@article {12247, title = {Multimorbidity Accumulation Among Middle-Aged Americans: Differences by Race/Ethnicity and Body Mass Index.}, journal = {The Journals of Gerontology: Series A }, volume = {77}, year = {2022}, pages = {e89-e97}, abstract = {

BACKGROUND: Obesity and multimorbidity are more prevalent among U.S. racial/ethnic minority groups. Evaluating racial/ethnic disparities in disease accumulation according to body mass index (BMI) may guide interventions to reduce multimorbidity burden in vulnerable racial/ethnic groups.

METHOD: We used data from the 1998-2016 Health and Retirement Study on 8 106 participants aged 51-55 at baseline. Disease burden and multimorbidity (>=2 co-occurring diseases) were assessed using 7 chronic diseases: arthritis, cancer, heart disease, diabetes, hypertension, lung disease, and stroke. Four BMI categories were defined per convention: normal, overweight, obese class 1, and obese class 2/3. Generalized estimating equations models with inverse probability weights estimated the accumulation of chronic diseases.

RESULTS: Overweight and obesity were more prevalent in non-Hispanic Black (82.3\%) and Hispanic (78.9\%) than non-Hispanic White (70.9 \%) participants at baseline. The baseline burden of disease was similar across BMI categories, but disease accumulation was faster in the obese class 2/3 and marginally in the obese class 1 categories compared with normal BMI. Black participants across BMI categories had a higher initial burden and faster accumulation of disease over time, while Hispanics had a lower initial burden and similar rate of accumulation, compared with Whites. Black participants, including those with normal BMI, reach the multimorbidity threshold 5-6 years earlier compared with White participants.

CONCLUSIONS: Controlling weight and reducing obesity early in the lifecourse may slow the progression of multimorbidity in later life. Further investigations are needed to identify the factors responsible for the early and progressing nature of multimorbidity in Blacks of nonobese weight.

}, keywords = {Body Mass Index, Disease accumulation, multimorbidity, Race/ethnicity}, issn = {1758-535X}, doi = {10.1093/gerona/glab116}, author = {Anda Botoseneanu and Markwardt, Sheila and Corey L Nagel and Allore, Heather G and Jason T Newsom and David A Dorr and Ana R Qui{\~n}ones} } @article {11796, title = {Physical Activity as a Mediator between Race/Ethnicity and Changes in Multimorbidity.}, journal = {The Journals of Gerontology, Series B}, volume = {77}, year = {2022}, pages = {1529-1538}, abstract = {

OBJECTIVES: Studies report racial/ethnic disparities in multimorbidity (>=2 chronic conditions) and their rate of accumulation over time as well as differences in physical activity. Our study aimed to investigate whether racial/ethnic differences in the accumulation of multimorbidity were mediated by physical activity among middle-aged and older adults.

METHODS: We assessed racial/ethnic differences in the accumulation of multimorbidity (of nine conditions) over twelve years (2004-2016) in the Health and Retirement Study (HRS; N = 18,264, mean age = 64.4 years). Structural equation modeling was used to estimate latent growth curve models of changes in multimorbidity and investigate whether the relationship of race/ethnicity (non-Hispanic Black, Hispanic, non-Hispanic White participants) to changes in the number of chronic conditions was mediated by physical activity after controlling for age, sex, education, marital status, household wealth, insurance coverage, smoking, alcohol, and body-weight.

RESULTS: There was a significant increase in multimorbidity over time. Initial levels and changes in multimorbidity over time varied significantly across individuals. Indirect effects of the relationship between race/ethnicity and changes in multimorbidity as mediated by physical activity were significant, consistent with the mediational hypothesis. Black respondents engaged in significantly lower levels of physical activity than White respondents after controlling for covariates, but there were no differences between Hispanic and White respondents once education was included. These results provide important new information for understanding how modifiable lifestyle factors may help explain disparities in multimorbidity in mid-to-late life, suggesting greater need to intervene to reduce sedentary behavior and increase physical activity.

}, keywords = {Chronic illness, Disparities, Exercise}, issn = {1758-5368}, doi = {10.1093/geronb/gbab148}, author = {Jason T Newsom and Denning, Emily C and Elman, Miriam R and Anda Botoseneanu and Heather G. Allore and Corey L Nagel and David A Dorr and Ana R Qui{\~n}ones} } @article {11580, title = {Racial and Ethnic Differences in Multimorbidity Changes Over Time.}, journal = {Medical Care}, volume = {59}, year = {2021}, pages = {402-409}, abstract = {

BACKGROUND: Our understanding of how multimorbidity progresses and changes is nascent.

