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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.

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.

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.

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 Assessing social media posts from Twitter, YouTube, Reddit, and Drugs-Forum.com for the presence of health misinformation about treatment for OUD. METHODS We developed a multi-stage analytic pipeline to assess social media posts from Twitter, YouTube, Reddit, and Drugs-Forum.com for the presence of health misinformation about treatment for OUD. Our approach first utilized 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 results for misinformation. RESULTS We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multi-stage analytic pipeline identified seven main clusters or discussion themes. Among a high-yield dataset of posts (N=303) for further public health expert review, these included discussion about potential treatments for opioid use disorder (N=90, 29.8%), the nature of addiction (N=68, 22.5%), pharmacologic properties of substances (N=52, 16.88%), injection drug use (N=36, 11.9%), pain and opioids (N=28, 9.27%), physical dependence of medications (N=22, 7.2%), and tramadol use (N=7, 2.3%). Public health expert review of 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 to 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.

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.

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.

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.

Falls disproportionately affect older adults (≥65) resulting in three million emergency department (ED) visits annually. The relationship between alcohol and falls is unclear, but side effects of alcohol consumption include gait and balance impairments, which are risk factors for falls. This study examined trends of older adult fall-related ED visits involving alcohol from 2011-2020. Alcohol data are not routinely collected during ED visits. Alcohol involvement was identified from National Electronic Injury Surveillance System-All Injury Program free-text narratives using natural language processing and text analytic techniques. Joinpoint regression was used and cross-validated with SAS to determine significant older adult fall changes from 2011-2020 by sex and to compare older adult trends to adults < 65 (20-34, 35-54, 55-64). From 2011-2020, 2.2% of all ED visits for older adult falls involved alcohol. Alcohol-involved fall ED visit rates for older adults increased annually by 7.2% (95%CI:6.1%-8.4%). These older adult ED visit rates increased by 6.8% (95%CI:5.4%-8.2%) for males and 7.4% (95%CI:6.0%-8.9%) for females annually. For adults aged 20-34 and 35-54, alcohol-involved fall ED visit rates increased annually by 1.3% (95%CI:0.1%-2.6%) and 3.8% (95%CI:2.7%-5.0%), respectively, until 2017 and then decreased by 4.4% (95%CI:1.2%-7.5%) and 4.0% (95%CI:0.3%-7.5%). For adults aged 55-64, ED visit rates increased annually by 8.5% (95%CI:6.8%-10.2%) until 2018 and then stabilized. Documenting alcohol use when treating falls is important for monitoring trends. Assessing alcohol use in conjunction with assessments for other fall risk factors can inform healthcare providers on effective strategies to recommend to reduce fall risk.

ObjectiveThe 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.DesignWe conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.MethodsFor 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.ResultsResults 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.ConclusionData 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.

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.

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

The National Poison Data System (NPDS) is a database and surveillance system for US poison centers (PCs) call data. The Centers for Disease Control and Prevention (CDC) and American Association of Poison Control Centers (AAPCC) use NPDS to identify incidents of potential public health significance. State health departments are notified by CDC of incidents identified by NPDS to be of potential public health significance. Our objective was to describe the public health impact of CDC's notifications and the use of NPDS data for surveillance.We described how NPDS data informed three public health responses: the Deepwater Horizon incident, national exposures to laundry detergent pods, and national exposures to e-cigarettes. Additionally, we extracted survey results of state epidemiologists regarding NPDS incident notification follow-up from 1 January 2015 to 31 December 2016 to assess current public health application of NPDS data using Epi Info 7.2 and analyzed data using SAS 9.3. We assessed whether state health departments were aware of incidents before notification, what actions were taken, and whether CDC notifications contributed to actions.NPDS data provided evidence for industry changes to improve laundry detergent pod containers safety and highlighted the need to regulate e-cigarette sale and manufacturing. NPDS data were used to improve situational awareness during the 2010 Deepwater Horizon oil spill. Of 59 health departments and PCs who responded to CDC notifications about anomalies (response rate = 49.2%), 27 (46%) reported no previous awareness of the incident, and 20 (34%) said that notifications contributed to public health action.Monitoring NPDS data for anomalies can identify emerging public health threats and provide evidence-based science to support public health action and policy changes.

