Evaluation of the acute respiratory infections surveillance system and associated factors: a cross-sectional study, Brazil, 2009-2021

Evaluación del sistema de vigilancia de infecciones respiratorias agudas y factores asociados: estudio transversal, Brasil, 2009-2021

Objective  To evaluate quality of acute respiratory infection reporting in Brazil, 2009-2021, and to analyze association with contextual factors, the COVID-19 pandemic and death as the clinical outcome.

Methods  Cross-sectional study of quality of completeness and timeliness of reporting held on the Influenza Epidemiological Surveillance System, via OpenDATASUS, based on Centers for Disease Control and Prevention guidelines. Pearson’s chi-square test was applied to compare sociodemographic and geographic factors, Bayesian structural time series were used to measure the impact of the COVID-19 pandemic and logistic regression was used to analyze association with the clinical outcome, using odds ratios (OR) and 95% confidence intervals (95% CI).

Results  Among the 3,401,881 reports, 53.6% had high completeness, ranging from 71.3% in the South to 37.3% in the Northeast. The surveillance stages with least timeliness were case identification (13.0%), sample collection (28.2%) and data entry (43.5%). During the COVID-19 pandemic, completeness reduced by 34.8%, mainly among sociodemographic (35.9%) and signs and symptoms (28.5%) variables. Completeness of signs and symptoms variables (OR 0.56; 95%CI 0.55; 0.56) and hospital care variables (OR 0.91; 95%CI 0.90; 0.92), as well as timely communication (OR 0.72; 95%CI 0.71; 0.72), sample collection (OR 0.90; 95%CI 0.89; 0.90) and data entry (OR 0.91; 95%CI 0.90; 0.92), was associated with lower odds of death.

Conclusion  This study demonstrated that completeness and timeliness of acute respiratory infection epidemiological surveillance actions has regional inequalities, with effects on filling out records during the COVID-19 pandemic and associations with clinical outcomes.

Keywords
Respiratory Tract Infections; Data Accuracy; Health Surveillance System; Interrupted Time Series Analysis; Evaluation Study