Abstract
Parental vaccine hesitancy is a key factor influencing children’s vaccination against infectious diseases such as the COVID-19. The current study aims to investigate how parent’s health literacy and health belief affect parental hesitancy toward the COVID-19 vaccination, and navigate effective measures to help parents make vaccination decision for children. A mixed-mode web survey was conducted among parents of children aged 3–11 years. Parental vaccine hesitancy, health literacy, and health beliefs were assessed. Parallel mediation model examined whether the association between parent's health literacy and vaccine hesitancy was mediated by health beliefs. In total, 11.3% of the 346 participants reported vaccine hesitancy. Hesitant parents were more likely to be he mother (Father: 4.5%; Mother: 12.9%) and with children having allergic issues (Allergic: 18.3%; Non-allergic: 9.8%). Meanwhile, parents with lower health literacy were more likely to show hesitancy towards vaccinating their children (β = −6.87, 95% CI = [−10.50, −3.11]). This relationship was partially mediated by more perceived barriers in vaccination (β = −2.53, 95%CI = [−4.09, −1.02]), but not other health beliefs. In other words, parents with better health literacy may perceive fewer barriers in making vaccination decision for their children, thus being less hesitant. Accordingly, healthcare professionals and policy makers could design education service to promote parents’ health literacy, and remove the perceived barriers as well as increase their confidence in following the COVID-19 vaccine guidance for children.
Keywords: Health literacy, Parental vaccine hesitancy, Health beliefs, Mediating effect
1. Introduction
In the past 12 months, the variants of SARS-CoV-2 such as Omicron have emerged and its high infection risk was identified by the World Health Organization (WHO) [1]. Moreover, its confluence with the influenza outbreaks in autumn and winter could result in considerable morbidity and mortality [2]. Epidemiological studies in Korea, the United States, and other countries have found that two years after the global outburst of the COVID-19 pandemic, the hospitalization and lethality rates of children under 11 years old was 3.1% and 0.4%, respectively. The rates were higher than the previous records reported during the strain period when comparing pediatric record and the data of life-span sample [3], [4]. In addition, there were case reports of children infected with COVID-19 leading to multisystem inflammatory syndrome with a mortality rate of 1 to 2% [5], [6]. Thus, the potentially serious consequences of COVID-19 in children warranted that vaccinating them against COVID-19 is not only necessary for protecting the children population, but also important for building a population-wide immune barrier and controlling the spread of coronavirus [7]. Since October 2021 when many developed countries have not yet approved vaccination for young children (e.g. the UK, Australia, New Zealand, etc.), China, as a developing country with a large population of young children (over 200 million in 2021 according to National Bureau of Statistics, 2023) [8], has started promoting the inactivated vaccine against COVID-19 for children aged 3–11 years. Understanding the public’s reaction, particularly parent’s attitude and associated factors, would provide important insights for developing vaccine-related policies in other countries worldwide.
Several studies have shown parental concerns about the safety and effectiveness of COVID-19 vaccines has increased [9], [10], [11], and become an essential barrier for the promotion of vaccination [12]. According to the WHO, vaccine hesitancy is one of the top ten threats to global health [13], which was defined as ‘delay in acceptance or refusal of vaccination despite availability of vaccination services’ [14]. With high vaccine hesitancy, people may refuse or delay vaccinations for themselves or their children [15], [16], [17], [18], which exposed them to greater risks of infection. According to the “7C” theoretical framework, vaccine hesitancy is related to people’s confidence, complacency, convenience, risk calculation, and collective responsibility, conspiracy, and compliance [19]. However, how parents’ attitude toward COVID-19 vaccination was shaped needs a closer look.
Prior research has identified multiple factors that are associated with parental attitudes toward children’s vaccination, including parent’s health literacy [20], [21], [22]. When launching Healthy People 2030, health literacy was referred as “the degree to which individuals can find, understand, and use information and services to inform health-related decisions and actions for themselves and others [23].” Vaccine literacy has been built on the similar idea as health literacy, and defined as “not only the simply knowledge about vaccines, but also a system with decreased complexity to communicate and offer vaccines as sine qua non of a functioning health system [24]”. Previous study by the our research team has found that health literacy affects people’s attitude toward their decision of taking COVID-19 vaccine for themselves [25]. However, the relationship between parent's health literacy and their attitudes towards children’s vaccination has remained inconclusive [26].
