2022, categorized by race and ethnicity, presented both annually and by month (or quarterly) to track subannual changes.

The rate of firearm homicide in the United States rose sharply from 2019 through 2020, reaching a level not seen in more than 2 decades, with ongoing and widening racial and ethnic disparities (1). During 2020–2021, the rate increased again (2). This report provides provisional firearm homicide data for 2022, stratified by race and ethnicity, presented both annually and by month (or quarter) to document subannual changes.

BACKGROUND: Suicide mortality data are a critical source of information for understanding suicide-related trends in the United States. However, official suicide mortality data experience significant delays. The Google Symptom Search Dataset (SSD), a novel population-level data source derived from online search behavior, has not been evaluated for its utility in predicting suicide mortality trends. METHODS: We identified five mental health related variables (suicidal ideation, self-harm, depression, major depressive disorder, and pain) from the SSD. Daily search trends for these symptoms were utilized to estimate national and state suicide counts in 2020, the most recent year for which data was available, via a linear regression model. We compared the performance of this model to a baseline autoregressive integrated moving average (ARIMA) model and a model including all 422 symptoms (All Symptoms) in the SSD. RESULTS: Our Mental Health Model estimated the national number of suicide deaths with an error of -3.86 %, compared to an error of 7.17 % and 28.49 % for the ARIMA baseline and All Symptoms models. At the state level, 70 % (N = 35) of states had a prediction error of <10 % with the Mental Health Model, with accuracy generally favoring larger population states with higher number of suicide deaths. CONCLUSION: The Google SSD is a new real-time data source that can be used to make accurate predictions of suicide mortality monthly trends at the national level. Additional research is needed to optimize state level predictions for states with low suicide counts.

Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges like exposure to intimate partner violence and substance use in the home can have negative impacts on lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced ACEs. However, how social networks of those who experienced ACEs differ from those who did not is poorly understood. In the present study, we use Reddit and Twitter data to investigate and compare social networks among individuals with and without ACEs exposure. We first use a neural network classifier to identify the presence or absence of public ACEs disclosures in social media posts. We then analyze egocentric social networks comparing individuals with self-reported ACEs to those with no reported history. We found that, although individuals reporting ACEs had fewer total followers in online social networks, they had higher reciprocity in following behavior (i.e., mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. These results imply that individuals with ACEs may try to actively connect to others having similar prior traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections online for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.

BACKGROUND: Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges such as exposure to intimate partner violence and substance use in the home, can have negative impacts on the lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced them. However, how the social networks of those who experienced ACEs differ from the social networks of those who did not is poorly understood. OBJECTIVE: In this study, we used Reddit and Twitter data to investigate and compare social networks between individuals with and without ACE exposure. METHODS: We first used a neural network classifier to identify the presence or absence of public ACE disclosures in social media posts. We then analyzed egocentric social networks comparing individuals with self-reported ACEs with those with no reported history. RESULTS: We found that, although individuals reporting ACEs had fewer total followers in web-based social networks, they had higher reciprocity in following behavior (ie, mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. CONCLUSIONS: These results imply that individuals with ACEs may try to actively connect with others who have similar previous traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections on the web for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs.

The article is previously published with incorrect and missing information. The updates are as follows: | | The last sentence in the third paragraph of ‘Building hospital capacity for data collection’ in ‘Results’ section has been updated as ‘A one-way ANOVA revealed a significant difference between April 2015 and April 2016 triage times, F(1,2734)=5.33, p=0.02. Triage times were on average 16.2 s longer in April 2016 compared with April 2015. No post-hoc analyses were done to control for other, non-CMST-related changes that occurred during the triage process (eg, additional triage screen) from April 2015 to April 2016.’ | Below statement has been added in the sixth paragraph of the ‘Discussion’ section after ‘Nurse participation in the satisfaction … a different US hospital.’ | The statistically significant increase in triage time of 16.2 s, which is unlikely to be clinically significant, may reflect other non-CMST-related triage process changes - such as addition of another triage screen - that were not accounted for in the analyses.

