Late-life depression: assessment of validity and performance of depression scales in patients attending outpatient clinic at the institute of mental health
DOI:
https://doi.org/10.18203/2349-3933.ijam20233883Keywords:
Late-life depression, Depression scales, CSDD, MADRS, PHQ9, GDSAbstract
Background: Ageing and depression often coexist, with older individuals experiencing increased depressive symptoms. Factors include substance use, diabetes, cardiovascular disease, and rural elderly populations. This study aimed to assess the validity and performance of depression scales for late-life depression among patients attending the outpatient clinic at the Institute of Mental Health, Chennai.
Methods: This prospective study was conducted on 358 patients aged >50 years who reported to the OPD and were diagnosed with depression at the institute of mental health, Chennai. Baseline assessments were done at the time of recruitment into the study, and assessments were done (visit 1) for depression as in assessment tools. Scheduled visits were performed every six months for two years (visits 2-5). Adverse events were monitored and recorded periodically.
Results: The study found a significant positive correlation between CSDD, MADRS, and PHQ9 scores with HAMD, MADRS, and GDS. The HAMD had a higher correlation with all depression scales except the Geriatric Depression Scale (GDS). The GDS had a distinct dimensionality and varied items, while MADRS showed a good correlation with all depression scales except GDS. The PHQ9 and MADRS are more valid and accurate among the participants, with higher accuracy, sensitivity, and specificity values. After these two scales, the HAMD was better with higher values than all the other scales.
Conclusions: Various depression scales were found to have a strong correlation with each other in measuring late-life depression at a tertiary care psychiatric institution.
References
Grover S, Malhotra N. Depression in elderly: A review of Indian research. J Geriatr Ment Health. 2015;2:4.
Sousa S, Paúl C, Teixeira L. Predictors of major depressive disorder in older people. Int J Environ Res Public Health. 2021;18:11894.
Vishwakarma D, Gaidhane A, Bhoi SR. Depression and its associated factors among the elderly population in India: A review. Cureus. 2023;15:23-9.
Pilania M, Yadav V, Bairwa M, Behera P, Gupta SD, Khurana H, et al. Prevalence of depression among older people (60 years and above) population in India, 1997-2016: a systematic review and meta-analysis. BMC Public Health 2019;19:142-8.
Shilpi T. Depression in elderly life: Psychological and psychosocial approaches. Int J Depress Anxiety. 2020;3:20-9.
Nieto I, Robles E, Vazquez C. Self-reported cognitive biases in depression: A meta-analysis. Clin Psychol Rev. 2020;82:101934.
Heo M, Murphy CF, Meyers BS. Relationship between the Hamilton depression rating scale and the Montgomery-Åsberg depression rating scale in depressed elderly: A meta-analysis. Am J Geriatr Psychiatr. 2007;15:899-905.
Bajaj MK, Parashar K, Das S. Development and validation of geriatric clinical depression rating scale. Indian J Clin Psychol. 2022;49:788-92.
Patino CM, Ferreira JC. Internal and external validity: can you apply research study results to your patients? J Bras Pneumol. 2018;44:183.
Spurk D, Hirschi A, Wang M, Valero D, Kauffeld S. Latent profile analysis: A review and "how to" guide of its application within vocational behaviour research. J Vocat Behav. 2020;120:103445.
Roepke-Buehler SK, Simon M, Dong X. Association between depressive symptoms, multiple dimensions of depression, and elder abuse: A cross-sectional, population-based analysis of older adults in urban Chicago. J Aging Health. 2015;27:1003-25.
Girgus J, Yang K, Ferri C. The gender difference in depression: Are older women at greater risk for depression than elderly men? Geriatrics. 2017;2:35.
Barry LC, Zdanys K. Sex differences in late-life depression: Where do we go from here? Public Policy Aging Rep. 2023;22:93-9.
Chireh B, Li M, D'Arcy C. Diabetes increases the risk of depression: A systematic review, meta-analysis and estimates of population attributable fractions based on prospective studies. Prev Med Rep 2019;14:100822.
Bădescu SV, Tătaru C, Kobylinska L, Georgescu EL, Zahiu DM, Zăgrean AM, et al. The association between Diabetes mellitus and depression. J Med Life. 2016;9:739.
Husain-Krautter S, Ellison JM. Late-life depression: The essentials and the essential distinctions. Am Psychiatr Publ. 2021;19:282-93.
Kleinheksel AJ, Rockich-Winston N, Tawfik H, Wyatt TR. Demystifying content analysis. Am J Pharm Edu. 2020;84:7113.
Abma IL, Rovers M, van der Wees PJ. Appraising convergent validity of patient-reported outcome measures in systematic reviews: constructing hypotheses and interpreting outcomes. BMC Res Notes. 2016;9:34.
Hubley AM. Discriminant Validity. In: encyclopedia of quality of life and well-being research. Dordrecht: Springer. 2014.
Collins LM. Research Design and Methods. In: Birren JE, eds. Encyclopedia of Gerontology. USA: Elsevier; 2007:433-42.
Greene RR. Erikson's healthy personality: Resilience and development. Acad Lett. 2021;23:123-8.