How health literacy would influence people’s health-related decisions such as vaccine uptake? Previous studies have found that Health Belief Model (HBM) could be applied to understanding people’s health behavior [27], such as vaccine uptake of pandemic swine flu [28], or HPV (Human papillomavirus infection) [29]. According to HBM, different health beliefs including how to perceive the severity and susceptibility of the disease infection (e.g., such as COVID-19), and how to perceive the benefits and accessibility of prevention measures (e.g., vaccines) would together influence people’s vaccine hesitancy [30]. Health literacy, as an indicator of individual’s capacities of processing health information, would play a key role in developing different health beliefs [31], [32]. However, to our best knowledge, few studies have examined the roles of health literacy and HBM constructs in COVID-19 vaccine uptakes, particularly when making decisions for one’s children.
According to the existing literature, we hypothesized that parent's health literacy is associated with lower vaccine hesitancy and health beliefs may play a mediating role. The study has a two-fold purposes: (1) to test how parent’s health literacy is associated with their vaccine hesitancy; (2) to explore the role of health beliefs in the abovementioned relationships.
2. Methods
2.1. Participants and design
The cross-sectional survey was conducted from November to December 2021. In Late October, Chinese government started advocating COVID-19 vaccinations for children aged 3–11 years [33], such that parents should approve and bring their children to take the COVID-19 vaccination [34]. Parents of children aged 3–11 years were recruited at the outpatient sections of the hospitals. The inclusion criteria for the questionnaire study participants were: having at least one child aged 3–11 years; having no difficulties in understanding Chinese. G*power was used to determine the minimum number of participants (Gpower: Faul & Erdfelder, 1992) [35]. To detect a small main effect equivalent to f2 = 0.05 with a power of 0.80 and an alpha of 0.05, a sample size of 263 individuals would be sufficient.
2.2. Procedures
To ensure that eligible participants could be recruited to reach the required sample size, tow researchers (i.e., graduate students from the Public Health program) have been to the waiting room of Children’s hospital for recruitment. However, considering the restrained physical contact to prevent the spread of coronavirus, participant’s responses were submitted online, which means a mixed-mode web survey was used. Participants who are native Chinese speakers, having at least one child aged from 3 to 11, and willing to participate would receive a QR code of the online survey. The survey was voluntary and anonymous with no personal information being included. After completion, the participants would submit their responses via a predisposed front-end and receive a debriefing regarding the study. Grandparent, paid helpers, or those who do not understand Chinese will not be included in the sample. The study was approved by the Research Ethics Committee of XXX University (blinded for review), and informed consent was obtained from each participant.
In total, 450 participants were invited for the study, and 81.1% have completed the survey and submitted their responses. The average response time is 189 s. The responses with a completion time less than 120 s, or with consecutive repeated answers for over 10% of the questions, would be considered invalid [36]. After screening, there were 346 valid responses, and the valid response rate was 94.8%.
2.2.1. Demographic variables
Participants’ socio-demographic characteristics were collected, including age, gender, education level, employment, health insurance, etc. In addition, their number of children, and Covid-19 vaccination were also included. For children’s situation, their history of chronic illness and allergies, Covid-19 vaccination were asked.
2.2.2. Parental vaccine hesitancy
Parent Attitudes about Childhood Vaccines (PACV) Scale Was used to evaluate participants’ attitude toward children’s vaccination. PACV was developed by Opel et al. (2011) [37], which is now widely used to measure parental vaccine hesitancy, e.g., “It is better for children to get fewer vaccines at the same time”. The scale has been validated in the USA, Italy, Malaysia, and China [38], [39], [40], [41], [42], [43], and a short scale has been developed [44]. The short version of PACV includes 15 items in 3 dimensions, namely immunization behavior (items 1 and 2), vaccine safety and efficacy (items 7 to 10), and overall attitude (items 3 to 6 and 11 to 15). The total score of all entries was summed and converted to a simple linear score on a percentage scale, with a score over 50 indicating hesitant attitude. The Cronbach's alpha for this scale was 0.705.
2.2.3. Health literacy
A 12-item short version of the European Health Literacy Questionnaire (HLS-EU-Q) [45] was used to assess people’s information processing skills in three domains, namely, health promotion (e.g., if you want to, how difficult is joining a sports club or exercise class for you?), health care (e.g., how difficult is finding information on treatments of illnesses that concern you?), and disease prevention(e.g., how difficult is finding information on how to manage mental health problems like stress or depression?). A 4-point Likert scale was used, with responses ranging from “1″ (very difficult) to ”4″ (very easy), and “unclear” was coded as a missing value. The total score was obtained to indicate the level of health literacy, with higher scores indicating better health literacy. In our study, the Cronbach's alpha for the health literacy scale was 0.925.