IMPORTANCE: Firearm homicides are a major public health concern; lack of timely mortality data presents considerable challenges to effective response. Near real-time data sources offer potential for more timely estimation of firearm homicides. OBJECTIVE: To estimate near real-time burden of weekly and annual firearm homicides in the US. DESIGN, SETTING, AND PARTICIPANTS: In this prognostic study, anonymous, longitudinal time series data were obtained from multiple data sources, including Google and YouTube search trends related to firearms (2014-2019), emergency department visits for firearm injuries (National Syndromic Surveillance Program, 2014-2019), emergency medical service activations for firearm-related injuries (biospatial, 2014-2019), and National Domestic Violence Hotline contacts flagged with the keyword firearm (2016-2019). Data analysis was performed from September 2021 to September 2022. MAIN OUTCOMES AND MEASURES: Weekly estimates of US firearm homicides were calculated using a 2-phase pipeline, first fitting optimal machine learning models for each data stream and then combining the best individual models into a stacked ensemble model. Model accuracy was assessed by comparing predictions of firearm homicides in 2019 to actual firearm homicides identified by National Vital Statistics System death certificates. Results were also compared with a SARIMA (seasonal autoregressive integrated moving average) model, a common method to forecast injury mortality. RESULTS: Both individual and ensemble models yielded highly accurate estimates of firearm homicides. Individual models' mean error for weekly estimates of firearm homicides (root mean square error) varied from 24.95 for emergency department visits to 31.29 for SARIMA forecasting. Ensemble models combining data sources had lower weekly mean error and higher annual accuracy than individual data sources: the all-source ensemble model had a weekly root mean square error of 24.46 deaths and full-year accuracy of 99.74%, predicting the total number of firearm homicides in 2019 within 38 deaths for the entire year (compared with 95.48% accuracy and 652 deaths for the SARIMA model). The model decreased the time lag of reporting weekly firearm homicides from 7 to 8 months to approximately 6 weeks. CONCLUSIONS AND RELEVANCE: In this prognostic study of diverse secondary data on machine learning, ensemble modeling produced accurate near real-time estimates of weekly and annual firearm homicides and substantially decreased data source time lags. Ensemble model forecasts can accelerate public health practitioners' and policy makers' ability to respond to unanticipated shifts in firearm homicides.

INTRODUCTION: Stigma associated with substance use and addiction is a major barrier to overdose prevention. Although stigma reduction is a key goal of federal strategies to prevent overdose, there is limited data to assess progress made in reducing use of stigmatizing language about addiction. METHODS: Using language guidelines published by the federal National Institute on Drug Abuse (NIDA), we examined trends in use of stigmatizing terms about addiction across four popular public communication modalities: news articles, blogs, Twitter, and Reddit. We calculate percent changes in the rates of articles/posts using stigmatizing terms over a five-year period (2017-2021) by fitting a linear trendline and assess statistically significant trends using the Mann-Kendall test. RESULTS: The rate of articles containing stigmatizing language decreased over the past five years for news articles (-68.2 %, p < 0.001) and blogs (-33.6 %, p < 0.001). Among social media platforms, the rate of posts using stigmatizing language increased (Twitter [43.5 %, p = 0.01]) or remained stable (Reddit [3.1 %, p = 0.29]). In absolute terms, news articles had the highest rate of articles containing stigmatizing terms over the five-year period (324.9 articles per million) compared to 132.3, 18.3, and 138.6 posts per million for blogs, Twitter, and Reddit, respectively. CONCLUSIONS: Use of stigmatizing language about addiction appears to have decreased across more traditional, longer-format communication modalities such as news articles. Additional work is needed to reduce use of stigmatizing language on social media.

