Identification of Central Symptoms in Depression of Older Adults With the Geriatric Depression Scale Using Network Analysis and Item Response Theory
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Objective This study aimed to identify the central symptoms of late-life depression using network analysis and the item response theory.Methods A total of 3,472 older adults were enrolled and the Geriatric Depression Scale-15 (GDS-15) was used to evaluate the depressive symptoms. To identify the central symptoms and the network structures among the individual symptoms, the analyses of symptom network structures and item response theory were performed.Results Among items on the GDS-15, “Happy,” “Hopeless,” “Empty,” “Bored,” “Worthless,” and “Good spirits” showed significantly higher strength centrality than the other symptoms. Among all the edges, the edge between “Empty” and “Bored” was the strongest; however, these two symptoms were not connected strongly to other symptoms. In the analysis of item response theory, “Empty,” “Bored,” “Hopeless,” “Worthless,” “Happy,” “Helpless,” and “Satisfied” presented a very high value on the discrimination parameter.Conclusion Our study identified the central symptoms and the network structures among symptoms listed on the GDS-15. Most of central symptoms identified by network analysis and item response theory coincided. Our results suggest that these central symptoms need to be prioritized as highly comorbid symptoms and can contribute to the development of a brief screening tool for the elderly.
Abstract
Objective This study aimed to identify the central symptoms of late-life depression using network analysis and the item response theory.Methods A total of 3,472 older adults were enrolled and the Geriatric Depression Scale-15 (GDS-15) was used to evaluate the depressive symptoms. To identify the central symptoms and the network structures among the individual symptoms, the analyses of symptom network structures and item response theory were performed.Results Among items on the GDS-15, “Happy,” “Hopeless,” “Empty,” “Bored,” “Worthless,” and “Good spirits” showed significantly higher strength centrality than the other symptoms. Among all the edges, the edge between “Empty” and “Bored” was the strongest; however, these two symptoms were not connected strongly to other symptoms. In the analysis of item response theory, “Empty,” “Bored,” “Hopeless,” “Worthless,” “Happy,” “Helpless,” and “Satisfied” presented a very high value on the discrimination parameter.Conclusion Our study identified the central symptoms and the network structures among symptoms listed on the GDS-15. Most of central symptoms identified by network analysis and item response theory coincided. Our results suggest that these central symptoms need to be prioritized as highly comorbid symptoms and can contribute to the development of a brief screening tool for the elderly.
참고문헌 (53)
- Beekman AT, "Review of community prevalence of depression in later life", Br J Psychiatry, 174, pp.307~311 (1999)
- Luppa M, "Age-and gender-specific prevalence of depression in latest-life-Systematic review and meta-analysis", J Affect Disord, 136, pp.212~221 (2012)
- Fiske A, "Depression in older adults", Annu Rev Clin Psychol, 5, pp.363~389 (2009)
- Gallo JJ, "Age differences in the symptoms of depression : a latent trait analysis", J Gerontol, 49, pp.P251~ (1994)
- Kok RM, "Management of depression in older adults : a review", JAMA, 317, pp.2114~2122 (2017)
- Alexopoulos GS, "Depression in the elderly", Lancet, 365, pp.1961~1970 (2005)
- Fried EI, "Problematic assumptions have slowed down depression research : why symptoms, not syndromes are the way forward", Front Psychol, 6, pp.309~ (2015)
- Borsboom D, "Network analysis : an integrative approach to the structure of psychopathology", Annu Rev Clin Psychol, 9, pp.91~121 (2013)
- Mullarkey MC, "Using network analysis to identify central symptoms of adolescent depression", J Clin Child Adolesc Psychol, 48, pp.656~668 (2019)
- Fried EI, "Depression sum-scores don’t add up : why analyzing specific depression symptoms is essential", BMC Med, 13, pp.1~11 (2015)
- Fried EI, "Moving forward : challenges and directions for psychopathological network theory and methodology", Perspect Psychol Sci, 12, pp.999~1020 (2017)
- Olino TM, "Measuring depression using item response theory : an examination of three measures of depressive symptomatology", Int J Methods Psychiatr Res, 21, pp.76~85 (2012)
- Borsboom D, "A network theory of mental disorders", World Psychiatry, 16, pp.5~13 (2017)
- Fried EI, "What are’good’depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis", J Affect Disord, 189, pp.314~320 (2016)
- Murri MB, "The symptom network structure of depressive symptoms in late-life : results from a European population study", Mol Psychiatry, 25, pp.1447~1456 (2020)
- Beevers CG, "Efficiently assessing negative cognition in depression : an item response theory analysis of the Dysfunctional Attitude Scale", Psychol Assess, 19, pp.199~209 (2007)
