Investigating multimorbidity patterns and associated risk factors in the fasa adults cohort study (FACS): A latent class analysis

TL;DR

This study identifies patterns of multiple chronic diseases in Iranian adults and finds risk factors like age, gender, and lifestyle.

Contribution

The study uses latent class analysis to reveal hidden multimorbidity subgroups and their associated risk factors in a regional population.

Findings

Three multimorbidity classes were identified: healthy, dyslipidemia, and cardio-metabolic conditions.

Older age and being female increased odds of dyslipidemia and cardio-metabolic conditions.

Higher physical activity and sleep duration reduced odds of cardio-metabolic conditions.

Abstract

Multimorbidity, defined as the co-occurrence of multiple health conditions, is a major global public health concern. This study aimed to identify latent classes of multimorbidity and associated risk factors in Iranian adults.

This cross-sectional study analyzed baseline data from 10,131 adults who participated in the Fasa Adults Cohort Study (FACS) in southern Iran. Multimorbidity was defined as the presence of two or more of 11 chronic diseases, including hypertension, dyslipidemia, stroke, osteoarthritis, depression, type two diabetes mellitus, obesity, osteoporosis, cardiovascular disease, thyroid disease, and respiratory disease. Latent class analysis (LCA) was used for cluster participants, and multinomial logistic regression was conducted to investigate the association between age, sex, education level, socioeconomic status, daily sleep duration, physical activity, and multimorbidity.

The prevalence of multimorbidity was 40.3%. Three latent classes were identified: healthy (66.8%), dyslipidemia (14.1%), and cardio-metabolic conditions (19.1%). Older age increased the odds of belonging to dyslipidemia (odds ratio (OR) = 1.04 [95% confidence interval (CI): 1.03–1.05]) and cardio-metabolic conditions (OR = 1.10 [95% CI: 1.09–1.11]) classes. Similarly, women were at higher odds than men of being in dyslipidemia (OR = 2.49 [95% CI: 2.05–3.02]) and cardio-metabolic conditions (OR = 3.35, 95% CI: 2.79–4.03]) classes. Employed participants showed decreased odds of having cardio-metabolic conditions (OR = 0.66 [95% CI: 0.55–0.80]). However, very high socioeconomic status was a risk factor for cardio-metabolic conditions (OR = 1.44 [95% CI: 1.16–1.78]) and dyslipidemia (OR = 1.35 [95% CI: 1.10–1.65]). Higher physical activity and sleeping for 8 hours or more were protective factors against cardio-metabolic conditions (OR = 0.74 [95% CI: 0.63–0.87]). Moreover, medium or high dietary intake increased the odds of belonging to the dyslipidemia class (OR = 1.46 [95% CI: 1.09–1.94] and OR = 1.57 [95% CI: 1.16–2.11], respectively).

Using LCA, we identified distinct subgroups of chronic diseases, showing hidden patterns of multimorbidity associated with several risk factors. This approach offers deeper knowledge of disease clustering, contributes to a more comprehensive understanding of multimorbidity, and shows the importance of regional health challenges in designing targeted public health interventions.

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