Participation in communities is essential to individual mental and physical health and can yield further benefits for members. With a growing amount of time spent participating in virtual communities, it's increasingly important that we understand how the community experience manifests in and varies across these online spaces. In this paper, we investigate Sense of Virtual Community (SOVC) in the context of live-streaming communities. Through a survey of 1,944 Twitch viewers, we identify that community experiences on Twitch vary along two primary dimensions: belonging, a feeling of membership and support within the group, and cohesion, a feeling that the group is a well-run collective with standards for behavior. Leveraging the Social-Ecological Model, we map behavioral trace data from usage logs to various levels of the social ecology surrounding an individual user's participation within a community, in order to identify which of these can be associated with lower or higher SOVC. We find that features describing activity at the individual and community levels, but not features describing the community member's dyadic relationships, aid in predicting the SOVC that community members feel within channels. We consider implications for the design of live-streaming communities and for fostering the well-being of their members, and we consider theoretical implications for the study of SOVC in modern, interactive online contexts, particularly those fostering large-scale or pseudonymized interactions. We also explore how the Social-Ecological Model can be leveraged in other contexts relevant to Computer-Supported Cooperative Work (CSCW), with implications for future work. © 2022 ACM.

Illicit or 'designer' benzodiazepines are a growing contributor to overdose deaths. We employed natural language processing (NLP) to study benzodiazepine mentions over 10 years on 270 online drug forums (subreddits) on Reddit. Using NLP, we automatically detected mentions of illicit and prescription benzodiazepines, including their misspellings and non-standard names, grouping relative mentions by quarter. On a collection of 17 861 755 posts between 2012 and 2021, we searched for 26 benzodiazepines (8 prescription; 18 illicit), detecting 173 275 mentions. The rate of posts about both prescription and illicit benzodiazepines increased consistently with increases in deaths involving both drug classes, illustrating the utility of surveillance via Reddit.

OBJECTIVE: The aim of this study was to assess associations of various content areas of Twitter posts with help-seeking from the US National Suicide Prevention Lifeline (Lifeline) and with suicides. METHODS: We retrieved 7,150,610 suicide-related tweets geolocated to the United States and posted between 1 January 2016 and 31 December 2018. Using a specially devised machine-learning approach, we categorized posts into content about prevention, suicide awareness, personal suicidal ideation without coping, personal coping and recovery, suicide cases and other. We then applied seasonal autoregressive integrated moving average analyses to assess associations of tweet categories with daily calls to the US National Suicide Prevention Lifeline (Lifeline) and suicides on the same day. We hypothesized that coping-related and prevention-related tweets are associated with greater help-seeking and potentially fewer suicides. RESULTS: The percentage of posts per category was 15.4% (standard deviation: 7.6%) for awareness, 13.8% (standard deviation: 9.4%) for prevention, 12.3% (standard deviation: 9.1%) for suicide cases, 2.4% (standard deviation: 2.1%) for suicidal ideation without coping and 0.8% (standard deviation: 1.7%) for coping posts. Tweets about prevention were positively associated with Lifeline calls (B=1.94, SE=0.73, p=0.008) and negatively associated with suicides (B=-0.11, standard error=0.05, p=0.038). Total number of tweets were negatively associated with calls (B=-0.01, standard error =0.0003, p=0.007) and positively associated with suicide, (B=6.410(-5), standard error =2.610(-5), p=0.015). CONCLUSION: This is the first large-scale study to suggest that daily volume of specific suicide-prevention-related social media content on Twitter corresponds to higher daily levels of help-seeking behaviour and lower daily number of suicide deaths. PREREGISTRATION: As Predicted, #66922, 26 May 2021.

IMPORTANCE: Opioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more. OBJECTIVES: To build and validate a statistical model for estimating national opioid overdose deaths in near real time. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, signals from 5 overdose-related, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality. EXPOSURES: Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. MAIN OUTCOMES AND MEASURES: Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System. RESULTS: Statistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and -1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model). CONCLUSIONS AND RELEVANCE: Results of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.

BACKGROUND: Despite recent rises in fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medications for opioid use disorder (PTMOUD). A better understanding of the timing and patterns in stimulant co-use among PWUO based on mentions of these substances on social media can help inform prevention programs, policy, and future research directions. This study examines stimulant co-mention trends among PWUO/PTMOUD on social media over multiple years. METHODS: We collected publicly available data from 14 forums on Reddit (subreddits) that focused on prescription and illicit opioids, and medications for opioid use disorder (MOUD). Collected data ranged from 2011 to 2020, and we also collected timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to generate lexical variants of all included prescription and illicit opioids and stimulants and detect mentions of them on the chosen subreddits. Finally, we analyzed and described trends and patterns in co-mentions. RESULTS: Posts collected for 13,812 Redditors showed that 12,306 (89.1%) mentioned at least 1 opioid, opioid-related medication, or stimulant. Analyses revealed that the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions by the same Redditor of heroin and methamphetamine, the substances most commonly co-mentioned, decreased in recent years, while co-mentions of both fentanyl and MOUD with methamphetamine increased. CONCLUSION: Our analyses reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD, which closely resembles the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment.

