Measuring suicide risk in the elderly with non-institutionalized depression: an integrative review

Silva, Sabrina Piccinelli Zanchettin; Bocchi, Sílvia Cristina Mangini

doi:10.1590/0034-7167-2020-0106

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…In several studies, depression is consistently confirmed as the most important factor in suicidal ideation in the elderly, and the more severe the depressive symptoms, the higher the suicidal ideation. 14,15 Representative examples of these factors include age, gender, economic status, social support, smoking, sleep, chronic diseases, life satisfaction, and psychosocial well-being. 16 However, predicting whether a particular person will develop suicidal ideation even if the risk factors above are identified is difficult.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Suicidal Ideation Predictive Model for Community-Dwelling Elderly Aged >55 Years

Lee¹,

Geem²,

Cho

2022

NDT

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Suicide is an important health and social concern worldwide. Both suicidal ideation and suicide rates are higher in the elderly population than in other age groups; thus, more careful attention and targeted interventions are required. Therefore, we have developed a model to predict suicidal ideation in the community-dwelling elderly aged of >55 years. Patients and Methods: A random forest algorithm was applied to those who participated in the Korea Welfare Panel. We used a total of 26 variables as potential predictors. To resolve the imbalance in the dataset resulting from the low frequency of suicidal ideation, training was performed by applying the synthetic minority oversampling technique. The performance index was calculated by applying the predictive model to the test set, which was not included in the training process. Results: A total of 6410 elderly Korean aged of >55 (mean, 71.48; standard deviation, 9.56) years were included in the analysis, of which 2.7% had suicidal ideation. The results for predicting suicidal ideation using the 26 chosen variables showed an AUC of 0.879, accuracy of 0.871, sensitivity of 0.750, and specificity of 0.874. The most significant variable in the predictive model was the severity of depression, followed by life satisfaction and self-esteem factors. Basic demographic variables such as age and gender demonstrated a relatively small effect. Conclusion: Machine learning can be used to create algorithms for predicting suicidal ideation in community-dwelling elderly. However, there are limitations to predicting future suicidal ideation. A predictive model that includes both biological and cognitive indicators should be created in the future.

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Section: Introductionmentioning

confidence: 99%

The Development of a Suicidal Ideation Predictive Model for Community-Dwelling Elderly Aged >55 Years

Lee¹,

Geem²,

Cho

2022

NDT

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Development and Validation of the Elderly Suicide Screening Scale

Silva,

Phinney,

Rebustini

et al. 2024

TONURSJ

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Background This study stems from the need to develop and validate a tool for interprofessional teams in Primary health care for screening for suicide among the elderly in the community. Objective To evaluate the psychometric properties of the Non-Institutionalized Elderly Suicide Screening Scale (Escala de Rastreamento de Renúncia à Vida no Idoso não institucionalizado - ERRVI). Methods This is a psychometric study focused on evaluating evidence of content validity and internal structure. The ERRVI construction process followed the guidelines for scale development based on theoretical models derived from qualitative research, which followed the methodological and theoretical approaches of grounded theory and symbolic interaction. The instrument underwent content validity evidence analysis carried out by a panel of experts, considering the Content Validity Index (CVR). After the pre-test, the internal structure was evaluated using Exploratory Factor Analysis (AFE). Reliability was assessed using three indicators: Cronbach's alpha, Omega, and ORION scores. It involved 300 elderly individuals from two municipalities in the central-western region of Sao Paulo, Brazil, and was conducted from September to November 2020. Results The final version comprised 31 items, categorized into four dimensions: autonomy, self-governance, self-care, and life satisfaction. The total explained variance was 50.82%, with factor loadings ranging from 0.31 to 0.86. The reliability indicators revealed a Cronbach’s alpha of 0.88, McDonald's Omega of 0.95, and scores for the dimensions assessed by the Overall Reliability of Fully-Informative Prior Oblique N-EAP (ORION) ranging from 0.78 to 0.84. Conclusion The ERRVI showed evidence of content validity and internal structure in accordance with the recommended psychometric parameters. This is an innovative tool with the social value, given the scarcity of tools that screen the risk of suicide among the elderly in the community. It is the first of its kind in Brazil and the third globally.

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Measuring suicide risk in the elderly with non-institutionalized depression: an integrative review

Cited by 2 publications

References 33 publications

The Development of a Suicidal Ideation Predictive Model for Community-Dwelling Elderly Aged >55 Years

The Development of a Suicidal Ideation Predictive Model for Community-Dwelling Elderly Aged >55 Years

Development and Validation of the Elderly Suicide Screening Scale

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