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.

Objective - To discuss the development of a set of tools for interagency collaborations on health surveillance - To determine the core contents of the tools based on known gaps in health surveillance - To determine collaborators in development and timelines for completion Introduction In 2010, the Council of State and Territorial Epidemiologists (CSTE) identified data collected by poison centers (PCs) as an important tool for all-hazards exposure and illness surveillance. In response to this, the Centers for Disease Control and Prevention (CDC), CSTE, and the American Association of Poison Control Centers (AAPCC) members created the Poison Center Public Health Community of Practice (CoP). The CoP acts as a platform, to facilitate sharing experiences, identify best practices, and develop relationships among federal agencies, state and local health departments (HD), and PCs. Since its inception, the CoP garnered over 250 members, hosted more than 25 webinars regarding PC-HD collaborations, and produced five newsletters highlighting subjects pertinent to PC and HD personnel. Description Findings and lessons learned from activities outlined in the introduction include the need for: 1) standardized inter-agency communication, 2) increased knowledge and utilization of state reporting and monitoring systems, and 3) inter-agency collaborations to prevent the duplication of efforts. In this roundtable, we will: 1) discuss how to develop information and tools for inter-agency public health communication and messaging, 2) identify key stakeholders including potential national, state, and local agencies who can help bolster communication messaging, and 3) develop appropriate points of contact within these agencies. Potential components of the guidance may include: 1) a comprehensive list of state resources available to PC and PH personnel, 2) recommended inter-agency points of contact, 3) lessons learned from collaborative projects, and 4) PC abilities to share and analyze data for public health practice and health surveillance. How the Moderator Intends to Engage the Audience in Discussions on the Topic This roundtable session will consider the following questions: - Which agencies--local, state, national, or otherwise-- would benefit from inter-agency collaborations health surveillance efforts? - What should the proposed tools include? Who should be involved in developing the proposed materials? Following this roundtable, the CoP hopes to have tangible next steps in creating inter-agency collaborations health surveillance guidance and establish a timeline for completion.

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

E-cigarette use is increasing, and the long-term impact on public health is unclear. We described the acute adverse health effects from e-cigarette exposures reported to U.S. poison centers. We compared monthly counts and demographic, exposure, and health effects data of calls about e-cigarettes and conventional cigarettes made to poison centers from September 2010 through December 2014. Monthly e-cigarette calls increased from 1 in September 2010, peaked at 401 in April 2014, and declined to 295 in December 2014. Monthly conventional cigarette calls during the same period ranged from 302 to 514. E-cigarette calls were more likely than conventional cigarette calls to report adverse health effects, including vomiting, eye irritation, and nausea. Five e-cigarette calls reported major health effects, such as respiratory failure, and there were two deaths associated with e-cigarette calls. E-cigarette calls to U.S. poison centers increased over the study period, and were more likely than conventional cigarettes to report adverse health effects. It is important for health care providers and the public to be aware of potential acute health effects from e-cigarettes. Developing strategies to monitor and prevent poisonings from these novel devices is critical.

Key Points Question Can real-time streams of secondary information related to suicide be used to accurately estimate suicide fatalities in the US in real time? Findings In this national cross-sectional study, combining information from 8 data streams encompassing various health services and online data sources enabled accurate, real-time estimation of US suicide fatalities with meaningful correlation to week-to-week epidemiological trends and a less than 1% error compared with actual counts. Meaning These findings advance the first efforts to create a population-level system for enabling real-time epidemiological trend monitoring of suicide fatalities., This cross-sectional study uses machine learning to estimate weekly suicide fatalities in the US in near real time., 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

