在加拿大开发和验证一个以人口为基础的风险算法,用于早期死亡:早期死亡人口风险工具 (PreMPoRT)
Meghan O'Neill1, Mackenzie Hurst1, Lief Pagalan1
1Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
BMJ public health
|February 28, 2025
概括
一个名为PreMPoRT的新工具准确地预测了加拿大成年人的5年过早死亡风险. 这种经过验证的算法识别了针对性公共卫生干预的个人,改善了健康结果.
科学领域:
- 人口健康 人口健康
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 过早死亡率构成了重大的公共卫生挑战.
- 准确的风险预测工具对于有针对性的干预措施至关重要.
- 现有的模型可能缺乏通用性或最佳性能.
研究的目的:
- 开发和验证早期死亡率人口风险工具 (PreMPoRT).
- 预测加拿大成年人口中5年过早死亡的发病率.
- 为了确定公共卫生干预的高风险子组.
主要方法:
- 使用加拿大社区健康调查和生命统计数据库 (2000-2017年) 进行回顾性队列分析.
- 使用社会人口统计,健康行为和慢性状况数据开发三个预测模型 (最小,初级,完整).
- 通过分组方法进行内部验证和跨延期周期进行外部验证,评估预测准确性,歧视 (c-统计) 和校准.
主要成果:
- 该队列包括500,870名成年人 (18-74岁);过早死亡发生在1.40% (女性) 和2.05% (男性) 中.
- 主要模型表现出强的表现:女性c-统计值0.856 (外部验证),男性c-统计值0.846 (外部验证).
- 在高风险群体中,PreMPoRT表现出色的歧视和校准,表现优于现有模型,在高风险群体中略过预测.
结论:
- PreMPoRT是一种经过验证的,高性能的风险算法,用于预测加拿大成年人过早死亡率.
- 与现有模型相比,该工具提供了更好的区分和校准.
- PreMPoRT可以有效地识别受益于有针对性的公共卫生倡议的个人和子组.
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