BACKGROUND: Timely data from official sources regarding the impact of the COVID-19 pandemic on people who use prescription and illegal opioids is lacking. We conducted a large-scale, natural language processing (NLP) analysis of conversations on opioid-related drug forums to better understand concerns among people who use opioids. METHODS: In this retrospective observational study, we analyzed posts from 14 opioid-related forums on the social network Reddit. We applied NLP to identify frequently mentioned substances and phrases, and grouped the phrases manually based on their contents into three broad key themes: (i) prescription and/or illegal opioid use; (ii) substance use disorder treatment access and care; and (iii) withdrawal. Phrases that were unmappable to any particular theme were discarded. We computed the frequencies of substance and theme mentions, and quantified their volumes over time. We compared changes in post volumes by key themes and substances between pre-COVID-19 (1/1/2019-2/29/2020) and COVID-19 (3/1/2020-11/30/2020) periods. RESULTS: Seventy-seven thousand six hundred fifty-two and 119,168 posts were collected for the pre-COVID-19 and COVID-19 periods, respectively. By theme, posts about treatment and access to care increased by 300%, from 0.631 to 2.526 per 1000 posts between the pre-COVID-19 and COVID-19 periods. Conversations about withdrawal increased by 812% between the same periods (0.026 to 0.235 per 1,000 posts). Posts about drug use did not increase (0.219 to 0.218 per 1,000 posts). By substance, among medications for opioid use disorder, methadone had the largest increase in conversations (20.751 to 56.313 per 1,000 posts; 171.4% increase). Among other medications, posts about diphenhydramine exhibited the largest increase (0.341 to 0.927 per 1,000 posts; 171.8% increase). CONCLUSIONS: Conversations on opioid-related forums among people who use opioids revealed increased concerns about treatment and access to care along with withdrawal following the emergence of COVID-19. Greater attention to social media data may help inform timely responses to the needs of people who use opioids during COVID-19.

BACKGROUND: Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. OBJECTIVE: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. METHODS: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder-related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post's language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. RESULTS: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. CONCLUSIONS: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

BACKGROUND: Beyond alcohol retail establishments, most business and property types receive limited attention in studies of violent crime. We sought to provide a comprehensive examination of which properties experience the most violent crime in a city and how that violence is distributed throughout a city. METHODS: For a large urban city, we merged violent incident data from police reports with municipal tax assessor data from 2012-2017 and tabulated patterns of violent crime for 15 commercial and public property types. To describe outlier establishments, we calculated the proportion of individual parcels within each property-type that experienced more than 5 times the average number of crimes for that property-type and also mapped the 25 parcels with the highest number of violent incidents to explore what proportion of violent crime in these block groups were contributed by the outlier establishments. RESULTS: While the hotel/lodging property-type experienced the highest number of violent crimes per parcel (2.72), each property-type had outlier establishments experiencing more than 5 times the average number of violent crimes per business. Twelve of 15 property-types (80%) had establishments with more than 10 times the mean number of violent incidents. The 25 parcels with the most violent crime comprised a wide variety of establishments, ranging from a shopping center, grocery store, gas station, motel, public park, vacant lot, public street, office building, transit station, hospital, pharmacy, school, community center, and movie theatre, and were distributed across the city. Eight of the 25 parcels with the highest amount of violent crime, accounted for 50% or more of the violent crime within a 400-meter buffer. CONCLUSIONS: All property-types had outlier establishments experiencing elevated counts of violent crimes. Furthermore, the 25 most violent properties in the city demonstrated remarkable diversity in property-type. Further studies assessing the risk of violent crime among additional property-types may aid in violence prevention.

