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Forecasting and clustering the burden of non communicable diseases in Europe to support public health policy

2026-06-20Artigo de investigação Acesso aberto
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dc.contributor.authorVinhal, Cláudia
dc.contributor.authorOliveira, Alexandra
dc.contributor.authorFaria, Brígida Mónica
dc.contributor.authorPimenta, Rui
dc.contributor.authorNascimento, Ana Paula
dc.contributor.authorOliveira, Alexandra
dc.contributor.authorFaria, Brigida Monica
dc.contributor.authorPimenta, Rui
dc.contributor.authorNascimento, Ana Paula
dc.date.accessioned2026-09-10T10:41:46Z
dc.date.available2026-09-10T10:41:46Z
dc.date.issued2026-06-20
dc.description.abstractThis study aims to model Disability Adjusted Life Years (DALYs) for cardiovascular diseases and neurological disorders using ARIMA models, exploring regional disparities and evaluating model performance across countries. This is a quantitative, exploratory time series study using secondary data from the Global Burden of Disease (GBD) database, covering annual DALYs across 48 European countries, from 1990 until 2019. ARIMA models were applied using an automated algorithm. Model performance was assessed using MAE, RMSE, MSE, and MAPE. Time series were compared using Piccolo distances, and countries were clustered with hierarchical average linkage. Cluster quality was evaluated using Silhouette, Dunn, McClain, and C-index. Simpler ARIMA models often yielded better forecasts than more complex ones, particularly in smaller countries such as San Marino and Monaco. Cardiovascular disease time series were grouped into 2 clusters, while neurological disorders formed 15 clusters, reflecting diverse epidemiological and reporting patterns. The mean MAPE was 6.8% for cardiovascular diseases and 0.95% for neurological disorders, indicating greater predictive accuracy and stability for the latter. ARIMA modelling is effective for capturing temporal dynamics in DALY data, but should be tailored to avoid overfitting. Clustering countries based on time series similarities reveals insights into health system differences and regional disease patterns. These findings support the use of data-driven approaches to improve forecasting and inform global public health planning.eng
dc.identifier.citationVinhal, C., Oliveira, A., Faria, B. M., Pimenta, R., & Nascimento, A. P. (2026). Forecasting and clustering the burden of non communicable diseases in Europe to support public health policy. Discover Public Health, 23(1), 996. https://doi.org/10.1186/s12982-026-02205-5
dc.identifier.doi10.1186/s12982-026-02205-5
dc.identifier.eissn3005-0774
dc.identifier.urihttp://hdl.handle.net/10400.22/32696
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationUID/00027/2025
dc.relation.hasversionhttps://link.springer.com/article/10.1186/s12982-026-02205-5
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNon-communicable diseases (NCDs)
dc.subjectDisability Adjusted Life Years (DALYs)
dc.titleForecasting and clustering the burden of non communicable diseases in Europe to support public health policyeng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage24
oaire.citation.startPage1
oaire.citation.titleDiscover Public Health
oaire.citation.volume23
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameOliveira
person.familyNameFaria
person.familyNamePimenta
person.familyNameNascimento
person.givenNameAlexandra
person.givenNameBrigida Monica
person.givenNameRui
person.givenNameAna Paula
person.identifierR-000-T1F
person.identifier846820
person.identifier.ciencia-id161A-55D9-C256
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person.identifier.ciencia-idD914-641B-E379
person.identifier.ciencia-id3A15-0245-285D
person.identifier.orcid0000-0001-5872-5504
person.identifier.orcid0000-0003-2102-3407
person.identifier.orcid0000-0002-1985-8395
person.identifier.orcid0000-0002-4423-2706
person.identifier.ridC-6649-2012
person.identifier.ridJUU-6485-2023
person.identifier.scopus-author-id56340903500
person.identifier.scopus-author-id6506476517
person.identifier.scopus-author-id56340941800
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relation.isAuthorOfPublication.latestForDiscoveryd6f940a1-3dba-41d2-9a5e-dc1f313eec07

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