OBJECTIVES: Assess multimorbidity changes among racially/ethnically diverse middle-aged and older adults.

DESIGN, SETTING, AND PARTICIPANTS: Prospective cohort study using latent class analysis to identify multimorbidity combinations over 16 years, and multinomial logistic models to assess change relative to baseline class membership. Health and Retirement Study respondents (age 51 y and above) in 1998 and followed through 2014 (N=17,297).

MEASURES: Multimorbidity latent classes of: hypertension, heart disease, lung disease, diabetes, cancer, arthritis, stroke, high depressive symptoms.

RESULTS: Three latent classes were identified in 1998: minimal disease (45.8\% of participants), cardiovascular-musculoskeletal (34.6\%), cardiovascular-musculoskeletal-mental (19.6\%); and 3 in 2014: cardiovascular-musculoskeletal (13\%), cardiovascular-musculoskeletal-metabolic (12\%), multisystem multimorbidity (15\%). Remaining participants were deceased (48\%) or lost to follow-up (12\%) by 2014. Compared with minimal disease, individuals in cardiovascular-musculoskeletal in 1998 were more likely to be in multisystem multimorbidity in 2014 [odds ratio (OR)=1.78, P<0.001], and individuals in cardiovascular-musculoskeletal-mental in 1998 were more likely to be deceased (OR=2.45, P<0.001) or lost to follow-up (OR=3.08, P<0.001). Hispanic and Black Americans were more likely than White Americans to be in multisystem multimorbidity in 2014 (OR=1.67, P=0.042; OR=2.60, P<0.001, respectively). Black compared with White Americans were more likely to be deceased (OR=1.62, P=0.01) or lost to follow-up (OR=2.11, P<0.001) by 2014.

CONCLUSIONS AND RELEVANCE: Racial/ethnic older adults are more likely to accumulate morbidity and die compared with White peers, and should be the focus of targeted and enhanced efforts to prevent and/or delay progression to more complex multimorbidity patterns.

}, keywords = {multimorbidity, race and ethnicity}, issn = {1537-1948}, doi = {10.1097/MLR.0000000000001527}, author = {Ana R Qui{\~n}ones and Jason T Newsom and Elman, Miriam R and Markwardt, Sheila and Corey L Nagel and David A Dorr and Heather G. Allore and Anda Botoseneanu} } @article {10147, title = {Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults.}, journal = {PLoS One}, volume = {14}, year = {2019}, pages = {e0218462}, 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.

}, keywords = {Chronic conditions, Comorbidity, Racial/ethnic differences}, issn = {1932-6203}, doi = {10.1371/journal.pone.0218462}, author = {Ana R Qui{\~n}ones and Anda Botoseneanu and Markwardt, Sheila and Corey L Nagel and Jason T Newsom and David A Dorr and Heather G. Allore} } @article {10450, title = {TRACKING CHANGES IN MULTIMORBIDITY AMONG RACIALLY AND ETHNICALLY DIVERSE POPULATIONS}, journal = {Innovation in Aging}, volume = {3}, year = {2019}, pages = {S354-S354}, abstract = {Multimorbidity is widely recognized as having adverse effects on health and wellbeing above and beyond the risk attributable to individual chronic disease. Much of what is known about multimorbidity rests on research that has largely focused on one point-in-time, or from a static perspective, with little consideration to issues involved in assessing longitudinal changes in multimorbidity. In addition, less focus has been placed on assessing racial and ethnic variations in longitudinal changes of multimorbidity. Addressing this knowledge gap, we highlight important issues and considerations in addressing multimorbidity research from a longitudinal perspective and present findings from longitudinal models that examine differences in the rate of chronic disease accumulation and multimorbidity onset between non-Hispanic white (white), non-Hispanic black (black), and Hispanic study participants in the Health and Retirement Study starting in middle-age and followed for up to 16 years.}, keywords = {multimorbidity, race-ethnicity, Racial and ethnic differences}, isbn = {2399-5300}, doi = {10.1093/geroni/igz038.1285}, author = {Ana R Qui{\~n}ones and Anda Botoseneanu and Markwardt, Sheila and Corey L Nagel and Jason T Newsom and David A Dorr and Heather G. Allore} } @article {8904, title = {Racial and ethnic differences in smoking changes after chronic disease diagnosis among middle-aged and older adults in the United States.}, journal = {BMC Geriatrics}, volume = {17}, year = {2017}, month = {2017 Feb 08}, pages = {48}, abstract = {

BACKGROUND: Middle-aged and older Americans from underrepresented racial and ethnic backgrounds are at risk for greater chronic disease morbidity than their white counterparts. Cigarette smoking increases the severity of chronic illness, worsens physical functioning, and impairs the successful management of symptoms. As a result, it is important to understand whether smoking behaviors change after the onset of a chronic condition. We assessed the racial/ethnic differences in smoking behavior change after onset of chronic diseases among middle-aged and older adults in the US.

