Andrea E Carmichael 1,2 , Michael F Ballesteros 2 , Judith R Qualters 2 , Karin A Mack 2 Show Affiliations »
Abstract
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. © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.
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. © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.
Entities: Chemical
Keywords:
coding systems; injury diagnosis; surveillance
Mesh： See more »
Year: 2022
PMID： 35210312 PMCID： PMC9133150 DOI： 10.1136/injuryprev-2021-044397
Source DB: PubMed Journal: Inj Prev ISSN： 1353-8047 Impact factor: 3.770