ABSTRACT
OBJECTIVE
Flattening the curve was the most promoted public health strategy worldwide, during the pandemic, to slow down the spread of the SARS-CoV-2 virus, and, consequently, to avoid overloading the healthcare systems. In Brazil, a relative success of public policies was evidenced. However, the association between public policies and the “flatten the curve” objectives remain unclear, as well as the association of different policies to reach this aim. This study aims to verify if the adoption of different public policies was associated with the flattening of the infection and death curves by covid-19 first wave in 2020.
METHODS
Data from the Sistema de Informação da Vigilância Epidemiológica da Gripe (Influenza Epidemiological Surveillance Information System – SIVEP-Gripe) and the Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics – IBGE) were used to compute standardized incidence and mortality rates. The Oxford Covid-19 Government Response Tracker (OxCGRT) was used to obtain information about governmental responses related to the mitigation of pandemic effects, and the Human Development Index (HDI) was used as a measure of socioeconomic status. A non-linear least-square method was used to estimate parameters of the five-parameter sigmoidal curve, obtaining the time to reach the peak and the incremental rate of the curves. Additionally, ordinary least-square linear models were used to assess the correlation between the curves and the public policies adopted.
RESULTS
Out of 51 municipalities, 261,326 patients had SARS-CoV-2 infection. Stringency Index was associated with reducing covid-19 incremental incidence and death rates,in addition to delaying the time to reach the peak of both pandemic curves. Considering both parameters, economic support policies did not affect the incidence nor the mortality rate curves.
CONCLUSION
The evidence highlighted the importance and effectiveness of social distancing policies during the first year of the pandemic in Brazil, flattening the curves of mortality and incidence rates. Other policies, such as those focused on economic support, were not effective in flattening the curves but met humanitarian and social outcomes.
Public Policy; Mortality; SARS-CoV-2; COVID-19; Communicable Disease Control
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Note: Absolute (dark gray) and cumulative (light gray) case/death rates, and first (solid blue) and second (solid green) derivative curves of the latter. The incremental rate (dashed black line) indicates the growth rate during the “window of linearity,” which is the largest linearity region of the cumulative curve. The boundaries for this region are points (red and green rings) corresponding to the maximum (red circle/dashed red line) and minimum (green circle/dashed green line) points of the second derivate curve. The time to reach the peak of the absolute rate curve is the corresponding time inside the “window of linearity” where the second derivate is zero (dashed blue line), or, in other words, where the rate start to decline in a municipality.
Note: Blue, red, and green lines correspond to Manaus, Campo Grande, and Florianópolis, respectively.
Note: the first boundary corresponds to the First Week of Infection Case Notification and the last indicates the End of the Highest Peak of the Curve. Estimated (dotted line) and Observed (solid line) Cumulate Incidence Curve for Manaus [D], Florianópolis [E], and Campo Grande [F] in the region circumscribed in A, B, and C, respectively. The two vertical lines delimitate the “window of linearity,” and the inclined line is made of the region’s first and last incidence rate of the estimated curve. The tangent of the angle formed by the inclined line and the x-axis represents the slope.