ObjectiveTo discuss the use of poison center (PC) data for public health (PH) surveillance at the local, state, and federal levels. To generate meaningful discussion on how to facilitate greater PC and PH collaboration.IntroductionSince 2008, poisoning is the leading cause of injury-related death in the United States; since 1980, the poisoning-related fatality rate in the United States (U.S.) has almost tripled1. Many poison-related injuries and deaths are reported to regional PCs which receive about 2.4 million reports of human chemical and poison exposures annually2. Federal, state, and local PH agencies often collaborate with PCs and use PC data for PH surveillance to identify poisoning-related health issues. Many state and local PH agencies have partnerships with regional PCs for direct access to local PC data which help them perform this function. At the national level, the National Center for Environmental Health (NCEH) of the Centers for Disease Control and Prevention (CDC) conducts PH surveillance for exposures and illnesses of PH significance using the National Poison Data System (NPDS), the national PC reporting database and real-time surveillance system.Though most PC and PH officials agree that PC data play an important role in PH practice and surveillance, collaboration between PH agencies and PCs can be hindered by numerous challenges. To address these challenges and bolster collaboration, the PC and PH Collaborations Community of Practice (CoP) has collaborated with members to provide educational webinars; newsletters highlighting the intersection of PH and PC work; and in-person meetings at relevant national and international conferences. The CoP includes over 200 members from state and local PH departments, regional PCs, CDC, the American Association of Poison Control Centers (AAPCC), and the U.S. Environmental Protection Agency (EPA).DescriptionThe panel will consist of 3 presenters and 1 moderator, who are members of the CoP. Each presenter will bring a unique perspective on the use of PC data for PH practice and surveillance. Dr. Prakash Mulay is the surveillance coordinator for chemical related illnesses and injuries in Florida. His primary focus is on carbon monoxide, pesticide, mercury, and arsenic poisoning. He also works as a liaison between the Florida Poison Information Centers and Department of Health. Dr. Mulay has a Medical Degree from India and a Masters of Public Health (MPH) in epidemiology from Florida International University, Miami. For the purpose of the panel discussion, Dr. Mulay will provide PC PH collaboration from the state perspective.Dr. Jay Schauben is the Director of the Florida/United States Virgin Islands Poison Information Center in Jacksonville, the Florida Poison Information Center Network Data Center, and the Clinical Toxicology Fellowship Program at University of Florida Health-Jacksonville Medical Center/University of Florida Health Science Center. He is board-certified in clinical toxicology and is a Fellow of the American Academy of Clinical Toxicology. In 1992, Dr. Schauben implemented the Florida Poison Information Center in Jacksonville and played a major role in crafting the Statewide Florida Poison Information Center Network. On the panel, Dr. Schauben will provide collaboration insight from the PC perspective.Dr. Royal Law is the surveillance and technical lead for the National Chemical and Radiological Surveillance Program, housed within the Health Studies Branch at the CDC. He received his PhD in Public Health from Georgia State University and his MPH at Emory University. Dr. Law will provide insight from the national level including CDC use of PC data for public health surveillance activities.How The Moderator Intends to Engage the AudienceAfter the panel members have been introduced and shared their contributions and experiences with PC PH collaboration the moderator will engage the audience by facilitating discussion of the successes and challenges to using PC data for PH practice and surveillance.Sample questions:What are your current capacities and collaborative activities between your state/local health department and your PC?What non-funding related barriers hinder the collaboration between your state/local health department and PC?If no increase in funding were available, how would you increase the level of interactivity with the PC and state/local health department? What if funding was available?References1Warner M, Chen LH, Makuc DM, Anderson RN, and Minino AM. Drug Poisoning Deaths in the United States, 1980–2008. National Center for Health Statistics Data Brief, December 2011. Accessed 8/29/2012.2Mowry JB, Spyker DA, Brooks DE, Zimmerman A, Schauben JL (2016) 2015 Annual Report of the American Association of Poison Control Centers’ National Poison Data Systems (NPDS): 33rd Annual Report, Clinical Toxicology, 54:10, 924-1109.

