| Citation: |
BAO Xiaolin, WEI Hongjuan, BIAN Xinxin, MA Xiumei, GAO Yin, ZHANG Yingyan, LIU Wei, MA Yuexian, ZHANG Weixin, YANG Xuewen. Analysis and Prediction of Disease Burden of Senile Depression in China from 1990 to 2021[J]. Medical Journal of Peking Union Medical College Hospital. DOI: 10.12290/xhyxzz.2024-0664
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Objective To analyze the trends in the burden of elderly depression in China from 1990 to 2021 and its risk factors, providing a theoretical basis for the prevention and policy-making of elderly depression in China. Methods Data on the burden of elderly depression in China from 1990 to 2021, including the number of cases, disability-adjusted life years (DALY), incidence rate, and DALY rate, were extracted from the Global Burden of Disease 2021 (GBD 2021). The Joinpoint model was used to analyze the trends through annual percentage change (APC) and average annual percentage change (AAPC). A time series model was used to predict the burden of elderly depression in the next five years. Population attributable fraction (PAF) was used to describe the burden of disease caused by risk factors for elderly depression in China in 1990 and 2021. Results From 1990 to 2021, the number of cases and the incidence rate of elderly depression in China showed an overall upward trend. Among them, the incidence in the 60-64 age group increased most significantly, the prevalence in the 95 and above age group showed a significant upward trend, and the DALY rate in the 65-69 age group showed the most pronounced increasing trend. The incidence, prevalence and DALY rate of elderly depression in females were higher than those in males. Major risk factors included childhood sexual abuse and intimate partner violence, with intimate partner violence having a particularly significant impact on females. The ARIMA model predicted that in the next five years, the incidence, prevalence and DALY rate of elderly depression in China will show a downward trend, with a larger decline in females than in males. Conclusion From 1990 to 2021, the incidence, prevalence and DALY rate of elderly depression in China showed an overall upward trend, with higher rates in females. It is crucial to focus on early prevention, particularly for elderly women, to reduce the burden of elderly depression.
| [1] |
Marx W, Penninx B, Solmi M, et al. Major depressive disorder[J]. Nature Reviews Disease Primers, 2023, 9(1): 44.
|
| [2] |
Reavley N, Jorm AF. Recognition of mental disorders and beliefs about treatment and outcome: findings from an Australian national survey of mental health literacy and stigma[J]. Aust N Z J Psychiatry, 2011, 45(11): 947-956.
|
| [3] |
Seedat S, Scott KM, Angermeyer MC, et al. Cross-national associations between gender and mental disorders in the World Health Organization World Mental Health Surveys[J]. Arch Gen Psychiatry,2009,66(7):785-95.
|
| [4] |
Park LT, Zarate CA. Depression in the primary care setting[J]. N Engl J Med,2019,380(6):559-568.
|
| [5] |
Gühne U, Stein J, Riedel-Heller S. Depression in old age--challenge of an ageing society[J]. Psychiatr Prax,2016,43(2):107-110.
|
| [6] |
Aziz R, Steffens DC. What are the causes of late-life depression[J]. Psychiatr Clin North Am,2013,36(4):497-516.
|
| [7] |
Wassink-Vossen S, Oude Voshaar RC, Naarding P, Collard RM. Effectiveness of late-life depression interventions on functional limitations: A systematic review[J]. Int J Ment Health Nurs. 2022, 31(4):823-842.
|
| [8] |
van den Berg K S, Wiersema C, Hegeman J M, et al. Clinical characteristics of late-life depression predicting mortality[J]. Aging & Mental Health, 2021, 25(3): 476-483.
|
| [9] |
Kopf D, Hewer W. Prevention of depression in old age: individual and societal relevance[J]. Z Gerontol Geriatr,2024,57(3):175-178.
|
| [10] |
GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet,2020,396(10258):1204-1222.
|
| [11] |
GBD 2021 Demographics Collaborators. Global age-sex-specific mortality, lifeexpectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021[J]. Lancet,2024,403(10440):1989-2056.
|
| [12] |
李子悦,方珈文,林凯程.1990—2019年中国归因于高体质指数的2 型糖尿病疾病负担分析与预测研究[J].中国全科医学,2024,27(9):1126-1133.
|
| [13] |
Benvenuto D, Giovanetti M, Vassallo L, et al. Application of the ARIMA model on the COVID-2019 epidemic dataset[J]. Data Brief,2020,29:105340.
|
| [14] |
曾四清. Joinpoint回归模型及其在传染病流行趋势分析中的应用[J]. 中国卫生统计, 2019, 36(5):787–791.
|
| [15] |
谢丽，张焕玲，唐认桥，等．队列研究中多因素调整人群归因危险度的估计及其应用[J].中国肿瘤, 2013, 22(5):373-378.
|
| [16] |
Schaakxs R, Comijs H C, Lamers F, et al. Age-related variability in the presentation of symptoms of major depressive disorder[J]. Psychological Medicine, 2017, 47(3): 543-552.
|
| [17] |
Fellinger M, Waldhör T, Serretti A, et al. Seasonality in major depressive disorder: effect of sex and age[J]. J Affect Disord,2022,296:111-116.
|
| [18] |
Carvalho Silva R, Pisanu C, et al. Biological markers of sex-based differences in major depressive disorder and in antidepressant response[J]. Eur Neuropsychopharmacol,2023,76:89-107.
|
| [19] |
Goldstein RD, Gruenberg AM. Major depressive disorder in the older adult: implications for women[J]. J Women Aging,2007,19(1-2):63-77.
|
| [20] |
Arias de la Torre J, Vilagut G, Ronaldson A, et al. Prevalence and age patterns of depression in the United Kingdom. A population-based study[J]. J Affect Disord. 2021, 279:164-172.
|
| [21] |
Takizawa T, Kondo T, Sakihara S, et al. Stress buffering effects of social support on depressive symptoms in middle age: reciprocity and community mental health[J]. Psychiatry Clin Neurosci,2006,60(6):652-61.
|
| [22] |
Ahmadabadi Z, Najman JM, Williams GM, et al. Intimate partner violence and subsequent depression and anxiety disorders[J]. Soc Psychiatry Psychiatr Epidemiol, 2020, 55: 611-620.
|
| [23] |
Burnette D, Ye X, Cheng Z, Ruan H. Living alone, social cohesion, and quality of life among older adults in rural and urban China: a conditional process analysis[J]. IntPsychogeriatr. 2021, 33(5):469-479.
|
| [24] |
Rosa AR, Bücker J. Sex representation in mental health research[J]. Eur Neuropsychopharmacol. 2024, 85:21-22.
|
| [25] |
Almeida O. Prevention of depression in older age[J]. Maturitas, 2014, 79(2): 136-141.
|
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