- Tang WK, "The geriatric depression scale should be shortened : results of Rasch analysis", Int J Geriatr Psychiatry, 20, pp.783~789 (2005)
- An X, "Item response theory : what it is and how you can use the IRT procedure to apply it", SAS Institute Inc, 364, pp.1~14 (2014)
- Brodey BB, "Development of the Perinatal Depression Inventory(PDI)-14 using item response theory : a comparison of the BDI-II, EPDS, PDI, and PHQ-9", Arch Womens Ment Health, 19, pp.307~316 (2016)
- Jabrayilov R, "Comparison of classical test theory and item response theory in individual change assessment", Appl Psychol Meas, 40, pp.559~572 (2016)
- van Wanrooij LL, "A network approach on the relation between apathy and depression symptoms with dementia and functional disability", Int Psychogeriatr, 31, pp.1655~1663 (2019)
- An MH, "Depressive symptom network associated with comorbid anxiety in late-life depression", Front Psychiatry, 10, pp.856~ (2019)
- Jang Y, "Cross-cultural comparability of the Geriatric Depression Scale : comparison between older Koreans and older Americans", Aging Ment Health, 5, pp.31~37 (2001)
- Hong J, "The aging work force in Korea", Int Arch Occup Environ Health, 85, pp.253~260 (2012)
- Park JH, "A nationwide survey on the prevalence and risk factors of late life depression in South Korea", J Affect Disord, 138, pp.34~40 (2012)
- Lee JH, "Development of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet(CERAD-K) : clinical and neuropsychological assessment batteries", J Gerontol B Psychol Sci Soc Sci, 57, pp.P47~ (2002)
- Yoo SW, "Validity of Korean version of the mini-international neuropsychiatric interview", Anxiety Mood, 2, pp.50~55 (2006)
- Yesavage JA, "Development and validation of a geriatric depression screening scale : a preliminary report", J Psychiatr Res, 17, pp.37~49 (1982)
- Sheik J, "Clinical Gerontology: A Guide to Assessment and Intervention", pp.165~173 (1986), The Hawthorne Press
- Azulai A, "Screening for geriatric depression in residential care facilities : a systematic narrative review", J Gerontol Soc Work, 58, pp.20~45 (2015)
- Mitchell AJ, "Diagnostic validity and added value of the Geriatric Depression Scale for depression in primary care : a meta-analysis of GDS30 and GDS15", J Affect Disord, 125, pp.10~17 (2010)
- Bae JN, "Development of the Korean version of the Geriatric Depression Scale and its short form among elderly psychiatric patients", J Psychosom Res, 57, pp.297~305 (2004)
- van Borkulo CD, "A new method for constructing networks from binary data", Sci Rep, 4, pp.5918~ (2014)
- Epskamp S, "Estimating psychological networks and their accuracy : a tutorial paper", Behav Res Methods, 50, pp.195~212 (2018)
- Tibshirani R, "Regression shrinkage and selection via the lasso", J R Stat Soc Series B Stat Method, 58, pp.267~288 (1996)
- Chen JH, "Extended Bayesian information criteria for model selection with large model spaces", Biometrika, 95, pp.759~771 (2008)
- Thomas ML, "The value of item response theory in clinical assessment : a review", Assessment, 18, pp.291~307 (2011)
- Reise SP, "The Encyclopedia of Clinical Psychology", pp.1~10 (2015), Wiley-Blackwell
- Baker FB, "Item Response Theory: Parameter Estimation Techniques" (2004), CRC Press
- Osman A, "The mindful attention awareness scale : further examination of dimensionality, reliability, and concurrent validity estimates", J Pers Assess, 98, pp.189~199 (2016)
- Montorio I, "The Geriatric Depression Scale : a review of its development and utility", Int Psychogeriatr, 8, pp.103~112 (1996)
- Ziegler M, "Short scales–five misunderstandings and ways to overcome them", J Individ Diff, 35, pp.185~189 (2014)
- Izal M, "Optimising the diagnostic performance of the Geriatric Depression Scale", Psychiatry Res, 178, pp.142~146 (2010)
- Weeks SK, "Comparing various short-form Geriatric Depression Scales leads to the GDS-5", 15, 2003, pp.35, 133~137
- "Psychiatr Prax", 2011, 280~286
- Elliott, "Central symptoms predict post-treatment outcomes and clinical impairment in anorexia nervosa : a network analysis", Clin Psychol Sci, 8, pp.139~154 (2020)
- Beard C, "Network analysis of depression and anxiety symptom relationships in a psychiatric sample", Psychol Med, 46, pp.3359~3369 (2016)
- Chiesi F, "The local reliability of the 15-item version of the Geriatric Depression Scale : an item response theory(IRT)study", J Psychosom Res, 96, pp.84~88 (2017)
- American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders(DSM-5®)" (2013), American Psychiatric Pub
- Fried EI, "The 52 symptoms of major depression : Lack of content overlap among seven common depression scales", J Affect Disord, 208, pp.191~197 (2017)
- Wang S, "Depression and cognition in the elderly", Annu Rev Clin Psychol, 11, pp.331~360 (2015)
- Chiesi F, "Does the 15-item Geriatric Depression Scale function differently in old people with different levels of cognitive functioning?", J Affect Disord, 227, pp.471~476 (2018)
- Midden AJ, "Differential item functioning analysis of items on the Geriatric Depression Scale-15 based on the presence or absence of cognitive impairment", Aging Ment Health, 22, pp.1136~1142 (2018)