Are there seasonal patterns for emergency department visits for head injuries in the USA? Findings from the National Electronic Injury Surveillance System-All Injury Program.

INTRODUCTION: Previous international research suggests that the incidence of head injuries may follow seasonal patterns. However, there is limited information about how the numbers and rates of head injuries, particularly sports- and recreation-related head injuries, among adults and children evaluated in the emergency department (ED) vary by month in the USA. This information would provide the opportunity for tailored prevention strategies. METHODS: We analysed data from the National Electronic Injury Surveillance System-All Injury Program from 2016 to 2019 to examine both monthly variation of ED visit numbers and rates for head injuries overall and those due to sports and recreation. RESULTS: The highest number of head injuries evaluated in the ED occurred in October while the lowest number occurred in February. Among males, children ages 0-4 years were responsible for the highest rates of head injury-related ED visits each year, while in females the highest rates were seen in both children ages 0-4 and adults ages 65 and older. The highest number of head injuries evaluated in the ED due to sports and recreation were seen in September and October. Head injury-related ED visits due to sports and recreation were much more common in individuals ages 5-17 than any other age group. CONCLUSION: This study showed that head injury-related ED visits for all mechanisms of injury, as well as those due to sports- and recreation-related activities, followed predictable patterns-peaking in the fall months. Public health professionals may use study findings to improve prevention efforts and to optimise the diagnosis and management of traumatic brain injury and other head injuries.

Morbidity and Mortality of Unintentional Carbon Monoxide Poisoning: United States 2005 to 2018.

STUDY OBJECTIVE: Centers for Disease Control and Prevention conducts case surveillance through the National Notifiable Diseases Surveillance System (NNDSS). This study aimed to provide surveillance report of unintentional carbon monoxide poisoning across multiple data sources to provide baseline data for the new NNDSS carbon monoxide poisoning surveillance. METHODS: For the period 2005 to 2018, we used 4 data sources to describe unintentional carbon monoxide poisoning: exposures reported by poison centers, emergency department (ED) visits, hospitalizations, and deaths. We conducted descriptive analyses by the cause of exposure (fire, nonfire, or unknown), age, sex, season, and US census region. Additional analyses were conducted using poison center exposure case data focusing on the reported signs and symptoms, management site, and medical outcome. RESULTS: Annually, we observed 39.5 poison center exposure calls (per 1 million, nationally), 56.5 ED visits (per 1 million, across 17 states), 7.3 hospitalizations (per 1 million, in 26 states), and 3.3 deaths (per 1 million, nationally) due to unintentional carbon monoxide poisoning. For 2005 to 2018, there was a decrease in the crude rate for non-fire-related carbon monoxide poisonings from hospital, and death data. Non-fire-related cases comprised 74.0% of ED visits data, 60.1% of hospitalizations, and 40.9% of deaths compared with other unintentional causes. Across all data sources, unintentional carbon monoxide poisonings were most often reported during the winter season, notably in January and December. Children aged 0 to 9 years had the highest reported rates in poison center exposure case data and ED visits (54.1 and 70.5 per 1 million, respectively); adults older than 80 years had the highest rates of hospitalization and deaths (20.2 and 9.9 per 1 million, respectively); and deaths occurred more often among men and in the Midwest region. Poison center exposure call data revealed that 45.9% of persons were treated at a health care facility. Headaches, nausea, and dizziness/vertigo were the most reported symptoms. CONCLUSION: The crude rates in non-fire-related carbon monoxide poisonings from hospitalizations, and mortality significantly decreased over the study period (ie, 2005 to 2018). This surveillance report provides trends and characteristics of unintentional carbon monoxide poisoning and the baseline morbidities and mortality data for the Centers for Disease Control and Prevention national surveillance system of carbon monoxide poisoning.

Emergency Department Visits for Alcohol-Associated Falls Among Older Adults in the United States, 2011 to 2020.

