Evidence map›Paper›PMID 38424187›Full record

ArticleScientific reports2024

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

Andreia Penso Pereira, João Macedo, Ana Afonso, Raul M S Laureano, Fernando Buarque de Lima Neto

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Andreia Penso PereiraInformation Sciences, Technologies and Architecture Research Center (ISTAR-IUL), Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026, Lisboa, Portugal. andreia_marisa_pereira@iscte-iul.pt.
João MacedoEscola Politécnica, Computer Engineering, (POLI/EComp), Universidade de Pernambuco (UPE), Recife, 50720-001, Brazil.
Ana AfonsoGlobal Health and Tropical Medicine, GHTM, Associate Laboratory in Translation and Innovation Towards Global Health, LA-REAL, Instituto de Higiene e Medicina Tropical, IHMT, Universidade NOVA de Lisboa, UNL, Rua da Junqueira 100, 1349-008, Lisboa, Portugal.
Raul M S LaureanoInformation Sciences, Technologies and Architecture Research Center (ISTAR-IUL), Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026, Lisboa, Portugal. raul.laureano@iscte-iul.pt.
Fernando Buarque de Lima NetoEscola Politécnica, Computer Engineering (POLI/PPG-EC), Universidade de Pernambuco (UPE), Rua Benfica, 455-Bloco 'C', Recife, 50720-001, Brazil.

Funding

FCT - Fundação para a Ciência e Tecnologia, I.P UID/GES/00315/2020FCT - Fundação para a Ciência e Tecnologia, I.P. UIDB/04466/2020ISCTE - Instituto Universitário de Lisboa Project HOPE
6 · The paper itself

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. 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.

Indexed as

Diabetes MellitusDiabetic RetinopathyComputer SimulationFuzzy LogicHumansLogistic ModelsMass ScreeningAgent-based modelsComputational simulationDiabetic retinopathyFuzzy logicLogistic regressionScreening adherence rate

Identifiers

PMID38424187
PMCPMC10904866

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.