2.2.4. The health beliefs model
HBM questionnaire was partially based on the questionnaire developed by Shahrabani and Benzion (2012) [46], and the constructs of the HBM categories were tested for reliability and validity in the previous study [47], which were specifically tailored to address the attitude and behaviors related to the COVID-19 vaccine. The Scale has 13 items from four categories, namely perceived susceptibility (e.g., working with many people each day increases my chances of getting the COVID-19), perceived severity (e.g., getting the COVID-19 would disrupt my family), perceived benefits (e.g., getting a COVID-19 shot would prevent me from getting the COVID-19), and perceived barriers (e.g., getting a COVID-19 shot can be painful). Each category has three items. With a 5-point Likert scale (strongly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, strongly disagree = 1), the total score of each HBM subscale was generated. A higher score means a greater level in the corresponding category.
2.3. Statistical analyses
A descriptive analysis was conducted on the demographic characteristics, health beliefs, health literacy, and vaccine hesitancy of the participants. Then, using linear regression, we examined the effect of health literacy on vaccine hesitancy, and parallel mediation analysis was conducted to examine four mediator variables. The following covariates were included in the regression: child's age, parent's age, number of children, relationship to child (0 = father, 1 = mother), and parent educational level (0 = high school or higher, 1 = junior high school or below). We used histograms to test for normality for continuous variables, and it has shown that people’s s age, number of children were normally distributed. After converting categorical variables (i.e., parent’s vaccination status) into dummy variables, Pearson correlation was used to address the correlation between all variables. A 2-sided significance level of 0.05 was adopted for all statistical tests. The analyses were performed using the Lavaan package version 0.6–8 in R.
3. Results
A total of 346 parents were enrolled in this study with an average age of 36.21 years. The majority (80.6%) of our sample were mothers, which is consistent with that mother is usually the primary caregivers who make the vaccination decision for children aged 3–11. 93.1% of the participants have obtained high school education or above. Regarding vaccine hesitancy, the average total score for vaccine hesitancy was 28.29 (SD = 15.63). 88.7% of participants got a score of 0–49 indicating “not hesitant”, while 11.3% got a score above 50, indicating “vaccine hesitant”. Compared with those who were not hesitant, hesitant parents were more likely to be the mother (marginally significant), and have children with allergic problems. In addition, the average total score of health literacy was 35.59 (SD = 7.09), and the non-hesitant parents showed higher health literacy than hesitant parents. Regarding health beliefs, hesitant parents tended to perceive more barriers (p = 0.02) and fewer benefits (p = 0.06) in having their children vaccinated. In the model of health beliefs, the presence or absence of perceived barriers differed statistically significantly between the vaccine hesitant and non-hesitant groups (p < 0.05). Details of descriptive results and correlations were present in Table 1 and Table 2 .
Table 1.
The profile of the participants (N = 346).