BACKGROUND: Online communities provide support for individuals looking for help with suicidal ideation and crisis. As community data are increasingly used to devise machine learning models to infer who might be at risk, there have been limited efforts to identify both risk and protective factors in web-based posts. These annotations can enrich and augment computational assessment approaches to identify appropriate intervention points, which are useful to public health professionals and suicide prevention researchers. OBJECTIVE: This qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. METHODS: We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct validity and the social sciences of measurement. We then applied the scheme to annotate 200 posts from r/SuicideWatch-a Reddit community focused on suicide crisis. RESULTS: We documented our results on producing an annotation scheme that is consistent with leading public health information coding schemes for suicide and advances attention to protective factors. Our study showed high internal validity, and we have presented results that indicate that our approach is consistent with findings from prior work. CONCLUSIONS: Our work formalizes a framework that incorporates construct validity into the development of annotation schemes for suicide risk on social media. This study furthers the understanding of risk and protective factors expressed in social media data. This may help public health programming to prevent suicide and computational social science research and investigations that rely on the quality of labels for downstream machine learning tasks.

IMPORTANCE: The association between online activities and youth suicide is an important issue for parents, clinicians, and policy makers. However, most information exploring potential associations is drawn from survey data and mainly focuses on risk related to overall screen time. OBJECTIVE: To evaluate the association between a variety of online risk factors and youth suicide-related behavior using real-world online activity data. DESIGN, SETTING, AND PARTICIPANTS: A matched case-control study was conducted from July 27, 2019, to May 26, 2020, with the sample drawn from more than 2600 US schools participating in an online safety monitoring program via the Bark online safety tool. For 227 youths having a severe suicide/self-harm alert requiring notification of school administrators, cases were matched 1:5 to 1135 controls on location, the amount of follow-up time, and general volume of online activity. EXPOSURES: Eight potential online risk factors (cyberbullying, violence, drug-related, hate speech, profanity, sexual content, depression, and low-severity self-harm) through assessment of text, image, and video data. MAIN OUTCOMES AND MEASURES: Severe suicide/self-harm alert requiring notification of school administrators; severe suicide alerts are statements by youths indicating imminent or recent suicide attempts and/or self-harm. RESULTS: The 1362 participants had a mean (SD) age of 13.3 (2.41) years; 699 (51.3%) were male. All 8 online risk factors studied exhibited differences between case and control populations and were significantly associated with subsequent severe suicide/self-harm alerts when examining total direct and indirect pathways. These associations ranged from an adjusted odds ratio (aOR) of 1.17 (95% CI, 1.09-1.26) for drug-related content to an aOR of 1.82 (95% CI, 1.63-2.03) for depression-related content. When considering the total number of different types of online risk factors among the 8 measured, there was an exponentially larger risk of severe suicide/self-harm alerts; youths with 5 or more of the 8 risk factors present in their online activity had a more than 70-fold increased odds of subsequently having a severe suicide/self-harm alert (aOR, 78.64; 95% CI, 34.39-179.84). CONCLUSIONS AND RELEVANCE: The findings of this study suggest that many discrete types of risk factors are identifiable from online data and associated with subsequent youth suicide-related behavior. Although each risk factor carries a specific association with suicide-related behavior, the greatest risk is evident for youths demonstrating multiple types of online risk factors.

Substances involved in overdose deaths have shifted over time and continue to undergo transition. Early detection of emerging drugs involved in overdose is a major challenge for traditional public health data systems. While novel social media data have shown promise, there is a continued need for robust natural language processing approaches that can identify emerging substances. Consequently, we developed a new metric, the relative similarity ratio, based on diachronic word embeddings to measure movement in the semantic proximity of individual substance words to 'overdose' over time. Our analysis of 64,420,376 drug-related posts made between January 2011 and December 2018 on Reddit, the largest online forum site, reveals that this approach successfully identified fentanyl, the most significant emerging substance in the overdose epidemic, >1 year earlier than traditional public health data systems. Use of diachronic word embeddings may enable improved identification of emerging substances involved in drug overdose, thereby improving the timeliness of prevention and treatment activities.

