Panel analysis
Information on sample composition and intersectionality is located in the appendix.
In the panel-analysis five dependent variables are put under investigation via pooled OLS regressions and one pooled Logit regression (vaccination readiness with min. 1 dose). Results regarding pre-existing conditions and comorbidities are listed, followed by findings regarding age. P-values and 95% confidence intervals are put in parentheses only when significant at the p<0.100 level. People who reported one underlying medical condition exhibited higher compliance with preventive measures (p=0.074, 95% CI -0.003 to 0.061), whereas persons with comorbidities significantly complied less (p=0.039, 95% CI -0.108 to -0.003). Looking at vaccination readiness, as at least one dose received vs. none-received, neither having one nor having several medical conditions produces significant results. People with one underlying condition took fewer Corona-tests (p=0.001, 95% CI -0.079 to -0.019), while for people with comorbidities there is no significant result. Persons with one medical condition supported compulsory vaccination (p=0.031, 95% CI 0.005 to 0.101), and so did those with comorbidities (p=0.064, 95% CI -0.004 to 0.131). Last, both people with one condition (p=0.000 95% CI -0.010 to -0.030) and with comorbidities (p=0.006, 95% CI -0.105 to -0.017) reported having met fewer social contacts.
Now turning to results for age, people above 65 years report higher compliance with preventive measures (p=0.000, 95% CI 0.069 to 0.125), higher vaccination readiness (p=0.000, 95% CI 0.576 to 1.088), a lower test uptake (p=0.000, 95% CI -0.121 to -0.062), stronger support for compulsory vaccination (p=0.000, 95% CI 0.083 to 0.177), and fewer contacts (p=0.000, 95% CI -0.145 to -0.090). Regression tables are provided in the appendix.
Cross-sectional analysis
The cross-sectional analysis considers eight dependent variables with data from late November/early December 2021. Low values on the dimension 1 of the MCA correspond to having no or few indicated diseases, disorders or injuries. High values relate to suffering from several medical conditions, i.e. from comorbidities.
Turning to preferences for the governmental response to contain the Coronavirus, people that scored high on the MCA exhibited more support for stringent measures (p=0.069, 95% CI -0.001 to 0.031). Persons, who had high MCA scores, were more likely to be vaccinated against COVID-19 (p=0.002, 95% CI 0.012 to 0.050). By contrast, those with low MCA scores reported higher test uptakes than those with high MCA scores (p=0.004, 95% CI -0.051 to -0.001).
High MCA scores correlate positively with self-reported compliance with preventive behaviour (p=0.031, 95% CI 0.002 to 0.040). People, who were high on the first dimension of the MCA, perceived others’ preventive behaviour to be less compliant (p=0.034, 95% CI -0.034 to -0.001) and also perceived others’ opinions on the government responses to be less supportive (p=0.068, 95% CI -0.032 to 0.001).
Finally, persons with comorbidities were more in favour of compulsory vaccination (p=0.016, 95% CI 0.006 to 0.056) and reported fewer social contacts (p=0.037, 95% CI -0.037 to -0.001).
People above the age of 65 exhibited more supportive preferences for government measures (p=0.001, 95% CI 0.031 to 0.115), a higher vaccination readiness (p=0.000 , 95% CI 0.090 to 0.191), a lower test uptake (p=0.000, 95% CI -0.160 to -0.054), stronger compliance (p= 0.000, 95% CI 0.068 to 0.166), more supportive preferences for compulsory vaccination (p=0.000, 95% CI 0.080 to 0.210), and fewer social contacts (p=0.000, 95% CI -0.204 to -0.111). However, persons in the advanced age group perceived others’ compliant behaviour (p=0.016, 95% CI 0.010 to 0.093) and others’ opinions towards government measures (p=0.001, 95% CI 0.029 to 0.114) more favourably than the younger cohorts did. Regression tables are provided in the appendix.
Robustness checks with control variables
For the robustness checks four control variables are considered: gender (binary), trust in the public health care system, trust in the federal government, and perceived personal health risk from COVID-19. Gender has been previously identified as a significant variable in partially explaining preventive behaviour (21, 22) and so has trust in governments (23, 24), as well as risk perception (25).
In the cross-sectional analysis perceived personal health risk strongly predicts stronger preferences (p=0.000, 95% CI 0.289 to 0.386) for and better compliance (p=0.000, 95% CI 0.194 to 0.326) with preventive measures. It also correlates positively with perceived behaviour (p=0.005, 95% CI 0.025 to 0.140) and opinions (p=0.000, 95% CI 0.083 to 0.199) of others, and negatively with number of social contacts (p=0.000, 95% CI -0.290 to -0.160). Trust in the public health care system seems to facilitate stronger compliance (p=0.000, 95% CI 0.109 to 0.247) and support for preventive measures (p=0.000, 95% CI 0.232 to 0.334. The same is true for trust in the federal government with compliance (p=0.002, 95% CI 0.040 to 0.175) and preferences for preventive measures (p=0.000, 95% CI 0.122 to 0.221). Finally, women appear to show stronger compliance (p=0.014, 95% CI 0.009 to 0.077) and report fewer social contacts (p=0.000, 95% CI -0.094 to -0.027). Detailed results of all robustness checks are located in the appendix.
As for the main predictors - underlying medical conditions, comorbidities and age - the robustness checks largely corroborate the initial main findings.