STUDY OBJECTIVE: The aim of this study was to examine the epidemiology of alcohol-associated fall injuries among older adults aged ≥65 years in the United States. METHODS: We included emergency department (ED) visits for unintentional fall injuries by adults from the National Electronic Injury Surveillance System-All Injury Program during 2011 to 2020. We estimated the annual national rate of ED visits for alcohol-associated falls and the proportion of these falls among older adults' fall-related ED visits using demographic and clinical characteristics. Joinpoint regression was performed to examine trends in alcohol-associated ED fall visits between 2011 and 2019 among older adult age subgroups and to compare these trends with those of younger adults. RESULTS: There were 9,657 (weighted national estimate: 618,099) ED visits for alcohol-associated falls, representing 2.2% of ED fall visits during 2011 to 2020 among older adults. The proportion of fall-related ED visits that were alcohol-associated was higher among men than among women (adjusted prevalence ratio [aPR]=3.6, 95% confidence interval [CI] 2.9 to 4.5). The head and face were the most commonly injured body parts, and internal injury was the most common diagnosis for alcohol-associated falls. From 2011 to 2019, the annual rate of ED visits for alcohol-associated falls increased (annual percent change 7.5, 95% CI 6.1 to 8.9) among older adults. Adults aged 55 to 64 years had a similar increase; a sustained increase was not detected in younger age groups. CONCLUSION: Our findings highlight the rising rates of ED visits for alcohol-associated falls among older adults during the study period. Health care providers in the ED can screen older adults for fall risk and assess for modifiable risk factors such as alcohol use to help identify those who could benefit from interventions to reduce their risk.

Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative Study.

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.

Leveraging data science to enhance suicide prevention research: a literature review.

OBJECTIVE: The purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research. DESIGN: We conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases. METHODS: For the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population. RESULTS: Results showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups. CONCLUSION: Data science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

Emergency Department Visits for Suspected Suicide Attempts Among Persons Aged 12-25 Years Before and During the COVID-19 Pandemic - United States, January 2019-May 2021.

Beginning in March 2020, the COVID-19 pandemic and response, which included physical distancing and stay-at-home orders, disrupted daily life in the United States. Compared with the rate in 2019, a 31% increase in the proportion of mental health-related emergency department (ED) visits occurred among adolescents aged 12-17 years in 2020 (1). In June 2020, 25% of surveyed adults aged 18-24 years reported experiencing suicidal ideation related to the pandemic in the past 30 days (2). More recent patterns of ED visits for suspected suicide attempts among these age groups are unclear. Using data from the National Syndromic Surveillance Program (NSSP),* CDC examined trends in ED visits for suspected suicide attempts† during January 1, 2019-May 15, 2021, among persons aged 12-25 years, by sex, and at three distinct phases of the COVID-19 pandemic. Compared with the corresponding period in 2019, persons aged 12-25 years made fewer ED visits for suspected suicide attempts during March 29-April 25, 2020. However, by early May 2020, ED visit counts for suspected suicide attempts began increasing among adolescents aged 12-17 years, especially among girls. During July 26-August 22, 2020, the mean weekly number of ED visits for suspected suicide attempts among girls aged 12-17 years was 26.2% higher than during the same period a year earlier; during February 21-March 20, 2021, mean weekly ED visit counts for suspected suicide attempts were 50.6% higher among girls aged 12-17 years compared with the same period in 2019. Suicide prevention measures focused on young persons call for a comprehensive approach, that is adapted during times of infrastructure disruption, involving multisectoral partnerships (e.g., public health, mental health, schools, and families) and implementation of evidence-based strategies (3) that address the range of factors influencing suicide risk.

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach.

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.

Identifying Incidents of Public Health Significance Using the National Poison Data System, 2013-2018.

Data System. The American Association of Poison Control Centers (AAPCC) and the Centers for Disease Control and Prevention (CDC) jointly monitor the National Poison Data System (NPDS) for incidents of public health significance (IPHSs).Data Collection/Processing. NPDS is the data repository for US poison centers, which together cover all 50 states, the District of Columbia, and multiple territories. Information from calls to poison centers is uploaded to NPDS in near real time and continuously monitored for specific exposures and anomalies relative to historic data.Data Analysis/Dissemination. AAPCC and CDC toxicologists analyze NPDS-generated anomalies for evidence of public health significance. Presumptive results are confirmed with the receiving poison center to correctly identify IPHSs. Once verified, CDC notifies the state public health department.Implications. During 2013 to 2018, 3.7% of all NPDS-generated anomalies represented IPHSs. NPDS surveillance findings may be the first alert to state epidemiologists of IPHSs. Data are used locally and nationally to enhance situational awareness during a suspected or known public health threat. NPDS improves CDC's national surveillance capacity by identifying early markers of IPHSs.

Cleaning and Disinfectant Chemical Exposures and Temporal Associations with COVID-19 - National Poison Data System, United States, January 1, 2020-March 31, 2020.

