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      Structural Racism and Well-Being Among Young People in the U.S.

      research-article
      , PhD 1 , , PhD 1 , , PhD 1 , 2
      American journal of preventive medicine

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          Abstract

          Introduction:

          Structural racism has clear and pernicious effects on population health. However, there is a limited understanding of how structural racism impacts young people’s well-being. The objective of this ecologic cross-sectional study was to assess the relationship between structural racism and well-being for 2,009 U.S. counties from 2010 to 2019.

          Methods:

          Population-based data on demographics, health, and other variables related to young people’s ability to thrive are used to construct a previously validated composite index that serves as a proxy of young people’s well-being. The index is regressed on several forms of structural racism (segregation, economic, and educational) both independently and jointly while accounting for county-fixed effects, time trends, and state-specific trends as well as weighting for child population. Data were analyzed from November 2021 through March 2023.

          Results:

          Higher levels of structural racism are associated with lower well-being. A 1-SD increase in Black–White child poverty disparity is associated with a −0.034 (95% CI= −0.019, −0.050) SD change in index score. When accounting for multiple structural racism measures, associations remain statistically significant. In joint models, only estimates for economic racism measures remain significant when additionally controlling for demographic, socioeconomic, and adult health measures ( β= −0.015; 95% CI= −0.001, −0.029). These negative associations are heavily concentrated in counties where Black and Latinx children are overrepresented.

          Conclusions:

          Structural racism—particularly of the kind that produces racialized poverty outcomes—has a meaningful adverse association with child and adolescent well-being, which may produce lifelong effects. Studies of structural racism among adults should consider a lifecourse perspective.

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          Most cited references59

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          The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies.

          Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
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            Multiple imputation using chained equations: Issues and guidance for practice

            Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. 2010 John Wiley & Sons, Ltd.
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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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                Author and article information

                Journal
                8704773
                1656
                Am J Prev Med
                Am J Prev Med
                American journal of preventive medicine
                0749-3797
                1873-2607
                30 May 2024
                December 2023
                28 June 2023
                06 June 2024
                : 65
                : 6
                : 1078-1091
                Affiliations
                [1 ]Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California
                [2 ]Department of Urban Planning, Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, California
                Author notes
                Address correspondence to: Nathaniel W. Anderson, PhD, Department of Health Policy and Management, University of California, Los Angeles, 650 Charles E. Young Drive South, Los Angeles CA 90095. nanderson14@ 123456ucla.edu .
                Article
                NIHMS1994441
                10.1016/j.amepre.2023.06.017
                11155270
                37385571
                5dbd7920-66d3-4db4-84b3-20dd0d2df947

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Medicine

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