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Updated: May 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Determinants for undetected dementia and late-life depression
Ruoling Chen1, Zhi Hu, Ruo-Li Chen
1Division of Health and Social Care Research, King's College London, 42 Weston St., London, UK. ruoling.chen@kcl.ac.uk
Background:
Determinants for undetected dementia and late-life depression have been not well studied.
Aims:
To investigate risk factors for undetected dementia and depression in older communities.
Method:
Using the method of the 10/66 algorithm, we interviewed a random sample of 7072 participants aged ≥60 years in six provinces of China during 2007-2011. We documented doctor-diagnosed dementia and depression in the interview. Using the validated 10/66 algorithm we diagnosed dementia (n = 359) and depression (n = 328).
Results:
We found that 93.1% of dementia and 92.5% of depression was undetected. Both undetected dementia and depression were significantly associated with low levels of education and occupation, and living in a rural area. The risk of undetected dementia was also associated with 'help available when needed', and inversely, with a family history of mental illness and having functional impairment. Undetected depression was significantly related to female gender, low income, having more children and inversely with having heart disease.
Conclusions:
Older adults in China have high levels of undetected dementia and depression. General socioeconomic improvement, associated with mental health education, targeting high-risk populations are likely to increase detection of dementia and depression in older adults, providing a backdrop for culturally acceptable service development.
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