Depression Assessment
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2021 ◽  
Vol 12 (04) ◽  
pp. 757-767
Selva Muthu Kumaran Sathappan ◽  
Young Seok Jeon ◽  
Trung Kien Dang ◽  
Su Chi Lim ◽  
Yi-Ming Shao ◽  

Abstract Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

Chifundo Colleta Zimba ◽  
Christopher F. Akiba ◽  
Maureen Matewere ◽  
Annie Thom ◽  
Michael Udedi ◽  

Abstract Background Integration of depression services into infectious disease care is feasible, acceptable, and effective in sub-Saharan African settings. However, while the region shifts focus to include chronic diseases, additional information is required to integrate depression services into chronic disease settings. We assessed service providers’ views on the concept of integrating depression care into non-communicable diseases’ (NCD) clinics in Malawi. The aim of this analysis was to better understand barriers, facilitators, and solutions to integrating depression into NCD services. Methods Between June and August 2018, we conducted nineteen in-depth interviews with providers. Providers were recruited from 10 public hospitals located within the central region of Malawi (i.e., 2 per clinic, with the exception of one clinic where only one provider was interviewed because of scheduling challenges). Using a semi structured interview guide, we asked participants questions related to their understanding of depression and its management at their clinic. We used thematic analysis allowing for both inductive and deductive approach. Themes that emerged related to facilitators, barriers and suggested solutions to integrate depression assessment and care into NCD clinics. We used CFIR constructs to categorize the facilitators and barriers. Results Almost all providers knew what depression is and its associated signs and symptoms. Almost all facilities had an NCD-dedicated room and reported that integrating depression into NCD care was feasible. Facilitators of service integration included readiness to integrate services by the NCD providers, availability of antidepressants at the clinic. Barriers to service integration included limited knowledge and lack of training regarding depression care, inadequacy of both human and material resources, high workload experienced by the providers and lack of physical space for some depression services especially counseling. Suggested solutions were training of NCD staff on depression assessment and care, engaging hospital leaders to create an NCD and depression care integration policy, integrating depression information into existing documents, increasing staff, and reorganizing clinic flow. Conclusion Findings of this study suggest a need for innovative implementation science solutions such as reorganizing clinic flow to increase the quality and duration of the patient-provider interaction, as well as ongoing trainings and supervisions to increase clinical knowledge. Trial registration This study reports finding of part of the formative phase of “The Sub-Saharan Africa Regional Partnership (SHARP) for Mental Health Capacity Building—A Clinic-Randomized Trial of Strategies to Integrate Depression Care in Malawi” registered as NCT03711786

2021 ◽  
Maxime D. Armstrong ◽  
Diego Maupomé ◽  
Marie-Jean Meurs

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250492
Isabelle Jalenques ◽  
Chloé Rachez ◽  
Urbain Tauveron Jalenques ◽  
Silvia Alina Nechifor ◽  
Lucile Morel ◽  

BackgroundMajor depressive disorder with psychotic features (MDDPsy), compared to nonpsychotic MDD, involves an increased risk of suicide and failure to achieve treatment response. Symptom scales can be useful to assess patients with MDDPsy. The aim of the present study was to validate French versions of the Delusion Assessment Scale (DAS) and Psychotic Depression Assessment Scale (PDAS).MethodsOne hundred patients were included. The scales were filled out by psychiatrists. Data from participants who accepted a second interview were used for inter-judge reliability. The scalability and psychometric properties of both scales were assessed.ResultsData from 94 patients were used. Owing to low score variability between patients, the predefined threshold for scalability (≥0.40) was not reached for both scales. Factorial analysis of the DAS identified five factors, different from those of the original version. Five factors were also identified in the PDAS, of which two comprised items from the HDRS and the other three items from the BPRS. Floor and ceiling effects were observed in both scales, due in part to the construction of certain subscales. Unlike the PDAS, the DAS had good internal consistency. Multiple correlations were observed between the DAS dimensions but none between those of the PDAS. Both scales showed good inter-judge reliability. Convergent validity analyses showed correlations with HDRS, BPRS and CGI.LimitationsInter-judge reliability was calculated from a relatively small number of volunteers.ConclusionsThe good psychometric properties of the French versions of the DAS and PDAS could help in assessing MDDPsy, in particular its psychotic features, and hence improve response to treatment and prognosis.

