Early detection of respiratory disease outbreaks through primary healthcare data

Bibliographic Data

ID19551663
AuthorsThiago Cerqueira-Silva (0000-0003-4534-2509), Izabel Marcilio (0000-0002-2914-6535), Vinicius De Araújo Oliveira (0000-0001-7858-9650), Pilar Tavares Veras Florentino (0000-0001-8077-8100), Gerson Oliveira Penna (0000-0001-8967-536X, Universidade de Brasília), Pablo Ivan Pereira Ramos (0000-0002-9075-7861), Viviane S Boaventura (0000-0002-7241-6844, Universidade Federal da Bahia), Manoel Barral-Netto (0000-0002-5823-7903)
Year2023
Volume13
Pages04124-04124
Publication date2023-11-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Global Health (JOURNAL)
Journal identifiersISSN: 2047-2978 • E-ISSN: 2047-2986
PublisherInternational Society of Global Health (PUBLISHER • GB)
DOI10.7189/jogh.13.04124
PMID37917874
OpenAlexW4388212747
LanguageEN
Citations received2
References cited14

Background: The emergence of coronavirus disease 2019 (COVID-19) in 2020 highlighted the relevance of surveillance systems in detecting early signs of potential outbreaks, thus enabling public health authorities to act before the pathogen becomes widespread. Syndromic digital surveillance through web applications has played a crucial role in monitoring the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, this approach requires expensive infrastructure, which is not available in developing countries. Pre-existing sources of information, such as encounters in primary health care (PHC), can provide valuable data for a syndromic surveillance system. Here we evaluated the utility of PHC data to identify early warning signals of the first COVID-19 outbreak in Bahia-Brazil in 2020. Methods: We compared the weekly counts of PHC encounters due to respiratory complaints and the number of COVID-19 cases in 2020 in Bahia State - Brazil. We used the data from December 2016 to December 2019 to predict the expected number of encounters in 2020. We analysed data aggregated by geographic regions (n = 34) and included those where historical PHC data was available for at least 70% of the population. Results: Twenty-one out of 34 regions met the inclusion criteria. We observed that notification of COVID-19 cases was preceded by at least two weeks with an excess of encounters of respiratory complaints in 18/21 (86%) of the regions analysed and four weeks or more in 10/21 (48%) regions. Conclusions: Digital syndromic surveillance systems based on already established PHC databases may add time to preparedness and response to emerging epidemics

Disease · Disease surveillance · Environmental health · Epidemiologic Surveillance · Health care · Medical emergency · Outbreak · Pathology · Population · Preparedness · Public health · Public health surveillance · COVID-19 Digital Contact Tracing · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Epidemiology · Internal Medicine · Virology