Epidemiology and Quality of Dengue Notifications in the State of Goiás (2015–2024): A Completeness Analysis

Abstract

Dengue represents one of the most critical contemporary public health challenges, requiring robust epidemiological surveillance based on the quality of data from the Notifiable Diseases Information System (SINAN). To analyze the quality of pre-filling (completeness) of variables in dengue notification forms registered in SINAN in the State of Goiás between 2015 and 2024. A cross-sectional and descriptive study using secondary data from DATASUS. Quality was evaluated by the completeness attribute, using the Romero and Cunha score to classify variables into five performance categories (Excellent to Very Poor). Data processing was performed using Tabwin and IBM SPSS Statistics version 24 software. A sharp discrepancy in data quality was observed. Identification variables (age, sex, municipality) showed excellent completeness (100%), induced by technical system constraints. In contrast, socioeconomic variables such as race/color (77%) and schooling (71%) were classified as poor. A clinical "blackout" was identified in warning and severity signs, with negligible completeness (1.92% to 0.15%), revealing an inverted logic where clinical data is only filled in after the final classification of the case. Virological surveillance also proved fragile, with only 2.06% completeness for the viral serotype. SINAN in Goiás functions efficiently as an administrative flow but fails seriously in clinical-epidemiological documentation. The inverted logic in filling in gravity signs compromises lethality monitoring. It is imperative to implement Permanent Education programs so that health professionals value the record as an assistance tool rather than a merely bureaucratic one.