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BMC Geriatrics
Published in: BMC Geriatrics 1/2024

Open Access 01-12-2024 | Osteoporosis | Research

Determinants of multimorbidity in older adults in Iran: a cross-sectional study using latent class analysis on the Bushehr Elderly Health (BEH) program

Authors: Maryam Marzban, Ali Jamshidi, Zahra Khorrami, Marlous Hall, Jonathan A Batty, Akram Farhadi, Mehdi Mahmudpour, Mohamad Gholizade, Iraj Nabipour, Bagher Larijani, Sima Afrashteh

Published in: BMC Geriatrics | Issue 1/2024

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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.
Literature
1.
Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2(1):1–12.CrossRef
2.
Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity Nat Reviews Disease Primers. 2022;8(1):48.PubMedCrossRef
3.
Walker V, Perret-Guillaume C, Kesse-Guyot E, Agrinier N, Hercberg S, Galan P, et al. Effect of multimorbidity on health-related quality of life in adults aged 55 years or older: results from the SU. VI. MAX 2 cohort. PLoS ONE. 2016;11(12):e0169282.PubMedPubMedCentralCrossRef
4.
Lu J, Wang Y, Hou L, Zuo Z, Zhang N, Wei A. Multimorbidity patterns in old adults and their associated multi-layered factors: a cross-sectional study. BMC Geriatr. 2021;21(1):1–11.CrossRef
5.
Zemedikun DT, Gray LJ, Khunti K, Davies MJ, Dhalwani NN, editors. Patterns of multimorbidity in middle-aged and older adults: an analysis of the UK Biobank data. Mayo Clinic Proceedings; 2018: Elsevier.
6.
Moftakhar L, Rezaeianzadeh R, Ghoddusi Johari M, Hosseini SV, Rezaianzadeh A. Epidemiology and predictors of multimorbidity in Kharameh cohort study: a population-based cross-sectional study in southern Iran. Health Sci Rep. 2023;6(1):e988.PubMedCrossRef
7.
Ahmadi B, Alimohammadian M, Yaseri M, Majidi A, Boreiri M, Islami F et al. Multimorbidity: epidemiology and risk factors in the Golestan cohort study, Iran: a cross-sectional analysis. Medicine. 2016;95(7).
8.
Mirzaie M, Darabi S. Population aging in Iran and rising health care costs. Iran J Ageing. 2017;12(2):156–69.
9.
Park B, Lee HA, Park H. Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older. PLoS ONE. 2019;14(11):e0216259.PubMedPubMedCentralCrossRef
10.
Aminisani N, Rastgou L, Shamshirgaran SM, Sarbakhsh P, Ghaderi S, Hyde M. Predictors of multimorbidity among the kurdish population living in the Northwest of Iran. BMC Public Health. 2020;20(1):1–8.CrossRef
11.
Marengoni A, Von Strauss E, Rizzuto D, Winblad B, Fratiglioni L. The impact of chronic multimorbidity and disability on functional decline and survival in elderly persons. A community-based, longitudinal study. J Intern Med. 2009;265(2):288–95.PubMedCrossRef
12.
Macri E, Lewis J, Khan KM, Ashe MC, de Morton NA. The de Morton mobility index: normative data for a clinically useful mobility instrument. Journal of aging research. 2012;2012.
13.
Guisado-Clavero M, Roso-Llorach A, López-Jimenez T, Pons-Vigués M, Foguet-Boreu Q, Muñoz MA, et al. Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis. BMC Geriatr. 2018;18(1):1–11.CrossRef
14.
Garin N, Olaya B, Moneta MV, Miret M, Lobo A, Ayuso-Mateos JL, et al. Impact of multimorbidity on disability and quality of life in the Spanish older population. PLoS ONE. 2014;9(11):e111498.PubMedPubMedCentralCrossRefADS
15.
