Special Report from the CDC: Suicide rates, sodium nitrite-related suicides, and online content, United States.

BACKGROUND: In 2022, suicide ranked as the 11th leading cause of death in the United States with 49,513 deaths. Provisional mortality data from 2022 indicate a 2.8% increase in the number of suicides compared to 2021. This paper examines overall suicide trends, sodium nitrite ingestion as an emerging suicide method, and the role that online forums play in sharing information about suicide methods (including sodium nitrite ingestion). METHODS: Suicides were identified from CDC's National Vital Statistics System (2018-July 2023 provisional) multiple cause-of-death mortality files using International Classification of Diseases (ICD), Tenth Revision underlying cause-of-death codes U03, X60-X84, and Y87.0 and T code T50.6 (antidotes and chelating agents). Google search popularity metrics were captured from January 2019 to January 2023. Case reports of sodium nitrite related suicide and suicide attempts (through February 2024) were identified in the medical and forensic literature. RESULTS: At least 768 suicides involving antidotes and chelating agents (including sodium nitrite) occurred between 2018 and July 2023, set in the context of 268,972 total suicides during that period. Overall, suicides involving antidotes and chelating agents (including sodium nitrite) represent <1% of all suicides, however, numbers are rising. CONCLUSIONS: Suicide methods are known to change over time. These changes can be influenced by, among other factors, online forums and means accessibility, such as internet purchase availability. CDC remains committed to prevention through comprehensive public health strategies that protect individuals, families, and communities. PRACTICAL APPLICATIONS: States and community partners might consider leveraging physicians, emergency responders, and other appropriate crisis response groups to disseminate information on sodium nitrite self-poisoning and its antidote, methylene blue. Efforts should be part of a comprehensive public health approach to suicide prevention.

Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study.

BACKGROUND: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. OBJECTIVE: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. METHODS: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. RESULTS: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. CONCLUSIONS: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.

Predicting state level suicide fatalities in the united states with realtime data and machine learning.

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.