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.

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.

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.

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.

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.

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.

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.

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.

Injury-Related Emergency Department Visits During the COVID-19 Pandemic.

INTRODUCTION: On March 13, 2020, the U.S. declared COVID-19 to be a national emergency. As communities adopted mitigation strategies, there were potential changes in the trends of injuries treated in emergency department. This study provides national estimates of injury-related emergency department visits in the U.S. before and during the pandemic. METHODS: A secondary retrospective cohort study was conducted using trained, on-site hospital coders collecting data for injury-related emergency department cases from medical records from a nationally representative sample of 66 U.S. hospital emergency departments. Injury emergency department visit estimates in the year before the pandemic (January 1, 2019-December 31, 2019) were compared with estimates of the year of pandemic declaration (January 1, 2020-December 31, 2020) for overall nonfatal injury-related emergency department visits, motor vehicle, falls-related, self-harm-, assault-related, and poisoning-related emergency department visits. RESULTS: There was an estimated 1.7 million (25%) decrease in nonfatal injury-related emergency department visits during April through June 2020 compared with those of the same timeframe in 2019. Similar decreases were observed for emergency department visits because of motor vehicle‒related injuries (199,329; 23.3%) and falls-related injuries (497,971; 25.1%). Monthly 2020 estimates remained relatively in line with 2019 estimates for self-harm‒, assault-, and poisoning-related emergency department visits. CONCLUSIONS: These findings provide updates for clinical and public health practitioners on the changing profile of injury-related emergency department visits during the COVID-19 pandemic. Understanding the short- and long-term impacts of the pandemic is important to preventing future injuries.

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.

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.

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.

Trends in US Emergency Department Visits for Mental Health, Overdose, and Violence Outcomes Before and During the COVID-19 Pandemic.

Importance: The coronavirus disease 2019 (COVID-19) pandemic, associated mitigation measures, and social and economic impacts may affect mental health, suicidal behavior, substance use, and violence. Objective: To examine changes in US emergency department (ED) visits for mental health conditions (MHCs), suicide attempts (SAs), overdose (OD), and violence outcomes during the COVID-19 pandemic. Design, Setting, and Participants: This cross-sectional study used data from the Centers for Disease Control and Prevention's National Syndromic Surveillance Program to examine national changes in ED visits for MHCs, SAs, ODs, and violence from December 30, 2018, to October 10, 2020 (before and during the COVID-19 pandemic). The National Syndromic Surveillance Program captures approximately 70% of US ED visits from more than 3500 EDs that cover 48 states and Washington, DC. Main Outcomes and Measures: Outcome measures were MHCs, SAs, all drug ODs, opioid ODs, intimate partner violence (IPV), and suspected child abuse and neglect (SCAN) ED visit counts and rates. Weekly ED visit counts and rates were computed overall and stratified by sex. Results: From December 30, 2018, to October 10, 2020, a total of 187 508 065 total ED visits (53.6% female and 46.1% male) were captured; 6 018 318 included at least 1 study outcome (visits not mutually exclusive). Total ED visit volume decreased after COVID-19 mitigation measures were implemented in the US beginning on March 16, 2020. Weekly ED visit counts for all 6 outcomes decreased between March 8 and 28, 2020 (March 8: MHCs = 42 903, SAs = 5212, all ODs = 14 543, opioid ODs = 4752, IPV = 444, and SCAN = 1090; March 28: MHCs = 17 574, SAs = 4241, all ODs = 12 399, opioid ODs = 4306, IPV = 347, and SCAN = 487). Conversely, ED visit rates increased beginning the week of March 22 to 28, 2020. When the median ED visit counts between March 15 and October 10, 2020, were compared with the same period in 2019, the 2020 counts were significantly higher for SAs (n = 4940 vs 4656, P = .02), all ODs (n = 15 604 vs 13 371, P < .001), and opioid ODs (n = 5502 vs 4168, P < .001); counts were significantly lower for IPV ED visits (n = 442 vs 484, P < .001) and SCAN ED visits (n = 884 vs 1038, P < .001). Median rates during the same period were significantly higher in 2020 compared with 2019 for all outcomes except IPV. Conclusions and Relevance: These findings suggest that ED care seeking shifts during a pandemic, underscoring the need to integrate mental health, substance use, and violence screening and prevention services into response activities during public health crises.

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

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.

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.

Advancing injury and violence prevention through data science.