Juul-Larsen HG, Christensen LD, Andersen O, Bandholm T, Kaae S, Petersen J. Development of the chronic condition measurement guide: a new tool to measure chronic conditions in older people based on ICD-10 and ATC-codes. Eur Geriatr Med. 2019;10(3):431–44.PubMedCrossRef
16.
Larsen FB, Pedersen MH, Friis K, Glümer C, Lasgaard M. A latent class analysis of multimorbidity and the relationship to socio-demographic factors and health-related quality of life. A national population-based study of 162,283 Danish adults. PLoS ONE. 2017;12(1):e0169426.PubMedPubMedCentralCrossRef
17.
Khorrami Z, Rezapour M, Etemad K, Yarahmadi S, Khodakarim S, Hezaveh AM, et al. The patterns of non-communicable disease multimorbidity in Iran: a multilevel analysis. Sci Rep. 2020;10(1):1–11.CrossRef
18.
Azadnajafabad S, Mohammadi E, Aminorroaya A, Fattahi N, Rezaei S, Haghshenas R et al. Non-communicable diseases’ risk factors in Iran; a review of the present status and action plans. J Diabetes Metabolic Disorders. 2021:1–9.
19.
Weller BE, Bowen NK, Faubert SJ. Latent class analysis: a guide to best practice. J Black Psychol. 2020;46(4):287–311.CrossRef
20.
Olaya B, Moneta MV, Caballero FF, Tyrovolas S, Bayes I, Ayuso-Mateos JL, et al. Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: a prospective cohort study. BMC Geriatr. 2017;17(1):186.PubMedPubMedCentralCrossRef
21.
Ostovar A, Nabipour I, Larijani B, Heshmat R, Darabi H, Vahdat K, et al. Bushehr elderly health (BEH) Programme, phase I (cardiovascular system). BMJ open. 2015;5(12):e009597.PubMedPubMedCentralCrossRef
22.
Shafiee G, Ostovar A, Heshmat R, Darabi H, Sharifi F, Raeisi A, et al. Bushehr Elderly Health (BEH) programme: study protocol and design of musculoskeletal system and cognitive function (stage II). BMJ open. 2017;7(8):e013606.PubMedPubMedCentralCrossRef
23.
Klishadi R, Khosravi A, Famouri F, Sadeghi M, Shirani S. Assessment of physical activity of adolescents in Isfahan. J Shahrekord Univ Med Sci. 2001;3(2).
24.
Aadahl M, Jørgensen T. Validation of a new self-report instrument for measuring physical activity. Med Sci Sports Exerc. 2003;35(7):1196–202.PubMedCrossRef
25.
Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DRJ, Tudor-Locke C et al. 2011 Compendium of Physical activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8).
26.
Ohkawara K, Ishikawa-Takata K, Park JH, Tabata I, Tanaka S. How much locomotive activity is needed for an active physical activity level: analysis of total step counts. BMC Res Notes. 2011;4(1):512.PubMedPubMedCentralCrossRef
27.
Mirzaei M, Mirzaei M, Bagheri B, Dehghani A. Awareness, treatment, and control of hypertension and related factors in adult Iranian population. BMC Public Health. 2020;20(1):667.PubMedPubMedCentralCrossRef
28.
Shahraz S, Saeedi Moghaddam S, Azmin M, Peykari N, Yoosefi M, Mohebi F, et al. Prevalence of diabetes and Prediabetes, and achievements in Diabetes Control in Iran; the results of the STEPS of 2016. Arch Iran Med. 2022;25(9):591–9.PubMedPubMedCentralCrossRef
29.
Health UDo, Services H. National Institutes of Health Common Terminology Criteria for adverse events. CTCAE) National Cancer Institute; 2009.
30.
Quaranta D, Piccininni C, Caprara A, Malandrino A, Gainotti G, Marra C. Semantic relations in a categorical verbal fluency test: an exploratory investigation in mild cognitive impairment. Front Psychol. 2019;10:2797.PubMedPubMedCentralCrossRef
31.
