| Literature DB >> 35671384 |
Ruitong Wang1, Chenyuan Qin1, Min Du1, Qiao Liu1, Liyuan Tao2, Jue Liu1,3,4.
Abstract
COVID-19 vaccine booster shots are necessary to provide durable immunity and stronger protection against the emerging SARS-CoV-2 variants. As a major platform for access to information, social media plays an important role in disseminating health information. This study aimed to evaluate hesitancy toward COVID-19 vaccine booster shots in China, assess its association with social media use, and provide information to manage social media. We conducted a cross-sectional study across all 31 provinces in mainland China from November 12, 2021, to November 17, 2021. In total, 3,119 of 3,242 participants completed the questionnaire (response rate = 96.2%). COVID-19 vaccine booster shot hesitancy rate in China was 6.5% (95% CI: 5.6-7.3). Unemployment (adjusted odds ratio [aOR] 2.428, 95% CI: 1.590-3.670), low monthly income (aOR 2.854,95% CI: 1.561-5.281), low scores of knowledge (aOR 0.917, 95% CI: 0.869-0.968) and low level of cues to action (aOR 0.773, 95% CI: 0.689-0.869) were associated with vaccine hesitancy. Compared with public social media, lower vaccine hesitancy was associated with high perceived importance of social media (aOR 0.252, 95% CI: 0.146-0.445) and official social media use (aOR 0.671, 95% CI: 0.467-0.954), while higher vaccine hesitancy was associated with traditional media use (aOR 3.718, 95% CI: 1.282-10.273). More efforts are needed to regulate the content of social media and filtering out misinformation. The role of official social media in disseminating health information should be enhanced.Entities:
Keywords: COVID-19; Social media; booster shot; third dose; vaccine hesitancy
Mesh:
Substances:
Year: 2022 PMID: 35671384 PMCID: PMC9302496 DOI: 10.1080/21645515.2022.2065167
Source DB: PubMed Journal: Hum Vaccin Immunother ISSN: 2164-5515 Impact factor: 4.526
Hesitancy toward COVID-19 vaccine booster shots in China by characteristics (N = 3119).
| Characteristics | Number of participants | Hesitancy toward COVID-19 vaccine booster shots (N,%) | |
|---|---|---|---|
| .102 | |||
| Eastern | 1,473 | 110 (54.5) | |
| Central | 1,019 | 56 (27.7) | |
| Western | 627 | 36 (17.8) | |
| .066 | |||
| Male | 1,511 | 111(55.0) | |
| Female | 1,608 | 91 (45.0) | |
| . | |||
| ≤20 | 162 | 12 (5.9) | |
| 21–30 | 1,747 | 98 (48.5) | |
| 31–40 | 934 | 59 (29.2) | |
| 41–50 | 194 | 17 (8.4) | |
| >50 | 82 | 16 (7.9) | |
| . | |||
| Less than high school | 29 | 1 (0.5) | |
| High school or some college | 568 | 63 (31.2) | |
| Bachelor‘s degree | 2,357 | 122 (60.4) | |
| Postgraduate degree | 165 | 16 (7.9) | |
| . | |||
| ≤3,000 | 411 | 28 (13.9) | |
| 3,001–5,000 | 558 | 52 (25.7) | |
| 5,001–10,000 | 1,370 | 86 (42.6) | |
| 10,001–20,000 | 641 | 31 (15.3) | |
| >20,000 | 139 | 5 (2.5) | |
| unmarried | 970 | 88 (43.6) | |
| married | 2,149 | 114 (56.4) | |
| unemployed | 677 | 68 (33.7) | |
| employed | 2442 | 134 (66.3) | |
| .052 | |||
| Yes | 238 | 23 (11.4) | |
| No | 2,881 | 179 (88.6) | |
| Traditional media or from friends and family members | 30 | 10 (5.0) | |
| Official social media | 1,365 | 54 (26.7) | |
| Professional social media | 149 | 18 (8.9) | |
| Public social media | 1,575 | 120 (59.4) | |
| Unimportant | 114 | 24 (11.9) | |
| Moderate | 414 | 89 (44.1) | |
| Important | 1,367 | 71 (35.1) | |
| Very important | 1,224 | 18 (8.9) | |
| .132 | |||
| <1 h | 2,710 | 183 (90.6) | |
| >1 h | 409 | 19 (9.4) | |
| 3119 | 202 (100.0) |
Note: p < .05 are marked in bold.
