Table 1_Dynamic trends, spatial clustering, and multi-model projections of the global burden of Alzheimer’s disease and other dementias: an analysis of GBD 1990–2021 data to 2050.docx

BackgroundAlzheimer’s disease and other dementias (ADOD) are a leading causes of disability and mortality among older adults worldwide. While the rising burden is recognized, comprehensive analyses of its dynamic growth rates, spatial clustering patterns, and comparative long-term forecasts remain limited, hindering targeted policy response.
MethodsUsing data from the Global Burden of Disease (GBD) 2021 study for individuals aged ≥60 years across 204 countries (1990–2021), we analyzed six burden indicators. We calculated age-standardized rates (ASR) and estimated annual percentage changes (EAPC) to quantify trends. Spatial clustering of EAPC patterns was performed using hierarchical clustering. Future burden to 2050 was projected using both exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) models, with comparative analysis across sex, age, and Socio-demographic Index (SDI).
ResultsFrom 1990 to 2021, global ADOD burden increased markedly in absolute terms. EAPC analysis revealed accelerated annual growth (>1%) in East Asia and Eastern Europe, surpassing the global average. Spatial clustering identified four distinct geographic archetypes, with rapid-growth clusters spanning middle-income regions in Latin America and Southeast Asia. Women and adults aged ≥80 years, especially those ≥95, bore a disproportionately high and increasing burden. Both ES and ARIMA models projected a continued rise in absolute burden to 2050, forecasting a near-doubling of the disease burden (DALYs) among women.
ConclusionThe global ADOD burden is escalating with pronounced dynamic heterogeneity in growth velocity and distinct spatial patterns. Our multi-model projections warn of a mounting crisis, disproportionately impacting women, the oldest-old, and rapidly aging middle-income regions. Public health strategies must evolve from static assessments to dynamic surveillance and geographically tailored interventions, with urgent investment in prevention and care systems in high-growth clusters.

研究背景：阿尔茨海默病及其他痴呆（Alzheimer’s disease and other dementias, ADOD）是全球老年人群致残与致死的首要病因之一。尽管该病负担日益加重已成为学界共识，但针对其动态增长率、空间聚集模式及长期对比预测的全面分析仍较为匮乏，这极大制约了针对性公共卫生政策的制定与落地实施。
研究方法：本研究采用1990-2021年全球疾病负担（Global Burden of Disease, GBD）2021研究的数据，纳入全球204个国家的60岁及以上人群，共分析6项疾病负担指标。通过计算年龄标化率（age-standardized rates, ASR）与年度变化百分比估计值（estimated annual percentage changes, EAPC）量化疾病负担的变化趋势；采用系统聚类法对EAPC的空间分布模式开展空间聚集性分析；分别采用指数平滑（exponential smoothing, ES）与自回归积分滑动平均（autoregressive integrated moving average, ARIMA）模型，对2050年之前的疾病负担进行预测，并按性别、年龄组及社会人口指数（Socio-demographic Index, SDI）进行对比分析。
研究结果：1990-2021年，全球ADOD疾病负担的绝对数值显著上升。EAPC分析显示，东亚与东欧地区的年度增长率超过1%，增速高于全球平均水平。空间聚类分析共识别出4种不同的地理模式，其中快速增长的聚类区域覆盖拉丁美洲与东南亚的中等收入地区。女性及80岁及以上老年人群（尤其是95岁及以上高龄者）所承担的疾病负担远超其人口占比，且呈不断加重趋势。指数平滑与自回归积分滑动平均两种模型均预测，至2050年全球ADOD疾病负担的绝对数值将持续上升，其中女性群体的伤残调整寿命年（Disability-Adjusted Life Years, DALYs）所代表的疾病负担预计将接近翻倍。
研究结论：全球ADOD疾病负担正持续升级，其增长速度存在显著的动态异质性，且空间分布模式各具特征。本研究通过多模型预测结果警示，一场日益严峻的公共卫生危机即将到来，该危机对女性、高龄老年人群以及老龄化速度较快的中等收入地区影响尤为严重。公共卫生策略需从静态评估转向动态监测与因地制宜的干预措施，并在高增长聚集区域加大对疾病预防与照护体系的紧急投入。