INTRODUCTION: The volume of new data that is created each year relevant to injury and violence prevention continues to grow. Furthermore, the variety and complexity of the types of useful data has also progressed beyond traditional, structured data. In order to more effectively advance injury research and prevention efforts, the adoption of data science tools, methods, and techniques, such as natural language processing and machine learning, by the field of injury and violence prevention is imperative. METHOD: The Centers for Disease Control and Prevention's (CDC) National Center for Injury Prevention and Control has conducted numerous data science pilot projects and recently developed a Data Science Strategy. This strategy includes goals on expanding the availability of more timely data systems, improving rapid identification of health threats and responses, increasing access to accurate health information and preventing misinformation, improving data linkages, expanding data visualization efforts, and increasing efficiency of analytic and scientific processes for injury and violence, among others. RESULTS: To achieve these goals, CDC is expanding its data science capacity in the areas of internal workforce, partnerships, and information technology infrastructure. Practical Application: These efforts will expand the use of data science approaches to improve how CDC and the field address ongoing injury and violence priorities and challenges.

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.

Noninfectious Foodborne Exposures Reported to 2 National Reporting Systems, United States, 2000-2010.

OBJECTIVES: Foodborne disease is a pervasive problem caused by consuming food or drink contaminated by infectious or noninfectious agents. The 55 US poison centers receive telephone calls for advice on foodborne disease cases that may be related to a foodborne disease outbreak (FBDO). Our objective was to assess whether poison center call records uploaded to the National Poison Data System (NPDS) can be used for surveillance of noninfectious FBDOs in the United States. METHODS: We matched NPDS records on noninfectious FBDO agents in the United States with records in the Foodborne Disease Outbreak Surveillance System (FDOSS) for 2000-2010. We conducted multivariable logistic regression analysis comparing NPDS matched and unmatched records to assess features of NPDS records that may indicate a confirmed noninfectious FBDO. RESULTS: During 2000-2010, FDOSS recorded 491 noninfectious FBDOs of known etiology and NPDS recorded 8773 calls for noninfectious foodborne disease exposures. Of 8773 NPDS calls, 469 (5.3%) were matched to a noninfectious FBDO reported to FDOSS. Multivariable logistic regression indicated severity of medical outcome, whether the call was made by a health care professional, and etiology as significant predictors of NPDS records matching an FDOSS noninfectious FBDO. CONCLUSIONS: NPDS may complement existing surveillance systems and response activities by providing timely information about single cases of foodborne diseases or about a known or emerging FBDO. Prioritizing NPDS records by certain call features could help guide public health departments in the types of noninfectious foodborne records that most warrant public health follow-up.

Evaluation of Oklahoma's Electronic Death Registration System and Event Fatality Markers for Disaster-Related Mortality Surveillance - Oklahoma USA, May 2013.

INTRODUCTION: Official counts of deaths attributed to disasters are often under-reported, thus adversely affecting public health messaging designed to prevent further mortality. During the Oklahoma (USA) May 2013 tornadoes, Oklahoma State Health Department Division of Vital Records (VR; Oklahoma City, Oklahoma USA) piloted a flagging procedure to track tornado-attributed deaths within its Electronic Death Registration System (EDRS). To determine if the EDRS was capturing all tornado-attributed deaths, the Centers for Disease Control and Prevention (CDC; Atlanta, Georgia USA) evaluated three event fatality markers (EFM), which are used to collate information about deaths for immediate response and retrospective research efforts. METHODS: Oklahoma identified 48 tornado-attributed deaths through a retrospective review of hospital morbidity and mortality records. The Centers for Disease Control and Prevention (CDC; Atlanta, Georgia USA) analyzed the sensitivity, timeliness, and validity for three EFMs, which included: (1) a tornado-specific flag on the death record; (2) a tornado-related term in the death certificate; and (3) X37, the International Classification of Diseases, 10th Revision (ICD-10) code in the death record for Victim of a Cataclysmic Storm, which includes tornadoes. RESULTS: The flag was the most sensitive EFM (89.6%; 43/48), followed by the tornado term (75.0%; 36/48), and the X37 code (56.2%; 27/48). The most-timely EFM was the flag, which took 2.0 median days to report (range 0-10 days), followed by the tornado term (median 3.5 days; range 1-21), and the X37 code (median >10 days; range 2-122). Over one-half (52.1%; 25/48) of the tornado-attributed deaths were missing at least one EFM. Twenty-six percent (11/43) of flagged records had no tornado term, and 44.1% (19/43) had no X37 code. Eleven percent (4/36) of records with a tornado term did not have a flag. CONCLUSION: The tornado-specific flag was the most sensitive and timely EFM. Using the flag to collate death records and identify additional deaths without the tornado term and X37 code may improve immediate response and retrospective investigations. Moreover, each of the EFMs can serve as quality controls for the others to maximize capture of all disaster-attributed deaths from vital statistics records in the EDRS.Issa AN, Baker K, Pate D, Law R, Bayleyegn T, Noe RS. Evaluation of Oklahoma's Electronic Death Registration System and event fatality markers for disaster-related mortality surveillance - Oklahoma USA, May 2013. Prehosp Disaster Med. 2019;34(2):125-131.