基于数据的亨廷顿病进展建模和估计英国社会成本
Andrew Pollard1, Danica Greetham1, James Myatt1
1Hybrid Intelligence, Capgemini Engineering, Stevenage, UK.
Royal Society open science
|November 21, 2024
概括
我们开发了一种亨廷顿病 (HD) 进展模型,以估计社会成本. 早期的认知衰退,目前没有测量,显著影响成本,由间接因素,如生产力损失驱动.
科学领域:
- 神经科学是一个神经科学.
- 卫生经济学 卫生经济学
- 生物统计学 生物统计学
背景情况:
- 亨廷顿氏病 (HD) 构成了重大的社会负担.
- 现有的模型可能无法完全捕捉疾病的进展或相关成本.
- 量化HD的经济影响对于资源分配和干预计划至关重要.
研究的目的:
- 开发和整合疾病进展模型与亨廷顿病的经济模型.
- 确定和量化导致HD社会成本的主要因素.
- 评估假设干预措施对人口成本的影响.
主要方法:
- 使用Enroll-HD数据,安装了一个连续隐藏的马尔科夫病进展模型,识别了五种不同的疾病状态.
- 用一种新的数据增强方法来纠正进展模型中的预期寿命偏差.
- 来自多个来源的成本数据被映射到临床变量,并使用模拟来估计人口成本.
主要成果:
- 该模型确定了五种不同的疾病状态,早期的认知衰退是一个显著的,可量化的因素,目前的临床分数没有捕捉到.
- 间接成本,包括国家福利和损失的国内生产总值,被确定为英国疾病发病总社会成本的主要驱动因素.
- 模拟方法证明了估计人口成本和评估假设干预场景的可行性.
结论:
- 开发的模型为了解亨廷顿病的进展及其经济影响提供了一个强大的框架.
- 早期的认知衰退是疾病进展的关键,可量化的方面,影响着社会成本.
- 疾病的社会成本在很大程度上是由直接的医疗保健和社会护理费用以外的间接经济因素驱动的.
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