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      Evaluation of Temporal Trends in Racial and Ethnic Disparities in Sleep Duration Among US Adults, 2004-2018

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          Abstract

          This cross-sectional study describes the temporal trends in racial and ethnic disparities in sleep duration during a 15-year period among US adults with sleep data available from the National Health Interview Survey.

          Key Points

          Question

          How have racial and ethnic differences in self-reported sleep duration among US adults changed from 2004 to 2018?

          Findings

          In this cross-sectional study of 429 195 US adults, the prevalence of short and long sleep duration were persistently higher among Black individuals during the 15-year study period. The disparities in short sleep duration were highest for Black women, Black individuals with middle or high income, and young and middle-aged Black adults.

          Meaning

          These findings suggest that marked racial and ethnic differences in sleep duration persisted from 2004 to 2018 and may contribute to health disparities among Black individuals.

          Abstract

          Importance

          Historically marginalized racial and ethnic groups are generally more likely to experience sleep deficiencies. It is unclear how these sleep duration disparities have changed during recent years.

          Objective

          To evaluate 15-year trends in racial and ethnic differences in self-reported sleep duration among adults in the US.

          Design, Setting, and Participants

          This serial cross-sectional study used US population-based National Health Interview Survey data collected from 2004 to 2018. A total of 429 195 noninstitutionalized adults were included in the analysis, which was performed from July 26, 2021, to February 10, 2022.

          Exposures

          Self-reported race, ethnicity, household income, and sex.

          Main Outcomes and Measures

          Temporal trends and racial and ethnic differences in short (<7 hours in 24 hours) and long (>9 hours in 24 hours) sleep duration and racial and ethnic differences in the association between sleep duration and age.

          Results

          The study sample consisted of 429 195 individuals (median [IQR] age, 46 [31-60] years; 51.7% women), of whom 5.1% identified as Asian, 11.8% identified as Black, 14.7% identified as Hispanic or Latino, and 68.5% identified as White. In 2004, the adjusted estimated prevalence of short and long sleep duration were 31.4% and 2.5%, respectively, among Asian individuals; 35.3% and 6.4%, respectively, among Black individuals; 27.0% and 4.6%, respectively, among Hispanic or Latino individuals; and 27.8% and 3.5%, respectively, among White individuals. During the study period, there was a significant increase in short sleep prevalence among Black (6.39 [95% CI, 3.32-9.46] percentage points), Hispanic or Latino (6.61 [95% CI, 4.03-9.20] percentage points), and White (3.22 [95% CI, 2.06-4.38] percentage points) individuals ( P < .001 for each), whereas prevalence of long sleep changed significantly only among Hispanic or Latino individuals (−1.42 [95% CI, −2.52 to −0.32] percentage points; P = .01). In 2018, compared with White individuals, short sleep prevalence among Black and Hispanic or Latino individuals was higher by 10.68 (95% CI, 8.12-13.24; P < .001) and 2.44 (95% CI, 0.23-4.65; P = .03) percentage points, respectively, and long sleep prevalence was higher only among Black individuals (1.44 [95% CI, 0.39-2.48] percentage points; P = .007). The short sleep disparities were greatest among women and among those with middle or high household income. In addition, across age groups, Black individuals had a higher short and long sleep duration prevalence compared with White individuals of the same age.

          Conclusions and Relevance

          The findings of this cross-sectional study suggest that from 2004 to 2018, the prevalence of short and long sleep duration was persistently higher among Black individuals in the US. The disparities in short sleep duration appear to be highest among women, individuals who had middle or high income, and young or middle-aged adults, which may be associated with health disparities.

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          Most cited references55

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          • Article: not found

          Sleep health: can we define it? Does it matter?

          Good sleep is essential to good health. Yet for most of its history, sleep medicine has focused on the definition, identification, and treatment of sleep problems. Sleep health is a term that is infrequently used and even less frequently defined. It is time for us to change this. Indeed, pressures in the research, clinical, and regulatory environments require that we do so. The health of populations is increasingly defined by positive attributes such as wellness, performance, and adaptation, and not merely by the absence of disease. Sleep health can be defined in such terms. Empirical data demonstrate several dimensions of sleep that are related to health outcomes, and that can be measured with self-report and objective methods. One suggested definition of sleep health and a description of self-report items for measuring it are provided as examples. The concept of sleep health synergizes with other health care agendas, such as empowering individuals and communities, improving population health, and reducing health care costs. Promoting sleep health also offers the field of sleep medicine new research and clinical opportunities. In this sense, defining sleep health is vital not only to the health of populations and individuals, but also to the health of sleep medicine itself.
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            • Article: not found

            Self-reported and measured sleep duration: how similar are they?

