Modelling demographic changes using simulation: Supportive analyses for socioeconomic studies

Abstract

Demographic analyses play a supporting role in many socioeconomic studies. The credibility of the decisions taken by regional and national authorities often depends on the accuracy of population forecasts. Among the mathematical methods used to study demographic phenomena, system dynamics is well suited to address the dynamic complexity that characterises population changes, and this method offers a proven approach called chronological ageing. The best results are achieved using a variation of the method that assumes the availability of historical data at the same level of aggregation. This level of detail is usually available at the national or regional level; however, when collecting data on smaller regions, there are difficulties in obtaining the necessary information. This study introduces an approach we call ‘hierarchical cohorting’, which could be a solution when empirical data are not as detailed as needed for making credible population projections. We also present a simple and effective algorithm that allows researchers to include population projections when modelling expected future demand for services. We demonstrate the application of this algorithm for forecasting the number of patient visits to a regional healthcare system through a case study; however, the approach can be applied wherever a non-stationary flow of events defines the size and structure of demand for services. Our approach combines two perspectives: the macro-level of system dynamics to study demographic changes and the operational level of discrete simulation to model forecasted demand for hospital inpatient services.