On January 19, 2020, the state of Washington reported the first U.S. laboratory-confirmed case of coronavirus disease 2019 (COVID-19) caused by infection with SARS-CoV-2 (1). As of April 19, a total of 720,630 COVID-19 cases and 37,202 associated deaths* had been reported to CDC from all 50 states, the District of Columbia, and four U.S. territories (2). CDC recommends, with precautions, the proper cleaning and disinfection of high-touch surfaces to help mitigate the transmission of SARS-CoV-2 (3). To assess whether there might be a possible association between COVID-19 cleaning recommendations from public health agencies and the media and the number of chemical exposures reported to the National Poison Data System (NPDS), CDC and the American Association of Poison Control Centers surveillance team compared the number of exposures reported for the period January-March 2020 with the number of reports during the same 3-month period in 2018 and 2019. Fifty-five poison centers in the United States provide free, 24-hour professional advice and medical management information regarding exposures to poisons, chemicals, drugs, and medications. Call data from poison centers are uploaded in near real-time to NPDS. During January-March 2020, poison centers received 45,550 exposure calls related to cleaners (28,158) and disinfectants (17,392), representing overall increases of 20.4% and 16.4% from January-March 2019 (37,822) and January-March 2018 (39,122), respectively. Although NPDS data do not provide information showing a definite link between exposures and COVID-19 cleaning efforts, there appears to be a clear temporal association with increased use of these products.

Characteristics of Tianeptine Exposures Reported to the National Poison Data System - United States, 2000-2017.

Tianeptine (marketed as Coaxil or Stablon) is an atypical tricyclic drug used as an antidepressant in Europe, Asia, and Latin America. In the United States, tianeptine is not approved by the Food and Drug Administration (FDA) for medical use and is an unscheduled pharmaceutical agent* (1). Animal and human studies show that tianeptine is an opioid receptor agonist (2). Several case studies have reported severe adverse effects and even death from recreational abuse of tianeptine (3-5). To characterize tianeptine exposures in the United States, CDC analyzed all exposure calls related to tianeptine reported by poison control centers to the National Poison Data System (NPDS)† during 2000-2017. Tianeptine exposure calls, including those for intentional abuse or misuse, increased across the United States during 2014-2017, suggesting a possible emerging public health risk. Most tianeptine exposures occurred among persons aged 21-40 years and resulted in moderate outcomes. Neurologic, cardiovascular, and gastrointestinal signs and symptoms were the most commonly reported health effects, with some effects mimicking opioid toxicity. A substantial number of tianeptine exposure calls also reported clinical effects of withdrawal. Among 83 tianeptine exposures with noted coexposures, the most commonly reported coexposures were to phenibut, ethanol, benzodiazepines, and opioids.

Reported Adverse Health Effects in Children from Ingestion of Alcohol-Based Hand Sanitizers - United States, 2011-2014.

Hand sanitizers are effective and inexpensive products that can reduce microorganisms on the skin, but ingestion or improper use can be associated with health risks. Many hand sanitizers contain up to 60%-95% ethanol or isopropyl alcohol by volume, and are often combined with scents that might be appealing to young children. Recent reports have identified serious consequences, including apnea, acidosis, and coma in young children who swallowed alcohol-based (alcohol) hand sanitizer (1-3). Poison control centers collect data on intentional and unintentional exposures to hand sanitizer solutions resulting from various routes of exposure, including ingestion, inhalation, and dermal and ocular exposures. To characterize exposures of children aged ≤12 years to alcohol hand sanitizers, CDC analyzed data reported to the National Poison Data System (NPDS).* The major route of exposure to both alcohol and nonalcohol-based (nonalcohol) hand sanitizers was ingestion. The majority of intentional exposures to alcohol hand sanitizers occurred in children aged 6-12 years. Alcohol hand sanitizer exposures were associated with worse outcomes than were nonalcohol hand sanitizer exposures. Caregivers and health care providers should be aware of the potential dangers associated with hand sanitizer ingestion. Children using alcohol hand sanitizers should be supervised and these products should be kept out of reach from children when not in use.

Notes from the Field: Kratom (Mitragyna speciosa) Exposures Reported to Poison Centers - United States, 2010-2015.

