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      Racial and ethnic disparities in excess mortality among U.S. veterans during the COVID‐19 pandemic

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          Abstract

          Objective

          The COVID‐19 pandemic disproportionately affected racial and ethnic minorities among the general population in the United States; however, little is known regarding its impact on U.S. military Veterans. In this study, our objectives were to identify the extent to which Veterans experienced increased all‐cause mortality during the COVID‐19 pandemic, stratified by race and ethnicity.

          Data Sources

          Administrative data from the Veterans Health Administration's Corporate Data Warehouse.

          Study Design

          We use pre‐pandemic data to estimate mortality risk models using five‐fold cross‐validation and quasi‐Poisson regression. Models were stratified by a combined race‐ethnicity variable and included controls for major comorbidities, demographic characteristics, and county fixed effects.

          Data Collection

          We queried data for all Veterans residing in the 50 states plus Washington D.C. during 2016–2020. Veterans were excluded from analyses if they were missing county of residence or race‐ethnicity data. Data were then aggregated to the county‐year level and stratified by race‐ethnicity.

          Principal Findings

          Overall, Veterans' mortality rates were 16% above normal during March–December 2020 which equates to 42,348 excess deaths. However, there was substantial variation by racial and ethnic group. Non‐Hispanic White Veterans experienced the smallest relative increase in mortality (17%, 95% CI 11%–24%), while Native American Veterans had the highest increase (40%, 95% CI 17%–73%). Black Veterans (32%, 95% CI 27%–39%) and Hispanic Veterans (26%, 95% CI 17%–36%) had somewhat lower excess mortality, although these changes were significantly higher compared to White Veterans. Disparities were smaller than in the general population.

          Conclusions

          Minoritized Veterans experienced higher rates excess of mortality during the COVID‐19 pandemic compared to White Veterans, though with smaller differences than the general population. This is likely due in part to the long‐standing history of structural racism in the United States that has negatively affected the health of minoritized communities via several pathways including health care access, economic, and occupational inequities.

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          Most cited references41

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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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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              Determinants of COVID-19 vaccine acceptance in the US

              Background The COVID-19 pandemic continues to adversely affect the U.S., which leads globally in total cases and deaths. As COVID-19 vaccines are under development, public health officials and policymakers need to create strategic vaccine-acceptance messaging to effectively control the pandemic and prevent thousands of additional deaths. Methods Using an online platform, we surveyed the U.S. adult population in May 2020 to understand risk perceptions about the COVID-19 pandemic, acceptance of a COVID-19 vaccine, and trust in sources of information. These factors were compared across basic demographics. Findings Of the 672 participants surveyed, 450 (67%) said they would accept a COVID-19 vaccine if it is recommended for them. Males (72%) compared to females, older adults (≥55 years; 78%) compared to younger adults, Asians (81%) compared to other racial and ethnic groups, and college and/or graduate degree holders (75%) compared to people with less than a college degree were more likely to accept the vaccine. When comparing reported influenza vaccine uptake to reported acceptance of the COVID-19 vaccine: 1) participants who did not complete high school had a very low influenza vaccine uptake (10%), while 60% of the same group said they would accept the COVID-19 vaccine; 2) unemployed participants reported lower influenza uptake and lower COVID-19 vaccine acceptance when compared to those employed or retired; and, 3) Black Americans reported lower influenza vaccine uptake and lower COVID-19 vaccine acceptance than all other racial groups reported in our study. Lastly, we identified geographic differences with Department of Health and Human Services (DHHS) regions 2 (New York) and 5 (Chicago) reporting less than 50 percent COVID-19 vaccine acceptance. Interpretation Although our study found a 67% acceptance of a COVID-19 vaccine, there were noticeable demographic and geographical disparities in vaccine acceptance. Before a COVID-19 vaccine is introduced to the U.S., public health officials and policymakers must prioritize effective COVID-19 vaccine-acceptance messaging for all Americans, especially those who are most vulnerable.
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                Author and article information

                Contributors
                kevin.griffith@vumc.org
                Journal
                Health Serv Res
                Health Serv Res
                10.1111/(ISSN)1475-6773
                HESR
                Health Services Research
                Blackwell Publishing Ltd (Oxford, UK )
                0017-9124
                1475-6773
                30 December 2022
                30 December 2022
                : 10.1111/1475-6773.14112
                Affiliations
                [ 1 ] Department of Health Law, Policy & Management Boston University School of Public Health Boston Massachusetts USA
                [ 2 ] Partnered Evidence‐Based Policy Resource Center VA Boston Healthcare System Boston Massachusetts USA
                [ 3 ] Department of Health Policy Vanderbilt University Medical Center Nashville Tennessee USA
                Author notes
                [*] [* ] Correspondence

                Kevin Griffith, West End Avenue, Suite 1204, Nashville, TN 37203, USA.

                Email: kevin.griffith@ 123456vumc.org

                Author information
                https://orcid.org/0000-0002-0776-4025
                https://orcid.org/0000-0002-7457-7211
                https://orcid.org/0000-0002-8304-8602
                Article
                HESR14112
                10.1111/1475-6773.14112
                9878051
                36478574
                ebe50ed8-9793-4d06-add6-80d73edc3074
                © 2022 Health Research and Educational Trust.

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                Page count
                Figures: 0, Tables: 6, Pages: 12, Words: 8078
                Funding
                Funded by: Agency for Healthcare Research and Quality , doi 10.13039/100000133;
                Award ID: K12 HS026395
                Funded by: VA Quality Enhancement Research Initiative , doi 10.13039/100007181;
                Award ID: PEC 16‐001
                Categories
                Research Article
                RESEARCH ARTICLES
                Veterans Health
                Custom metadata
                2.0
                corrected-proof
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.4 mode:remove_FC converted:26.01.2023

                Health & Social care
                quality of care/patient safety (measurement),racial/ethnic differences in health and health care,va health care system

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