ISSN (Electrónico): 2590-5708
Formato: Electrónico / Acceso Abierto
Frecuencia: Números Anuales
Revisión por Pares: Doble Ciego
Keywords
Data interpretation
public health
risk prediction
research design
models
epidemiological surveillance Interpretación de datos
salud pública
predicción de riesgo
diseño de investigación
modelos
vigilancia epidemiológica
public health
risk prediction
research design
models
epidemiological surveillance Interpretación de datos
salud pública
predicción de riesgo
diseño de investigación
modelos
vigilancia epidemiológica
How to Cite
Pinzón Castro, D. A., Arcila Echavarría, D. C., Arroyave Espinal, S. M., Maturana Cuesta , J. F., Herrera López, B. Y., & Araque Fernández , Álvaro A. (2025). Predictive models of epidemiological surveillance in public health using digital health and social and structural determinants of health (sdoh): systematic review. Ciencia, Tecnología E Innovación En Salud, 9, 35–49. https://doi.org/10.23850/25393871.7199
Abstract
Context: The social and structural determinants of health are those social and economic structures that mediate health inequalities, which are interrelated and overlapping to facilitate or limit an outcome. Including social and physical environments, health services and social factors, they can be divided into two categories: individual or patient-specific, such as education, employment, or housing; and community determinants, which measure the environment in which the individual develops or socioeconomic factors such as air pollution, housing characteristics, unemployment rate, and here it is important to highlight the importance of recording these in the different models of electronic medical records. It is concluded that tools with wide dissemination and inter-institutional use are required, which facilitate the inclusion of variables that refer to these social determinants and that can help identify risk factors for special populations.
Objective: To critically evaluate what kind of predictive models have been developed to predict health outcomes, using digital tools for recording and processing information and including SDOH. Methods: This is a systematic review of narrative literature, providing a critical view of the results found with respect to prognostic studies, which used digital tools for their construction and included social and structural determinants of health for risk prediction. For its preparation, the recommendations of the PRISMA declaration have been followed.
Results: The first searches were conducted in April 2022, using the MeSH terms: "social determinants", "forecast" and "health technologies", "public health" and the Boolean operators AND and OR as needed, the PubMed, Cochrane databases were used, in addition to the bioRxiv and medRxiv prepublication servers, Google Scholar was also used. Subsequently, a review by title was carried out in each database to identify articles related to the keywords and that could be included in the systematic review, eliminating 1020 articles, selecting 45 articles for evaluation by abstract and full text, of which 40 additional articles were eliminated for not finding a relationship with the topic of the systematic review and one for being a letter to the editor of a journal. We found 2 systematic reviews that had a similar theme to our review but did not take into account the same depth regarding the topic. Therefore, it was decided to include 5 prognostic articles in this systematic review.
Conclusions: This systematic review concludes that predictive models that incorporate SDOH, along with digital tools for data collection, significantly improve their predictive capacity and accuracy. The best results are observed when both individual and community SDOH are included
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