A Novel Chronic Disease Policy Model

TL;DR

This paper introduces a flexible simulation tool for healthcare policy analysis in chronic diseases, combining epidemiological modeling, a novel entropy-based survival fitting, and discrete event simulation to evaluate policy impacts.

Contribution

It presents a new simulation framework with an innovative entropy-based method for fitting survival models from limited data, enhancing policy decision support.

Findings

Effective simulation of chronic disease progression

Flexible survival model fitting from diverse data sources

Supports evaluation of healthcare policy options

Abstract

We develop a simulation tool to support policy-decisions about healthcare for chronic diseases in defined populations. Incident disease-cases are generated in-silico from an age-sex characterised general population using standard epidemiological approaches. A novel disease-treatment model then simulates continuous life courses for each patient using discrete event simulation. Ideally, the discrete event simulation model would be inferred from complete longitudinal healthcare data via a likelihood or Bayesian approach. Such data is seldom available for relevant populations, therefore an innovative approach to evidence synthesis is required. We propose a novel entropy-based approach to fit survival densities. This method provides a fully flexible way to incorporate the available information, which can be derived from arbitrary sources. Discrete event simulation then takes place on the fitted model using a competing hazards framework. The output is then used to help evaluate the potential impacts of policy options for a given population.