Identifying potentially depressed older Chinese adults in the community: Hong Kong's Elderly Health Service cohort.

Background: Depression is common at older ages, but is under-recognized due to stigma, misperception, and under-diagnosis; its manifestations may vary by setting. Identifying older adults at risk of depression in the community is urgently needed for timely support and early interventions. We assessed the performance of an existing risk prediction model developed in a European setting (i.e., Depression Risk Assessment Tool (DRAT-up)), and developed a new model (i.e., EHS-Depress model) to predict 2-year risk of the onset of later life depressive symptoms in older Chinese adults.

Methods: Among 185,538 participants aged ≥65 years from Hong Kong's Elderly Health Service (EHS) cohort, 174,806 without depressive symptoms at baseline were included. Two-thirds were randomly sampled for recalibration and new model development using Cox proportional-hazards models with backward elimination. Overall predictive performance, discrimination, and calibration were assessed using the remaining.

Results: The original DRAT-up model underestimated the risk of developing depressive symptoms in older Chinese adults; recalibrating it improved calibration. The new EHS-Depress model had better discrimination (Harrell's C statistic 0.68 and D statistic 2.74) and similarly good calibration (calibration slope 1.18 and intercept -0.002) probably due to the inclusion of more specific health measures, socio-demographics, lifestyle factors, and regular social contact as predictors.

Limitations: Predictors of depressive symptoms included in our models depend on the data availability.

Conclusions: The EHS-Depress model predicted 2-year risk of developing depressive symptoms better than the original and recalibrated DRAT-up models. The setting-specific risk prediction model is more applicable to older Chinese adults in primary care settings.