METHODS: We use longitudinal data from the Health and Retirement Study (HRS 1992-2010) to examine changes in smoking status and quantity of cigarettes smoked after a new heart disease, diabetes, cancer, stroke, or lung disease diagnosis among smokers.

RESULTS: The percentage of middle-aged and older smokers who quit after a new diagnosis varied by racial/ethnic group and disease: for white smokers, the percentage ranged from 14\% after diabetes diagnosis to 32\% after cancer diagnosis; for black smokers, the percentage ranged from 15\% after lung disease diagnosis to 40\% after heart disease diagnosis; the percentage of Latino smokers who quit was only statistically significant after stoke, where 38\% quit. In logistic models, black (OR = 0.43, 95\% CI: 0.19-0.99) and Latino (OR = 0.26, 95\% CI: 0.11-0.65) older adults were less likely to continue smoking relative to white older adults after a stroke, and Latinos were more likely to continue smoking relative to black older adults after heart disease onset (OR = 2.69, 95\% CI [1.05-6.95]). In models evaluating changes in the number of cigarettes smoked after a new diagnosis, black older adults smoked significantly fewer cigarettes than whites after a new diagnosis of diabetes, heart disease, stroke or cancer, and Latino older adults smoked significantly fewer cigarettes compared to white older adults after newly diagnosed diabetes and heart disease. Relative to black older adults, Latinos smoked significantly fewer cigarettes after newly diagnosed diabetes.

CONCLUSIONS: A large majority of middle-aged and older smokers continued to smoke after diagnosis with a major chronic disease. Black participants demonstrated the largest reductions in smoking behavior. These findings have important implications for tailoring secondary prevention efforts for older adults.

}, keywords = {Chronic disease, Health Conditions and Status, Older Adults, Racial/ethnic differences, Smoking}, issn = {1471-2318}, doi = {10.1186/s12877-017-0438-z}, author = {Ana R Qui{\~n}ones and Corey L Nagel and Jason T Newsom and Nathalie Huguet and Sheridan, Paige and Stephen M Thielke} } @article {8267, title = {Does Mode of Contact with Different Types of Social Relationships Predict Depression in Older Adults? Evidence from a Nationally Representative Survey}, journal = {Journal of the American Geriatrics Society}, volume = {63}, year = {2015}, pages = {2014}, publisher = {63}, abstract = {ObjectivesTo determine associations between use of three different modes of social contact (in person, telephone, written or e-mail), contact with different types of people, and risk of depressive symptoms in a nationally representative, longitudinal sample of older adults. DesignPopulation-based observational cohort. SettingUrban and suburban communities throughout the contiguous United States. ParticipantsIndividuals aged 50 and older who participated in the Health and Retirement Survey between 2004 and 2010 (N = 11,065). MeasurementsFrequency of participant use of the three modes of social contact with children, other family members, and friends at baseline were used to predict depressive symptoms (measured using the eight-item Center for Epidemiologic Studies Depression Scale) 2 years later using multivariable logistic regression models. ResultsProbability of having depressive symptoms steadily increased as frequency of in-personbut not telephone or written or e-mail contactdecreased. After controlling for demographic, clinical, and social variables, individuals with in-person social contact every few months or less with children, other family, and friends had a significantly higher probability of clinically significant depressive symptoms 2 years later (11.5 ) than those having in-person contact once or twice per month (8.1 ; P .001) or once or twice per week (7.3 ; P .001). Older age, interpersonal conflict, and depression at baseline moderated some of the effects of social contact on depressive symptoms. ConclusionFrequency of in-person social contact with friends and family independently predicts risk of subsequent depression in older adults. Clinicians should consider encouraging face-to-face social interactions as a preventive strategy for depression.}, keywords = {Demographics, Health Conditions and Status, Healthcare, Methodology, Retirement Planning and Satisfaction}, author = {Alan R Teo and Choi, Hwajung and Sarah B. Andrea and Marcia A. Valenstein and Jason T Newsom and Dobscha, Steven K. and Zivin, Kara} } @article {7811, title = {Predictors of Smoking Patterns After First Stroke}, journal = {Social Work in Health Care}, volume = {52}, year = {2013}, note = {Copyright - Copyright Taylor and Francis Group 2013 Last updated - 2013-06-04 CODEN - SWHCDO}, pages = {467}, publisher = {52}, abstract = {Persistent smoking following stroke is associated with poor outcomes including development of secondary stroke and increased mortality risk. This study uses longitudinal data from the U.S. Health and Retirement Study (1992-2008) to investigate whether depression and duration of inpatient hospital care impact smoking outcomes among stroke survivors (N = 745). Longer duration of care was associated with lower likelihood of persistent smoking. Depression was associated with greater cigarette consumption. Interaction effects were also significant, indicating that for survivors who experienced longer inpatient care there was a weaker association between depression and cigarette consumption. Implications for practice and research are discussed. PUBLICATION ABSTRACT}, keywords = {Health Conditions and Status, Public Policy}, url = {http://search.proquest.com.proxy.lib.umich.edu/docview/1364611528?accountid=14667http://mgetit.lib.umich.edu/?ctx_ver=Z39.88-2004andctx_enc=info:ofi/enc:UTF-8andrfr_id=info:sid/ProQ 3Apqrlandrft_val_fmt=info:ofi/fmt:kev:mtx:journalandrft.genre=articleandr}, author = {Michael J. McCarthy and Nathalie Huguet and Jason T Newsom and Mark S Kaplan and Bentson McFarland} } @article {7695, title = {Health behavior change following chronic illness in middle and later life.}, journal = {J Gerontol B Psychol Sci Soc Sci}, volume = {67}, year = {2012}, month = {2012 May}, pages = {279-88}, publisher = {67B}, abstract = {

