Accelerate the process of getting vaccinated: factors associated with consideration of and accessibility to COVID-19 vaccination in metropolises of…

General description of participants

Of the 8,990 eligible participants included in the survey analysis, 3,788 (42.14%) were vaccinated in Shanghai, 2,258 (25.12%) were vaccinated in Fuzhou, and 2,944 (32.75%) were vaccinated in Chengdu (see Table 1). Participants mean age was 29.55 (SD=11.63), most were male (55.98%), unmarried (59.21%), non-disabled (98.87%), white-collars or students (67.91%) and had graduated from university or junior college (63.16%). Most (75.45%) reported a monthly household income of 20,000 or less. The sample was representative of general population in terms of gender and income.

Table 2 presents the distribution and the univariate analysis of the possible factors relevant in the vaccination process. Some of the factors associated with both consideration and accessibility phases were statistically significant; they were region, education, occupation, brand preference, vaccination hesitancy, and domestic risk awareness (p<0.05). Disability status (p=0.001) and GP (p<0.001) were associated only with the consideration phase, while household income (p=0.002) difference was observed in the accessibility phase.

The logistic regression models (Fig.1) included the following variables: region, SES (education, occupation, income), attitudes towards COVID-19 and vaccines (vaccine brand preference, vaccination hesitancy, risk awareness for the domestic epidemic), and other basic characteristics of participants (age, sex, disability, contacted with GPs). Above variables were tested and found to be significant in at least one phase. All two models were statistically significant (p<0.05).

Multivariate analysis of factors associated with the two phases of the vaccination process. Binary logistic regression models were used to predict factors influencing the length of time categories to make an appointment and the length of time categories to receive a vaccination. The * was representative for p<0.05. Only the independent variables of the three dimensions (region, SES and personal attitudes towards COVID-19/vaccines) which are emphatically discussed in the study were represented in this figure. Covariates like disability and contacted with GPs were not presented

In the consideration phase (Fig.1), the odds of an appointment decision taking longer than one month were 2.26 (95% CI: 1.90 to 2.68) times greater for participants in Fuzhou and 2.48 (95%CI: 2.17 to 2.83) times greater for participants in Chengdu than for participants in Shanghai. Moreover, such odds increased for participants with master and above degree compared with those who were illiterate or graduated from primary school (OR: 1.76, 95% CI: 1.07 to 2.88). Participants with higher monthly household incomes (reference category:<5000) were also more likely to consider longer than one month. Occupational disparity was also significant. Compared with farmers, other professions, except for medical staff, were less likely to make an appointment within a month of hearing about COVID-19 vaccinations. For instance, the odds of the consideration phase being longer than one month were 3.37 (95%CI: 1.69 to 6.75) times greater for those engaged only in housework and for the unemployed than for farmers. Compared with participants with specific brand preference, the odds of the consideration phase being longer than one month were 1.13 (95%CI: 1.02 to 1.26) times greater for those without brand preference. The results also indicated that the odds of the consideration phase lasting more than a month increased with vaccination hesitancy (high hesitancy, OR: 2.98, 95%CI:2.50 to 3.55; medium hesitancy, OR:2.64, 95%CI:2.372.94; reference category: low hesitancy).

In the accessibility phase (Fig.1), the odds of waiting longer than one week to receive a vaccination were 8.82 (95% CI: 7.28 to 10.68) times greater for participants in Fuzhou and 2.28 (95%CI: 1.98 to 2.63) times greater for those in Chengdu than for participants in Shanghai. Such odds decreased only for participants with master and above degree compared to those who were illiterate or graduated from primary school (OR: 0.46, 95% CI: 0.29 to 0.75). Compared with farmers, teachers (OR: 0.51, 95%CI: 0.32 to 0.80) and students (OR: 0.32, 95%CI: 0.21 to 0.48) were less likely to wait longer than one week. The significant influence of monthly household income ()was merely found in one category(>=5000 and<10,000, OR:0.86, 95%CI:0.76 to 0.98, reference category:<5000). Meanwhile, participants without a brand preference (OR: 0.86, 95%CI: 0.77 to 0.95) were less likely to wait longer than a week after making an appointment to receive a vaccination. Moreover, this likelihood increased with higher risk awareness of a domestic epidemic (medium risk awareness, OR: 1.24, 95%CI:1.12 to 1.37; reference category: low risk awareness).

The multiple subgroup factor analysis for the vaccination processes in Shanghai, Fuzhou, and Chengdu are shown in Fig.2. The multi-variate models included following variables: SES (education, occupation, income), attitudes towards COVID-19 and vaccines (vaccine brand preference, vaccination hesitancy, risk awareness for the domestic epidemic), and other basic characteristics of participants (age, sex, disability, contacted with GPs). In Chengdu, no vaccine brand preference extended the consideration phase (OR:1.13, 95%CI:1.05 to 1.22, reference category: specific brand preference) but shortened the accessibility phase (OR:0.84, 95%CI:0.78 to 0.92). In Shanghai, the participants with no brand preference also tended to wait shorter in accessibility phase (OR:0.91, 95%CI:0.85 to 0.97). Participants graduating from senior high school were found to get vaccinated earlier after the appointment than those with lowest level of education (OR:0.45, 95%CI:0.27 to 0.75) in Shanghai. Higher household income in Shanghai and Chengdu and higher vaccination hesitancy in all three cities were significantly associated with longer consideration phase. Occupational disparities were found mainly in Shanghai. For example, house-based and unemployed participants were 3.47 (95% CI: 1.60 to 7.54) times more likely to have a longer consideration period than farmers.

Subgroup analysis of three cities: Shanghai, Fuzhou, and Chengdu. All six models were statistically significant (p<0.05). The * was representative for p<0.05. Variables included were the same in models for all three cities, while only variables that have at least one category that was significant in one or both phases are shown in the figure. Insignificant variables were not presented in the figure

Link:

Accelerate the process of getting vaccinated: factors associated with consideration of and accessibility to COVID-19 vaccination in metropolises of...