Avaliação da completude dos dados da síndrome respiratória aguda grave no município do Rio de Janeiro nos anos de 2020 a 2023

Abstract

The Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) is a critical tool for epidemiological surveillance in Brazil. It is responsible for collecting and providing essential data to inform public policies, resource allocation, and decision-making during health emergencies such as Influenza and COVID-19 outbreaks. Despite its significance, incomplete data records—caused by issues such as data collection failures, inadequate training of involved professionals, and system-related problems—undermine the quality of epidemiological analyses and the ability to respond to public health demands effectively. This study aimed to analyze the completeness of data recorded in SIVEP-Gripe for the municipality of Rio de Janeiro and its programmatic areas between 2020 and 2023. The research, based on secondary data extracted from the municipal TABNET system, evaluated the variables "Sample Collection," "Final Classification," "Closure Criteria," "Case Outcome," and "PCR Test Result," all considered critical for monitoring Severe Acute Respiratory Syndrome (SARS). The study applied the completeness criteria proposed by Ribas et al. (2020), adapted from Romero DE and Cunha CB et al. (2006), categorizing completeness into five levels, ranging from "excellent" (>95%) to "very poor" (<50%). Data analysis, conducted using Excel® after extraction in CSV format, revealed a positive evolution in the completeness of all variables over time for the municipality. By 2023, the only variable not rated as "excellent" was "Case Outcome." Among programmatic areas, APs 1.0, 5.1, and 5.3 achieved the highest ratings across all variables, while APs 2.1, 3.2, 4.0, and 5.2 had the lowest. The variable "PCR" showed significant improvement over the analyzed period, but by 2023, both "PCR" and "Case Outcome" still had the lowest classifications. These findings suggest a positive trend in the completeness of these variables over time, likely reflecting structural actions implemented by the Epidemiological Surveillance of Rio de Janeiro during the COVID-19 pandemic. However, targeted actions are still required for certain variables, such as "PCR" and "Case Outcome," to advance further in this area. The results underscore the importance of accurate data entry to support the early detection of new strains or viral variants and the implementation of rapid and effective responses to public health emergencies.