Objective Our objective was to compare the effectiveness of applying the historical limits method (HLM) to poison center (PC) call volumes with vs without stratifying by exposure type. Introduction The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States PCs. PCs provide triage and treatment advice for hazardous exposures through a free national hotline. Information on demographics, health effects, implicated substance(s), medical outcome of the patient, and other variables are collected. CDC uses automated algorithms to identify anomalies in both pure call volume and specific clinical effect volume, and to identify calls reporting exposure to high priority agents. Pure and clinical effect volume anomalies are identified when an hourly call count exceeds a threshold based on historical data using HLM. 1 Clinical toxicologists and epidemiologists at the American Association of Poison Control Centers and CDC apply standardized criteria to determine if the anomaly identifies a potential incident of public health significance (IPHS) and to notify the respective health departments and local PCs as needed. Discussions with NPDS users and analysis of IPHS showed that alerting based on pure call volume yielded excessive false positives. A study using a 5-year NPDS call dataset assessed the positive predictive value (PPV) of the call volume-based approach. This study showed that less than 4% of anomalies were IPHS. 2 A low PPV can cause unnecessary waste of staff time and resources analyzing false positive anomalies. As an alternative to pure call volume-based detection where all calls to each PC are aggregated for anomaly detection, we considered separating calls by toxicologically-relevant exposure categories for more targeted anomaly detection. We hypothesized that this stratified approach would reduce the number of false positives. Methods We derived our exposure categories based on the criteria that the categories must: 1) relate to hazardous exposures of public health importance, 2) reflect categories based on clinical effects and treatment modalities, 3) avoid high priority exposures that may be triggered by single calls, 4) be compatible with exposure substance identification codes currently used by PCs and NPDS, and 5) include enough calls for meaningful tracking. We queried all calls reporting exposures to the proposed categories between January 1, 2009 and July 31, 2015 for ten PCs. We applied the HLM method after stratifying by exposure category and tabulated the number of alerts triggered for each category during the study period. We then applied the HLM method for the ten PCs on all combined exposure calls to represent the traditional non-stratified approach. We compared the combined alert burden generated by stratifying by exposure category with the alert burden for the non-stratified approach for varying time windows (1-, 2-, 4-, 8- and 24-hours). We conducted analysis in R. Results We derived a total of 20 exposure categories, including chemicals (n=4), drugs of abuse (n=6), pesticides (n=3), gas/fume/vapors (n=2), contaminated food/water (n=1), and others (n=4). Call counts during 2015 for these categories ranged from approximately 5,000 to 90,000. Table 1 shows the total number of alerts triggered for each method by time windows. There was a marked reduction of alert burden when first stratifying by exposure category for time windows shorter than eight hours compared to the alert burden for the non-stratified approach. Conclusions Stratification of call volume by exposure category and time window suggests potential improvement over traditional non- stratified approach by having a lower alert burden. Further work should focus on refining the exposure categories, refining the time window for surveillance, and assessing other detection performance metrics, such as sensitivity. Table 1: Alert burden comparison for the non-stratified vs stratified approach

Objective Our objective was to determine if the detection performance of current surveillance algorithms to detect call clusters is improved by stratifying by exposure category. Introduction The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States poison centers (PCs) to identify clusters of reports of hazardous exposures and illnesses. NPDS stores basic information from PC calls including call type (information request only or call reporting a possible chemical exposure), exposure agent, demographics, clinical, and other variables. CDC looks for anomalies in PC data by using automated algorithms to analyze call and clinical effect volume, and by identifying calls reporting exposures to pre-specified high priority agents. Algorithms analyzing call and clinical effect volume identify anomalies when the number of calls exceeds a threshold using the historical limits method (HLM). Clinical toxicologists and epidemiologists at the American Association of Poison Control Centers and CDC apply standardized criteria to determine if the anomaly is a potential incident of public health significance (IPHS) and then notify the respective health departments and PCs as needed. Discussions with surveillance system users and analysis of past IPHS determined that call volume-based surveillance results in a high proportion of false positive anomalies. A study assessing the positive predictive value (PPV) of this approach determined that fewer than four percent of anomalies over a five-year period were IPHS. 1 A low PPV can cause an unnecessary waste of staff time and resources. We hypothesized that first stratifying call volume by exposure category would reduce the number of false positives. With the help of medical toxicologists, we created 20 toxicologically-relevant exposure categories to test this hypothesis. Methods To compare cluster detection performance between the two approaches, we used a historical testbed of hourly exposure call counts with and without initial stratification by exposure category from 10 selected PCs from Jan 1, 2006 - Jul 31, 2015. We ran the HLM for both non-stratified and stratified testbeds to estimate the monthly number of anomalies triggered (i.e., alert burden). Our target signals to assess detection performance consisted of call samples from three large public health events: the 2009 Salmonella food poisoning event from contaminated peanut butter, the 2012 Hurricane Sandy- associated carbon monoxide poisonings in New Jersey, and the 2014 Elk River contaminated water spill in West Virginia (WV). For each event, we chose 30 random calls one thousand times to obtain 1000 random sets of inject clusters. Each inject cluster was iteratively added into the testbed with and without initial stratification by exposure category. We then applied the HLM for each iteration to see if the algorithm identified the inject cluster. The sensitivity for each approach for each PC was calculated as the proportion of iterations where the algorithm identified the inject cluster. We reported median sensitivities from the ten PCs for each of the time windows of 1, 2, 4, 8, and 24 hours. Results Figure 1 summarizes results for the WV event with markers showing anomaly burden (x-axis) and sensitivity (y-axis) using the stratified ( Δ ) and the non-stratified (o) approach by different time windows (hrs). The results from the other two events are not shown but established similar patterns. Anomaly burden is shown as the estimated monthly anomaly count for each approach. For example, markers linked by the arrow show that with a 4-hour time window, the stratified approach achieves nearly perfect sensitivity with ~10 anomalies as the monthly anomaly burden while sensitivity of the non-stratified approach is below 20% with ~40 monthly anomalies. The stratified approach gave improved overall sensitivity across all time windows, and reduced anomaly burden for 1-, 2-, and 4-hour time windows. Conclusions We found a consistent detection advantage (higher sensitivity and lower anomaly burden) for the stratified vs traditional non- stratified approach for 1-, 2-, and 4-hour time windows. Further research should focus on refining the stratified approach and the specific surveillance parameters (such as time windows) that increase algorithm performance. Figure 1: Detection performance comparison: stratified vs non-stratified approach; 2014 Elk River contaminated water spill in West Virginia scenario

