LATENT CLASS ANALYSIS OF MULTIMORBIDITY COMPARING RACE/ETHNICITY GROUPS. (11th November 2018)

• Record Type: Journal Article
• Authors: Newsom, J; Quinones, A; Botoseneanu, A; Allore, H; Nagel, C; Dorr, D
• Is Part Of: Innovation in aging. Volume 2(2018)Supplement 1
• Page Start: 497
• Page End: 498
• Publication Date: 2018-11-11
• Subjects: Aging -- Periodicals; Gerontology -- Periodicals; 612.67
• DOI: 10.1093/geroni/igy023.1852
• Languages: English
• ISSNs: 2399-5300

Abstract

Abstract: Multimorbidity (multiple co-occurring chronic conditions), which is associated with poor quality of life, increased risk of disability, service utilization, and mortality, is important to understanding racial and ethnic disparities in health. Latent class analysis was used to explore multimorbidity combinations and to compare multimorbidity classes among Black, Hispanic, and White middle-aged and older adult participants in the 1998 Health and Retirement Study (N = 18, 917, mean age = 65.45). Latent class models examined race/ethnic group differences in class membership and response probabilities with Mplus Version 8. Results suggested four latent classes, characterized by individuals who are relatively disease free, individuals with a high number of chronic conditions (high multimorbidity), individuals with hypertension and diabetes (early risk condition), and individuals with arthritis (musculoskeletal only). Significant differences across the three race/ethnicity groups suggested Hispanics were at the highest risk among the three groups. Hispanic participants had approximately twice the odds of predicted class membership in the high multimorbidity group than Whites (OR = 1.929, p < .001) or Blacks (OR = 2.522, p < .001) and were twice as likely to be predicted members of the early risk group than Blacks (OR = 2.053, p < .005). Blacks and Hispanics were also significantly more likely than Whites to fall into the predicted multimorbidity class than the musculoskeletal only class (OR = 1.72, p < .05, and OR = 1.51, p < .05, respectively). These results provide important new information for understanding disparities in disability, service utilization, and mortality.