Citizens’ Perception of COVID-19 Passport Usefulness: A Cross Sectional Study
Abstract
:1. Introduction
- Several authors think that the IP must only be considered as a cause of permanent immunization after being vaccinated. In this case, the IP will become a vaccination certificate [7,14]. On the other hand, some opinions are favorable to considering immunity for those persons who have recovered from COVID-19 [15]. In this regard, it must be outlined that in many countries, e.g., such as in Spain, people who were infected by SARS-CoV-2 must wait several weeks after their recovery to receive a vaccine shot [16].
- The immunization duration of the COVID-19 vaccines is under study. However, it is commonly accepted that vaccine protection declines throughout time [17].
- Once the immunological requirements are met, IPs should be easy to obtain. In this regard, we must outline that if it is only possible to obtain certificates digitally through smartphones, older people who do not have the habits and skills to use these devices could experience a great barrier to obtaining an IP [14].
- Those people who, due to physiological reasons, cannot receive the vaccine could also be discriminated against. This same reflection can be extended to citizens from the poorest countries that will not have acceptable vaccination coverage for several years. The time needed to reach an acceptable vaccination rate in all countries is a critical issue. Despite all adults having been vaccinated at the end of 2021 in developed countries [18], this fact will not follow in Africa until 2023.
- The mandatory use of an IP to allow social activities could be in conflict with rights linked to privacy [7,9]. Vaccination and COVID-19 test results could be classified under personal health data, and its use is prohibited by the ninth article of the General Data Protection Regulation (GDPR). The exception could be related to the public interest, such as controlling the COVID-19 outbreak [19].
- The mandatory use of an IP can also violate other personal rights, such as freedom of movement, especially for people who would not be able to take the vaccine because of health constraints or cannot access it [20]. Other rights that are in conflict with the implementation of an IP are religious and ideological freedoms [7,9].
2. Materials and Methods
2.1. Materials
2.2. Variables
- Gender (GENDER) = a dichotomous variable that takes a value of 0 for males and 1 for females.
- Age (AGE) = a dummy variable that takes a value of 0 if the interviewee is under 45 years and 1 otherwise. In our sample, this is the median of that variable (see Table 1). Likewise, following [40], from that age, approximately, the probability that SARS-CoV-2 causes severe disease and death increases exponentially.
- Cultural status (CS) = a dummy variable whose value is 0 if the surveyed person has not completed a university degree and 1 if he/she is, at least, a graduate.
- Vaccination status (VAC) = a dichotomous variable that takes 0 if the person has not been vaccinated and 1 otherwise.
- Natural Immunization (NATIM) = a dichotomous variable that takes 0 if the person was never infected by SARS-CoV-2 and 1 otherwise.
- Number of PCRs done (NPCR) = takes 0 if the person never did any test and 1 otherwise.
2.3. Methods
+ a6 × NPCR
3. Results
- LQR in median (τ = 0.5) attains similar results to OLS. The principal explanatory variable is VAC, which showed a positive significant impact on UTILITY (cr = 0.482, p = 0.0047, 95%CI = [0.150, 0.815]). However, having immunity due to a previous infection also had a negative significant impact on the median value of UTILITY (cr = −0.327, p = 0.0446, 95%CI = [−0.645, −0.009]).
- Being vaccinated also had a significant impact on UTILITY at τ = 0.25 (p = 0.0026, 95%CI = [0.230, 1.074]) and τ = 0.75 (p = 0.0385, 95%CI = [0.011, 0.391]). Therefore, it seems that VAC is the key variable to explain UTILITY when IP is used to limit COVID-19 transmission due to mobility.
- NPCR and cultural status have a positive significant impact on the lower and upper quantiles of UTILITY, respectively. Therefore, whereas in the quantile τ = 0.25 we obtain for NPCR a cr = 0.484 (p = 0.0162, 95%CI= [0.091, 0.877]) in LQR at τ = 0.75, CS attains a cr = 0.201 (p = 0.0106, 95%CI = [0.048, 0.355]).
- If IP limits assembly in public spaces, Table 6 reports the following:
- Despite still being relevant in this second context, being vaccinated is less relevant to explaining acceptance. VAC only has a positive significant impact on the median (cr = 0.229, p = 0.0442, 95%CI = [0.162, 0.704]).
- Two sociodemographic factors, age and gender, seem to be the most relevant variables to explain UTILITY; whereas, age shows a significant positive relation with UTILITY in the median (cr = 0.229, p = 0.0442, 95%CI = [0.007, 0.451]) and at quantile τ = 0.75 (cr = 0.19, p = 0.0077, 95%CI = [0.051, 0.329]), being female is positively linked with a positive perception for the COVID-19 passport in the median (cr = 0.455, p < 0.0001, 95%CI = [0.243, 0.667]) and quantiles τ = 0.25 (cr = 0.427, p = 0.0045, 95%CI = [0.134, 0.719]).