2021 ◽  
Sarah Cassidy ◽  
Louise Bradley ◽  
Heather Cogger-Ward ◽  
Jessica Graham ◽  
Jacqui Rodgers

Abstract BackgroundDepression can be hard to accurately identify in autistic adults due to overlapping characteristics of autism and depression, and depression tools developed for the general population not including unique signs of depression experienced by autistic people. MethodsThree focus groups and 15 cognitive interviews with autistic adults identified response difficulties and missing autism specific items in a widely used depression assessment tool developed for the general population (PHQ-9). Feedback informed new items in the Autistic Depression Assessment Tool (ADAT-A). A further 9 cognitive interviews and two large online surveys with autistic adults refined the ADAT-A items. Subsequently, 236 autistic adults (87 male, 113 female, 33 non-binary, 18-61 years) completed the ADAT-A online, alongside self-report measures of camouflaging autistic traits (CAT-Q), Intolerance of Uncertainty (IUS-12), Suicidality (SBQ-ASC), Defeat and Entrapment (DES). Analyses explored structural validity, internal consistency, convergent and divergent validity of the ADAT-A in a community sample of autistic adults.ResultsExploratory factor analysis of the ADAT-A showed evidence in support of a three-factor solution, capturing cognitive-affective and somatic depression symptoms, and autistic specific depression symptoms. Internal consistency of each subscale and total scores were excellent (.87 - .94). The ADAT-A was significantly correlated with related constructs including self-reported suicidality, defeat and entrapment (rs>.49). The ADAT-A total score and subscales were significantly more strongly correlated with hypothesised proximally related compared to distally related constructs.LimitationsThe samples involved in the development and validation of the ADAT-A were largely female, and largely diagnosed as autistic in adulthood, which is not representative of the wider autistic population. The ADAT-A has initially been developed and validated for research purposes, and has not been validated for use in clinical contexts to screen for possible depression diagnosis.ConclusionsThe ADAT-A is a self-report autism specific depression assessment tool, developed and validated with and for autistic adults, without co-occurring intellectual disability. There is promising initial evidence in support of the measurement properties of this tool for use in research. Future research must explore whether the ADAT-A is useful in better identifying depression in autistic people in clinical settings, compared to other tools developed for the general population.

Quan Gao ◽  
Hye Eun Lee

This study examines how the framing and interactivity of messages influence the intentions of individuals to take a depression assessment. An experiment with a 2 (message framing: gain-versus loss-) × 2 (interactivity: low versus high) between-subject design was conducted among 269 Chinese participants (M = 30.70, SD = 7.34). The results showed that those reading loss-framed messages had a higher intention to take a depression assessment compared to those reading gain-framed messages. Secondly, those reading messages delivered with high interactivity had a higher intention to take a depression assessment than those reading messages delivered with low interactivity. Further, the interaction effect of framed messages and their varying degrees of interactivity was found to influence the intentions of individuals to take a depression assessment as well. Specifically, participants who read the loss-framed message reported stronger intention in the high interactivity group. In contrast, there was no significant difference between the effectiveness of loss-framed and gain-framed messages in promoting the intention to take a depression assessment in the low interactivity condition.

2020 ◽  
Kennedy Opoku Asare ◽  
Yannik Terhorst ◽  
Julio Vega ◽  
Ella Peltonen ◽  
Eemil Lagerspetz ◽  

BACKGROUND Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment by moment datasets to quantify human behaviours that have the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. OBJECTIVE The objective of this study is to investigate the feasibility of predicting depression with human behaviours quantified from a smartphone datasets, and to identify behaviours that can influence depression. METHODS Smartphone datasets and self-reported eight-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average 22.1 days (SD =17.90, min= 8, max=86). We quantified 22 regularity, entropy, and standard deviation behavioural markers from the smartphone usage data. We explore the linear relationship between the behavioural features and depression using correlation and bivariate linear mixed models (LMM). We leverage 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the Permutation Importance method, we find influential behavioural markers in predicting depression. RESULTS Of the 629 participants from at least 56 countries, 10.96% were females, 86.80% males, 2.22% non-binary. For participants’ age distribution; 11.61% were between 18–24 years, 32.43% 25–34, 24.80% 35–44, 26.39% 45–64 and 4.77% were 65 years and over. Of the 1374 PHQ-8 assessments 83.19% were non-depressed, 16.81% were depressed, based on PHQ-8 cut off. Significant positive Pearson’s correlation was found between screen status normalised entropy and depression (r=0.14, P<.001). LMM demonstrates intra-class correlation of 0.7584 and significant positive association between screen status normalised entropy and depression (beta=.48, P=0.03). The best ML algorithms obtained precision (85.55%–92.50%), recall (92.19%–94.38%), F1 (88.73%–93.41%), area under the curve receiver operating characteristic AUC (94.68%–98.83%), Cohen’s kappa (86.61%–92.21%), and accuracy (96.44%–97.97%). Including age group and gender as predictors improved the ML performances. Screen and Internet connectivity features were the most influential in predicting depression. CONCLUSIONS Our findings demonstrate that behavioural markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Traditional assessment of depression can be augmented with behavioural markers from smartphones for depression diagnosis and monitoring.

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