Applying data science to improve timeliness of injury data analysis and visualization

BackgroundCDC uses the National Electronic Injury Surveillance System-All Injuries Program (NEISS-AIP) to track injury-related emergency department (ED) visits. Although data are received weekly, previously data were annually processed with 12+ month delay.AimsImprove timeliness of NEISS-AIP and determine national estimates of injury-related ED visits in the United States before and during the COVID-19 pandemic.MethodsWe used CDC’s Enterprise Data and Visualization Platform, an Azure cloud-based platform, for analysis. We integrated a Tableau server for creating a interactive data visualization dashboard. We compared injury ED visits before the COVID-19 pandemic (January 1 through December 31, 2019) to the year of the pandemic declaration (January 1 through December 31, 2020).ResultsWe improved timeliness of NEISS-AIP data from 12+ months to six weeks by developing a cloud-based data pipeline and significantly reduced manual processing efforts by nearly fully automating the data management process. There was an estimated 1.7 million (25%) decrease in nonfatal injury-related ED visits during April-June 2020 compared to the same time frame in 2019. Similar decreases were observed for ED visits due to motor vehicle-related injuries (199,329;23.3%) and falls-related injuries (497,971;25.1%). Monthly 2020 estimates remained relatively similar with 2019 estimates for self-harm-, assault-, and poisoning-related ED visits.ConclusionThe use of data science significantly improved the timeliness of data analysis. Timely access to data is crucial to monitor emerging trends to prevent future injuries.Learning OutcomesRecognize benefits of applying data science to monitoring injury trends. Knowledge of methods to improve data timeliness.

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.