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Non-fatal injury data: characteristics to consider for surveillance and research
  1. Andrea E. Carmichael1,2,
  2. Michael F. Ballesteros2,
  3. Judith R. Qualters2,
  4. Karin A. Mack2
  1. 1 Oak Ridge Associated Universities (ORAU), Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  2. 2 Division of Injury Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  1. Correspondence to Andrea E. Carmichael, Centers for Disease Control and Prevention, Atlanta, USA; acarmichael{at}


Background All data systems used for non-fatal injury surveillance and research have strengths and limitations that influence their utility in understanding non-fatal injury burden. The objective of this paper was to compare characteristics of major data systems that capture non-fatal injuries in the USA.

Methods By applying specific inclusion criteria (eg, non-fatal and non-occupational) to well-referenced injury data systems, we created a list of commonly used non-fatal injury data systems for this study. Data system characteristics were compiled for 2018: institutional support, years of data available, access, format, sample, sampling method, injury definition/coding, geographical representation, demographic variables, timeliness (lag) and further considerations for analysis.

Results Eighteen data systems ultimately fit the inclusion criteria. Most data systems were supported by a federal institution, produced national estimates and were available starting in 1999 or earlier. Data source and injury case coding varied between the data systems. Redesigns of sampling frameworks and the use of International Classification of Diseases, 9th Revision, Clinical Modification/International Classification of Diseases, 10th Revision, Clinical Modification coding for some data systems can make longitudinal analyses complicated for injury surveillance and research. Few data systems could produce state-level estimates.

Conclusion Thoughtful consideration of strengths and limitations should be exercised when selecting a data system to answer injury-related research questions. Comparisons between estimates of various data systems should be interpreted with caution, given fundamental system differences in purpose and population capture. This research provides the scientific community with an updated starting point to assist in matching the data system to surveillance and research questions and can improve the efficiency and quality of injury analyses.

  • surveillance
  • coding systems
  • injury diagnosis

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  • Contributors AEC was involved in all aspects of the study, including planning, writing and reviewing the paper, and analysing and reporting the results. MFB, JRQ and KAM were involved in planning, writing and reviewing the article. AEC is the guarantor for this work.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.