This cross-sectional study aimed to identify patterns of non-communicable disease (NCD) multimorbidity among Iranian adults and to assess how these patterns are associated with sociodemographic characteristics and health-related quality of life (HRQOL).
The analysis used data collected through the 2021 Iranian STEPS Survey following the WHO STEPwise approach to NCD risk factor surveillance. The investigation focused on how combinations of chronic conditions cluster in the population and the demographic and socioeconomic correlates of those clusters.
The analysis used a nationally representative, community-based household survey conducted across Iran in 2021. The analytic sample comprised 17,517 adults with complete survey data. Sample composition included 56.7% women and 67.4% urban residents. The STEPS framework was the primary data collection method described.
Multimorbidity clusters were derived from participant chronic disease profiles. The chronic conditions incorporated in the latent class analysis included myocardial infarction, stroke, asthma/chronic obstructive pulmonary disease, cancer, obesity (measured both as abdominal obesity and BMI-defined obesity), hypertension, diabetes, chronic kidney disease and dyslipidaemia.
Health-related quality of life was measured using the EuroQol Visual Analogue Scale (EQ-VAS). Associations between identified clusters, demographic variables and EQ-VAS scores were examined using multinomial logistic regression models.
Latent class analysis identified four distinct multimorbidity clusters in the sample:
These clusters distinguish groups characterized by higher burdens of obesity and metabolic conditions versus a low-comorbidity group and a smaller non-obese cardiometabolic group.
Cluster membership varied significantly by multiple sociodemographic factors. Key associations reported were:
The two obesity-related clusters (OSMS and OEMS) were associated with older age, female sex, lower education, unpaid work and urban residence.
NOCM membership was associated with male sex, older age, retirement and holding complementary insurance.
The LC cluster represented the largest single group (42.4%) and served as the reference for HRQOL comparisons.
These patterns indicate that both biological factors (age, sex) and social determinants (education, occupation, insurance, urbanicity) shape multimorbidity profiles.
Compared with the LC cluster, mean EQ-VAS scores were significantly lower in all three other clusters:
These findings show the greatest decrement in self-reported HRQOL in the OSMS cluster, with intermediate reductions for NOCM and smaller but significant reductions for OEMS.
The study reports heterogeneous multimorbidity clusters in Iran that are strongly influenced by obesity and social disadvantage. Associations between cluster membership and sociodemographic variables underscore the role of social determinants in shaping patterns of chronic disease.
Authors conclude that findings support the need for integrated, equity-oriented policies that address both clinical complexity and social determinants through prevention strategies and coordinated care models targeted to high-burden clusters.
The source text does not report certain methodological details and limitations within the provided excerpt. Specifically, information on survey response rates, weighting procedures, latent class model selection criteria, adjustment covariates in regression models beyond those listed, and study limitations were not reported in the supplied text.
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