ObjectiveCarbon monoxide (CO) is an odorless, colorless gas produced by fossil fuel combustion. On October 29, 2012, Hurricane Sandy moved ashore near Atlantic City, New Jersey, causing widespread morbidity and mortality, $30 to $50 billion in economic damage, and 8.5 million households to be without power. The combination of power outages and unusually low temperatures led people to use alternate power sources, placing many at risk for CO exposure.MethodsWe examined Hurricane Sandy–related CO exposures from multiple perspectives to help identify risk factors and develop strategies to prevent future exposures. This report combined data from 3 separate sources (health departments, poison centers via the National Poison Data System, and state and local public information officers).ResultsResults indicated that the number of CO exposures in the wake of Hurricane Sandy was significantly greater than in previous years. The persons affected were mostly females and those in younger age categories and, despite messaging, most CO exposures occurred from improper generator use.ConclusionsOur findings emphasize the continued importance of CO-related communication and ongoing surveillance of CO exposures to support public health response and prevention during and after disasters. Additionally, regional poison centers can be a critical resource for potential on-site management, public health promotion, and disaster-related CO exposure surveillance. (Disaster Med Public Health Preparedness. 2017;11:562–567)

Poisonings from lamp oil ingestion continue to occur worldwide among the pediatric population despite preventive measures such as restricted sale of colored and scented lamp oils. This suggests that optimal prevention practices for unintentional pediatric exposures to lamp oil have yet to be identified and/or properly implemented.To characterize demographic, health data, and potential risk factors associated with reported exposures to lamp oil by callers to poison centers (PCs) in the US and discuss their public health implications.This was a two part study in which the first part included characterizing all exposures to a lamp oil product reported to the National Poison Data System (NPDS) with regard to demographics, exposure, health, and outcome data from 1/1/2000 to 12/31/2010. Regional penetrance was calculated using NPDS data by grouping states into four regions and dividing the number of exposure calls by pediatric population per region (from the 2000 US census). Temporal analyses were performed on NPDS data by comparing number of exposures by season and around the July 4th holiday. Poisson regression was used to model the count of exposures for these analyses. In the second part of this project, in order to identify risk factors we conducted a telephone-based survey to the parents of children from five PCs in five different states. The 10 most recent lamp oil product exposure calls for each poison center were systematically selected for inclusion. Calls in which a parent or guardian witnessed a pediatric lamp oil product ingestion were eligible for inclusion. Data on demographics, exposure information, behavioral traits, and health were collected. A descriptive analysis was performed and Fisher's exact test was used to evaluate associations between variables. All analyses were conducted using SAS v9.3.Among NPDS data, 2 years was the most common patient age reported and states in the Midwestern region had the highest numbers of exposure calls compared to other regions. Exposure calls differed by season (p0.0001) and were higher around the July 4th holiday compared to the rest of the days in July (2.09 vs. 1.89 calls/day, p0.002). Most exposures occurred inside a house, were managed on-site and also had a "no effect" medical outcome. Of the 50 PC-administered surveys to parents or guardians, 39 (78%) met inclusion criteria for analysis. The majority of ingestions occurred in children that were 2 years of age, that were not alone, involved tiki torch fuel products located on a table or shelf, and occurred inside the home. The amount of lamp oil ingested did not appear to be associated with either the smell (p = 0.19) or the color of the oil (p = 1.00) in this small sample. Approximately half were asymptomatic (n = 18; 46%), and of those that reported symptoms, cough was the most common (n = 20, 95%) complaint.Lamp oil product exposures are most common among young children (around 2 years of age) while at home, not alone and likely as a result of the product being in a child-accessible location. Increasing parental awareness about potential health risks to children from these products and teaching safe storage and handling practices may help prevent both exposures and associated illness. These activities may be of greater benefit in Midwestern states and during summer months (including the period around the July 4th holiday).

