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Determinants of Multimorbidity in Older Adults in Iran: A Cross-Sectional Study Using Latent Class Analysis on the Bushehr Elderly Health (Beh) Program Publisher Pubmed



Marzban M1, 2 ; Jamshidi A2 ; Khorrami Z3 ; Hall M4, 5 ; Batty JA4, 5 ; Farhadi A2 ; Mahmudpour M2 ; Gholizade M6 ; Nabipour I6 ; Larijani B7 ; Afrashteh S8
Authors
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Authors Affiliations
  1. 1. Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
  2. 2. The Persian Gulf Tropical Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
  3. 3. Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
  5. 5. Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
  6. 6. The Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
  7. 7. Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
  8. 8. Department of Biostatistics and Epidemiology, Faculty of Health and Nutrition, Bushehr University of Medical Sciences, Bushehr, Iran

Source: BMC Geriatrics Published:2024


Abstract

Background and objectives: Multimorbidity, defined as the presence of two or more long-term health conditions in an individual, is one of the most significant challenges facing health systems worldwide. This study aimed to identify determinants of classes of multimorbidity among older adults in Iran. Research Design and methods: In a cross-sectional sample of older adults (aged ≥ 60 years) from the second stage of the Bushehr Elderly Health (BEH) program in southern Iran, latent class analysis (LCA) was used to identify patterns of multimorbidity. Multinomial logistic regression was conducted to investigate factors associated with each multimorbidity class, including age, gender, education, household income, physical activity, smoking status, and polypharmacy. Results: In 2,426 study participants (mean age 69 years, 52% female), the overall prevalence of multimorbidity was 80.2%. Among those with multimorbidity, 3 latent classes were identified. These comprised: class 1, individuals with a low burden of multisystem disease (56.9%); class 2, individuals with predominantly cardiovascular-metabolic disorders (25.8%) and class 3, individuals with predominantly cognitive and metabolic disorders (17.1%). Compared with men, women were more likely to belong to class 2 (odds ratio [OR] 1.96, 95% confidence interval [CI] 1.52–2.54) and class 3 (OR 4.52, 95% CI 3.22–6.35). Polypharmacy was associated with membership class 2 (OR 3.52, 95% CI: 2.65–4.68) and class 3 (OR 1.84, 95% CI 1.28–2.63). Smoking was associated with membership in class 3 (OR 1.44, 95% CI 1.01–2.08). Individuals with higher education levels (59%) and higher levels of physical activity (39%) were less likely to belong to class 3 (OR 0.41; 95% CI: 0.28–0.62) and to class 2 (OR 0.61; 95% CI: 0.38–0.97), respectively. Those at older age were less likely to belong to class 2 (OR 0.95). Discussion and implications: A large proportion of older adults in Iran have multimorbidity. Female sex, polypharmacy, sedentary lifestyle, and poor education levels were associated with cardiovascular-metabolic multimorbidity and cognitive and metabolic multimorbidity. A greater understanding of the determinants of multimorbidity may lead to strategies to prevent its development. © The Author(s) 2024.
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