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



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


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


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).


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.





Published Version (Please cite this version)


Publication Info

Tan, Jie, Chenxinan Ma, Chonglin Zhu, Yin Wang, Xiaoshuang Zou, Han Li, Jiarun Li, Yanxuan He, et al. (2022). Prediction models for depression risk among older adults: systematic review and critical appraisal. Ageing research reviews. p. 101803. 10.1016/j.arr.2022.101803 Retrieved from

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Chenxinan Ma


Chenxinan Ma is a second-year master's student studying global health at Duke Kunshan University and Duke University. Prior to studying at Duke, he completed a five-year medical education undergraduate program in China with a focus on preventive medicine at the Medical College of Soochow University, China. Chenxinan had a background in life science and population health sciences and his research internests are healthy aging and the management of chronic diseases.


Chenkai Wu

Assistant Professor of Global Health at Duke Kunshan University

Dr. Chenkai Wu is an Assistant Professor and Director of Graduate Studies in Global Health at DKU. Before joining DKU, he was a faculty in the Department of Epidemiology and Community Health at New York Medical College. His research interests include (1) measurement, epidemiology, and clinical implications of frailty, (2) the interplay of genetic predisposition with the social and natural environment in shaping healthy aging, and (3) implications of machine learning for improving clinical practice. Since 2016, he has published over 80 peer-reviewed papers in epidemiology and gerontology/geriatrics. 

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