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      Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults

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          Abstract

          Background

          Multimorbidity–having two or more coexisting chronic conditions–is highly prevalent, costly, and disabling to older adults. Questions remain regarding chronic diseases accumulation over time and whether this differs by racial and ethnic background. Answering this knowledge gap, this study identifies differences in rates of chronic disease accumulation and multimorbidity development among non-Hispanic white, non-Hispanic black, and Hispanic study participants starting in middle-age and followed up to 16 years.

          Methods and findings

          We analyzed data from the Health and Retirement Study (HRS), a biennial, ongoing, publicly-available, longitudinal nationally-representative study of middle-aged and older adults in the United States. We assessed the change in chronic disease burden among 8,872 non-Hispanic black, non-Hispanic white, and Hispanic participants who were 51–55 years of age at their first interview any time during the study period (1998–2014) and all subsequent follow-up observations until 2014. Multimorbidity was defined as having two or more of seven somatic chronic diseases: arthritis, cancer, heart disease (myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart problems), diabetes, hypertension, lung disease, and stroke. We used negative binomial generalized estimating equation models to assess the trajectories of multimorbidity burden over time for non-Hispanic black, non-Hispanic white, and Hispanic participants. In covariate-adjusted models non-Hispanic black respondents had initial chronic disease counts that were 28% higher than non-Hispanic white respondents (IRR 1.279, 95% CI 1.201, 1.361), while Hispanic respondents had initial chronic disease counts that were 15% lower than non-Hispanic white respondents (IRR 0.852, 95% CI 0.775, 0.938). Non-Hispanic black respondents had rates of chronic disease accumulation that were 1.1% slower than non-Hispanic whites (IRR 0.989, 95% CI 0.981, 0.998) and Hispanic respondents had rates of chronic disease accumulation that were 1.5% faster than non-Hispanic white respondents (IRR 1.015, 95% CI 1.002, 1.028). Using marginal effects commands, this translates to predicted values of chronic disease for white respondents who begin the study period with 0.98 chronic diseases and end with 2.8 chronic diseases; black respondents who begin the study period with 1.3 chronic diseases and end with 3.3 chronic diseases; and Hispanic respondents who begin the study period with 0.84 chronic diseases and end with 2.7 chronic diseases.

          Conclusions

          Middle-aged non-Hispanic black adults start at a higher level of chronic disease burden and develop multimorbidity at an earlier age, on average, than their non-Hispanic white counterparts. Hispanics, on the other hand, accumulate chronic disease at a faster rate relative to non-Hispanic white adults. Our findings have important implications for improving primary and secondary chronic disease prevention efforts among non-Hispanic black and Hispanic Americans to stave off greater multimorbidity-related health impacts.

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          Most cited references27

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          Multimorbidity in older adults.

          M Salive (2013)
          Multimorbidity, the coexistence of 2 or more chronic conditions, has become prevalent among older adults as mortality rates have declined and the population has aged. We examined population-based administrative claims data indicating specific health service delivery to nearly 31 million Medicare fee-for-service beneficiaries for 15 prevalent chronic conditions. A total of 67% had multimorbidity, which increased with age, from 50% for persons under age 65 years to 62% for those aged 65-74 years and 81.5% for those aged ≥85 years. A systematic review identified 16 other prevalence studies conducted in community samples that included older adults, with median prevalence of 63% and a mode of 67%. Prevalence differences between studies are probably due to methodological biases; no studies were comparable. Key methodological issues arise from elements of the case definition, including type and number of chronic conditions included, ascertainment methods, and source population. Standardized methods for measuring multimorbidity are needed to enable public health surveillance and prevention. Multimorbidity is associated with elevated risk of death, disability, poor functional status, poor quality of life, and adverse drug events. Additional research is needed to develop an understanding of causal pathways and to further develop and test potential clinical and population interventions targeting multimorbidity. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2013.
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            Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure.

            Questionnaires are used to estimate disease burden. Agreement between questionnaire responses and a criterion standard is important for optimal disease prevalence estimates. We measured the agreement between self-reported disease and medical record diagnosis of disease. A total of 2,037 Olmsted County, Minnesota residents > or =45 years of age were randomly selected. Questionnaires asked if subjects had ever had heart failure, diabetes, hypertension, myocardial infarction (MI), or stroke. Medical records were abstracted. Self-report of disease showed >90% specificity for all these diseases, but sensitivity was low for heart failure (69%) and diabetes (66%). Agreement between self-report and medical record was substantial (kappa 0.71-0.80) for diabetes, hypertension, MI, and stroke but not for heart failure (kappa 0.46). Factors associated with high total agreement by multivariate analysis were age 12 years, and zero Charlson Index score (P < .05). Questionnaire data are of greatest value in life-threatening, acute-onset diseases (e.g., MI and stroke) and chronic disorders requiring ongoing management (e.g.,diabetes and hypertension). They are more accurate in young women and better-educated subjects.
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              On the unnecessary ubiquity of hierarchical linear modeling.

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 June 2019
                2019
                : 14
                : 6
                : e0218462
                Affiliations
                [1 ] Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, United States of America
                [2 ] School of Public Health, Oregon Health & Science University, Portland, Oregon, United States of America
                [3 ] Department of Health & Human Services, University of Michigan, Dearborn, Michigan, United States of America
                [4 ] Institute of Gerontology, University of Michigan, Ann Arbor, Michigan, United States of America
                [5 ] College of Nursing, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
                [6 ] Department of Psychology, Portland State University, Portland, Oregon, United States of America
                [7 ] Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
                [8 ] Department of Internal Medicine, School of Medicine, Yale University, New Haven, Connecticut, United States of America
                [9 ] Department of Biostatistics School of Public Health, Yale University, New Haven, Connecticut, United States of America
                Nathan S Kline Institute, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6554-7734
                Article
                PONE-D-19-08789
                10.1371/journal.pone.0218462
                6576751
                31206556
                9fa1a5e0-eb01-49f9-8a8c-577e59cc16f4
                © 2019 Quiñones et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 March 2019
                : 3 June 2019
                Page count
                Figures: 1, Tables: 3, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG055681
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG047891
                Award Recipient :
                Funded by: Yale Claude D. Pepper Older Americans Independence Center
                Award ID: P30AG021342
                Award Recipient :
                This work was supported by: Grant Number: R01AG055681, National Institute on Aging of the National Institutes of Health, https://www.nia.nih.gov/ to ARQ; Grant Number: R01AG047891, National Institute on Aging of the National Institutes of Health, https://www.nia.nih.gov/ to HGA; Grant Number: P30AG021342, Yale Claude D. Pepper Older Americans Independence Center, https://medicine.yale.edu/intmed/geriatrics/peppercenter/ to HGA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funder/National Institutes of Health.
                Categories
                Research Article
                People and Places
                Population Groupings
                Ethnicities
                Hispanic People
                Medicine and Health Sciences
                Public and Occupational Health
                People and Places
                Population Groupings
                Age Groups
                Elderly
                Medicine and Health Sciences
                Epidemiology
                Ethnic Epidemiology
                People and places
                Geographical locations
                North America
                United States
                Physical Sciences
                Mathematics
                Algebra
                Polynomials
                Binomials
                People and places
                Population groupings
                Ethnicities
                Hispanic people
                Hispanic American people
                Medicine and Health Sciences
                Cardiology
                Myocardial Infarction
                Custom metadata
                The data underlying the results presented in this study are publicly available from the Health and Retirement Study, http://hrsonline.isr.umich.edu/.

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