Evaluation of the National Electronic Injury Surveillance System – All injury program’s self-directed violence data,United States, 2018 |
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| Institution: | 1. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Injury Prevention, 4770 Buford Highway, Atlanta, GA 30341, United States;2. Centers for Disease Control and Prevention, Center for Surveillance, Epidemiology, and Laboratory Services, Epidemic Intelligence Service, 2400 Century Center, Atlanta, GA 30345, United States;1. College of Engineering, Civil Engineering Department University of Wasit, Kut, Iraq;2. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907-2051, USA;1. School of Transportation, Southeast University, China;2. Jiangsu Key Laboratory of Urban ITS, China;3. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China;4. Transport Strategy Centre, Imperial College London, UK;1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Center for Connected Multimodal Mobility, Clemson University, Clemson, SC 29634, USA;3. School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;1. College of Engineering, Zhejiang Normal University, Zhejiang 321005, China;2. Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Zhejiang 321005, China;3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;4. Sichuan Vocational and Technical College of Communications, Chengdu, Sichuan 611130, China;1. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;2. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3261, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;1. Connecticut Transportation Safety Research Center, Connecticut Transportation Institute, University of Connecticut, United States;2. Department of Civil, Environmental & Construction Engineering, University of Central Florida, United States |
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| Abstract: | Introduction: National estimates for nonfatal self-directed violence (SDV) presenting at EDs are calculated from the National Electronic Injury Surveillance System – All Injury Program (NEISS–AIP). In 2005, the Centers for Disease Control and Prevention and Consumer Product Safety Commission added several questions on patient characteristics and event circumstances for all intentional, nonfatal SDV captured in NEISS–AIP. In this study, we evaluated these additional questions along with the parent NEISS–AIP, which together is referred to as NEISS–AIP SDV for study purposes. Methods: We used a mixed methods design to evaluate the NEISS–AIP SDV as a surveillance system through an assessment of key system attributes. We reviewed data entry forms, the coding manual, and training materials to understand how the system functions. To identify strengths and weaknesses, we interviewed multiple key informants. Finally, we analyzed the NEISS–AIP SDV data from 2018—the most recent data year available—to assess data quality by examining the completeness of variables. Results: National estimates of SDV are calculated from NEISS–AIP SDV. Quality control activities suggest more than 99% of the cause and intent variables were coded consistently with the open text field that captures the medical chart narrative. Many SDV variables have open-ended response options, making them difficult to efficiently analyze. Conclusions: NEISS–AIP SDV provides the opportunity to describe systematically collected risk factors and characteristics associated with nonfatal SDV that are not regularly available through other data sources. With some modifications to data fields and yearly analysis of the additional SDV questions, NEISS–AIP SDV can be a valuable tool for informing suicide prevention. Practical Applications: NEISS-AIP may consider updating the SDV questions and responses and analyzing SDV data on a regular basis. Findings from analyses of the SDV data may lead to improvements in ED care. |
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| Keywords: | Suicide Self-harm Self-directed violence Surveillance NEISS |
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