A widely-used model for determining the long-term health impacts of public health interventions, often called a "multistate lifetable", requires estimates of incidence, case fatality, and sometimes also remission rates, for multiple diseases by age and gender. Generally, direct data on both incidence and case fatality are not available in every disease and setting. For example, we may know population mortality and prevalence rather than case fatality and incidence. This paper presents Bayesian continuous-time multistate models for estimating transition rates between disease states based on incomplete data. This builds on previous methods by using a formal statistical model with transparent data-generating assumptions, while providing accessible software as an R package. Rates for people of different ages and areas can be related flexibly through splines or hierarchical models. Previous methods are also extended to allow age-specific trends through calendar time. The model is used to estimate case fatality for multiple diseases in the city regions of England, based on incidence, prevalence and mortality data from the Global Burden of Disease study. The estimates can be used to inform health impact models relating to those diseases and areas. Different assumptions about rates are compared, and we check the influence of different data sources.
翻译:用于确定公共卫生干预措施对健康的长期影响的广泛使用的模式,通常称为“多州生命”,要求按年龄和性别对多种疾病的发病率、病例死亡率以及有时还有发病率进行估计。一般而言,在每一种疾病和背景中,都无法获得关于发病率和病例死亡率的直接数据。例如,我们可能知道死亡率和发病率,而不是病例死亡率和发病率。本文介绍了根据不完全的数据估算疾病国之间过渡率的巴耶斯连续时间多州模型。这以以往方法为基础,采用具有透明数据生成假设的正式统计模型,同时提供可检索的软件作为R组合。不同年龄和地区的人的死亡率可以通过螺纹或等级模型灵活地联系起来。以前的方法也通过日历时间来扩大,以便允许特定年龄趋势。该模型用于根据疾病发病率、流行率和死亡率全球研究得出的数据估算英格兰城区多重疾病的死亡率。这些估计数可用于向与这些疾病和地区有关的健康影响模型提供信息。对比率的不同假设进行了比较,我们检查不同数据来源的影响。