The many faces of depression in primary care

Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques

(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634-0.767, and the corresponding value range for hypomanic symptoms was 0.770-0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants' mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

PURPOSE

Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices.

PATIENTS AND METHODS

We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words.

RESULTS

Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented.

CONCLUSION

Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.

CHALLENGES FOR PRIMARY HEALTHCARE PROFESSIONALS IN CARING FOR WOMEN WITH POSTPARTUM DEPRESSION

ABSTRACT The aim of this study was to explore the challenges for primary healthcare professionals in caring for women with postpartum depression, in order to learn which tools are used for the identification of these women, as well as ways of working to restore their health. This qualitative descriptive study was conducted in healthcare units in Campina Grande, Paraíba. Data were collected by field observation and a semi-structured interview, which was administered to 16 healthcare professionals. The results describe healthcare professionals' difficulties in identifying and treating women with postpartum depression, because of the limited focus on physiological developmental aspects of gestation and postpartum. The professionals reported limited knowledge on how to assess emotional changes related to the postpartum period. Primary healthcare professionals need to be educated to identify and treat women with postpartum depression, and practice guidelines and protocols must be established to guide evidence-based practice.

Participation in nighttime activities in the genesis of depression in public school teachers from the State of Pernambuco, Brazil

Teachers often undertake nighttime work involving exam corrections, projects and devising lesson plans in their homes. Many present excessive daytime sleepiness (EDS) and depression.

Sintomas depressivos em idosos: análise dos itens da Escala de Depressão Geriátrica

OBJETIVOS: Verificar a estrutura fatorial da Escala de Depressão Geriátrica de 15 itens em uma amostra de idosos assistidos pela Estratégia Saúde da Família, descrever o perfil social e analisar as respostas aos itens da Escala de Depressão Geriátrica. MÉTODOS: Estudo de delineamento transversal com 503 idosos assistidos pela Estratégia Saúde da Família, em Dourados, MS. Para analisar as respostas da EDG 15, utilizou-se o teste de Qui-quadrado de Mantel-Haenzsel (p <0,05). A análise fatorial, a consistência interna e a generalidade dos resultados para a população foram realizadas. RESULTADOS: Dos 503 idosos pesquisados 69,0% eram mulheres, 53,1% não letrados, 53,7% tinham 70 anos ou mais e 34,4% apresentavam depressão. A análise fatorial identificou quatro fatores (apatia, desesperança, desmotivação e isolamento). A estrutura da EDG 15 não se mostrou apropriada para a generalização de resultados. CONCLUSÃO: Dentre os idosos com depressão, predominaram a apatia e o isolamento. Cabe às equipes de saúde promover atividades físicas, recreativas e culturais para minimizar esse quadro. Novas pesquisas serão necessárias, sobretudo para análise da estrutura fatorial.