| Literature DB >> 34852168 |
Marco Antonio Catussi Paschoalotto1, Eduardo Polena Pacheco Araújo Costa1, Sara Valente de Almeida1,2, Joana Cima3, Joana Gomes da Costa4, João Vasco Santos5,6,7, Pedro Pita Barros1, Claudia Souza Passador8, João Luiz Passador8.
Abstract
OBJECTIVE: To investigate how sociodemographic conditions, political factors, organizational confidence, and non-pharmaceutical interventions compliance affect the COVID-19 vaccine hesitancy in Brazil.Entities:
Mesh:
Substances:
Year: 2021 PMID: 34852168 PMCID: PMC8639140 DOI: 10.11606/s1518-8787.2021055003903
Source DB: PubMed Journal: Rev Saude Publica ISSN: 0034-8910 Impact factor: 2.106
Sample characteristics (Survey Data) x National characteristics (National Data).
| Variable | Survey Data | National Data | |||||
|---|---|---|---|---|---|---|---|
| States and municipalities (number) | |||||||
| 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 | ||||
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).
FigureBivariate analysis plots (except for 1A), respectively: Willingness to be vaccinated (A); Willingness to be vaccinated and NPI Compliance Index (B); Willingness to be vaccinated and Age (C); Willingness to be vaccinated and Gender (D); Willingness to be vaccinated and Schooling level (E); Willingness to be vaccinated and Vaccine side effects (F); Willingness to be vaccinated and Political leaning (G); Willingness to be vaccinated and Federal Government performance (H).
Logit models analyzing the explanatory capacity of the independent variables concerning the willingness to be vaccinated.
| (1) | (1) | (2) | (2) | ||
|---|---|---|---|---|---|
| OR | dydx | OR | dydx | ||
| Compliance Index | 1.123 | 0.014 | |||
| 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 | 0.054 | 1.218 | 0.023 | |
| Professional situation (baseline group: Unemployed) | |||||
| Retired | 2.93 | 0.179 | 3.867 | 0.145 | |
| 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 | 0.328 | 2.692 | 0.121 | |
| University – PhD | 5.103 | 0.338∗ | 2.049 | 0.091 | |
| Vaccine side effects (baseline group: do not disagree or agree) | |||||
| Fully disagree | 3.454 | 0.077 | |||
| Partially disagree | 2.346 | 0.060 | |||
| Partially agree | 0.503 | -0.077c | |||
| Fully agree | 0.108c | -0.344c | |||
| Political leaning (baseline group: Center) | |||||
| 1- Far left | 0.896 | -0.014 | |||
| 2 | 1.869 | 0.072 | |||
| 3 | 1.553 | 0.053 | |||
| 5 | 0.690 | -0.050 | |||
| 6 | 0.476c | -0.104 | |||
| 7 - Far right | 0.388 | -0.136 | |||
| Federal government - Performance (baseline group: Fair) | |||||
| Very bad | 2.355c | 0.107c | |||
| 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 | |
indicate significance at 10%, 5% and 1% level, respectively.
Note: We also ran ordered logit models, which presented the same significative results.