| Total N = 346 | Not hesitant N = 307 | Hesitant N = 39 | t/χ2 Value | p Value | |
|---|---|---|---|---|---|
| Age, Mean(SD) | 36.21(4.30) | 36.19(4.23) | 36.51(4.90) | 1.16 | 0.28 |
| Sex | 3.86 | 0.05 | |||
| Mother (1) | 279(80.6%) | 243(87.1%) | 36(12.9%) | ||
| Father (0) | 67(19.4%) | 64(95.5%) | 3(4.5%) | ||
| Educational level | 0.74 | 0.39 | |||
| Junior high school or below (1) | 24(6.9%) | 20(83.3%) | 4(16.7%) | ||
| High school or higher (0) | 322(93.1%) | 287(89.1%) | 35(10.9%) | ||
| Number of children, Mean(SD) | 1.67 (0.58) | 1.69(0.59) | 1.51(0.51) | 1.21 | 0.32 |
| Child’s age, Mean(SD) | 6.51(2.77) | 6.53(2.73) | 6.41(3.03) | 0.75 | 0.70 |
| Vaccination status | 7.03 | 0.07 | |||
| Unvaccinated (0) | 9(2.6%) | 7(77.8%) | 2(22.2%) | ||
| One dose (1) | 8(2.3%) | 5(62.5%) | 3(37.5%) | ||
| Two doses (2) | 221(63.9%) | 197(89.1%) | 24(10.9%) | ||
| Three doses (3) | 108(31.2%) | 98(90.7%) | 10(9.3%) | ||
| Children’s vaccination | 11.00 | <0.01 | |||
| Unvaccinated (0) | 53(15.3%) | 40 (75.5%) | 13(24.5%) | ||
| Vaccinated (1) | 293(84.7%) | 267(91.1%) | 26(8.9%) | ||
| Children’s chronic condition | 0.004 | 0.95 | |||
| Yes (1) | 17(4.9%) | 15(88.2%) | 2(11.8%) | ||
| No (0) | 329(95.1%) | 292(88.8%) | 37(11.2%) | ||
| Children’s allergies | 3.58 | 0.06 | |||
| Yes (1) | 60 (17.3%) | 49 (81.7%) | 11 (18.3%) | ||
| No (0) | 286 (82.7%) | 258 (90.2%) | 28 (9.8%) | ||
| Vaccine Hesitancy, Mean(SD) | 28.29(15.63) | 24.57(11.98) | 57.61(8.27) | ||
| Health Literacy, Mean(SD) | 35.55(7.06) | 35.81(7.05) | 33.52 (6.82) | 1.38 | 0.05 |
| Health beliefs | |||||
| Perceived Susceptibility, Mean(SD) | 2.52(0.73) | 2.51(0.74) | 2.56(0.665) | 0.63 | 0.81 |
| Perceived Severity, Mean(SD) | 3.88(0.83) | 3.87(0.85) | 4.02(0.61) | 0.57 | 0.87 |
| Perceived Benefits, Mean(SD) | 3.33(0.70) | 3.37(0.71) | 3.01(0.50) | 1.75 | 0.06 |
| Perceived Barriers, Mean(SD) | 2.42(0.61) | 2.37(0.59) | 2.76(0.62) | 2.00 | 0.02 |
Table 2.
Pearson correlation matrix between the variables.
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.Sex | 1 | |||||||||||||||||
| 2.Educational level | 0.13* | 1 | ||||||||||||||||
| 3.Age | −0.12* | 0.09 | 1 | |||||||||||||||
| 4.Children's age | −0.02 | 0.18** | 0.49** | 1 | ||||||||||||||
| 5.Number of children | 0.04 | 0.12* | 0.03 | 0.07 | 1 | |||||||||||||
| 6.Vaccination status (Unvaccinated) | −0.01 | 0.03 | −0.06 | −0.08 | −0.00 | 1 | ||||||||||||
| 7.Vaccination status (One shot) | 0.03 | −0.04 | 0.01 | 0.01 | 0.02 | −0.03 | 1 | |||||||||||
| 8.Vaccination status (Two shots) | 0.10 | 0.11* | −0.11* | 0.01 | 0.01 | −0.22** | −0.21** | 1 | ||||||||||
| 9.Vaccination status (Three shots) | −0.11* | −0.11* | 0.13* | 0.02 | −0.01 | −0.11* | −0.10 | −0.90** | 1 | |||||||||
| 10.Chidren’s vaccination | 0.03 | −0.05 | −0.16** | −0.33* | 0.05 | 0.13* | --0.07 | 0.09 | −0.11* | 1 | ||||||||
| 11.Children’s chronic condition | 0.11* | −0.06 | 0.02 | 0.02 | −0.03 | 0.13* | 0.05 | −0.05 | −0.01 | 0.16** | 1 | |||||||
| 12.Children’s allergies | −0.07 | −0.10 | 0.08 | −0.05 | −0.08 | 0.12* | 0.08 | −0.10 | 0.04 | 0.10 | 0.28** | 1 | ||||||
| 13.Health Literacy | 0.05 | −0.08 | −0.14* | −0.10 | 0.07 | 0.03 | −0.07 | −0.06 | 0.07 | 0.02 | 0.07 | −0.09 | 1 | |||||
| 14.Perceived Susceptibility | 0.03 | 0.05 | −0.03 | −0.06 | −0.03 | 0.08 | 0.05 | −0.02 | −0.02 | 0.04 | −0.03 | 0.01 | −0.18** | 1 | ||||
| 15.Perceived Severity | 0.09 | 0.05 | −0.02 | −0.01 | 0.03 | 0.07 | 0.01 | 0.01 | −0.03 | −0.05 | 0.04 | 0.02 | −0.12* | 0.32* | 1 | |||
| 16.Perceived Benefits | −0.06 | 0.00 | −0.05 | −0.04 | 0.02 | −0.05 | 0.03 | −0.06* | 0.07 | −0.09 | −0.13* | −0.12* | 0.09 | 0.06 | 0.13* | 1 | ||
| 17.Perceived Barriers | 0.07 | 0.02 | 0.03 | 0.03 | 0.02 | 0.09 | −0.09 | −0.01 | −0.05 | −0.03 | 0.03 | 0.06 | −0.21** | 0.27** | 0.11* | −0.03 | 1 | |
| 18.Vaccine Hesitancy | 0.16** | 0.04 | 0.04 | 0.03 | −0.04 | 0.12* | 0.12* | 0.05 | −0.14* | 0.14* | 0.01 | 0.04 | −0.19** | 0.13* | 0.09 | −0.16** | 0.36* | 1 |
P＜0.05,**P＜0.01.