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.

Introduction Synthetic cannabinoids include various psychoactive chemicals that are sprayed onto plant material, which is then 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 are sold in retail outlets as herbal products and are often labeled not for human consumption. Law enforcement agencies regulate many of these substances; however, manufacturers may frequently change the formulation and mask their intended purpose to avoid detection and regulation. On April 6, 2015, automated surveillance algorithms via surveillance through the National Poison Data System (NPDS), a web-based surveillance system of all calls to United States (US) poison centers (PCs), identified an increase in calls to PCs related to synthetic cannabinoid use. To identify risk factors and adverse health effects, CDC analyzed all calls to PCs about synthetic cannabinoid use from January to May, 2015.

In March of 2011, an earthquake struck Japan causing a tsunami that resulted in a radiological release from the damaged Fukushima Daiichi nuclear power plant. Surveillance for potential radiological and any iodine/iodide product exposures was initiated on the National Poison Data System (NPDS) to target public health messaging needs within the United States (US). Our objectives are to describe self-reported exposures to radiation, potassium iodide (KI) and other iodine/iodide products which occurred during the US federal response and discuss its public health impact.All calls to poison centers associated with the Japan incident were identified from March 11, 2011 to April 18, 2011 in NPDS. Exposure, demographic and health outcome information were collected. Calls about reported radiation exposures and KI or other iodine/iodide product ingestions were then categorized with regard to exposure likelihood based on follow-up information obtained from the PC where each call originated. Reported exposures were subsequently classified as probable exposures (high likelihood of exposure), probable non-exposures (low likelihood of exposure), and suspect exposure (unknown likelihood of exposure).We identified 400 calls to PCs associated with the incident, with 340 information requests (no exposure reported) and 60 reported exposures. The majority (n = 194; 57%) of the information requests mentioned one or more substances. Radiation was inquired about most frequently (n = 88; 45%), followed by KI (n = 86; 44%) and other iodine/iodide products (n = 47; 24%). Of the 60 reported exposures, KI was reported most frequently (n = 25; 42%), followed by radiation (n = 22; 37%) and other iodine/iodide products (n = 13; 22%). Among reported KI exposures, most were classified as probable exposures (n = 24; 96%); one was a probable non-exposure. Among reported other iodine/iodide product exposures, most were probable exposures (n = 10, 77%) and the rest were suspect exposures (n = 3; 23%). The reported radiation exposures were classified as suspect exposures (n = 16, 73%) or probable non-exposures (n = 6; 27%). No radiation exposures were classified as probable exposures. A small number of the probable exposures to KI and other iodide/iodine products reported adverse signs or symptoms (n = 9; 26%). The majority of probable exposures had no adverse outcomes (n = 28; 82%). These data identified a potential public health information gap regarding KI and other iodine/iodide products which was then addressed through public health messaging activities.During the Japan incident response, surveillance activities using NPDS identified KI and other iodine/iodide products as potential public health concerns within the US, which guided CDC's public health messaging and communication activities. Regional PCs can provide timely and additional information during a public health emergency to enhance data collected from surveillance activities, which in turn can be used to inform public health decision-making.

