The use of social simulation modelling to understand adherence to diabetic retinopathy screening programs

Abstract

Abstract The success of screening programs depends to a large extent on the adherence of the target population, so it is therefore of fundamental importance to develop computer simulation models that make it possible to understand the factors that correlate with this adherence, as well as to identify population groups with low adherence to define public health strategies that promote behavioral change. Our aim is to demonstrate that it is possible to simulate screening adherence behavior using computer simulations. This is the first article in the literature in which three versions of an agent-based model are presented simultaneously: a) Logistic regression to determine the individual decision to adhere to screening; b) Logistic regression is replaced by fuzzy logic components and c) A combination of the previous. All versions were calibrated and validated using real data from 271867 calls for diabetic retinopathy screening. The results obtained are statistically very close to the real ones, which allows us to conclude that despite having a high degree of abstraction from the real data, the simulations are very valid and useful as a predictive tool for health planning, both in implementing new screening programs and in identifying problem factors and improving existing interventions.