When looking at each item of PACV (see Table 3 ), it showed that 52 participants reported to have delayed (39) or decided not to (13) have a child vaccination for reasons other than illness or allergy. In addition, the primary concerns for parents about taking COVID-19 vaccine is that: the COVID-19 vaccine may have side effects (56.4%), may not prevent the infection (54.9%), or may not be safe (45.1%).
Table 3.
The frequency of choosing different responses in PACV (N = 346).
| Item | Responses | N (%) |
|---|---|---|
| 1.Have you ever delayed having your child get a shot for reasons other than illness or allergy | Hesitant | 39(11.3) |
| Not hesitant | 300(86.7) | |
| Not sure | 7(2.0) | |
| 2.Have you ever decided not to have your child get a shot for reasons other than illness or allergy? | Hesitant | 13(3.8) |
| Not hesitant | 324(93.6) | |
| Not sure | 9(2.6) | |
| 3.How sure are you that following the recommended shot schedule is a good idea for your child? | Hesitant | 27(7.8) |
| Not hesitant | 293(84.7) | |
| Not sure | 26(7.5) | |
| 4.Children get more shots than are good for them | Hesitant | 140(40.5) |
| Not hesitant | 75(21.7) | |
| Not sure | 131(37.9) | |
| 5.I believe that many of the illnesses shots prevent are severe. | Hesitant | 99(28.6) |
| Not hesitant | 178(51.4) | |
| Not sure | 69(19.9) | |
| 6.It is better for my child to develop immunity by getting sick than to get a shot. | Hesitant | 42(12.1) |
| Not hesitant | 215(62.1) | |
| Not sure | 89(25.7) | |
| 7.It is better for children to get fewer vaccines at the same time. | Hesitant | 64(18.5) |
| Not hesitant | 154(44.5) | |
| Not sure | 128(37.0) | |
| 8.How concerned are you that your child might have a serious side effect from the COVID-19 vaccination? | Hesitant | 195(56.4) |
| Not hesitant | 121(35.0) | |
| Not sure | 30(8.7) | |
| 9.How concerned are you that any one of the childhood shots might not be safe? | Hesitant | 156(45.1) |
| Not hesitant | 154(44.5) | |
| Not sure | 36(10.4) | |
| 10.How concerned are you that the COVID-19 vaccination might not prevent the disease? | Hesitant | 190(54.9) |
| Not hesitant | 120(34.7) | |
| Not sure | 36(10.4) | |
| 11.If you had another infant today, would you want him/her to get all the recommended shots? | Hesitant | 8(2.3) |
| Not hesitant | 312(90.2) | |
| Not sure | 26(7.5) | |
| 12.Overall, how hesitant about childhood shots would you consider yourself to be? | Hesitant | 92(26.6) |
| Not hesitant | 241(69.7) | |
| Not sure | 13(3.8) | |
| 13.I trust the information I receive from the official authorities about vaccination. | Hesitant | 4(1.2) |
| Not hesitant | 295(85.3) | |
| Not sure | 47(13.6) | |
| 14.I am able to openly discuss my concerns about shots with my child’s doctor. | Hesitant | 9(2.6) |
| Not hesitant | 300(86.7) | |
| Not sure | 37(10.7) | |
| 15.All things considered, how much do you trust your child's doctor? | Hesitant | 27(7.8) |
| Not hesitant | 276(79.8) | |
| Not sure | 43(12.4) |
