Undiagnosed depression: A community diagnosis

Williams, Sharifa Z.; Chung, Grace S.; Muennig, Peter

doi:10.1016/j.ssmph.2017.07.012

Cited by 88 publications

(57 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some patients may also have depression or anxiety that goes undiagnosed. However, undiagnosed depression has previously been associated with increased healthcare costs in a community sample (Williams, Chung, & Muennig, 2017). Similar results were also found in a large nationally representative sample of health patients in Japan (Yamabe, Liebert, Flores, & Pashos, 2019).…”

Section: Discussionmentioning

confidence: 99%

Healthcare use and costs in adult cancer patients with anxiety and depression

Decastro

Schwab

Tiamson-Kassab

et al. 2020

Depression and Anxiety

View full text Add to dashboard Cite

Objective Anxiety and depression are common in individuals with cancer and may impact healthcare service use and costs in this population. This study examined the effects of anxiety alone, depression alone, and comorbid anxiety and depressive disorder on healthcare use and costs among patients with cancer. Method This was a retrospective cohort analysis of administrative data of patients aged 18 or older with an International Classification of Diseases, Ninth Revision diagnosis of cancer. Key outcomes were any visit to emergency department (ED), any inpatient hospitalization, length of hospital stays, and annual healthcare costs 1 year from cancer diagnosis. Results A total of 13,426 patients were included. Relative to patients with neither anxiety nor depression, those with anxiety alone, depression alone, or comorbid anxiety and depression were more likely to experience an ED visit and be hospitalized. Length of hospital stays were also longer and annual healthcare costs were significantly higher in all three clinical groups. Conclusions Cancer patients with anxiety and depression were at greater risk for ED visits and hospitalizations, experienced longer hospital stays, and accrued higher healthcare costs. Future researchers should determine whether screening and treating comorbid anxiety and depression may decrease healthcare utilization and improve turnover wellbeing among cancer patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Healthcare use and costs in adult cancer patients with anxiety and depression

Decastro

Schwab

Tiamson-Kassab

et al. 2020

Depression and Anxiety

View full text Add to dashboard Cite

show abstract

“…[ 6 ] Depression is frequently undiagnosed and untreated. [ 12 ] Many reasons hamper help-seeking behavior, one of which is the lack of mental health literacy. [ 13 ] Another reason is the stigma or negative attitude towards people with mental disorders.…”

Section: Introductionmentioning

confidence: 99%

The prevalence and associated factors of depression among medical students of Saudi Arabia: A systematic review

AlJaber

2020

J Family Med Prim Care

View full text Add to dashboard Cite

The main purpose of our review study was to estimate depression prevalence among Saudi Arabian medical students according to the published articles and try to reveal the main associated factors. A systematic search was performed through PubMed/Medline and Google Scholar databases. A review of 18 studies published between January 2010 and March 2019 was composed of the following selection of necessary articles approved by PICO (population, intervention, control, and outcomes) criteria. The prevalence of depression among medical students of Saudi Arabia ranged from 30.9% to 77.6% with a mean prevalence of 51.5%. Depression severity was evaluated by various questionnaires, so we summarized the extracted data and revealed that medical students tend to have moderate to severe depression to a greater extent than mild depressive symptoms (33.27% vs 29.9%). The findings of this review suggest a high incidence of depression among medical students and the influence of associate sociodemographic factors. Females are considered to be at a higher risk of depression. First-year medical students are the most susceptible to develop depressive symptoms. Smoking is strongly associated with depression severity. Marital status, eating habits, usage of stimulants, and sleep disturbances are within significant findings of our review study.

show abstract

“…The need for effective mental illness surveillance to improve individual coping, in addition to reducing public burden, has been widely discussed [ 54 , 55 ]. The proposed multi-domain system addresses existing gaps in the detection, monitoring, and treatment of individuals who suffer from mood disorders, and who do not yet receive appropriate mental health support.…”

Section: Moving Beyond the Individual To A Multi-domain Neurophenomentioning

confidence: 99%

“…Specifically, three main populations are expected to significantly benefit from such a system. First, individuals who are not diagnosed with mood disorders due to low accessibility to mental health services or low awareness of the symptoms of mood disorders (estimated at 66% of individuals in the community who suffer from depression [ 55 ]). The proposed system could track individual behavioral and physiological signs and prompt the individual when necessary to seek treatment.…”

Section: Moving Beyond the Individual To A Multi-domain Neurophenomentioning

confidence: 99%

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

Sela

Santamaría

Amichai-Hamburge

et al. 2020

Sensors

View full text Add to dashboard Cite

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.

show abstract

Undiagnosed depression: A community diagnosis

Cited by 88 publications

References 21 publications

Healthcare use and costs in adult cancer patients with anxiety and depression

Healthcare use and costs in adult cancer patients with anxiety and depression

The prevalence and associated factors of depression among medical students of Saudi Arabia: A systematic review

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

Contact Info

Product

Resources

About