Markov models are widely employed in cost-effectiveness analysis of healthcare interventions. Although such models are usually formulated at the individual level, it is also useful to examine outcomes at the population level. Analysts may wish to know the impact of a health intervention on a whole population instead of an individual or a given cohort. For example, it will always be highly desirable to determine the change of the equilibrium population level--the size and structure of the population in equilibrium--before and after the intervention, and then factor in the change in evaluating the intervention. We review methods from the stochastic processes literature that can be used for this purpose. We first examine homogeneous population-level Markov models and derive equilibrium distributions using Jackson and Whittle networks. We also apply age-dependent stochastic population dynamics to study non-homogeneous population-level medical Markov models Secondly, we study population models for the spread of infectious diseases in populations. The transmittable feature makes the modeling of infectious diseases different from other types of diseases. These models are usually not Jackson or Whittle processes and difficult to handle. We apply approximation methods for Markov population processes to study epidemic equilibriums. Finally, we study stochastic networks with Markov modulated transition rates, and obtain exact analytic results for equilibrium means. We then apply the results to a special type of epidemic models. We present several measures of population health quality to evaluate healthcare interventions and illustrate how they may differ in applications. Compared with the individual measures and cohort measures currently used, population measures provide useful adjuncts to population-based medical decision making

Last modified

• 08/16/2018

Creator

• Huang Min

DOI

• https://doi.org/10.21985/N25M7X

Subject

• Industrial Engineering and Management Sciences

Keyword

• Industrial Engineering

Date created

• 2007-10-11

Resource type

• Dissertation

Rights statement

• In Copyright