The National Poison Data System (NPDS) is a national near-real-time surveillance system that improves situational awareness for chemical and poison exposures, according to data from US poison centers. NPDS is the successor to the Toxic Exposure Surveillance System. The Centers for Disease Control and Prevention (CDC) use these data, which are owned and managed by the American Association of Poison Control Centers, to improve public health surveillance for chemical and poison exposures and associated illness, identify early markers of chemical events, and enhance situational awareness during outbreaks. Information recorded in this database is from self-reported calls from the public or health care professionals. In 2009, NPDS detected 22 events of public health significance and CDC used the system to monitor several multistate outbreaks. One of the limitations of the system is that exposures do not necessarily represent a poisoning. Incorporating NPDS data into the public health surveillance network and subsequently using NPDS to rapidly identify chemical and poison exposures exemplifies the importance of the poison centers and NPDS to public health surveillance. This integration provides the opportunity to improve the public health response to chemical and poison exposures, minimizes morbidity and mortality, and serves as an important step forward in surveillance technology and integration.

This presentation compares surveillance algorithms used in the National Poison Data System to identify incidents of public health significance with recently expanded filtering capabilities and with methods beyond the NPDS generalized historical limits model. Collected data series from 55 poison centers over 7 years include hourly counts of general call volumes and of substance-specific (e.g. CO exposure) calls. By applying current, modified, and novel methods to known and simulated clusters among these data, the authors will present the most efficient algorithms for identifying incidents of public health significance.

Background: In the United States, national surveillance for foodborne disease outbreaks is conducted by the Foodborne Disease Outbreak Surveillance System (FDOSS), which relies on voluntary reporti...

The role of public health surveillance in disaster response continues to expand as timely, accurate information is needed to mitigate the impact of disasters. Health surveillance after a disaster involves the rapid assessment of the distribution and determinants of disaster-related deaths, illnesses, and injuries in the affected population. Public health disaster surveillance is one mechanism that can provide information to identify health problems faced by the affected population, establish priorities for decision makers, and target interventions to meet specific needs. Public health surveillance traditionally relies on a wide variety of data sources and methods. Poison center (PC) data can serve as data sources of chemical exposures and poisonings during a disaster. In the US, a system of 57 regional PCs serves the entire population. Poison centers respond to poison-related questions from the public, health care professionals, and public health agencies. The Centers for Disease Control and Prevention (CDC) uses PC data during disasters for surveillance of disaster-related toxic exposures and associated illnesses to enhance situational awareness during disaster response and recovery. Poison center data can also be leveraged during a disaster by local and state public health to supplement existing surveillance systems. Augmenting traditional surveillance data (ie, emergency room visits and death records) with other data sources, such as PCs, allows for better characterization of disaster-related morbidity and mortality. Poison center data can be used during a disaster to detect outbreaks, monitor trends, track particular exposures, and characterize the epidemiology of the event. This timely and accurate information can be used to inform public health decision making during a disaster and mitigate future disaster-related morbidity and mortality.WolkinA, SchnallAH, LawR, SchierJ. Using poison center data for postdisaster surveillance. Prehosp Disaster Med. 2014;29(5):1-4.

The Centers for Disease Control and Prevention (CDC) and the American Association of Poison Control Centers conduct national surveillance on data collected by US poison centers to identify incidents of potential public health significance (IPHS). The overarching goals of this collaboration are to improve CDC's national surveillance capacity for public health threats, identify early markers of public health incidents and enhance situational awareness. The National Poison Data System (NPDS) is used as a surveillance system to automatically identify data anomalies.To characterize data anomalies and IPHS captured by national surveillance of poison center data over 5 years.Data anomalies are identified through three surveillance methodologies: call-volume, clinical effect, and case-based. Anomalies are reviewed by a team of epidemiologists and clinical toxicologists to determine IPHS using standardized criteria. The authors reviewed IPHS identified by these surveillance activities from 2008 through 2012.Call-volume surveillance identified 384 IPHS; most were related to gas and fume exposures (n = 229; 59.6%) with the most commonly implicated substance being carbon monoxide (CO) (n = 92; 22.8%). Clinical-effect surveillance identified 138 IPHS; the majority were related to gas and fume exposures (n = 58; 42.0%) and gastrointestinal complaints (n = 84; 16.2%), and the most commonly implicated substance was CO (n = 20; 14.4%). Among the 11 case-based surveillance definitions, the botulism case definition yielded the highest percentage of identified agent-specific illness.A small proportion of data anomalies were designated as IPHS. Of these, CO releases were the most frequently reported IPHS and gastrointestinal syndromes were the most commonly reported illness manifestations. poison center data surveillance may be used as an approach to identify exposures, illnesses, and incidents of importance at the national and state level.