- Cultural status also has a positive impact on UTILITY in the upper quantile (cr = 0.192, p = 0.0065, 95%CI = [0.054, 0.329]).
4. Discussion
5. Conclusions and Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Observation |
---|---|
GENDER | 50% men and 50% women |
AGE | 18–24 years = 25% 25–44 years = 25% 45–64 years = 25% plus 64 years = 25% median = 44.5 years mean = 45.38 years standard deviation = 19.87 years |
CULTURAL STATUS | 59.5% of people have a university degree |
VACCINATION STATUS | 21.25% of people were vaccinated |
NATURAL IMMUNITY | 22.5% of people are naturally immunized |
NUMBER OF PCRs | 67.75% of people reported getting tested at least once |
Traveling | Leisure | Tests on Differences | ||||||
---|---|---|---|---|---|---|---|---|
Question | Mean | Std. Dev. | Median | Mean | Std. Dev. | Median | t-Test | WSRT |
Question 1: I feel that implementing IP as a prophylactic measure is a bad/good idea | 5.97 | 3.43 | 7 | 5.78 | 3.63 | 7 | 1.442 (0.399) | 0.957 (0.339) |
Question 2: I feel that implementing IP as a prophylactic measure is a foolish/wise idea | 5.95 | 3.40 | 7 | 5.74 | 3.54 | 6 | 1.674 (0.095) | 1.265 (0.206) |
Question 3: I feel that implementing IP as a prophylactic measure is an Ineffective/effective idea | 6.17 | 3.23 | 7 | 5.88 | 3.40 | 7 | 2.606 (0.009) | 2.338 (0.019) |
Question 4: I feel that implementing IP as a prophylactic measure is negative/positive idea | 6.20 | 3.21 | 7 | 6.05 | 3.41 | 7 | 1.419 (0.339) | 0.807 (0.419) |
Traveling Purpose | Leisure Purpose | |||||
---|---|---|---|---|---|---|
Question | Loading | CA | AVE | Loading | CA | AVE |
0.953 | 0.834 | 0.969 | 0.888 | |||
Question 1: I feel that implementing IP as a prophylactic measure is a bad/good idea | 0.904 | 0.929 | ||||
Question 2: I feel that implementing IP as a prophylactic measure is a foolish/wise idea | 0.907 | 0.939 | ||||
Question 3: I feel that implementing IP as a prophylactic measure is an ineffective/effective idea | 0.872 | 0.928 | ||||
Question 4: I feel that implementing IP as a prophylactic measure is negative/positive idea | 0.821 | 0.868 |
Traveling Purpose | Leisure Purpose | |||||
---|---|---|---|---|---|---|
Variable | Regression Coefficient | 95%CI | p-Value | Regression Coefficient | 95%CI | p-Value |
Intercept | −0.098 | [−0.307, 0.111] | 0.357 | −0.22 | [−0.430 −0.010] | 0.041 |
GENDER | 0.097 | [−0.097, 0.291] | 0.328 | 0.21 | [0.013, 0.407] | 0.038 |
AGE | −0.090 | [−0.293, 0.113] | 0.386 | 0.09 | [−0.115, 0.295] | 0.391 |
CS | 0.059 | [−0.140, 0.258] | 0.565 | 0.01 | [−0.270, 0.290] | 0.942 |
VAC | 0.415 | [0.165, 0.665] | 0.001 | 0.27 | [0.023, 0.517] | 0.033 |
NATIM | −0.212 | [−0.451, 0.027] | 0.082 | −0.07 | [−0.307, 0.167] | 0.563 |
NPCR | 0.074 | [−0.156, 0.304] | 0.531 | 0.09 | [−0.133, 0.313] | 0.431 |
R2 = 0.0376 | R2 = 0.0299 | |||||
Snedecor’s F = 2.56 (0.0229) | Snedecor’s F = 2.02 (0.062) | |||||
White’s LM = 35.826 (0.0229) | White’s LM = 31.529 (0.0643) | |||||
Normality (χ2) = 89.972 (<0.001) | Normality (χ2) = 76.597 (<0.001) |
Level | τ = 0.25 | τ = 0.5 | τ = 0.75 | |||
---|---|---|---|---|---|---|
