Public health · 2026
The hidden burden of disease in Brazil: A quantitative comparison between official notifications and modeled estimates from the Global Burden of Disease study.
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da Silva Milena D, Procianoy Guilherme S, Behar Paulo Renato P
Аннотация
OBJECTIVES: To quantify the divergence between compulsory notifiable disease notifications in Brazil and modeled incidence estimates, by comparing official notifications from the Information System for Notifiable Diseases (SINAN) with estimates from the Global Burden of Diseases (GBD) study, identifying discrepancies between reported and estimated disease incidence.
STUDY DESIGN: Ecological, quantitative, nationwide study.
METHODS: Data for the year 2021 were obtained for 18 notifiable diseases with available information in both SINAN and GBD. For each condition, the number of reported cases in SINAN and the corresponding GBD estimates were collected. Ratios between the two sources (GBD/SINAN) were calculated, and measures of central tendency and dispersion were derived. Associations were assessed using Spearman's correlation coefficient and a log-transformed linear regression model to explore proportionality between reported and estimated cases.
RESULTS: A total of 734,447 cases reported in SINAN were analyzed. The GBD/SINAN ratio ranged from 0.36 (leprosy) to 1045 (malaria), with a median ratio of 4.8, suggesting that, for most conditions, modeled incidence substantially exceeds notifications. There was a strong positive correlation between the two sources (ρ = 0.70; p = 0.0011). The log-log regression model showed a slope of 0.72 and an R2 of 0.66, suggesting that divergence may increase with higher modeled burden.
CONCLUSIONS: The findings reveal widespread and heterogeneous divergence between notifiable disease notifications and modeled estimates in Brazil. Strengthening SINAN through improved automation, training, and integration is essential to enhance data reliability and support more effective public health planning.
STUDY DESIGN: Ecological, quantitative, nationwide study.
METHODS: Data for the year 2021 were obtained for 18 notifiable diseases with available information in both SINAN and GBD. For each condition, the number of reported cases in SINAN and the corresponding GBD estimates were collected. Ratios between the two sources (GBD/SINAN) were calculated, and measures of central tendency and dispersion were derived. Associations were assessed using Spearman's correlation coefficient and a log-transformed linear regression model to explore proportionality between reported and estimated cases.
RESULTS: A total of 734,447 cases reported in SINAN were analyzed. The GBD/SINAN ratio ranged from 0.36 (leprosy) to 1045 (malaria), with a median ratio of 4.8, suggesting that, for most conditions, modeled incidence substantially exceeds notifications. There was a strong positive correlation between the two sources (ρ = 0.70; p = 0.0011). The log-log regression model showed a slope of 0.72 and an R2 of 0.66, suggesting that divergence may increase with higher modeled burden.
CONCLUSIONS: The findings reveal widespread and heterogeneous divergence between notifiable disease notifications and modeled estimates in Brazil. Strengthening SINAN through improved automation, training, and integration is essential to enhance data reliability and support more effective public health planning.