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      Addressing health disparities using multiply imputed injury surveillance data

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          Abstract

          Background

          Assessing disparities in injury is crucial for injury prevention and for evaluating injury prevention strategies, but efforts have been hampered by missing data. This study aimed to show the utility and reliability of the injury surveillance system as a trustworthy resource for examining disparities by generating multiple imputed companion datasets.

          Methods

          We employed data from the National Electronic Injury Surveillance System-All Injury Program (NEISS-AIP) for the period 2014–2018. A comprehensive simulation study was conducted to identify the appropriate strategy for addressing missing data limitations in NEISS-AIP. To evaluate the imputation performance more quantitatively, a new method based on Brier Skill Score (BSS) was developed to assess the accuracy of predictions by different approaches. We selected the multiple imputations by fully conditional specification (FCS MI) to generate the imputed companion data to NEISS-AIP 2014–2018. We further assessed health disparities systematically in nonfatal assault injuries treated in U.S. hospital emergency departments (EDs) by race and ethnicity, location of injury and sex.

          Results

          We found for the first time that significantly higher age-adjusted nonfatal assault injury rates for ED visits per 100,000 population occurred among non-Hispanic Black persons (1306.8, 95% Confidence Interval [CI]: 660.1 – 1953.5), in public settings (286.3, 95% CI: 183.2 – 389.4) and for males (603.5, 95% CI: 409.4 – 797.5). We also observed similar trends in age-adjusted rates (AARs) by different subgroups for non-Hispanic Black persons, injuries occurring in public settings, and for males: AARs of nonfatal assault injury increased significantly from 2014 through 2017, then declined significantly in 2018.

          Conclusions

          Nonfatal assault injury imposes significant health care costs and productivity losses for millions of people each year. This study is the first to specifically look at health disparities in nonfatal assault injuries using multiply imputed companion data. Understanding how disparities differ by various groups may lead to the development of more effective initiatives to prevent such injury.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12939-023-01940-4.

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          Most cited references40

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          Structural racism and health inequities in the USA: evidence and interventions

          The Lancet, 389(10077), 1453-1463
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            Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

            Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
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              Multiple Imputation for Nonresponse in Surveys

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                Author and article information

                Contributors
                wcq6@cdc.gov
                Journal
                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central (London )
                1475-9276
                3 July 2023
                3 July 2023
                2023
                : 22
                : 126
                Affiliations
                [1 ]GRID grid.416738.f, ISNI 0000 0001 2163 0069, Division of Injury Prevention, National Center for Injury Prevention and Control, , U.S. Centers for Disease Control and Prevention, ; Atlanta, GA USA
                [2 ]GRID grid.420322.5, ISNI 0000 0001 2299 1421, Division of Hazard and Injury Data Systems, , U.S. Consumer Product Safety Commission, ; Bethesda, MD USA
                Author information
                http://orcid.org/0000-0001-7806-5881
                Article
                1940
                10.1186/s12939-023-01940-4
                10316636
                37400819
                e0f9d45e-889f-4742-b224-bd2b3552e30e
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 27 February 2023
                : 18 June 2023
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Health & Social care
                health disparity,missing data,multiple imputation,non-fatal assault injury,national electronic injury surveillance system-all injury program (neiss-aip)

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