Summary of the association between use of social media and hesitancy toward COVID-19 vaccine booster shots in China by logistic regression models.
| Social media use | Univariate model | Multivariate modela | ||
|---|---|---|---|---|
| Crude odds ratio (95%CI) | Adjusted odds ratio (95%CI) | |||
| <1 h | Reference | Reference | ||
| >1 h | 0.673 (0.402–1.064) | .109 | 0.651 (0.363–1.109) | .130 |
| Traditional media or from friends and family members | 6.063 (2.668–12.964) | <.001 | 3.725 (1.308–10.050) | . |
| Official social media | 0.499 (0.357–0.691) | <.001 | 0.650 (0.454–0.922) | . |
| Professional social media | 1.666 (0.955–2.756) | .057 | 1.669 (0.890–2.991) | .096 |
| Public social media | Reference | Reference | ||
| Unimportant | Reference | Reference | ||
| Moderate | 1.027 (0.626–1.734) | .918 | 1.206 (0.705–2.126) | .505 |
| Important | 0.205 (0.125–0.347) | <.001 | 0.260 (0.152–0.458) | |
| Very important | 0.056 (0.029–0.106) | <.001 | 0.077 (0.039–0.153) | |
p < .05 are marked in bold.
Odds ratios were adjusted for sociodemographic factors (region, sex, education, age group, marital status, occupation, monthly income (RMB), history of chronic diseases (cardiovascular disease, cancer, diabetes, hypertension, or respiratory diseases), knowledge about COVID-19 and vaccines, perceived susceptibility, perceived severity, perceived barriers, perceived benefits, cues to action, and the other two variables about social media.
Summary of factors associated with hesitancy toward COVID-19 vaccine booster shots in China by stepwise logistic regression model.
| Characteristics | Adjusted odds ratio (95%CI) | |
|---|---|---|
| Traditional media or from friends and family members | 4.115 (1.497–10.869) | . |
| Official social media | 0.676 (0.472–0.958) | . |
| Professional social media | 1.595 (0.841–2.891) | .137 |
| Public social media | Reference | |
| Unimportant | Reference | |
| Moderate | 1.131 (0.664–1.986) | .658 |
| Important | 0.233 (0.137–0.407) | |
| Very important | 0.066 (0.033–0.130) | |
| ≤3,000 | Reference | |
| 3,001–5,000 | 2.854 (1.561–5.281) | |
| 5,001– 10,000 | 2.883 (1.568–5.389) | |
| 10,000– 20,000 | 2.312 (1.145–4.682) | . |
| >20,000 | 1.851 (0.550–5.319) | .280 |
| Employed | Reference | |
| Unemployed | 2.428 (1.590–3.670) | |
| 0.917 (0.869–0.968) | . | |
| 0.773 (0.689–0.869) |
p < .05 are marked in bold.
Figure 1.The association between hesitancy toward COVID-19 vaccine booster shots and time spent on social media among people who spent more than one hour on social media per day.
