O impacto da disponibilidade de dados e informação oportuna para a vigilância epidemiológica

Abstract

Cad. Saúde Pública 2022; 38(7):e00115122 EDITORIAL EDITORIAL This article is published in Open Access under the Creative Commons Attribution license, which allows use, distribution, and reproduction in any medium, without restrictions, as long as the original work is correctly cited. Today, in Brazil, a wide range of health problems is monitored through information systems from the national epidemiological surveillance. In May 2022, more than 50 health problems or diseases were listed as notifiable diseases, including events associated with the COVID-19 epidemic, such as cases of severe acute respiratory infection (SARI) and multi-system inflammatory syndrome in adults and children 1. These systems aggregate data to monitor the situation in the national territory, identify new outbreaks, and formulate public health policies 2 , acting as mechanisms of the Brazilian Unified National Health System (SUS) to support the network in several important aspects. Any occurrence of data unavail-ability in these systems can seriously compromise many relevant monitoring mechanisms to public health emergencies. One of the objectives of information systems in health surveillance is to provide fast response to epidemics or unexpected events involving infectious agents. For example, the H1N1 virus epidemic showed the clear need to expand the Epidemiological Surveillance System for SARI (SIVEP-Gripe), to monitor the progress of cases 3. In addition, several analytical studies for academic research are conducted using data from these systems, generating important conclusions and recommendations for public health 4,5,6. In the beginning of the COVID-19 pandemic, the fast growth in the number of cases required recommendations of non-pharmacological measures to mitigate the transmission of the SARS-CoV-2. Recently, indicators built from COVID-19 morbidity assessments have supported decisions that recommended priority groups in early COVID-19 vaccination campaign. Later, analyses of COVID-19 vaccine efficacy showed important results indicating the need for booster doses of COVID-19 vaccines 6,7. Therefore, any data unavailability in these systems implies delayed response, no identification of events of interest, and no provision of analytically-based recommendations. Some delay is expected between the disease notification, which is usually made through notification forms, and the subsequent entry of information into the databases. In this sense, nowcasting procedures (to estimate the number of cases in a given moment) have been developed using statistical models that handle notification time patterns 8. With data quality and availability, it is possible to assess temporal patterns in the reporting process. Clear examples that use such techniques are InfoDengue (https://info.dengue.mat.br/) and