Department of Psychology, Faculty of Psychology, University of Almeria, Almeria, Spain.
Health Research Center CEINSA, University of Almeria, Almeria, Spain.
BMC Public Health. 2024 Oct 11;24(1):2781. doi: 10.1186/s12889-024-20173-w.
Depressive disorders are a critical public health concern in Chile. Nonetheless, there is a lack of evidence regarding the identification of depressive symptom clusters. The objective was to identify depressive symptom clusters among Chilean young adults and examine how demographic, and lifestyle factors as well as social support can influence and predict them.
Cross-sectional study conducted among 1,000 participants from the Limache cohort 2. A latent class analysis (LCA) was performed to identify depressive symptom clusters, using the Patient Health Questionnaire (PHQ-9). Multinomial logistic regression was then applied to explore the associations between identified classes and potential predictors. The models were adjusted by age and sex.
Three latent classes of depressive symptoms were identified: minimal (25.7%); somatic (50.7%) and severe (23.6%). In the severe class for eight out nine depressive symptoms the probabilities were above 50%, and the probability of suicidal ideation was almost a third in this class. Being female (Adjusted Odds ratio [AOR], 2.49; 95% confidence interval [CI] [1.63-3.81]), current smoker (AOR, 1.74; 95% CI [1.15-2.65]), having basic education (AOR, 3.12; 95% CI [1.30-7.53]) and obesity (AOR, 2.72; 95% CI [1.61-4.59]) significantly increased the likelihood of belonging to severe class. Higher social support decreased the odds of being in the somatic (OR, 0.96; 95% CI [0.93-0.98]) and severe (OR, 0.92; 95% CI [0.90-0.94]) classes.
These findings highlight the importance of individualized intervention strategies for depression management. Also, the study suggests that nutritional status and social support should be considered when addressing depression in this population.
抑郁障碍是智利的一个重大公共卫生问题。然而,对于抑郁症状群的识别,目前还缺乏相关证据。本研究旨在鉴定智利青年人群中的抑郁症状群,并探讨人口统计学和生活方式因素以及社会支持对其的影响和预测作用。
本研究为横断研究,共纳入来自利马切队列 2 的 1000 名参与者。采用患者健康问卷(PHQ-9)对抑郁症状群进行潜类别分析(LCA)。然后,采用多项逻辑回归来探索鉴定的类别与潜在预测因素之间的关联。模型通过年龄和性别进行调整。
共鉴定出 3 种抑郁症状潜类别:轻度(25.7%)、躯体(50.7%)和重度(23.6%)。在重度类别中,9 个抑郁症状中的 8 个症状的概率均高于 50%,且该类别中有近三分之一的人有自杀意念。女性(调整后的优势比[OR],2.49;95%置信区间[CI] [1.63-3.81])、当前吸烟者(OR,1.74;95% CI [1.15-2.65])、接受基本教育(OR,3.12;95% CI [1.30-7.53])和肥胖(OR,2.72;95% CI [1.61-4.59])显著增加了属于重度类别的可能性。较高的社会支持降低了躯体(OR,0.96;95% CI [0.93-0.98])和重度(OR,0.92;95% CI [0.90-0.94])类别的可能性。
这些发现强调了针对抑郁症管理制定个体化干预策略的重要性。此外,该研究表明,在该人群中,应考虑营养状况和社会支持,以解决抑郁问题。