通过基于医疗保健行政数据的新型识别算法估计痴呆和帕金森症的负担
Jacopo Sabbatinelli1,2, Leonardo Biscetti3, Marco Lilla4
1Department of Clinical and Molecular Sciences, Università Politecnica Delle Marche, Ancona, Italy.
Frontiers in public health
|November 17, 2025
概括
这项研究估计了意大利痴呆症和帕金森症的负担,发现老年人患病率更高. 使用行政数据和TREND协议的新方法改善了这些神经系统疾病的病例识别.
科学领域:
- 流行病学 流行病学
- 老年学是一门学科.
- 公共卫生 公共卫生
背景情况:
- 神经系统疾病,特别是痴呆症和帕金森症,对老年人群构成重大公共卫生挑战.
- 准确估计患病率和发病率对于医疗保健规划和有针对性的干预措施至关重要.
研究的目的:
- 为了估计意大利马尔克地区痴呆症和帕金森症的负担.
- 利用新的识别方法与行政医疗保健数据,以改善病例确定.
主要方法:
- 使用行政数据库 (2016-2021) 进行的横截面研究,包括药物处方,医院出院记录和慢性疾病登记册.
- 在40岁及以上的个体中应用TREND协议以加强病例识别.
- 根据年龄和性别调整的流行率和发病率的计算,使用地理信息系统 (GIS) 进行空间分析.
主要成果:
- 2021年,65岁以上的年龄调整后患病率为帕金森症的22.6‰,痴呆症的65.8‰.
- 帕金森症的五年发病率为1.7‰,痴呆症为6.9‰.
- 地理信息系统揭示了帕金森症 (南部地区) 和痴呆症 (中部/内陆地区) 的独特空间模式.
结论:
- 行政数据与TREND协议相结合,有效地提高了神经退行性疾病的病例识别.
- 观察到的地理模式为马尔喀地区的区域医疗保健规划提供了宝贵的见解.
- 可扩展的方法支持可重复的,数据驱动的关于老龄化人口的公共卫生政策策略.
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