            Recent epidemiologic studies have found that self-reported duration of sleep is associated with obesity, diabetes, hypertension, and mortality. The extent to which self reports of sleep duration are similar to objective measures and whether individual characteristics influence the degree of similarity are not known. Eligible participants at the Chicago site of the Coronary Artery Risk Development in Young Adults Study were invited to participate in a 2003-2005 ancillary sleep study; 82% (n = 669) agreed. Sleep measurements collected in 2 waves included 3 days each of wrist actigraphy, a sleep log, and questions about usual sleep duration. We estimate the average difference and correlation between subjectively and objectively measured sleep by using errors-in-variables regression models. Average measured sleep was 6 hours, whereas the average from subjective reports was 6.8 hours. Subjective reports increased on average by 34 minutes for each additional hour of measured sleep. Overall, the correlation between reported and measured sleep duration was 0.47. Our model suggests that persons sleeping 5 hours over-reported their sleep duration by 1.2 hours, and those sleeping 7 hours over-reported by 0.4 hours. The correlations and average differences between self-reports and measured sleep varied by health, sociodemographic, and sleep characteristics. In a population-based sample of middle-aged adults, subjective reports of habitual sleep are moderately correlated with actigraph-measured sleep, but are biased by systematic over-reporting. The true associations between sleep duration and health may differ from previously reported associations between self-reported sleep and health.
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              Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation.

              Sleep disturbance is associated with inflammatory disease risk and all-cause mortality. Here, we assess global evidence linking sleep disturbance, sleep duration, and inflammation in adult humans.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                7 April 2022
                April 2022
                7 April 2022
                : 5
                : 4
                : e226385
                Affiliations
                [1 ]Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
                [2 ]Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [3 ]Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [4 ]Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas
                [5 ]Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas
                [6 ]Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
                [7 ]Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
                [8 ]Division of Critical Care Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
                [9 ]SEICHE Center for Health and Justice, Section of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [10 ]Equity Research and Innovation Center, Section of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [11 ]Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
                [12 ]Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina
                [13 ]Intramural Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland
                [14 ]Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
                Author notes
                Article Information
                Accepted for Publication: February 19, 2022.
                Published: April 7, 2022. doi:10.1001/jamanetworkopen.2022.6385
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Caraballo C et al. JAMA Network Open.
                Corresponding Author: Harlan M. Krumholz, MD, SM, 195 Church St, Fifth Floor, New Haven, CT 06510 ( harlan.krumholz@ 123456yale.edu ).
                Author Contributions: Dr Caraballo had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Caraballo, Mahajan, Massey, Nunez-Smith, Krumholz.
                Acquisition, analysis, or interpretation of data: Caraballo, Mahajan, Valero-Elizondo, Lu, Roy, Riley, Annapureddy, Murugiah, Elumn, Nasir, Forman, Jackson, Herrin, Krumholz.
                Drafting of the manuscript: Caraballo, Mahajan.
                Critical revision of the manuscript for important intellectual content: Mahajan, Valero-Elizondo, Massey, Lu, Roy, Riley, Annapureddy, Murugiah, Elumn, Nasir, Nunez-Smith, Forman, Jackson, Herrin, Krumholz.
                Statistical analysis: Caraballo, Annapureddy.
                Obtained funding: Jackson.
                Administrative, technical, or material support: Valero-Elizondo.
                Supervision: Riley, Nunez-Smith, Jackson, Krumholz.
                Conflict of Interest Disclosures: Dr Lu reported receiving grants from the National Heart, Lung, and Blood Institute and the Yale Center for Implementation Science outside the submitted work. Dr Roy reported consulting for the Institute for Healthcare Improvement. Dr Riley reported receiving personal fees from Heluna Health and consulting for the Institute for Healthcare Improvement outside the submitted work. Dr Nasir reported serving on the advisory boards of Novartis International AG, Esperion Therapeutics Inc, and Novo Nordisk. Dr Krumholz reported receiving expenses and/or personal fees from UnitedHealthcare, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried & Jensen law firm, the Arnold & Porter law firm, and the Martin Baughman law firm; being a co-founder of Refactor Health and Hugo Health; and being associated with contracts through Yale New Haven Hospital from the Centers for Medicare & Medicaid Services and through Yale University from Johnson & Johnson outside the submitted work. No other disclosures were reported.
                Funding/Support: This study was supported in part by award Z1AES103325-01 from the Intramural Program at the National Institutes of Health, National Institute of Environmental Health Sciences.
                Role of the Funder/Sponsor: The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Data Sharing Statement: All data are publicly available from the Integrated Public Use Microdata Series Health Surveys ( https://nhis.ipums.org/nhis). The code used to analyze these data is publicly available at https://zenodo.org/record/6028375.
                Article
                zoi220200
                10.1001/jamanetworkopen.2022.6385
                8990329
                35389500
                bdb53387-add4-4ebd-8653-36b25a59664a
                Copyright 2022 Caraballo C et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 27 October 2021
                : 19 February 2022
                Categories
                Research
                Original Investigation
                Online Only
                Diversity, Equity, and Inclusion

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