Choosing an Appropriate Depression Assessment Tool for Chinese Older Adults: A Review of 11 Instruments

Wu, Chiung-Man; Kelley, Lisa Skemp

doi:10.3928/00989134-20070801-04

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…The 20‐item Center for Epidemiological Studies Depression Scale (CES‐D) (Radloff, ), which has been extensively validated in the Chinese older adults (Wu and Kelley, ; Cheng and Chan, ), was used to measure depressive symptoms in the current study. Four subscales of the CES‐D: Depressed Affect , Somatic Symptoms , Lack of Well‐Being , and Interpersonal Problems (Nguyen and Zonderman, ) were examined.…”

Section: Methodsmentioning

confidence: 99%

Prevalence and correlates of depressive symptoms in Chinese older adults: a population‐based study

Cuijpers

et al. 2011

Int J Geriat Psychiatry

156

109

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The present study found a higher prevalence of depression in the Chinese older population compared with those reported two decades ago. Family support and health status were the most significant protective factors for depression in Chinese older adults. Traditional Chinese culture, which values family significantly and contributed to the previously reported lower prevalence rate, changed dramatically, which may explain the current higher prevalence. In addition to the deterioration of family support, the worsening of health status is another significant factor.

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Section: Methodsmentioning

confidence: 99%

Prevalence and correlates of depressive symptoms in Chinese older adults: a population‐based study

Cuijpers

et al. 2011

Int J Geriat Psychiatry

156

109

View full text Add to dashboard Cite

show abstract

“…The following factors were considered to be covariates, which were obtained from comprehensive questionnaires: (a) demographic characteristics including age, gender, education, marital status, living condition, and monthly income; (b) lifestyle factors such as tobacco and alcohol use, and exercise habits; (c) clinical indicators including physician-diagnosed medical conditions, number of regular medications, hospital admissions and history of falls in the past one year; (d) physical function assessment on basic/instrumental activities of daily living (ADL/IADL) evaluated by the Katz scale [ 12 ] and Lawton instrumental activities of daily living scale [ 13 ]; balance capacity evaluated with the timed up and go test [ 14 ]; (e) sensory assessments including vision, hearing, and continence, answering “normal, mild impaired, impaired” on a three-point scale; (f) a psychophysiological assessment in which depression, anxiety, and insomnia were assessed by the geriatric depression scale-4 (GDS-4) [ 15 ], generalized anxiety disorder scale-7 (GAD-7) [ 16 ], and Athens insomnia scale (AIS) [ 17 ], respectively; chronic pain assessed with the numerical rating scale (NRS) [ 18 ]; (g) cognitive function evaluated using the minicog scale [ 19 ]; (h) dietary diversity score focusing on nine types of food including grains, vegetables, fruits, meat, seafood, eggs, dairy products, beans, and tea. The dietary diversity score was calculated based on the sum of the frequency of each type of food.…”

Section: Measurementsmentioning

confidence: 99%

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

et al. 2022

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Background We have witnessed frailty, which characterized by a decline in physiological reserves, become a major public health issue in older adults. Understanding the influential factors associated with frailty may help prevent or if possible reverse frailty. The present study aimed to investigate factors associated with frailty status and frailty transition in a community-dwelling older population. Methods A prospective cohort study on community-dwelling subjects aged ≥ 60 years was conducted, which was registered beforehand (ChiCTR 2,000,032,949). Participants who had completed two visits during 2020–2021 were included. Frailty status was evaluated using the Fried frailty phenotype. The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. Bayesian network analysis with the max-min hill-climbing (MMHC) algorithm was used to identify factors related to frailty status and frailty transition. Results Of 1,981 subjects at baseline, 1,040 (52.5%) and 165 (8.33%) were classified as prefrailty and frailty. After one year, improved, stable, and worsening frailty status was observed in 460 (35.6%), 526 (40.7%), and 306 (23.7%) subjects, respectively. Based on the variables screened by LASSO regression, the Bayesian network structure suggested that age, nutritional status, instrumental activities of daily living (IADL), balance capacity, and social support were directly related to frailty status. The probability of developing frailty is 14.4% in an individual aged ≥ 71 years, which increases to 20.2% and 53.2% if the individual has balance impairment alone, or combined with IADL disability and malnutrition. At a longitudinal level, ADL/IADL decline was a direct predictor of worsening in frailty state, which further increased the risk of hospitalization. Low high-density lipoprotein cholesterol (HDL-C) and diastolic blood pressure (DBP) levels were related to malnutrition, and further had impacts on ADL/IADL decline, and ultimately led to the worsening of the frailty state. Knowing the status of any one or more of these factors can be used to infer the risk of frailty based on conditional probabilities. Conclusion Older age, malnutrition, IADL disability, and balance impairment are important factors for identifying frailty. Malnutrition and ADL/IADL decline further predict worsening of the frailty state.

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“…Until now there has been no pooled resource from which researchers or implementers can identify the best-performing tools for their needs. Several smaller reviews have been conducted of screening tool validation studies for particular disorders in particular settings or populations–such as perinatal depression in Africa [ 18 ], depression in Spanish-speaking populations [ 19 ], and depression in Chinese older adults [ 20 ]–but to date there has been no review of screening tools for all CMDs across all LMIC populations. The project presented here updates and builds upon a 2012 systematic review of depression (as opposed to all CMD) screening tools validated in LMIC [ 21 ], which identified 19 studies.…”

Section: Introductionmentioning

confidence: 99%

Validated Screening Tools for Common Mental Disorders in Low and Middle Income Countries: A Systematic Review

2016

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BackgroundA wide range of screening tools are available to detect common mental disorders (CMDs), but few have been specifically developed for populations in low and middle income countries (LMIC). Cross-cultural application of a screening tool requires that its validity be assessed against a gold standard diagnostic interview. Validation studies of brief CMD screening tools have been conducted in several LMIC, but until now there has been no review of screening tools for all CMDs across all LMIC populations.MethodsA systematic review with broad inclusion criteria was conducted, producing a comprehensive summary of brief CMD screening tools validated for use in LMIC populations. For each validation, the diagnostic odds ratio (DOR) was calculated as an easily comparable measure of screening tool validity. Average DOR results weighted by sample size were calculated for each screening tool, enabling us to make broad recommendations about best performing screening tools.Results153 studies fulfilled our inclusion criteria. Because many studies validated two or more screening tools, this corresponded to 273 separate validations against gold standard diagnostic criteria. We found that the validity of every screening tool tested in multiple settings and populations varied between studies, highlighting the importance of local validation. Many of the best performing tools were purposely developed for a specific population; however, as these tools have only been validated in one study, it is not possible to draw broader conclusions about their applicability in other contexts.ConclusionsOf the tools that have been validated in multiple settings, the authors broadly recommend using the SRQ-20 to screen for general CMDs, the GHQ-12 for CMDs in populations with physical illness, the HADS-D for depressive disorders, the PHQ-9 for depressive disorders in populations with good literacy levels, the EPDS for perinatal depressive disorders, and the HADS-A for anxiety disorders. We recommend that, wherever possible, a chosen screening tool should be validated against a gold standard diagnostic assessment in the specific context in which it will be employed.

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Choosing an Appropriate Depression Assessment Tool for Chinese Older Adults: A Review of 11 Instruments

Cited by 9 publications

References 30 publications

Prevalence and correlates of depressive symptoms in Chinese older adults: a population‐based study

Prevalence and correlates of depressive symptoms in Chinese older adults: a population‐based study

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

Validated Screening Tools for Common Mental Disorders in Low and Middle Income Countries: A Systematic Review

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