Were public interventions relevant for containing the covid-19 pandemic in Brazil in 2020?

Cattani, Vitória Berg; Santos, Thaís Araujo dos; Ribeiro-Alves, Marcelo; Castro-Alves, Julio

doi:10.11606/s1518-8787.2023057005030

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Original Article • Rev. Saúde Pública 57 • 2023 • https://doi.org/10.11606/s1518-8787.2023057005030 linkcopy

Were public interventions relevant for containing the covid-19 pandemic in Brazil in 2020?

Authorship SCIMAGO INSTITUTIONS RANKINGS

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

Variables	SIR ^a		SMR ^b

	Low incidence Mean	High incidence Mean	Low mortality Mean	High mortality Mean
Slope
HDI	0.77	0.75	0.78	0.74
HDI Education	0.7	0.69	0.71	0.68
HDI Health	0.84	0.82	0.85	0.82
HDI Income	0.78	0.75	0.79	0.72
Demographic Density Rate	295.23	298.17	377.86	244.48
Crowding Rate	20.13	34.9	19.92	39.97
ICU Hospital Beds Rate	2.97	2.77	2.84	2.37
Physician Rate	2.74	2.03	3.05	1.66
Stringency Index	62.2	54.5	67.56	66.1
Economic Support Index	4.82	10.61	8.59	8.1
Containment and Health Index ^c	41.02	36.14	47.88	43.3
Government Response Index ^d	36.45	32.95	42.9	38.9
Time
HDI	0.74	0.79	0.72	0.79
HDI Education	0.67	0.72	0.66	0.72
HDI Health	0.82	0.85	0.81	0.85
HDI Income	0.73	0.81	0.71	0.81
Demographic Density Rate	344.32	275.64	296.02	263.18
Crowding Rate	38.95	18.28	39.69	18.1
ICU Hospital Beds Rate	2.71	2.94	2.57	2.78
Physician Rate	1.82	3.27	1.66	3.02
Stringency Index	54.26	62.4	65.99	65.51
Economic Support Index	1.48	10.71	3.18	14.11
Containment and Health Index ^c	35.15	43.38	42.48	47.59
Government Response Index ^d	30.92	39.3	37.54	43.4

Variables	SIR ^a	p-value	SMR ^b	p-value

	Ab (95%CI)		Ab (95%CI) p-value
Slope
Stringency Index	-1.17 (-2.02 to -0.31)	0.01	-0.16 (-0.59 to 0.26)	0.44
Economic Support Index	0.64 (-0.19 to 1.47)	0.13	0.18 (-0.16 to 0.52)	0.3
Containment and Health Index ^c	-0.8 (-1.8 to 0.21)	0.12	-0.15 (-0.56 to 0.26)	0.46
Government Response Index ^d	-0.72 (-1.88 to 0.44)	0.22	-0.12 (-0.59 to 0.36)	0.62
Time
Stringency Index	0.35 (0.12 to 0.57)	< 0.01	0.21 (0.03 to 0.4)	0.02
Economic Support Index	-0.21 (-0.42 to 0.01)	0.06	-0.07 (-0.24 to 0.09)	0.35
Containment and Health Index ^c	0.45 (0.21 to 0.69)	< 0.01	0.27 (0.11 to 0.44)	< 0.01
Government Response Index ^d	0.46 (0.18 to 0.75)	< 0.01	0.3 (0.1 to 0.5)	< 0.01

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Faculdade de Saúde Pública da Universidade de São Paulo Avenida Dr. Arnaldo, 715, 01246-904 São Paulo SP Brazil, Tel./Fax: +55 11 3061-7985 - São Paulo - SP - Brazil
E-mail: revsp@usp.br

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[TFN1] ^a SIR: standardized incidence rate of hospitalization by covid-19 curves, estimated considering the day of infection as 9 days before the date of registration at the SIVEP system. ^b SMR: standardized mortality rate of hospitalization by covid-19 curves. ^c Stringency Index augmented by health policies. ^d Containment and Health Index plus Economic Support Index.