Early Response to COVID-19 in Brazil: The Impact of a Targeted Approach to Suspected Cases and on Epidemiologic Surveillance Efforts

Background SARS-CoV-2 entered Brazil before travel restrictions and border closures were imposed. This study reports characteristics of suspected and confirmed COVID-19 cases among symptomatic international travelers in Brazil and their contacts. Methods We analyzed the REDCap platform developed by the Brazilian Ministry of Health to notify and investigate suspect cases of COVID-19 from January 1 to March 20, 2020. We analyzed the impact of Brazil's targeted approach to suspected cases from specific countries on epidemiologic surveillance efforts during the early stages of the COVID-19 pandemic. Results Based on molecular RT-PCR tests, there were 217 (4.2%) confirmed, 1,030 (20.1%) unconfirmed, 722 (14.1%) suspected, and 3,157 (61.6%) non-investigated cases among travelers returning from countries included in the alert list for surveillance, as defined by the Ministry of Health. Among those who traveled to countries not included in the alert list (n=3,372), there were 66 (2.0%) confirmed, 845 (25.3%) unconfirmed, 521 (15.6%) suspected, and 1,914 (57.2%) non-investigated cases. A comparison of the characteristics of confirmed cases returning from alert and non-alert countries did not reveal a statistically significant difference in symptoms. Almost half of the hospitalized travelers with known travel dates and hospitalization status (53.6%) were inbound from countries not included in the alert list and RT-PCR tests were reported for only 30.5%. Conclusions Policies adopted at entry points to contain the introduction of SARS-CoV-2 in Brazil were not ideal. An analysis of the early response shows that surveillance of travelers, including testing strategies, data standards, and reporting systems, was insufficient.

The Relationship between Ideology and COVID-19 Deaths: What We Know and What We Still Need to Know

Several recent studies have investigated if support for Jair Bolsonaro in the presidential election of 2018 is positively associated with COVID-19 infections and deaths in Brazil. In these studies, COVID-19 outcomes in 2020 and 2021 are the dependent variables, and votes for Jair Bolsonaro in the 2018 presidential election (as a proxy for ideology) are the key explanatory variable. This article discusses why ecological research designs are difficult to test empirically. We discuss why correlations between vote shares and COVID-19 outcomes using aggregate data can produce biased inferences, and we specifically focus on measurement error, aggregation bias, and spatial and temporal dynamics.

Evaluation of the effectiveness of surveillance policies to control the COVID-19 pandemic in São Paulo, Brazil.

BACKGROUND: Surveillance efforts are essential to pandemic control, especially where the state is the primary health provider, such as Brazil. When public health testing guidelines limit molecular tests, there are reductions in detection efforts aimed at early recognition, isolation, and treatment of those infected with the virus. This study evaluates the effectiveness of surveillance policies to control the COVID-19 pandemic in São Paulo. METHODS: We conducted an interrupted time series analysis with a segmented regression model to analyze if changes in the state's guidelines improved RT-PCR testing outcomes in Brazil's most affluent and largest state, São Paulo. Anonymized daily data on the RT-PCR tests conducted in public laboratories belonging to the state-wide network from March 1, 2020 to June 5, 2021 were extracted from the Sao Paulo State open-source database, while the data on the genomic sequences were obtained from GISAID. We then aggregated these data for the 17 regional health departments in the state to evaluate regional-level outcomes. RESULTS: The public health system restricted RT-PCR testing to hospitalized cases in the first months. Testing was expanded to permit symptomatic testing of non-hospitalized persons only in July 2020, but a statistically significant increase in surveillance efforts was not observed. Case definition was expanded to allow case confirmation based on clinical, laboratory and image data criteria other than an RT-PCR test without increasing the testing effort for asymptomatic suspicious cases in September 2020. There was an increase in the mean volume of testing in each RHD, but the test positivity rate increased due to insufficient testing expansion. Results also show an uneven improvement in testing outcomes following these changes across the state's regional health departments. CONCLUSIONS: Evidence suggests that lower RT-PCR testing and genomic surveillance efforts are associated with areas characterized by a higher population concentration and a greater population reliance on the public health system. Our results highlight the need to structure health surveillance and information systems for disease control and prevention in emergency settings considering local demographics and vulnerabilities. In high prevalence settings, efforts at identifying and including vulnerable populations in routine and enhanced surveillance programs during COVID-19 must be significantly improved.