Depression Symptom Patterns and Social Correlates among Chinese Americans.
Lin ZhuPublished in: Brain sciences (2018)
The aim of this study is to examine and compare the depression symptoms pattern and social correlates in three groups: foreign-born Chinese Americans, US-born Chinese Americans, and non-Hispanic whites. This study used data from the Collaborative Psychiatric Epidemiology Surveys (CPES). The study sample consists of 599 Chinese Americans (468 for the foreign-born and 121 for the US-born) and 4032 non-Hispanic whites. Factor analysis was used to examine the depression symptom patterns by each subgroup. Four depression symptoms dimensions were examined: negative affect, somatic symptoms, cognitive symptoms, and suicidality. Logistic regression was used to investigate the effects of sociodemographic (age, gender, marital status, and education), physical health condition, and social relational factors (supports from and conflict with family and friends) on specific types of depression symptoms separately for the three subgroups. The findings showed little differences in depression symptom patterns but clear variation in the social correlates to the four depression dimensions across the three ethnocultural groups, foreign-born Chinese Americans, US-born Chinese Americans, and non-Hispanic whites. Clinicians should take into account the sociocultural factors of patients when making diagnosis and suggesting treatments. In addition, psychiatrists, psychologists, or other mental health service providers should offer treatment and coping suggestions based on the specific symptom dimensions of patients, and patients' ethnocultural backgrounds.
Keyphrases
- sleep quality
- depressive symptoms
- mental health
- healthcare
- end stage renal disease
- gestational age
- ejection fraction
- chronic kidney disease
- low birth weight
- clinical trial
- patient reported
- randomized controlled trial
- physical activity
- prognostic factors
- risk factors
- patient reported outcomes
- african american
- machine learning
- social support
- risk assessment
- palliative care
- electronic health record
- climate change
- deep learning