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

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 populat...

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Bibliographic Details
Published in:Scientific reports 2024-02, Vol.14 (1), p.4963-4963, Article 4963
Main Authors: Pereira, Andreia Penso, Macedo, João, Afonso, Ana, Laureano, Raul M. S., de Lima Neto, Fernando Buarque
Format: Article
Language:English
Subjects:
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Agent-based models
Computational simulation
Computer simulation
Diabetes
Diabetes mellitus
Diabetic retinopathy
Fuzzy logic
Health planning
Humanities and Social Sciences
Logistic regression
Mathematical models
multidisciplinary
Public health
Retinopathy
Science
Science (multidisciplinary)
Screening adherence rate
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Summary: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. Three versions of an agent-based model are presented using different methods to determine the agent’s individual decision to adhere to screening: (a) logistic regression; (b) fuzzy logic components and (c) a combination of the previous. All versions were based on real data from 271,867 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 tool to support decisions in health planning, while evaluating multiple scenarios and accounting for emergent behavior.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-55517-4