Patterns and trends in the burden of Alzheimer’s disease and related dementias in China (1990

Abstract

Background

The epidemiology of Alzheimer's disease and related dementias (ADRD) in China is understudied as compared to global levels.

Objective

The aim of this study was to examine the trend of dementia epidemiology in China from 1990 to 2021 and provide predictions for the next two decades.

Methods

The Global Burden of Disease study (GBD) 2021 were used to analyze the prevalence, incidence, mortality, and disability-adjusted life years (DALYs) rates due to ADRD in China and globally. Joinpoint regression analysis was used to analyze the epidemiological trends from 1990 to 2021. A forecast of ADRD prevalence trends was conducted utilizing Autoregressive Integrated Moving Average (ARIMA) models.

Results

China was experiencing a growing burden of ADRD. As of 2021, the number of people with dementia in China had risen to 56.85 million (95%CI: 49.38, 64.98), up from 21.80 (95%CI: 19.07, 24.84) million in 1990. The prevalence, incidence, mortality, and DALY rates all indicated a greater disease burden among the Chinese population compared to global levels, with a significantly higher burden in the female group. The projected prevalence rate was expected to increase by 60% compared to the current prevalence rate.

Conclusions

As the population in China continues to age, ADRD presents an undeniable challenge. To mitigate the growing burden of ADRD and improve the overall health of the population, it is essential to establish a comprehensive plan that focuses on increasing public awareness and enhancing the quality of life for all, with special attention given to women.

Keywords

Alzheimer's disease and related dementias joinpoint analysis prediction. temporal trend

Get full access to this article

View all access options for this article.

References

Förstl

Kurz

. Clinical features of Alzheimer’s disease. Eur Arch Psychiatry Clin Neurosci 1999; 249: 288–290.

Nichols

Steinmetz

Vollset

, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. Lancet Public Health 2022; 7: e105–e125.

Chandra

Coile

Mommaerts

. What can economics say about Alzheimer’s disease? J Econ Lit 2023; 61: 428–470.

Rudnicka

Napierała

Podfigurna

, et al. The world health organization (WHO) approach to healthy ageing. Maturitas 2020; 139: 6–11.

Alzheimer’s Association. 2023 Alzheimer’s disease facts and figures. Alzheimers Dement 2023; 19: 1598–1695.

World Health Organization. World health statistics 2024: Monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization, 2024.

Ren

Lin

, et al. The China Alzheimer report 2022. Gen Psychiatr 2022; 35: e100751.

Feng

Sun

, et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2019. Front Aging Neurosci 2022; 14: 937486.

Sun

Zhang

, et al. Time trends in Alzheimer’s disease mortality attributable to metabolic risks and smoking in China from 1990 to 2019: an age-period-cohort analysis. Front Aging Neurosci 2024; 16: 1425577.

10.

Zhang

P-D

Y-B

Z-H

, et al. Age, period, and cohort effects on activities of daily living, physical performance, and cognitive functioning impairment among the oldest-old in China. J Gerontol A Biol Sci Med Sci 2020; 75: 1214–1221.

11.

Anderson

Rosenberg

. Age standardization of death rates: implementation of the year 2000 standard. Natl Vital Stat Rep 1998; 47: 1–16. 20.

12.

Devleesschauwer

Havelaar

Maertens De Noordhout

, et al. DALY Calculation in practice: a stepwise approach. Int J Public Health 2014; 59: 571–574.

13.

Murray

CJL

Acharya

. Understanding DALYs. J Health Econ 1997; 16: 703–730.

14.

The Global Burden of Disease [GBD]. Disease, injury, and risk factsheets, https://www.healthdata.org/research-analysis/diseases-injuries/factsheets (2021, accessed 25 November 2024).

15.

Wang

Yao

, et al. Estimating the prevalence and mortality of coronavirus disease 2019 (COVID-19) in the USA, the UK, Russia, and India. Infect Drug Resist 2020; 13: 3335–3350.

16.

Ceylan

. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ 2020; 729: 138817.

17.

Gao

Lin

Yin

, et al. Heat exposure and dementia-related mortality in China. JAMA Netw Open 2024; 7: e2419250.

18.

Chen

Yang

, et al. Global, regional, and national health inequalities of Alzheimer’s disease and Parkinson’s disease in 204 countries, 1990–2019. Int J Equity Health 2024; 23: 125.

19.

Han

Lyu

, et al. Aging in China: perspectives on public health. Glob Health J 2020; 4: 11–17.

20.

Zhao

Ding

, et al. The burden of Alzheimer’s disease mortality in the United States, 1999–2018. J Alzheimers Dis 2021; 82: 803–813.

21.