Objective To describe the use of poison center data for public health surveillance from the poison center, local, state, and federal public health perspectives and to generate meaningful discussion on how to address the challenges to collaboration. Introduction Since 2008, poisoning has become the leading cause of injury-related death in the United States (US); since 1980, the poisoning-related fatality rate in the US has almost tripled.1 Many poison-related injuries and deaths are reported to regional poison centers (PCs) which receive about 2.4 million reports of human chemical and poison exposures annually.2 Federal, state, and local public health (PH) agencies often collaborate with poison centers and use PC data for public health surveillance of poisoning-related health issues. Many state and local PH agencies have partnerships with regional PCs for direct access to local PC data which help them perform this function. At the national level, CDC conducts public health surveillance for exposures and illnesses of public health significance using the National Poison Data System (NPDS), the national PC reporting database. Though most PC and PH officials agree that PC data play an important role in PH practice and surveillance, collaboration between PH agencies and PCs has been hindered by numerous challenges. To address these challenges and bolster collaboration, the Poison Center and Public Health Collaborations Community of Practice (CoP) was created in 2010 by CDC as a means to share experiences, identify best practices, and facilitate relationships among federal, state and local public health agencies and PCs. To date, the Poison Center and Public Health Collaborations CoP includes over 200 members from state and local public health, regional PCs, CDC, the American Association of Poison Control Centers (AAPCC), and the Environmental Protection Agency (EPA). A leadership team was created with representatives of the many stakeholders of the community to drive its direction and oversee activities. Methods The panel will consist of 4 presenters and 1 moderator, who are members of the Poison Center and Public Health Collaborations CoP leadership team. Each presenter will bring a unique perspective of the use of PC data for PH practice and surveillance: CDC, state department of health, a local department of health, and a PC. Royal Law from the CDC National Center for Environmental Health will present on using PC data for identification of exposures and illnesses of public health significance identified from NPDS data collected from all 57 PCs. Dr. Jay Schauben from the Florida/USVI Poison Information Center - Jacksonville will discuss PC participation in surveillance and use of PC data for tracking and mitigation of PH events in Florida. Dr. Prakash Mulay from the Florida Department of Health will discuss utilization of PC data to enhance ESSENCE-based chemical-associated exposure and illness surveillance in Florida. Katherine Wheeler from the New York City (NYC) Department of Health and Mental Hygiene will discuss NYC’s use of PC data in surveillance of potential emerging issues, from energy drinks to synthetic marijuana. Each presenter will discuss the use of PC data for PH practice and surveillance in his or her organization and jurisdiction, the successes of using PC data, and their challenges. Results The moderator will engage the audience by facilitating discussion of the successes and challenges to using PC data for PH practice and surveillance with the audience. Sample questions: What are your current capacities and collaborative activities between your state/local health department and your poison center? What non-funding related barriers hinder the collaboration between your state/local health department and poison center? If more funding were available, how would you use this funding to increase the level of interactivity with the poison center and state/local health department?

CDC and the American Association of Poison Control Centers have conducted ongoing surveillance for exposures to radioactive materials reported from all 57 US poison centers to NPDS, a national poison center reporting database and surveillance system. Of 419 exposures identified Sept 2010 to June 2012, 25 were associated with a radiation-related incident. Public health or hospital radiation safety staff were involved in each of these events. The capability to conduct surveillance of exposures to radioactive materials is available for local public health use with their regional PC and may improve public health capacity to identify and respond to radiological emergencies.