Cosman F, de Beur SJ, LeBoff M, Lewiecki E, Tanner B, Randall S, et al. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25(10):2359–81.PubMedPubMedCentralCrossRef
32.
33.
Hussain MA, Katzenellenbogen JM, Sanfilippo FM, Murray K, Thompson SC. Complexity in disease management: a linked data analysis of multimorbidity in Aboriginal and non-aboriginal patients hospitalised with atherothrombotic disease in Western Australia. PLoS ONE. 2018;13(8):e0201496.PubMedPubMedCentralCrossRef
34.
Hall M, Dondo TB, Yan AT, Mamas MA, Timmis AD, Deanfield JE, et al. Multimorbidity and survival for patients with acute myocardial infarction in England and Wales: latent class analysis of a nationwide population-based cohort. PLoS Med. 2018;15(3):e1002501.PubMedPubMedCentralCrossRef
35.
Cassell A, Edwards D, Harshfield A, Rhodes K, Brimicombe J, Payne R, et al. The epidemiology of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract. 2018;68(669):e245–e51.PubMedPubMedCentralCrossRef
36.
Puth M-T, Weckbecker K, Schmid M, Münster E. Prevalence of multimorbidity in Germany: impact of age and educational level in a cross-sectional study on 19,294 adults. BMC Public Health. 2017;17(1):1–7.CrossRef
37.
Mohideen FS, Honest PCR, Syed MA, David KV, Abdulmajeed J, Ramireddy N. Prevalence of multimorbidity among adults attending primary health care centres in Qatar: a retrospective cross-sectional study. J Family Med Prim Care. 2021;10(5):1823.PubMedPubMedCentralCrossRef
38.
Emamian MH, Hashemi H, Fotouhi A. Predicted 10-year risk of cardiovascular disease in the Islamic Republic of Iran and the body mass index paradox. East Mediterr Health J. 2020;26(12):1465–72.PubMedCrossRef
39.
Khalagi K, Ansarifar A, Fahimfar N, Sanjari M, Gharibzdeh S, Sharifi F, et al. Cardio-metabolic and socio-demographic risk factors associated with dependency in basic and instrumental activities of daily living among older Iranian adults: Bushehr elderly health program. BMC Geriatr. 2021;21(1):1–9.CrossRef
40.
Ofori-Asenso R, Chin KL, Curtis AJ, Zomer E, Zoungas S, Liew D. Recent patterns of multimorbidity among older adults in high-income countries. Popul Health Manage. 2019;22(2):127–37.CrossRef
41.
Robins JLW, McCain NL, Elswick R. Exploring the complexity of cardiometabolic risk in women. Biol Res Nurs. 2012;14(2):160–70.PubMedCrossRef
42.
Albert PR. Why is depression more prevalent in women? J Psychiatry Neuroscience: JPN. 2015;40(4):219.CrossRef
43.
Karvinen S, Jergenson MJ, Hyvärinen M, Aukee P, Tammelin T, Sipilä S, et al. Menopausal status and physical activity are independently associated with cardiovascular risk factors of healthy middle-aged women: cross-sectional and longitudinal evidence. Front Endocrinol. 2019;10:589.CrossRef
44.
Blümel JE, Carrillo-Larco RM, Vallejo MS, Chedraui P. Multimorbidity in a cohort of middle-aged women: risk factors and disease clustering. Maturitas. 2020;137:45–9.PubMedPubMedCentralCrossRef
45.
Sergi G, Rui MD, Sarti S, Manzato E. Polypharmacy in the Elderly. Drugs Aging. 2011;28(7):509–18.PubMedCrossRef
46.
Jyrkkä J, Mursu J, Enlund H, Lönnroos E. Polypharmacy and nutritional status in elderly people. Curr Opin Clin Nutr Metab Care. 2012;15(1):1–6.PubMedCrossRef
47.
