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

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.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data

BACKGROUND

Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.

METHODS

Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.

RESULTS

We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.

CONCLUSIONS

Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Assessment of the relation between depression, frailty, nutrition and quality of life among older adults: findings from a cross-sectional study in Greece

BACKGROUND

Quality of life (QOL) among older adults is a crucial aspect of geriatric care, particularly in the context of global demographic shifts toward ageing societies. Understanding the determinants of QOL in older adults is essential for developing effective interventions to promote well-being in this population.

METHODS

This cross-sectional study conducted in Greece aimed to investigate the complex relationship between depression, frailty, nutritional status, and QOL on 90 older adults (aged ≤65). Assessment instruments including the World Health Organization Quality of Life (WHOQoL)-BREF questionnaire, Geriatric Depression Scale (GDS), Clinical Frailty Scale (CFS), and Mini Nutritional Assessment (MNA) were utilised to evaluate various dimensions of QOL, depressive symptoms, frailty, and nutritional status.

RESULTS

The study revealed significant negative correlations between depression and frailty with all domains of QOL (P < 0.05), indicating that higher levels of depressive symptoms and frailty were associated with lower QOL across physical, psychological, social, and environmental dimensions. Conversely, positive correlations were found between nutritional status and all QOL domains (P < 0.05), suggesting that better nutritional status was linked to higher QOL. Multivariate logistic regression analysis further demonstrated associations between nutritional status and participant characteristics, with females being more likely to be malnourished (odds ratio (OR) = 6.56, P = 0.013), while better health status (OR = 0.34, P = 0.486) and marital status (OR = 0.02, P = 0.019) were protective against malnutrition.

CONCLUSION

These findings underscore the interconnectedness of depression, frailty, and nutritional status in shaping QOL among individuals. Holistic interventions targeting mental health, physical vulnerability, and nutritional well-being are essential for promoting overall well-being and functional outcomes in this population.

Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases

Introduction

It remains unclear whether depressive symptoms are associated with increased all-cause mortality and to what extent depressive symptoms are associated with chronic disease and all-cause mortality. The study aims to explore the relationship between depressive symptoms and all-cause mortality, and how depressive symptoms may, in turn, affect all-cause mortality among Chinese middle-aged and older people through chronic diseases.

Methods

Data were collected from the China Health and Retirement Longitudinal Study (CHARLS). This cohort study involved 13,855 individuals from Wave 1 (2011) to Wave 6 (2020) of the CHARLS, which is a nationally representative survey that collects information from Chinese residents ages 45 and older to explore intrinsic mechanisms between depressive symptoms and all-cause mortality. The Center for Epidemiological Studies Depression Scale (CES-D-10) was validated through the CHARLS. Covariates included socioeconomic variables, living habits, and self-reported history of chronic diseases. Kaplan-Meier curves depicted mortality rates by depressive symptom levels, with Cox proportional hazards regression models estimating the hazard ratios (HRs) of all-cause mortality.

Results

Out of the total 13,855 participants included, the median (Q1, Q3) age was 58.00 (51.00, 63.00) years. Adjusted for all covariates, middle-aged and older adults with depressive symptoms had a higher all-cause mortality rate (HR = 1.20 [95% CI, 1.09-1.33]). An increased rate was observed for 55-64 years old (HR = 1.23 [95% CI, 1.03-1.47]) and more than 65 years old (HR = 1.32 [95% CI, 1.18-1.49]), agricultural Hukou (HR = 1.44, [95% CI, 1.30-1.59]), and nonagricultural workload (HR = 1.81 [95% CI, 1.61-2.03]). Depressive symptoms increased the risks of all-cause mortality among patients with hypertension (HR = 1.19 [95% CI, 1.00-1.40]), diabetes (HR = 1.41[95% CI, 1.02-1.95]), and arthritis (HR = 1.29 [95% CI, 1.09-1.51]).

Conclusion

Depressive symptoms raise all-cause mortality risk, particularly in those aged 55 and above, rural household registration (agricultural Hukou), nonagricultural workers, and middle-aged and older people with hypertension, diabetes, and arthritis. Our findings through the longitudinal data collected in this study offer valuable insights for interventions targeting depression, such as early detection, integrated chronic disease care management, and healthy lifestyles; and community support for depressive symptoms may help to reduce mortality in middle-aged and older people.

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

BACKGROUND

The prevalence of depression is increasing in the elderly population, and growing evidence suggests that malnutrition impacts mental health. Despites, research on the factors that predict depression is limited.

METHODS

We included 2946 elderly individuals from National Health and Nutrition Examination Survey (NHANES) spanning the years 2011 through 2014. Depressive symptoms were assessed using the PHQ-9 scale. Multinomial logistic regression was performed to evaluate the independent association between Geriatric Nutritional Risk Index (GNRI) and depression prevalence and scores. Subgroup analysis was conducted to explore potential factors influencing the negative correlation between GNRI and depression. Restricted cubic spline graph was employed to examine the presence of a non-linear relationship between GNRI and depression.

RESULTS

The depression group had a significantly lower GNRI than the non-depression group, and multivariate logistic regression showed that GNRI was a significant predictor of depression (P < 0.001). Subgroup analysis revealed that certain demographic characteristics were associated with a lower incidence of depression in individuals affected by GNRIs. These characteristics included being female (P < 0.0001), non-Hispanic black (P = 0.0003), having a moderate BMI (P = 0.0005), having a college or associates (AA) degree (P = 0.0003), being married (P = 0.0001), having a PIR between 1.50 and 3.49 (P = 0.0002), being a former smoker (P = 0.0002), and having no history of cardiovascular disease (P < 0.0001), hypertension (P < 0.0001), and diabetes (P = 0.0027). Additionally, a non-linear negative correlation (non-linear P < 0.01) was found between GNRI and depression prevalence, with a threshold identified at GNRI = 104.17814.

CONCLUSION

The GNRI demonstrates efficacy as a reliable indicator for forecasting depression in the elderly population. It exhibits a negative nonlinear correlation with the prevalence of depression among geriatric individuals.

New Horizons in artificial intelligence in the healthcare of older people

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Comorbid Depressive and Anxiety Symptomatology in Older Adults: The Role of Aging Self-Stereotypes, Loneliness, and Feelings of Guilt Associated with Self-Perception as a Burden

The main objective of this study was to analyze the differences between older adults' symptom profiles (subclinical, anxiety, depressive, and comorbid) in negative aging self-stereotypes, loneliness, and feelings of guilt associated with self-perception as a burden. Participants were 310 community-dwelling people aged 60 years and over. The sample was grouped into four symptom profiles of older adults: anxiety, depressive, comorbid anxiety-depression, and subclinical symptoms. We carried out multinomial logistic regression analyses to analyze the role of assessed variables in the explanation of the four symptom profiles. Older adults who reported a comorbid symptomatology presented higher negative aging self-stereotypes and feelings of loneliness than the other three profiles. Compared with the subclinical profile, older adults who reported clinical symptomatology (anxiety, depressive, and comorbid profile) presented higher feelings of guilt associated with self-perception as a burden. The findings of this study suggest potential associations that may contribute to understanding and treating comorbid anxiety and depressive symptoms in older adults.