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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.

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.

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.

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

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 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

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.

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.

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?