Using multiple linear regression, in the unadjusted model, parent’s health literacy was associated with lower vaccine hesitancy (β = −6.825, 95%CI = [−10.456, −3.051]). After controlling for parents’ age and children’s age, relationship to the child, parental educational level, and the number of children, it was shown that higher level of health literacy was still associated with lower parental vaccine hesitancy (β = −6.868, 95% CI = [−10.501, −3.107]). To explore the underlying mechanisms, we used multiple parallel mediation analysis to test the mediation role of HBM components (see Table 4 ). Parents’ perceived susceptibility, severity of COVID-19 infection, barriers and benefits of COVID-19 vaccine were included as mediators. In the adjusted model, health literacy was significantly related to lower perceived susceptibility (β = −0.321, 95%CI = [−0.509, −0.124]), lower perceived severity (β = −0.245, 95%CI = [−0.472, −0.008]), and fewer perceived barriers (β = −0.304, 95%CI = [−0.451, −0.155]), but not to perceived benefits (β = 0.146, 95%CI = [−0.046, 0.338]). Meanwhile, perceived benefits (β = −3.079, 95%CI = [−5.501, −0.765]) and perceived barriers (β = 8.302, 95%CI = [5.214,11.185]) were associated with vaccine hesitancy in opposite directions, while the relationship with perceived susceptibility and perceived severity were not significant. After entailing four components as mediators, only perceived barriers was found to have an indirect effect (β = −2.526, 95%CI = [−4.094, −1.024]) in the relationship between health literacy and vaccine hesitancy. To sum up, it was found that parents’ health literacy was related with lower perceived barriers, which was associated with reduced vaccine hesitancy for their children. Path coefficients of the mediation models are shown in Fig. 1 .
Table 4.
Multiple parallel mediation model predicting vaccine hesitancy.
| adjusted model† |
unadjusted model |
|||||
|---|---|---|---|---|---|---|
| β | P | 95%CI | β | P | 95%CI | |
| REGRESSION | ||||||
| Health Literacy- Vaccine Hesitancy | −3.604 | 0.050 | (−7.274,0.032) | −3.473 | 0.063 | (−7.148,0.402) |
| Health Literacy- HBM1-Susceptibility | −0.321 | <0.01 | (−0.509,-0.124) | −0.310 | <0.01 | (−0.503,0.127) |
| Health Literacy- HBM2-Severity | −0.245 | <0.05 | (−0.472,−0.008) | −0.228 | 0.047 | (−0.457,−0.014) |
| Health Literacy- HBM3-Benefits | 0.146 | 0.137 | (−0.046,0.338) | 0.151 | 0.092 | (−0.019,0.327) |
| Health Literacy- HBM4-Barriers | −0.304 | <0.001 | (−0.451,−0.155) | −0.297 | <0.001 | (−0.455,−0.150) |
| HBM1-Susceptibility- Vaccine Hesitancy | 0.198 | 0.855 | (−2.016,2.248) | 0.196 | 0.858 | (−1.943,2.296) |
| HBM2-Severity- Vaccine Hesitancy | 0.909 | 0.323 | (−0.897,2.662) | 1.124 | 0.231 | (−0.779,2.885) |
| HBM3-Benefits- Vaccine Hesitancy | −3.079 | <0.05 | (−5.501,−0.765) | −3.347 | <0.01 | (−5.712,−1.040) |
| HBM4-Barriers- Vaccine Hesitancy | 8.302 | <0.001 | (5.214,11.185) | 8.509 | <0.001 | (5.717,11.609) |
| Indirect effects | ||||||
| HBM1-Susceptibility as mediator | −0.064 | 0.860 | (−0.860,0.655) | −0.061 | 0.864 | (−0.812,0.604) |
| HBM2-Severity as mediator | −0.233 | 0.387 | (−0.789,0.255) | −0.256 | 0.324 | (−0.829,0.176) |
| HBM3-Benefits as mediator | −0.451 | 0.222 | (−1.324,0.120) | −0.505 | 0.176 | (−1.363,0.069) |
| HBM4-Barriers as mediator | −2.526 | <0.01 | (−4.094,−1.024) | −2.531 | <0.01 | (−4.459,−1.201) |
| Total effect | −6.868 | <0.001 | (−10.501,−3.107) | −6.825 | <0.001 | (−10.456,−3.051) |
Adjusted for sex, age, educational level, Child’s age, Number of children.
