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AbstractSystem dynamics models are increasingly being used to understand the underlying dynamics of populations and hypothesize causal system structures that can account for changes in a population's disease burden. A considerable challenge for public health modeling is understanding how changes in underlying determinants of a problem impact a population's measure of public health, such as the prevalence of a disease. Additionally, it is common to have limited insights and data for the dynamics of these determinants. This article presents an analytical method of estimating underlying distributions using model‐generated prevalence relying on a minimum number of model stratum. This method models the evolution of the underlying distribution parameters by combining statistical distribution theory and system dynamics models. This article provides general equations for various applications and an in‐depth example using body mass index. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.