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.
Background: Surveillance efforts are critical to pandemic control, especially where the state is the primary health provider, such as Brazil. When public health testing guidelines limit RT-PCRs, there are reductions in detection efforts aimed at early recognition, isolation, and treatment of those infected with the virus.Methods: We conducted an interrupted time series analysis with a segmented regression model using publicly available data to analyze if changes in the state’s guidelines improved RT-PCR testing outcomes in Brazil’s most affluent and largest state, São Paulo, from March 2020 to June 2021. Results: The São Paulo state’s policy guidelines have changed substantially over time. In the first months, the public health system restricted RT-PCR testing to hospitalized cases. Testing was expanded to permit symptomatic testing of non-hospitalized persons only in July 2020. In September 2020, there was a review of the national surveillance guidelines and case definition was expanded to permit case confirmation based on clinical, laboratory and image data criteria other than an RT-PCR test. In February 2021, policies were revised to instruct public health agencies to increase epidemiological monitoring with genomic data. Results 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 reliance of the population on the public health system.
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.
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