Bayesian multistate modelling of incomplete chronic disease burden data

TL;DR

This paper introduces Bayesian multistate models that estimate disease transition rates from incomplete data, enabling better health impact assessments with flexible, transparent assumptions and accessible software.

Contribution

It develops a Bayesian continuous-time multistate modeling framework that handles incomplete data and incorporates age and temporal trends, extending previous methods with new statistical and computational tools.

Findings

Estimated case fatality rates for multiple diseases in England.

Demonstrated model flexibility with splines and hierarchical structures.

Compared different assumptions and data sources for robustness.

Abstract

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.