Primary health care data-based early warning system for dengue outbreaks: a nationwide case study in Brazil

Background: Traditional surveillance presents limitations for early outbreak detection. Primary health care (PHC) administrative data applied to syndromic surveillance offers a cost-effective way to integrate early warning systems (EWS). We evaluate the potential of an EWS for dengue outbreaks using PHC data in Brazil.

Methods: We applied the Early Aberration Reporting System (EARS-C1 and EARS-C2) to arbovirus-related PHC encounters from October 1, 2022, to March 1, 2024, to establish an EWS across 5570 municipalities. We assessed EWS timeliness, sensitivity, and positive predictive value (PPV) against fixed-incidence dengue outbreak thresholds.

Findings: Arbovirus-related PHC encounters occurred in 5364 (96.3%) and dengue cases in 5269 (94.6%) Brazilian municipalities. PHC-based warnings anticipated 48.5% (100 cases/100,000 inhabitants), and 68.4% (300/100,000) of outbreaks detected by existing surveillance. Timeliness was higher in municipalities with over 100,000 inhabitants.

Interpretation: The EARS algorithm applied to PHC data anticipated outbreaks up to four weeks before suspected case reporting. Its use of routine data ensures broader coverage and scalability. This study demonstrates the feasibility of integrating PHC data into an EWS for early dengue outbreak detection in Brazil.

Funding: Rockefeller Foundation’s Health Initiative and Fundação de Amparo à Pesquisa do Estado da Bahia, Brazil.