Early warning system using primary health care data in the post-Covid-19 pandemic era

Brazil nationwide case-study

Bibliographic Data

ID5166899
AuthorsThiago Cerqueira-Silva (0000-0003-4534-2509, Fundação Oswaldo Cruz), Juliane F Oliveira (0000-0002-7167-8754, Fundação Oswaldo Cruz), Vinicius De Araújo Oliveira (0000-0001-7858-9650, Fundação Oswaldo Cruz), Pilar Tavares Veras Florentino (0000-0001-8077-8100, Fundação Oswaldo Cruz), Alberto Sironi (0000-0002-8964-0260, Fundação Oswaldo Cruz), Gerson Oliveira Penna (0000-0001-8967-536X, Universidade de Brasília), Pablo Ivan Pereira Ramos (0000-0002-9075-7861, Fundação Oswaldo Cruz), Viviane S Boaventura (0000-0002-7241-6844, Fundação Oswaldo Cruz), Manoel Barral-Netto (0000-0002-5823-7903, Fundação Oswaldo Cruz), Izabel Marcilio (0000-0002-2914-6535, Fundação Oswaldo Cruz)
Year2024
Volume40
Issue11
Pagese00010024-e00010024
Publication date2024-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCadernos de Saude Publica (JOURNAL)
Journal identifiersISSN: 0102-311X • E-ISSN: 1678-4464
PublisherFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/0102-311xen010024
PMID39775767
OpenAlexW4406296594
SCIELO_PIDS0102-311X2024001101407
LanguageEN
References cited19

Syndromic surveillance using primary health care (PHC) data is a valuable tool for early outbreak detection, as demonstrated by the potential to identify COVID-19 outbreaks. However, the potential of such an early warning system in the post-COVID-19 era remains largely unexplored. We analyzed PHC encounter counter of respiratory complaints registered in the database of the Brazilian Unified National Health System from October 2022 to July 2023. We applied EARS (variations C1/C2/C3) and EVI to estimate the weekly thresholds. An alarm was determined when the number of encounters exceeded the week-specific threshold. We used data on hospitalization due to respiratory disease to classify as anomalies the weeks in which the number of cases surpassed predetermined thresholds. We compared EARS and EVI efficacy in anticipating anomalies. A total of 119 anomalies were identified across 116 immediate regions during the study period. The EARS-C2 presented the highest early alarm rate, with 81/119 (68%) early alarms, and C1 the lowest, with 71 (60%) early alarms. The lowest true positivity was the EARS-C1 118/1,354 (8.7%) and the highest was EARS-C3 99/856 (11.6%). Routinely collected PHC data can be successfully used to detect respiratory disease outbreaks in Brazil. Syndromic surveillance enhances timeliness in surveillance strategies, albeit with lower specificity. A combined approach with other strategies is essential to strengthen accuracy, offering a proactive and effective public health response against future outbreaks

2019-20 coronavirus outbreak · Coronavirus disease 2019 (COVID-19) · Disease · Early warning system · Family medicine · Geography · Infectious disease (medical specialty) · Medical emergency · Outbreak · Pandemic · Political science · Primary care · Telecommunications · Warning system · Computer Science · COVID-19 Digital Contact Tracing · Data-Driven Disease Surveillance · Internal Medicine · Medical Coding and Health Information · Medicine · Virology

Tools

Open DOIOpen Access