Scheltens

De Strooper

Kivipelto

, et al. Alzheimer’s disease. Lancet 2021; 397: 1577–1590.

22.

Larson

Yaffe

Langa

. New insights into the dementia epidemic. N Engl J Med 2013; 369: 2275–2277.

23.

Yang

, et al. Global burden of dementia death from 1990 to 2019, with projections to 2050: an analysis of 2019 global burden of disease study. J Prev Alzheimers Dis 2024; 4: 1013–1021.

24.

Sindi

Kåreholt

Ngandu

, et al. Sex differences in dementia and response to a lifestyle intervention: evidence from nordic population-based studies and a prevention trial. Alzheimers Dement 2021; 17: 1166–1178.

25.

Gilstrap

Zhou

Alsan

, et al. Trends in mortality rates among Medicare enrollees with Alzheimer disease and related dementias before and during the early phase of the COVID-19 pandemic. JAMA Neurol 2022; 79: 342.

26.

Chen

Charpignon

M-L

Raquib

, et al. Excess mortality with Alzheimer disease and related dementias as an underlying or contributing cause during the COVID-19 pandemic in the US. JAMA Neurol 2023; 80: 919.

27.

Yanez

Weiss

Romand

J-A

, et al. COVID-19 mortality risk for older men and women. BMC Public Health 2020; 20: 1742.

28.

Bonanad

García-Blas

Tarazona-Santabalbina

, et al. The effect of age on mortality in patients with COVID-19: a meta-analysis with 611,583 subjects. J Am Med Dir Assoc 2020; 21: 915–918.

29.

Xiao

Wang

, et al. 2023 China Alzheimer’s disease: facts and figures. Hum Brain 2023; 2: 1–13.

30.

Joel

McCarthy

. Incorporating sex as a biological variable in neuropsychiatric research: where are we now and where should we be? Neuropsychopharmacology 2017; 42: 379–385.

31.

Podcasy

Epperson

. Considering sex and gender in Alzheimer disease and other dementias. Dialogues Clin Neurosci 2016; 18: 437–446.

32.

Zhang

Clarke

Rhynas

. A thematic analysis of Chinese people with dementia and family caregivers’ experiences of home care in China. Dementia 2020; 19: 2821–2835.

33.

Crane

Walker

Hubbard

, et al. Glucose levels and risk of dementia. N Engl J Med 2013; 369: 540–548.

34.

Pandini

Pace

Copani

, et al. Insulin has multiple antiamyloidogenic effects on human neuronal cells. Endocrinology 2013; 154: 375–387.

35.

Bartl

Meyer

Brendler

, et al. Different effects of soluble and aggregated amyloid β42 on gene/protein expression and enzyme activity involved in insulin and APP pathways. J Neural Transm 2013; 120: 113–120.

36.

Abbatecola

Rizzo

Barbieri

, et al. Postprandial plasma glucose excursions and cognitive functioning in aged type 2 diabetics. Neurology 2006; 67: 235–240.

37.

Liu

Cui

, et al. Prevalence of tobacco dependence and associated factors in China: findings from nationwide China health literacy survey during 2018–19. Lancet Reg Health West Pac 2022; 24: 100464.

38.

Cokkinides

Bandi

McMahon

, et al. Tobacco control in the United States–recent progress and opportunities. CA Cancer J Clin 2009; 59: 352–365.

39.

World Health Organization. WHO Report on the Global Tobacco Epidemic, 2023. Geneva: World Health Organization, 2023.

40.

Chudasama

Khunti

Gillies

, et al. Healthy lifestyle and life expectancy in people with multimorbidity in the UK biobank: a longitudinal cohort study. PLoS Med 2020; 17: e1003332.

41.

Kontopoulou

Panagopoulos

Kakkos

, et al. A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks. Future Internet 2023; 15: 255.

42.

Wilson

. Time series analysis: forecasting and control. J Time Ser Anal 2016; 37: 709–711.

43.

Zheng

Y-L

Zhang

L-P

Zhang

X-L

, et al. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS One 2015; 10: e0116832.

44.

Paliari

Karanikola

Kotsiantis

. A comparison of the optimized LSTM, XGBOOST and ARIMA in time series forecasting. In: 2021 12th international conference on information, intelligence, systems & applications (IISA), pp.1–7. Chania Crete, Greece: IEEE.

45.

Hua

. Bitcoin price prediction using ARIMA and LSTM. E3S Web Conf 2020; 218: 01050.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.17 MB

Patterns and trends in the burden of Alzheimer’s disease and related dementias in China (1990–2021) and predictions to 2040

Abstract

Background

Objective

Methods

Results

Conclusions

Keywords

Get full access to this article

References

Supplementary Material