Fig. 1.
The mediation model of health literacy predicting parental vaccine hesitancy (adjusted for sex, age, Child’s age, educational level, Number of children); *: P＜0.05; **: P＜0.01.
4. Discussion
Children are a vulnerable population to COVID-19, and kindergartens and primary schools are crowded places which are under high infection risk of diseases such as COVID-19. Compared with the unvaccinated population, the morbidity of infection was reduced at least 36% among the vaccinated people aged under 20 [48]. Children aged 3–11 years who have not been vaccinated or have not received the full covid-19 vaccine (two shots) are at constant risk of COVID-19 infection [49]. Therefore, understanding the factors affecting children vaccination is critical to promote disease prevention. In our study, by focusing on the primary barriers for vaccination ‘parental vaccine hesitancy’, we investigated how parent’s health literacy and health beliefs influence their attitudes about having their children vaccinated.
In our sample, only 11% of the parents showed hesitancy towards having their children taken the COVID-19 vaccine, which was much lower than the vaccine hesitancy of the general population (25% to 53%) [50], [51], [52]. Parental vaccine hesitancy rates are much lower than we expected, this may be related to the period of our survey and the strong government policy [53]. However, there is still a small group of parents who stubbornly insist on not having their children vaccinated. Therefore, looking at the modifiable predictors for their attitude may provide a starting point for behavior changing.
Interestingly, our findings showed that health literacy was significantly associated with reduced vaccine hesitancy and fewer perceived barriers, among which the latter further reduced parental vaccine hesitancy. The effect of health literacy was consistent with the existing literature [25], [54], [55], probably because health literacy can help people better identify misinformation and obtain valid information to make health-related decisions. In other words, people with greater health literacy would better understand the vaccination policy and find it easier to make decisions. This is also consistent with our previous findings that higher health literacy was associated with lower hesitancy towards taking COVID-19 vaccine for themselves [25], [56]. In other words, the high rate of COVID-19 vaccine uptake in mainland, China, may be associated with the increased health literacy of lay public in the past decades. However, although previous studies have looked at the effect of health beliefs [57], [58], [59], it lacks the evidence connecting health beliefs, health literacy and vaccine-related decisions. Our research has extended the existing findings by linking parent’s health literacy, health beliefs and vaccine hesitancy, and addressing the major pathway underlying the effect of health literacy was that it may reduce people’s perceived barriers in making decisions for children’s COVID-19 vaccination.
Nevertheless, there are some limitations of the current study that should be acknowledged. First, since the data was collected in December 2021, a period that China has just started promoting COVID-19 vaccination for children, our findings may only reflect the situation of the early stage of vaccination promotion. Second, with recruiting participants at the Children’s hospital, a convenience sampling was used, which may inevitably cause selection bias and skewness of the sample (e.g., the majority are mother and having high education). Therefore, the generalizability of the findings to the population in rural places could be reduced. At last, with self-report method, it is possible that parent’s vaccine hesitancy is subject to people’s adherence willingness of public measures or social desirability, rather than their true attitude. More objective measures in future studies should be developed.
5. Conclusion
Overall, our results suggest that parents with high health literacy had more positive attitudes toward their children's uptake of the COVID-19 vaccine during the COVID-19 pandemic, partially because they perceive fewer barriers in making this decision. From a practical perspective, healthcare professionals and health care systems should provide education programs to improve parent’s health literacy, and address parent’s concerns about COVID-19 vaccine, to help the parents better protect their children from infectious diseases.
6. Funding
The study was supported by the Humanity and Social Science Youth foundation (21YJCZH209).
7. Role of Funder/Sponsor (if any)
The funder had no role in the design and conduct of the study.
8. Author contribution statement
HQZ has contributed to study conceptualization, data collection, manuscript draft and revision.
LYC has contributed to questionnaire development, data analysis, and manuscript revision.
ZXH has contributed to the data analysis, and manuscript revision.
DXL has contributed to measurement development, data collection, and manuscript preparation.
QT has contributed to the conceptualization, data collection, and manuscript review.
FZ has conceptualized and designed the study, coordinated and supervised data collection, reviewed and finalized the manuscript.
All authors have reviewed and approved the final manuscript to be submitted and agreed to be accountable for each aspect of the work.
9. Data availability statement
The data is available upon reasonable request addressing to the correspondence author.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
There is no conflict of interest to be declared.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data is available upon reasonable request addressing to the correspondence author.