Kratom (Mitragyna speciosa) is a plant consumed throughout the world for its stimulant effects and as an opioid substitute (1). It is typically brewed into a tea, chewed, smoked, or ingested in capsules (2). It is also known as Thang, Kakuam, Thom, Ketum, and Biak (3). The Drug Enforcement Administration includes kratom on its Drugs of Concern list (substances that are not currently regulated by the Controlled Substances Act, but that pose risks to persons who abuse them), and the National Institute of Drug Abuse has identified kratom as an emerging drug of abuse (3,4). Published case reports have associated kratom exposure with psychosis, seizures, and deaths (5,6). Because deaths have been attributed to kratom in the United States (7), some jurisdictions have passed or are considering legislation to make kratom use a felony (8). CDC characterized kratom exposures that were reported to poison centers and uploaded to the National Poison Data System (NPDS) during January 2010-December 2015. The NPDS is a national database of information logged by the country's regional poison centers serving all 50 United States, the District of Columbia, and Puerto Rico and is maintained by the American Association of Poison Control Centers. NPDS case records are the result of call reports made by the public and health care providers.

Notes from the Field: Increase in Reported Adverse Health Effects Related to Synthetic Cannabinoid Use - United States, January-May 2015.

On April 6, 2015, CDC received notification of an increase in telephone calls to U.S. poison centers related to synthetic cannabinoid use. Monthly calls to all poison centers are tracked by the National Poison Data System, which reported that adverse health effects or concerns about possible adverse health effects related to synthetic cannabinoid use increased 330% from 349 in January 2015 to 1,501 in April 2015. Synthetic cannabinoids include various psychoactive chemicals or a mixture of such chemicals that are sprayed onto plant material, which is then often smoked or ingested to achieve a "high." These products are sold under a variety of names (e.g., synthetic marijuana, spice, K2, black mamba, and crazy clown) and can be sold in retail outlets as herbal products. Law enforcement agencies have regulated a number of these substances; however, manufacturers of synthetic cannabinoids frequently change the formulation to avoid detection and regulation. After the initial notification, CDC analyzed information from the National Poison Data System on reported adverse health effects related to synthetic cannabinoid use for the period January-May 2015.

Notes from the field: calls to poison centers for exposures to electronic cigarettes--United States, September 2010-February 2014.

Electronic nicotine delivery devices such as electronic cigarettes (e-cigarettes) are battery-powered devices that deliver nicotine, flavorings (e.g., fruit, mint, and chocolate), and other chemicals via an inhaled aerosol. E-cigarettes that are marketed without a therapeutic claim by the product manufacturer are currently not regulated by the Food and Drug Administration (FDA). In many states, there are no restrictions on the sale of e-cigarettes to minors. Although e-cigarette use is increasing among U.S. adolescents and adults, its overall impact on public health remains unclear. One area of concern is the potential of e-cigarettes to cause acute nicotine toxicity. To assess the frequency of exposures to e-cigarettes and characterize the reported adverse health effects associated with e-cigarettes, CDC analyzed data on calls to U.S. poison centers (PCs) about human exposures to e-cigarettes (exposure calls) for the period September 2010 (when new, unique codes were added specifically for capturing e-cigarette calls) through February 2014. To provide a comparison to a conventional product with known toxicity, the number and characteristics of e-cigarette exposure calls were compared with those of conventional tobacco cigarette exposure calls.

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.

Estimating national and state-level suicide deaths using a novel online symptom search data source.

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.

Harmful Algal Bloom Exposures Self-reported to Poison Centers in the United States, May-October 2019.

The National Poison Data System (NPDS) comprises self-reported information from people who call US poison center hotlines. NPDS data have proven to be important in identifying emerging public health threats. We used NPDS to examine records of people who had self-reported exposure to harmful algal blooms (HABs). Participating poison centers then contacted people who had called their centers from May through October 2019 about their HAB exposure to ask about exposure route, symptoms, health care follow-up, and awareness of possible risks of exposure. Of 55 callers who agreed to participate, 47 (85%) reported exposure to HABs while swimming or bathing in HAB-contaminated water. Nine callers reported health symptoms from being near waters contaminated with HABs, suggesting potential exposure via aerosolized toxins. Symptoms varied by the reported routes of exposure; the most commonly reported symptoms were gastrointestinal and respiratory. More public and health care provider education and outreach are needed to improve the understanding of HAB-related risks, to address ways to prevent HAB-related illnesses, and to describe appropriate support when exposures occur.

Development of a Machine Learning Model to Estimate US Firearm Homicides in Near Real Time.

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.