Variable | Regression Coefficient | p-Value | Regression Coefficient | p-Value | Regression Coefficient | p-Value |
Intercept | −0.829 | <0.0001 | 0.060 | 0.6712 | 0.701 | <0.0001 |
GENDER | 0.238 | 0.1581 | 0.080 | 0.545 | 0.002 | 0.9806 |
AGE | −0.239 | 0.1751 | −0.156 | 0.2619 | 0.038 | 0.6363 |
CS | −0.077 | 0.6599 | 0.234 | 0.089 | 0.201 | 0.0106 |
VAC | 0.652 | 0.0026 | 0.482 | 0.0047 | 0.201 | 0.0385 |
NATIM | −0.321 | 0.1193 | −0.327 | 0.0446 | −0.161 | 0.0833 |
NPCR | 0.484 | 0.0162 | 0.078 | 0.6226 | −0.119 | 0.1895 |
95%CI | 95%CI | 95%CI | ||||
Intercept | [−1.182, −0.476] | [−0.218, 0.338] | [0.542, 0.860] | |||
GENDER | [−0.092, 0.567] | [−0.179, 0.340] | [−0.147, 0.150] | |||
AGE | [−0.585, 0.106] | [−0.428, 0.116] | [−0.118, 0.193] | |||
CS | [−0.418, 0.264] | [−0.035, 0.502] | [0.048, 0.355] | |||
VAC | [0.230, 1.074] | [0.150, 0.815] | [0.011, 0.391] | |||
NATIM | [−0.725, 0.082] | [−0.645, −0.009] | [−0.343, 0.021] | |||
NPCR | [0.091, 0.877] | [−0.232, 0.387] | [−0.296, 0.058] | |||
Pseudo-R2 | 0.08241 | 0.03266 | 0.03762 |
Level | τ = 0.25 | τ = 0.5 | τ = 0.75 | |||
---|---|---|---|---|---|---|
Variable | Regression Coefficient | p-Value | Regression Coefficient | p-Value | Regression Coefficient | p-Value |
Intercept | −0.971 | <0.0001 | −0.126 | 0.2773 | 0.717 | <0.0001 |
GENDER | 0.427 | 0.0045 | 0.455 | <0.0001 | 0.031 | 0.6511 |
AGE | 0.023 | 0.8853 | 0.229 | 0.0442 | 0.190 | 0.0077 |
CS | −0.184 | 0.2331 | −0.057 | 0.6096 | 0.192 | 0.0065 |
VAC | 0.286 | 0.1351 | 0.433 | 0.0019 | 0.040 | 0.6438 |
NATIM | −0.178 | 0.3293 | −0.220 | 0.0975 | 0.067 | 0.4199 |
NPCR | 0.297 | 0.0957 | 0.082 | 0.5259 | 0.042 | 0.6044 |
95%CI | 95%CI | 95%CI | ||||
Intercept | [−1.285, −0.658] | [−0.353, 0.101] | [0.575, 0.859] | |||
GENDER | [0.134, 0.719] | [0.243, 0.667] | [−0.102, 0.163] | |||
AGE | [−0.284, 0.329] | [0.007, 0.451] | [0.051, 0.329] | |||
CS | [−0.487, 0.118] | [−0.276, 0.162] | [0.054, 0.329] | |||
VAC | [−0.088, 0.660] | [0.162, 0.704] | [−0.130, 0.210] | |||
NATIM | [−0.537, 0.180] | [−0.479, 0.040] | [−0.096, 0.229] | |||
NPCR | [−0.052, 0.646] | [−0.171, 0.335] | [−0.116, 0.200] | |||
Pseudo-R2 | 0.05550 | 0.03231 | 0.04617 |
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Andrés-Sánchez, J.d.; Arias-Oliva, M.; Pelegrin-Borondo, J. Citizens’ Perception of COVID-19 Passport Usefulness: A Cross Sectional Study. Behav. Sci. 2022, 12, 140. https://doi.org/10.3390/bs12050140
Andrés-Sánchez Jd, Arias-Oliva M, Pelegrin-Borondo J. Citizens’ Perception of COVID-19 Passport Usefulness: A Cross Sectional Study. Behavioral Sciences. 2022; 12(5):140. https://doi.org/10.3390/bs12050140
Chicago/Turabian StyleAndrés-Sánchez, Jorge de, Mario Arias-Oliva, and Jorge Pelegrin-Borondo. 2022. "Citizens’ Perception of COVID-19 Passport Usefulness: A Cross Sectional Study" Behavioral Sciences 12, no. 5: 140. https://doi.org/10.3390/bs12050140
APA StyleAndrés-Sánchez, J. d., Arias-Oliva, M., & Pelegrin-Borondo, J. (2022). Citizens’ Perception of COVID-19 Passport Usefulness: A Cross Sectional Study. Behavioral Sciences, 12(5), 140. https://doi.org/10.3390/bs12050140