ArticleBMC public health2025
Hospitalisations in Brazil: an ecological time series analysis of the impact of medical decision support data as an exogenous variable.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Dengue hospitalizations in Brazil: Forecasting with climatic and physicians' digital search data under real-world reporting delays.PLOS digital health · 2026Article
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3 authors.
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Abstract
purposePublic health surveillance depends on continuous monitoring to guide interventions and allocate resources effectively. This study aimed to evaluate whether structured medical search data from the Afya Whitebook®, a clinical decision-support platform, can serve as exogenous variables to enhance the explanatory capacity of time series models characterising hospitalisation patterns within Brazil's public health system.
methodsAn ecological time series analysis was conducted using hospitalisation data (SIH/SUS) and Afya Whitebook® search volumes from 2021 to 2024. SARIMAX models assessed temporal associations between search activity and hospital admissions across Brazilian states, compared to univariate SARIMA models to evaluate the added value of search data.
resultsIn 278 of the 478 time series, SARIMAX models provided a better fit than univariate SARIMA models, particularly for conditions such as chronic obstructive pulmonary disease, dengue, urinary tract infections, type 2 diabetes, asthma, depression, and chronic kidney disease. Model fit varied by disease and region, underscoring the influence of contextual factors in the association between search behaviour and hospital admissions.
conclusionThis study demonstrates that structured medical search data can serve as exogenous variables to improve the explanatory capacity of time series models of hospitalisation patterns. Despite variation between diseases and regions, this approach shows promise in supporting public health surveillance and could be strengthened by incorporating contextual data in future studies.
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