Esmaeili Elham Davtalab, Ghaffari Alireza, Kalankesh Leila R, Zeinalzadeh Ali Hossein, Dastgiri Saeed
Research Center of Psychiatry and Behavioral Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Internal Medicine, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
BMC Public Health. 2025 May 10;25(1):1728. doi: 10.1186/s12889-025-23001-x.
This study aimed to identify distinct population classes with different risk profiles using Latent Class Analysis (LCA) in Iran, as well as, to evaluate the association between various classes of risky behavior and Socio -Economic Status (SES) levels.
This cross-sectional study was conducted on 860 participants in Tabriz, northwestern Iran from September to November 2023. The source population included clients who visited the Asadabadi Family Medicine Clinic. Data were collected using two standard self-report questionnaires. LCA was utilized to categorize the data. Twelve variables were utilized to determine the classes of risky behaviors. After considering the model selection indices, we found that the model with three latent classes was the most suitable. Multi-nominal logistic regression was employed to assess the association between risky behavior and SES.
The results of this study showed that the prevalence of the middle-risk class and high-risk class among the study population was 13% and 21%, respectively. Individuals with a very high SES were less likely to engage in multiple risky behavior classes compared to those with a low SES (OR: 0.82, 95% CI: 0.59-0.97 and OR: 0.79, 95% CI: 0.48-1.29). Additionally, divorced participants (OR: 1.7, 95% CI: 1.08-2.71 and 4.31,95% CI: 1.61-11.56).
In the present study, the co-occurrence of risky behaviors was reported as 10 and 3 for the high-risk behavior class and the middle-risk behavior class, respectively. The findings suggest that prevention and treatment interventions aimed at modifying multiple high-risk behaviors should be integrated into the healthcare system, in addition to those focused on altering a single behavior. Furthermore, the results of this study indicate that individuals with lower socioeconomic status are given higher priority in screening programs designed to identify high-risk behaviors.
本研究旨在利用潜在类别分析（LCA）在伊朗识别具有不同风险特征的不同人群类别，并评估各类危险行为与社会经济地位（SES）水平之间的关联。
本横断面研究于2023年9月至11月在伊朗西北部大不里士的860名参与者中进行。源人群包括访问阿萨达巴迪家庭医学诊所的患者。使用两份标准的自我报告问卷收集数据。采用LCA对数据进行分类。使用12个变量来确定危险行为类别。在考虑模型选择指标后，我们发现具有三个潜在类别的模型最为合适。采用多项逻辑回归评估危险行为与SES之间的关联。
本研究结果表明，研究人群中中度风险类别和高风险类别的患病率分别为13%和21%。与社会经济地位低的人相比，社会经济地位非常高的人参与多种危险行为类别的可能性较小（比值比：0.82，95%置信区间：0.59 - 0.97；比值比：0.79，95%置信区间：0.48 - 1.29）。此外，离婚参与者（比值比：1.7，95%置信区间：1.08 - 2.71；4.31，95%置信区间：1.61 - 11.56）。
在本研究中，高风险行为类别和中度风险行为类别的危险行为共现率分别报告为10和3。研究结果表明，除了针对单一行为改变的干预措施外，旨在改变多种高风险行为的预防和治疗干预措施应纳入医疗保健系统。此外，本研究结果表明，在旨在识别高风险行为的筛查项目中，社会经济地位较低的个体应被给予更高的优先级。