This study investigates emergency department visits for violence-related injuries occurring at home and outside the home in Cardiff, Wales, before and after COVID-19 lockdown measures were instituted in March 2020.

IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making. OBJECTIVE: To estimate weekly suicide fatalities in the US in near real time. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017. EXPOSURES: Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017). MAIN OUTCOMES AND MEASURES: Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System. RESULTS: Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%). CONCLUSIONS AND RELEVANCE: The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.

Cannabis is the most widely used illicit drug in the U.S. In 2018, 16% of Americans aged 12 years or older reported using cannabis.1 Vaping, or e-cigarette product use, is an emerging mode of cannabis use because of perceptions that it is less harmful than smoking and with better taste and a stronger “high.”2 Along with a range of health effects associated with cannabis use, chemicals, such as vitamin E acetate, present in some tetrahydrocannabinol (THC)-containing vaping products can be harmful, as demonstrated by the recent national outbreak of lung injury.3 Data are limited on vaping as a mode of use of cannabis as traditional public health data systems typically do not capture these behaviors.4

PURPOSE: Youth suicide clusters may be exacerbated by suicide contagion-the spread of suicidal behaviors. Factors promoting suicide contagion are poorly understood, particularly in the advent of social media. Using cross-sectional data from an ongoing youth suicide cluster in Ohio, this study examines associations between suicide cluster-related social media and suicidal behaviors. METHODS: We surveyed 7th- to 12th-grade students in northeastern Ohio during a 2017-2018 suicide cluster to assess the prevalence of suicidal ideation (SI), suicide attempts (SAs), and associations with potential contagion-promoting factors such as suicide cluster-related social media, vigils, memorials, news articles, and watching the Netflix series 13 Reasons Why before or during the cluster. Generalized estimating equations examined associations between potential contagion-promoting factors and SI/SA, adjusting for nonmodifiable risk factors. Subgroup analyses examined whether associations between cluster-related factors and SI/SA during the cluster varied by previous history of SI/SA. RESULTS: Among participating students, 9.0% (876/9,733) reported SI and 4.9% attempted suicide (481/9,733) during the suicide cluster. Among students who posted suicide cluster-related content to social media, 22.9% (267/1,167) reported SI and 15.0% (175/1,167) attempted suicide during the suicide cluster. Posting suicide cluster-related content was associated with both SI (adjusted odds ratio 1.7, 95% confidence interval 1.4-2.0) and SA during the cluster (adjusted odds ratio 1.7, 95% confidence interval 1.2-2.5). In subgroup analyses, seeing suicide cluster-related posts was uniquely associated with increased odds of SI and SA during the cluster among students with no previous history of SI/SA. CONCLUSIONS: Exposure to suicide cluster-related social media is associated with both SI and SA during a suicide cluster. Suicide interventions could benefit from efforts to mitigate potential negative effects of social media and promote prevention messages.

This study aims to quantify the prevalence of forced sex, pressured sex, and related pregnancy among adolescent girls and young women in five low- and middle-income countries. Nationally representative, cross-sectional household surveys were conducted in Haiti, Malawi, Nigeria, Zambia, and Uganda among girls and young women aged 13 to 24 years. A stratified three-stage cluster sample design was used. Respondents were interviewed to assess prevalence of sexual violence, pregnancy related to the first or most recent experience of forced or pressured sex, relationship to perpetrator, mean age at sexual debut, mean age at pregnancy related to forced or pressured sex, and prevalence of forced/coerced sexual debut. Frequencies, weighted percentages, and weighted means are presented. The lifetime prevalence of forced or pressured sex ranged from 10.4% to 18.0%. Among these adolescent girls and young women, the percentage who experienced pregnancy related to their first or most recent experience of forced or pressured sex ranged from 13.2% to 36.6%. In three countries, the most common perpetrator associated with the first pregnancy related to forced or pressured sex was a current or previous intimate partner. Mean age at pregnancy related to forced or pressured sex was similar to mean age at sexual debut in all countries. Preventing sexual violence against girls and young women will prevent a significant proportion of adverse effects on health, including unintended pregnancy. Implementation of strategies to prevent and respond to sexual violence against adolescent girls and young women is urgently needed.