Background: Depression is a serious mental health issue and a major concern among middle-aged women, especially during the menopause transition time. This study aimed to determine the prevalence and associated factors of depression among the middle-aged women of the menopause transition in Dhaka.

Methodology: A cross-sectional study was conducted among 41 to 60 years older women in Dhaka city using a multistage cluster sampling technique and face to face interview.

Result: In total 326 middle-aged women participated in the study, and among them, 30.4% had major depression. No statistically significant association was observed between menopause status and major depression. However, peri-(34.2%) and post-menopausal (33.3%) groups were more depressed compared to pre-menopausal (26.8%) groups. Our adjusted analysis indicates, marital status (p = 0.004), having salaried job (p < 0.001), number of offspring (p = 0.003), sedentary hours (p = 0.002), smoking habit (p = 0.012), hypertension (p = 0.012), chronic disease other than diabetes/hypertension (p = 0.006), vasomotor symptoms (p = 0.004) and sleep problem (p = 0.007) were significantly associated with depression status.

Conclusion: The study result indicates a high prevalence of major depression among middle-aged Bangladeshi women during the menopause transition. Therefore, depression should be routinely evaluated among middle-aged women for the monitoring and prevention of depression.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajp.2020.102312DOI Listing

Publication Analysis

Top Keywords

middle-aged women
16
prevalence associated
8
associated factors
8
factors depression
8
women dhaka
8
dhaka city
8
women menopause
8
menopause transition
8
major depression
8
depression
5

Similar Publications

Axial spondyloarthritis (ax-SpA) causes pain, fatigue, stiffness, loss of physical function, and poor health status, which can influence sexual activity and enjoyment. To explore whether patients with ax-SpA perceive that their health status effects their sexual activity and to identify predictors of these perceived effects on sexual activity after a 5-year follow-up. Data about demographics, disease, medication, health-related quality of life (HRQOL), and sexual quality of life (SQOL) were collected at the baseline and 5-year follow-up.

View Article and Find Full Text PDF

Breast cancer is a leading cause of cancer-related deaths among women globally. It is imperative to explore novel biomarkers to predict breast cancer treatment response as well as progression. Here, we collected six breast cancer samples and paired normal tissues for high-throughput sequencing.

View Article and Find Full Text PDF

The COVID-19 pandemic has not only posed alarming health challenges but also exacerbated the scenarios of intimate partner violence (IPV) against women globally. While global studies indicate a conspicuous increase in IPV during COVID-19 lockdowns; Indian studies exhibit mixed evidence. This ambiguity in world's most populous country underscores a greater need to examine the nexus between exposure to COVID-19 and IPV using a large nationally representative sample of India.

View Article and Find Full Text PDF

To evaluate the predictive utility of N6-methyladenosine (m6A)-associated long non-coding RNAs (lncRNAs) for the prognosis and immunotherapy response in papillary renal cell carcinoma (pRCC). Transcriptomic data of pRCC samples were extracted from the TCGA database. The m6A-related lncRNAs were identified by Pearson correlation analysis.

View Article and Find Full Text PDF

Aging is typically associated with declines in episodic memory, executive functions, and sleep quality. Therefore, the sleep-dependent stabilization of episodic memory is suspected to decline during aging. This might reflect in accelerated long-term forgetting, which refers to normal learning and retention over hours, yet an abnormal retention over nights and days.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!