Specifying prospective compartmental models of chronic disease

Abstract Background Determining the impact of preventive interventions on future health and economic outcomes in a given population often requires parameterising disease models with forecast disease incidence, remission and case fatality rates. This paper outlines a method to specify these forecasts, using period estimates over several years from source data that are not necessarily coherent from a cohort perspective. Methods For a given chronic disease, the model is specified by first obtaining and smoothing source data inputs. We used Global Burden of Disease (GBD) period estimates of disease prevalence ( p ) incidence rates ( i ) and mortality rates ( d ) from 1990 to 2019 as a starting point, from which case fatality rates ( f ) were calculated. Values were smoothed by birth cohort within each calendar year using a quadratic Bezier curve, from which remission rates ( r ) were inferred. Next, each measure was projected for 20 years using log-linear regression by year, with 10-year age knots, produced independently by sex. Finally, a stochastic optimization algorithm was used to adjust i,f and r to ensure longitudinal (cohort)-coherence such that they matched simulated forecasts of p and d , which were treated as the reference targets. Model output The method is illustrated by presenting model output for the 20 highest disability-adjusted life year-causing chronic diseases in Australia. Model fit was assessed visually, as well as through the relative and absolute differences between calibration output and targets ( p and d ). A subset model using GBD input data from 1990-2009 only was also produced and compared to 2019 GBD data to investigate the model’s predictive validity. Conclusions Our method is a useful optimization procedure to derive cohort-coherent estimates of chronic disease in simulation models. For this analysis, readily available GBD period rates were used as input data; however, other raw data sources could also be applied for a given disease.