OBJECTIVES: Understanding lifestyle improvements among individuals with chronic illness is vital for targeting interventions that can increase longevity and improve quality of life.

METHODS: Data from the U.S. Health and Retirement Study were used to examine changes in smoking, alcohol use, and exercise 2-14 years after a diagnosis of heart disease, diabetes, cancer, stroke, or lung disease.

RESULTS: Patterns of behavior change following diagnosis indicated that the vast majority of individuals diagnosed with a new chronic condition did not adopt healthier behaviors. Smoking cessation among those with heart disease was the largest observed change, but only 40\% of smokers quit. There were no significant increases in exercise for any health condition. Changes in alcohol consumption were small, with significant declines in excessive drinking and increases in abstention for a few health conditions. Over the long term, individuals who made changes appeared to maintain those changes. Latent growth curve analyses up to 14 years after diagnosis showed no average long-term improvement in health behaviors.

DISCUSSION: Results provide important new information on health behavior changes among those with chronic disease and suggest that intensive efforts are required to help initiate and maintain lifestyle improvements among this population.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Alcohol Drinking, Chi-Square Distribution, Chronic disease, Diabetes Mellitus, Exercise, Female, Health Behavior, Heart Diseases, Humans, Longitudinal Studies, Lung Diseases, Male, Middle Aged, Neoplasms, Smoking, Stroke, Time Factors}, issn = {1758-5368}, doi = {10.1093/geronb/gbr103}, author = {Jason T Newsom and Nathalie Huguet and Michael J. McCarthy and Pamela Ramage-Morin and Mark S Kaplan and Julie Bernier and Bentson McFarland and Jillian Oderkirk} } @book {5289, title = {Longitudinal Data Analysis: a Practical Guide for Researchers In Aging, Health, And Social Sciences}, year = {2012}, publisher = {Routledge}, organization = {Routledge}, address = {New York}, abstract = {This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from 12 major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings fur further study and a list of articles that further illustrate how to implement the analysis and report the results. An accompanying website provides syntax examples for several software packages for each of the chapter examples. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. Although most chapters emphasize the use of large studies collected over long term periods, much of the book is also relevant to researchers who analyze data collected in shorter time periods. The book opens with issues related to using publicly available data sets including a description of the goals, designs, and measures of the data. The next 10 chapters provide non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis, including: weighting samples and adjusting designs for longitudinal studies; missing data and attrition; measurement issues related to longitudinal research; the use of ANOVA and regression for averaging change over time; mediation analysis for analyzing causal processes; growth curve models using multilevel regression; longitudinal hypotheses using structural equation modeling (SEM); latent growth curve models for evaluating individual trajectories of change; dynamic SEM models of change; and survival (event) analysis. Examples from longitudinal data sets such as the Health and Retirement Study, the Longitudinal Study of Aging, and Established Populations for Epidemiologic Studies of the Elderly as well as international data sets such as the Canadian National Population Health Survey and the English Longitudinal Study of Aging, illustrate key concepts. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.}, keywords = {Cross-National, Health Conditions and Status, Methodology}, author = {Jason T Newsom and Richard N Jones and Scott M Hofer} }