Development of a Nomogram for Predicting Depression in the Elderly Using Patient Health Questionnaire-9 among a Nationwide Sample of Korean Elderly

Prediction models for depression risk among older adults: systematic review and critical appraisal

OBJECTIVE

To provide an overview of prediction models for the risk of major depressive disorder (MDD) among older adults.

METHODS

We conducted a systematic review combined with a meta-analysis and critical appraisal of published studies on existing geriatric depression risk models.

RESULTS

The systematic search screened 23,378 titles and abstracts; 14 studies including 20 prediction models were included. A total of 16 predictors were selected in the final model at least twice. Age, physical health, and cognitive function were the most common predictors. Only one model was externally validated, two models were presented with a complete equation, and five models examined the calibration. We found substantial heterogeneity in predictor and outcome definitions across models; important methodological information was often missing. All models were rated at high or unclear risk of bias, primarily due to methodological limitations. The pooled C-statistics of 12 prediction models was 0.83 (95%CI=0.77-0.89).

CONCLUSION

The usefulness of all models remains unclear due to several methodological limitations. Future studies should focus on methodological quality and external validation of depression risk prediction models.

The effects of changes in daily life due to the COVID-19 pandemic on the depressive symptoms among community-dwelling older adults in Korea

This study aims to identify the effects of daily life changes due to the coronavirus disease 2019 pandemic on the depressive symptoms among community-dwelling older adults. This cross-sectional and secondary data analysis study collected and analysed the data of 72 335 older adults aged older than 65 who participated in the 2020 Korean Community Health Survey. Changes in daily life due to the coronavirus disease 2019 pandemic were measured by changes in physical activity, sleep duration, consumption of instant foods or soda drinks, alcohol consumption, smoking, and social contact compared to before the pandemic as perceived by the participants. The Patient Health Questionnaire-9 was used to assess depressive symptoms, and multiple logistic regression analysis was conducted to explore the relationship between the two. After adjusting for socio-demographic and health-related factors, changes in daily life due to the coronavirus disease 2019 pandemic that affected depressive symptoms in older adults living in the community were observed. This study confirmed that changes in daily life due to the coronavirus disease 2019 pandemic negatively affected the mental health of older adults. Thus, there is a need to improve social support and care systems, by including non-face-to-face programmes using remote communication technology. It will allow older adults to maintain their daily lives and mental health during the COVID-19 pandemic.

Predicting High-Risk Groups for COVID-19 Anxiety Using AdaBoost and Nomogram: Findings from Nationwide Survey in South Korea

People living in local communities have become more worried about infection due to the extended pandemic situation and the global resurgence of COVID-19. In this study, the author (1) selected features to be included in the nomogram using AdaBoost, which had an advantage in increasing the classification accuracy of single learners and (2) developed a nomogram for predicting high-risk groups of coronavirus anxiety while considering both prediction performance and interpretability based on this. Among 210,606 adults (95,287 males and 115,319 females) in South Korea, 39,768 people (18.9%) experienced anxiety due to COVID-19. The AdaBoost model confirmed that education level, awareness of neighbors/colleagues’ COVID-19 response, age, gender, and subjective stress were five key variables with high weight in predicting anxiety induced by COVID-19 for adults living in South Korean communities. The developed logistic regression nomogram predicted that the risk of anxiety due to COVID-19 would be 63% for a female older adult who felt a lot of subjective stress, did not attend a middle school, was 70.6 years old, and thought that neighbors and colleagues responded to COVID-19 appropriately (classification accuracy = 0.812, precision = 0.761, recall = 0.812, AUC = 0.688, and F-1 score = 0.740). Prospective or retrospective cohort studies are required to causally identify the characteristics of anxiety disorders targeting high-risk COVID-19 anxiety groups identified in this study.

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.