Aggarwal P, Woolford SJ, Patel HP. Multi-morbidity and polypharmacy in older people: challenges and opportunities for clinical practice. Geriatrics. 2020;5(4):85.PubMedPubMedCentralCrossRef
48.
Knaeps S, Lefevre J, Wijtzes A, Charlier R, Mertens E, Bourgois JG. Independent associations between Sedentary Time, Moderate-To-Vigorous physical activity, Cardiorespiratory Fitness and Cardio-Metabolic Health: a cross-sectional study. PLoS ONE. 2016;11(7):e0160166.PubMedPubMedCentralCrossRef
49.
Lippi G, Henry BM, Sanchis-Gomar F. Physical inactivity and cardiovascular disease at the time of coronavirus disease 2019 (COVID-19). Eur J Prev Cardiol. 2020;27(9):906–8.PubMedPubMedCentralCrossRef
50.
Wang B, Gan L, Deng Y, Zhu S, Li G, Nasser MI, et al. Cardiovascular Disease and Exercise: from Molecular mechanisms to clinical applications. J Clin Med. 2022;11(24):7511.PubMedPubMedCentralCrossRef
51.
Darwish H, Farran N, Assaad S, Chaaya M. Cognitive Reserve Factors in a developing country: Education and Occupational Attainment Lower the risk of Dementia in a sample of Lebanese older adults. Front Aging Neurosci. 2018;10.
52.
Lenehan ME, Summers MJ, Saunders NL, Summers JJ, Vickers JC. Relationship between education and age-related cognitive decline: a review of recent research. Psychogeriatrics. 2015;15(2):154–62.PubMedCrossRef
53.
Cockerham WC, Hamby BW, Oates GR. The social determinants of chronic disease. Am J Prev Med. 2017;52(1):5–S12.CrossRef
54.
Hahad O, Daiber A, Michal M, Kuntic M, Lieb K, Beutel M, et al. Smoking and neuropsychiatric Disease—associations and underlying mechanisms. Int J Mol Sci. 2021;22(14):7272.PubMedPubMedCentralCrossRef
55.
Yuan S, Yao H, Larsson SC. Associations of cigarette smoking with psychiatric disorders: evidence from a two-sample mendelian randomization study. Sci Rep. 2020;10(1):1–9.CrossRef
56.
Boksa P. Smoking, psychiatric illness and the brain. J Psychiatry Neuroscience: JPN. 2017;42(3):147.CrossRef
57.
58.
Devore EE, Grodstein F, Schernhammer ES. Sleep duration in relation to cognitive function among older adults: a systematic review of Observational studies. Neuroepidemiology. 2016;46(1):57–78.PubMedCrossRef
59.
Lo JC, Groeger JA, Cheng GH, Dijk D-J, Chee MWL. Self-reported sleep duration and cognitive performance in older adults: a systematic review and meta-analysis. Sleep Med. 2016;17:87–98.PubMedCrossRef
60.
Zhou Y, Ni Y, Jones M, Dai X, Lim CC, Zhu A et al. Sleep behaviors and progression of multimorbidity in middle-aged and older adults: a prospective cohort study from China. Journals Gerontology: Ser A. 2023:glad087.
61.
Sinha P, Calfee CS, Delucchi KL. Practitioner’s guide to latent class analysis: methodological considerations and common pitfalls. Crit Care Med. 2021;49(1):e63.PubMedPubMedCentralCrossRef
Metadata
Title
Determinants of multimorbidity in older adults in Iran: a cross-sectional study using latent class analysis on the Bushehr Elderly Health (BEH) program
Authors
Maryam Marzban
Ali Jamshidi
Zahra Khorrami
Marlous Hall
Jonathan A Batty
Akram Farhadi
Mehdi Mahmudpour
Mohamad Gholizade
Iraj Nabipour
Bagher Larijani
Sima Afrashteh
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Geriatrics / Issue 1/2024
Electronic ISSN: 1471-2318
DOI
https://doi.org/10.1186/s12877-024-04848-y

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