Stratified analysis of the association between social media use and hesitancy toward COVID-19 vaccine booster shots.
| Time spent >1h (Reference: time spent <1h) | Use traditional media (Reference: use public social media) | Use official social media (Reference: use public social media) | Use professional social media (Reference: use public social media) | Moderate perceived importance (Reference: low perceived importance) | High perceived importance (Reference: low perceived importance) | Very high perceived importance (Reference: low perceived importance) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aOR a(95%CI) | aOR a (95%CI) | aOR a (95%CI) | aOR a (95%CI) | aOR a (95%CI) | aOR a (95%CI) | aOR (95%CI) | ||||||||
| .447 | .483 | .787 | .724 | .275 | .913 | .535 | ||||||||
| Male | 0.539 (0.228–1.158) | 3.126 (0.477–16.516) | 0.631 (0.382–1.028) | 1.633 (0.683–3.694) | 1.555 (0.719–3.539) | 0.219 (0.101–0.496) | 0.051 (0.017–0.141) | |||||||
| Female | 0.842 (0.348–1.818) | 6.811 (1.782–24.098) | 0.698 (0.399–1.189) | 1.283 (0.412–2.223) | 0.807 (0.347–1.998) | 0.234 (0.101–0.575) | 0.081 (0.029–0.222) | |||||||
| .557 | .728 | .837 | . | . | . | |||||||||
| ≤30 | 0.691 (0.299–1.439) | 5.458 (1.403–18.766) | 0.685 (0.411–1.119) | 0.780 (0.269–1.930) | 3.183 (1.203–10.365) | 0.919 (0.352–2.966) | 0.242 (0.080–0.842) | |||||||
| >30 | 0.486 (0.191–1.125) | 3.616 (0.534–23.778) | 0.721 (0.0399–1.279) | 4.403 (1.717–10.802) | 0.759 (0.360–1.627) | 0.081 (0.036–0.180) | 0.028 (0.009–0.076) | |||||||
| .621 | .416 | .978 | .820 | .410 | .416 | .508 | ||||||||
| Less than Bachelor’s degree | 0.789 (0.276–2.000) | 1.933 (0.391–9.151) | 0.650 (0.309–1.305) | 1.448 (0.373–4.811) | 1.911 (0.632–6.477) | 0.383 (0.126–1.290) | 0.110 (0.028–0.430) | |||||||
| Bachelor’s Degree or higher | 0.581 (0.269–1.144) | 4.592 (1.057–17.255) | 0.658 (0.429–0.995) | 1.716 (0.800–3.459) | 1.097 (0.584–2.142) | 0.221 (0.117–0.431) | 0.065 (0.028–0.146) | |||||||
| .348 | .859 | .867 | .983 | .624 | .742 | .962 | ||||||||
| Employed | 0.782 (0.399–1.436) | 4.386 (0.974–8.313) | 0.676 (0.436–1.035) | 1.600 (0.770–3.142) | 1.264 (0.662–2.525) | 0.219 (0.114–0.441) | 0.072 (0.031–0.207) | |||||||
| Unemployed | 0.398 (0.096–1.261) | 3.638 (0.817–15.793) | 0.632 (0.316–1.219) | 1.630 (0.319–7.028) | 0.912 (0.305–2.927) | 0.273 (0.092–0.858) | 0.069 (0.018–0.255) | |||||||
| .844 | .195 | .805 | .142 | .993 | .106 | .835 | ||||||||
| Married | 0.708 (0.318–1.443) | 9.047 (1.899–36.108) | 0.719 (0.443–1.150) | 2.545 (1.095–5.521) | 1.163 (0.561–2.554) | 0.155 (0.073–0.345) | 0.061 (0.024–0.153) | |||||||
| Unmarried | 0.629 (0.233–1.495) | 2.393 (0.545–9.378) | 0.656 (0.367–1.140) | 0.948 (0.308–2.540) | 1.157 (0.492–2.880) | 0.400 (0.175–0.968) | 0.072 (0.022–0.218) | |||||||
p < .05 are marked in bold.
Odds ratios were adjusted for sociodemographic factors (region, sex, education, age group, marital status, occupation, monthly income (RMB), chronic disease (cardiovascular disease, cancer, diabetes, hypertension, or respiratory diseases), knowledge about COVID-19 and vaccines, perceived susceptibility, perceived severity, perceived barriers, perceived benefits, cues to action, and the other two variables about social media.