Running away from the jab: factors associated with COVID-19 vaccine hesitancy in Brazil

Paschoalotto, Marco Antonio Catussi; Costa, Eduardo Polena Pacheco Araújo; Almeida, Sara Valente de; Cima, Joana; Costa, Joana Gomes da; Santos, João Vasco; Barros, Pedro Pita; Passador, Claudia Souza; Passador, João Luiz

doi:10.11606/s1518-8787.2021055003903

Marco Antonio Catussi Paschoalotto Correspondence: Marco Antonio Catussi Paschoalotto, Travessa do Hospital, 18 - 2° andar 1150-187 Lisboa, Portugal, Email: marcocatussi@gmail.com

Universidade NOVA de Lisboa. Nova School of Business and Economics. Carcavelos, Portugal

https://orcid.org/0000-0003-2276-8531

Eduardo Polena Pacheco Araújo Costa

Universidade NOVA de Lisboa. Nova School of Business and Economics. Carcavelos, Portugal

https://orcid.org/0000-0002-4347-6338

Sara Valente de Almeida

Universidade NOVA de Lisboa. Nova School of Business and Economics. Carcavelos, Portugal

Imperial College London. Faculty of Health Sciences. London, England

https://orcid.org/0000-0001-9178-2105

Joana Cima

Universidade do Minho. Núcleo de Investigação em Políticas Económicas e Empresariais. Braga, Portugal

https://orcid.org/0000-0002-2780-7467

Joana Gomes da Costa

Universidade do Porto. Faculdade de Economia e Gestão. Porto, Portugal

https://orcid.org/0000-0003-3233-1026

João Vasco Santos

Universidade do Porto. Faculdade de Medicina. MEDCIDS – Departamento Medicina da Comunidade, Informação e Decisão em Saúde. Porto, Portugal

Universidade do Porto. Faculdade de Medicina. Centro de Investigação em Tecnologias e Serviços de Saúde. Porto, Portugal

ARS Norte. ACES Grande Porto VIII - Espinho/Gaia. Unidade de Saúde Pública. Vila Nova de Gaia, Portugal

https://orcid.org/0000-0003-4696-1002

Pedro Pita Barros

Universidade NOVA de Lisboa. Nova School of Business and Economics. Carcavelos, Portugal

https://orcid.org/0000-0002-0881-4928

Claudia Souza Passador

Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto. Ribeirão Preto, São Paulo, Brasil

https://orcid.org/0000-0002-9333-563X

João Luiz Passador

Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto. Ribeirão Preto, São Paulo, Brasil

https://orcid.org/0000-0002-0460-8852

Correspondence: Marco Antonio Catussi Paschoalotto, Travessa do Hospital, 18 - 2° andar 1150-187 Lisboa, Portugal, Email: marcocatussi@gmail.com

Authors’ Contribution: Study design and planning: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Data collection, analysis and interpretation: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Manuscript drafting or review: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Approval of the final version: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Public responsibility for the content of the article: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP.

Conflict of Interests: The authors declare no conflict of interest.

Variable			Survey Data		National Data
States and municipalities (number)^a a Sample comprising 88.9% of the Brazilian states, 74.7% of the Brazilian capitals, and 4.7% of the Brazilian municipalities. More than 75% of the Brazilian municipalities are characterized as “small” (< 25,000 inhabitants), reducing the likelihood of achieving a substantial representativeness for them (31).
			States		24		27
	Capitals		20		27
	Municipalities		263		5,570
Gender (%)
	Male		37.9		48.2
	Female		61.7		51.8
	Other/No answer		0.4
Age (%)
	≤ 18 years	0.9		< 19 years		33.1
	19 to 25 years	30.6		20 to 24 years		9.0
	26 to 32 years	20.9		25 to 34 years		17.1
	33 to 45 years	27.4		35 to 44 years		14.0
	46 to 64 years	18.4		45 to 64 years		19.2
	65 to 79 years	1.9		65 to 79 years		6.0
	≥ 80 years	0.1		≥ 80 years		1.6
Education (%)
	Elementary school		0.5		55.8
	High school		14.4		30.1
	University – Bachelor		40.5		14.1
	University – MBAs and specializations		20.8
	University – Master’s		14.1
	University – Doctorate		9.7
Residence area (%)
	Urban		97.4		84.4
	Rural		2.6		15.6
Households (%; average number)
	One/Live alone		7.9		30.9
	Two		33.3
	Three		26.9		30.4
	Four		20.7		22.8
	Five		7.2		10.0
	More than five		4.0		5.9
Professional situation (%)
	Retired	2.8		Out of the workforce		37.2
	Student	21.2
	Unemployed	6.5		Unoccupied		6.6
	Public server	17.2		Occupied		39.1
	Worker – Own business	10.9
	Worker – SME enterprises	15.4
	Worker – Big enterprises	22.1
	Other/No answer	3.9		Other		17.1

		(1)	(1)	(2)	(2)
		OR	dydx	OR	dydx
Compliance Index				1.123^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.014^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
Age (years)(baseline group ≤ 18)
	19–25	0.912	-0.018	0.951	-0.006
	26–32	1.014	0.003	0.841	-0.020
	33–45	1.002	0.0004	0.877	-0.015
	46–64	0.785	-0.048	0.597	-0.063
	≥ 65	0.447	-0.174	0.619	-0.058
Gender (baseline group: Female)
	Male	1.324^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.054^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	1.218	0.023
Professional situation (baseline group: Unemployed)
	Retired	2.93^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.179^c a, b, c indicate significance at 10%, 5% and 1% level, respectively.	3.867^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.145^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
	Student	1.391	0.066	1.012	0.002
	Other	0.834	-0.039	1.080	0.010
	Public server	1.424	0.070	1.405	0.042
	Worker – Big enterprises	1.178	0.034	1.829	0.072
	Worker – SME enterprises	1.122	0.024	1.266	0.029
	Worker – Own business	0.936	-0.014	1.225	0.025
Schooling level (baseline group: Elementary school)
	High school	2.940	0.246	2.856	0.127
	University – Bachelor	1.960	0.161	1.218	0.027
	University – MBAs and specializations	2.827	0.238	1.700	0.069
	University – Master	4.747 ^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.328	2.692	0.121
	University – PhD	5.103 ^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.338^∗	2.049	0.091
Vaccine side effects (baseline group: do not disagree or agree)
	Fully disagree			3.454 ^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.077^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
	Partially disagree			2.346 ^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.060^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
	Partially agree			0.503^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	-0.077^c
	Fully agree			0.108^c	-0.344^c
Political leaning (baseline group: Center)
	1- Far left			0.896	-0.014
	2			1.869^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.072^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
	3			1.553 ^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.	0.053 ^a a, b, c indicate significance at 10%, 5% and 1% level, respectively.
	5			0.690	-0.050
	6			0.476^c	-0.104^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
	7 - Far right			0.388^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.	-0.136^b a, b, c indicate significance at 10%, 5% and 1% level, respectively.
Federal government - Performance (baseline group: Fair)
	Very bad			2.355^c	0.107^c
	Bad			1.337	0.039
	Good			0.699	-0.052
	Very good			0.532	-0.095
	N	1,623	1,623	1,261	1,261

[1] Correspondence: Marco Antonio Catussi Paschoalotto, Travessa do Hospital, 18 - 2° andar 1150-187 Lisboa, Portugal, Email: marcocatussi@gmail.com

Brasil

Brasil

Running away from the jab: factors associated with COVID-19 vaccine hesitancy in Brazil

ABSTRACT

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION: