Key factors influencing paediatric COVID-19 vaccine hesitancy: a brief overview and Decision-making Trial and Evaluation Laboratory analysis

Abstract

Objectives

The purpose of this study was to examine the current literature on paediatric COVID-19 vaccine hesitancy among parents and identify key influencing factors, thus enabling targeted policy development and implementation.

Study design

This was a systematic literature review and Decision-making Trial and Evaluation Laboratory (DEMATEL) analysis.

Methods

A review of the quantitative and qualitative literature focusing on factors influencing paediatric COVID-19 vaccine hesitancy was conducted. Searches were performed in PubMed, ScienceDirect, SpringerLink and Embase. Because of the immediacy of the topic, commentaries were included in addition to research and review articles. Influencing factors were categorised according to the Health Ecology Theory and screened using the DEMATEL method.

Results

A total of 44 articles were included in the study, and 44 factors influencing paediatric COVID-19 vaccine hesitancy were identified. Of these, 18 were categorised as key factors using the DEMATEL method, including a history of COVID-19 infection in parents and perceived safety of the paediatric COVID-19 vaccine.

Conclusions

Policymakers and public health personnel should pay more attention to the key factors influencing paediatric COVID-19 vaccine hesitancy. The outcome of this research will benefit and motivate decision-makers to consider strategies to overcome various challenges of COVID-19 vaccine hesitancy.

Introduction

The novel coronavirus pneumonia (COVID-19) pandemic has resulted in more than 600 million confirmed cases, including approximately 6.6 million deaths.¹ In addition to the threat to health, COVID-19 also impacts the daily life and mental health of the public and thus continues to receive much attention from researchers worldwide.^{1 , 2} According to the World Health Organisation, vaccines and vaccination are the most effective measures to halt the pandemic, thus emphasising the importance of vaccination.^{3 , 4} Since the start of the pandemic, many countries have invested a lot of resources into the research, development and practical application of COVID-19 vaccines.5, 6, 7 The age range of those eligible to receive the COVID-19 vaccination has extended from 18 to 59 years to ≥3 years in China⁸ and was gradually liberalised from >12 years to all ages in Canada, meaning that children can also now receive the COVID-19 vaccination.⁹

Vaccine hesitancy refers to the delay in acceptance or refusal of vaccines, despite the availability of the vaccine. Vaccine hesitancy is complex and context specific, varying across time, place and vaccine.¹⁰ The Strategic Advisory Group of Experts working group on vaccine hesitancy also recognised that vaccine hesitancy occurs along a continuum between full acceptance, including high demand for vaccines, and outright refusal of some or all vaccines, although acceptance of the vaccines was the norm in the majority of populations globally.¹⁰ In this study, paediatric vaccine hesitancy refers to parental hesitancy about the paediatric vaccine because, in most cases, parents are the decision-makers regarding whether or not a child should be vaccinated.^{2 , 11}

Previous investigations into the factors influencing paediatric vaccine hesitancy often used specific theories and models (e.g. the Health Belief Model and the Theory of Planned Behaviour).^{12 , 13} However, many researchers have pointed out that the insufficient inclusion of factors influencing vaccine hesitancy is a limitation of their studies,14, 15, 16 and studies based on specific theoretical models may lack comprehensiveness. At the same time, a systematic review of the factors influencing influenza vaccine hesitancy noted that the review only described the influencing factors and could not judge their importance.¹⁷ This is because when a factor is reported more frequently, it does not mean that it is more important but may simply be because of it being selected more often by the researcher or showing significance more often.¹⁷ Therefore, comprehensive identification of the key factors influencing paediatric COVID-19 vaccine hesitancy can help to reduce the hesitancy rate and ultimately improve vaccination coverage.

According to previous research, the common theoretical models used in the study of influences on vaccine hesitancy include the Knowledge-Attitude-Practice Theory,^{18 , 19} the Health Belief Model,^{20 , 21} the Protection Motivation Theory²² and the Theory of Planned Behaviour,²³ but they lack comprehensiveness to a certain extent. For example, these models lack policy-level constructs, such as culture and economics, when measured. In comparison, the Health Ecology Theory is more comprehensive and is derived from ecology theory.²⁴ McLeroy²⁴ applied ecology theory to the field of health promotion research in 1988 and argued that health promotion should focus on both individual and social factors, and more branches have since developed, including the Health Ecology Theory. According to the Health Ecology Theory, the determinants of health behaviours include (1) personal innate traits and disease biology; (2) personal psychology and behaviour; (3) interpersonal network; (4) living and working conditions; and (5) national and local social, economic, political, health, environmental conditions, and related policy factors.²⁴ The Health Ecology Theory emphasises that health behaviours are the result of the interdependence and interaction of many factors.

This study aimed to identify factors influencing paediatric COVID-19 vaccine hesitancy through a literature evaluation under the framework of the Health Ecology Theory and subsequently determine the key influencing factors through Decision-making Trial and Evaluation Laboratory (DEMATEL).

Methods

Literature search and selection procedure

The literature screening flowchart is shown in Fig. 1 . The keywords used for the literature search included paediatric vaccine; paediatric vaccine hesitancy/hesitation; vaccine intention/willing/behaviour; influencing factor; factor; kid; child/children/parent/kids. The retrieval databases were PubMed, ScienceDirect, SpringerLink and Embase, and Boolean operators “AND,” “OR” and “NOT” were used for the combination of retrieval terms during the process. The two study authors (Yonyi Wang was responsible for reading, screening and excluding, while Xinping Zhang checked and proofread) screened the retrieved articles and eliminated those not meeting the study needs. The purpose of the included literature was to measure or evaluate factors influencing paediatric COVID-19 vaccine hesitancy. Case reports, clinical guidelines, recommendations and articles in non-English languages were excluded. We also excluded studies that investigated children with diseases because each vaccine may have specific considerations for particular populations and health conditions.²⁵

Fig. 1.

Literature screening flowchart.

In terms of selecting influencing factors, those with significant outcomes and those frequently reported in the literature were included. This selection process was checked by the two authors based on the principle of ‘consistency of content’ and then discussed to determine the correct categorisation. Some factors could be categorised without doubt (e.g. psychological factors could be categorised as Dimension 2). For controversial factors, reference was made to the previous DEMATEL literature.

DEMATEL

DEMATEL was proposed by Gabus and Fontela at the Geneva Research Centre for the Science and Human Affairs Program from 1972 to 1976. DEMATEL uses graph theory and matrix theory to (1) analyse the complex problems of interlocking influencing factors, (2) identify the causal relationship between complex system factors and (3) extract key elements.

The following steps were used in the current study to determine the key influencing factors:

Step 1. Factors influencing paediatric COVID-19 vaccine hesitancy were determined. A group of effective factors S = {S₁, S₂, … …, S_n}, with significant impact on the system were identified.

Step 2. An initial direct influence matrix was established. An expert panel was set up, including four experts in preventive medicine, two in paediatrics, two in social medicine and one in health management. Experts formulated the direct influence matrix X=(x_ij)_n×n by indicating the influence that the factor S_i has on S_j, using an integer scale (0–4) of no influence (0); very low influence (1); small influence (2); moderate influence (3); very strong influence (4).

Step 3. A normalised direct influence matrix was calculated. The normalised direct influence matrix M can be obtained by normalising the initial direct influence matrix X according to the following equation.

$$M=\raisebox{1ex}{$X$}\!\left/ \!\raisebox{-1ex}{$\underset{1\le i\le n}{\max }$}\right.\sum _{j=1}^n{x}_{ij}$$

Step 4. Based on matrix X, the total influence matrix T = [t_ij]_n×n was calculated by summing the direct effects and all of the indirect effects by

$$T={\left(t_{ij}\right)}_{n\times n}=M{\left(I-M\right)}^{-1}$$

where, I–identity matrix;

Step 5. The Prominence and Relation values were calculated

$$R_{i=}\sum _{j-1}^n{t}_{ij},i=\mathrm{1,2},\dots ,n$$

$$C_{j=}\sum _{i-1}^n{t}_{ij},j=\mathrm{1,2},\dots ,n$$

Prominence (R_i+C_j) describes the strength of influence given and received by a given factor. The Relation (R_i-C_j) shows the net effect that a given factor brings into the system and is the basis for ranking factors. If R_i-C_i is positive, then S_i belongs to a group of causes (impact the system). If R_i-C_i is negative, then S_i is the effect of the net impact of other system elements and is classified in the group of effects.

Step 6. A cause and effect diagram was plotted. According to the values of array (R_i + C_j, R_i-C_j), the causality diagram was drawn, with the Prominence as abscissa and the Relation as ordinate, the values of (R_i + C_j, R_i-C_j) were indicated in the figure (Fig. 2), and the visualised figure was used to represent the importance of factors in the system. A line was drawn with the mean of R + C values as the cut-off point to divide the causality map into four quadrants. Due to their location in a specific quadrant, factors are classified as most important, important, independent or indirect.²⁶

Fig. 2.

Dimension 1. Cause and effect diagram.

Results

Systematic search results

Among the 44 articles identified during the search, 36 were cross-sectional studies, three were review articles, one was an intervention study, two were mixed methods studies (i.e. using both qualitative and quantitative research methods), one was a commentary and one was qualitative a study. The details of the selected studies are presented in Table 1 . A total of 95,497 participants were involved in the studies included in this review.

Table 1.

Literature information.

Author(s)	Study type	Region	Tool	Study period	Sample size
Humble RM et al.9	Cross-sectional study	Canada	Self-developed questionnaire	2020.12.20–2020.12.24	1702
Babicki M et al.27	Cross-sectional study	Poland	Self-developed questionnaire	2021.5.9–2021.5.14	4432
Zona S et al.28	Cross-sectional study	Italy	Self-developed questionnaire	2021.7.15–2021.8.16	1799
Kezhong A et al.29	Cross-sectional study	China	A 10-question adult vaccine hesitancy scale (aVHS)	2020.6–2020.7	13,451
Musa S et al.30	Cross-sectional study	Qatar	Vaccination scheduled records and information	2021.5.17–2021.6.3	4023
Skjefte M et al.31	Cross-sectional study	16 countries	Self-developed questionnaire	2020.10.28–2020.11.18	17,871
Fisher CB et al.32	Cross-sectional study	USA	Items from previous scales	2021.10	400
Xu Y et al.8	Cross-sectional study	China	Parental Attitudes About Childhood Vaccines (PACV)	2021.7.22–2021.8.14	917
Lackner CL et al.33	Cross-sectional study	Canada	Self-developed questionnaire	2020.5.15–2020.6.9	455
Wang Y and Zhang X2	Cross-sectional study	China	Parental Attitudes About Childhood Vaccines, PACV	2021.6–2021.7	382
Olusanya OA et al.34	Review	–	–	–	–
Kreuter MW et al.35	Cross-sectional study	USA	Self-developed questionnaire	2021.1.13–2021.1.31	1951
Russo L et al.36	Cross-sectional study	Italy	Self-developed questionnaire	2021.7.22–2021.8.31	1696
Cole JW37	Intervention study	USA	MOTIVE (MOtivational Interviewing Tool to Improve Vaccine AcceptancE)	2018.7–2019.6/2019.7–2020.3	2504/1954
Ellithorpe ME et al.38	Cross-sectional study	USA	Self-developed questionnaire	2020.11.13–2020.12.8	682
Phan TT39	Cross-sectional study	Mid-Atlantic	Self-developed questionnaire	2021.3.19–2021.4.16	513
Temsah MH et al.40	Cross-sectional study	Saudi Arabia	Vaccine Hesitancy Scale, VHS-Adjusted	–	3167
Alfieri NL et al.41	Cross-sectional study	USA	Self-developed questionnaire	2020.6.8–2020.6.29	1425
Teasdale CA et al.42	Cross-sectional study	USA	Self-developed questionnaire	2021.3.9–2021.4.11	1119
Xu Y et al.43	Cross-sectional study	China	Patient Health Questionnaire (PHQ-4) and self-developed questionnaire	2020.12.18–2020.12.31	4748
Bell S et al.44	Mixed Method Study	UK	Self-developed questionnaire	2020.4.19–2020.5.11	1252/19
Brandstetter S et al.45	Cross-sectional study	Germany	Self-developed questionnaire	2020.5.5–2020.5.28	612
Yılmaz M et al.46	Cross-sectional study	Turkey	Self-developed questionnaire	2021.2.8–2021.2.21	1035
Szilagyi PG et al.47	Cross-sectional study	USA	Vaccine Hesitancy Scale, VHS-Adjusted	2021.2.17–2021.3.30	1745
Gabriella DG et al.48	Cross-sectional study	Italy	Self-developed questionnaire	2021.4.18–2021.5.18	607
Ruggiero KM et al.49	Cross-sectional study	USA	Parental Attitudes About Childhood Vaccines, PACV	2020.11–2021.1	427
Teasdale CA et al.50	Cross-sectional study	USA	Self-developed questionnaire	2021.3.9–2021.4.2	2074
Urrunaga-Pastor D et al.51	Cross-sectional study	Latin America and Caribbean	Self-developed questionnaire	2021.5.20–2021.7.14	227,740
Kelly BJ et al.52	Cross-sectional study	USA	Self-developed questionnaire	2020.4	2247
Botha E et al.53	Review	–	–	–	–
Evans S et al.54	Mixed Method Study	Australia	Self-developed questionnaire	2020.4.8–2020.4.28/2021.1.18–2021.2.8	1094
Altulaihi BA et al.55	Cross-sectional study	Saudi Arabia	Self-developed questionnaire	–	333
Hetherington E et al.56	Cross-sectional study	Canada	Self-developed questionnaire	2020.5–2020.6	1321
Chemakina et al.57	Qualitative study	Russia	–	–	253
MacDonald NE and Dubé E58	Commentary	–	–	–	–
Wang Q et al.14	Cross-sectional study	China	Vaccine Hesitancy Scale, VHS	2020.9.21–2020.10.17	3095
Zhou Y et al.59	Cross-sectional study	China	Self-developed questionnaire	2020.7.1–2020.9.8	1071
Montalti M et al.60	Cross-sectional study	Italy	Self-developed questionnaire	2020.12–2021.1	5054
Aldakhil H et al.61	Cross-sectional study	Saudi Arabia	Vaccine Hesitancy Scale, VHS	2021.1.1–2021.2.28	270
Galanis P et al.62	Review	–	–	–	–
Middleman AB et al.63	Cross-sectional study	USA	Self-developed questionnaire	2020.8.11–2020.9.18/2021.2.4–2021.3.1/2021.6.10–2021.6.30	1613
Chiang, V. et al.64	Cross-sectional study	China	Medical records	2021.2–2021.6	1127
Goldman, R. D. et al.65	Cross-sectional study	USA	Self-developed questionnaire	2020.3.26–2020.5.31	1552
Wu Yue. et al.66	Cross-sectional study	China	Self-developed questionnaire	2021.6–2021.7	2538

From the included studies, most of the surveys were conducted using self-developed questionnaires. In these questionnaires, the outcome variable was parental paediatric COVID-19 vaccine hesitancy, and the questioning varied, mainly in terms of intention, willingness, propensity and attitude. Other main dimensions were sociodemographics (e.g. gender, age, region, economic status), vaccine safety, efficacy, priority, history of vaccination (e.g. influenza vaccination), perceived risk of COVID-19 and/or vaccine, negative COVID-19 experience, trust and psychological status. The current review identified 44 factors influencing paediatric COVID-19 vaccine hesitancy from the selected articles (Table 2 ).

Table 2.

Factors influencing paediatric COVID-19 vaccine hesitancy from the selected articles.

No.	Factors	Details
a. Dimension 1. Personal innate traits and disease biology.
S1	Gender27,38,42,52,60,62
S2	Age28,33,35,40,47,51,55,60,62
S3	Age of child/children30,36,40,48,55,60
S4	History of COVID-19 infection in parents30,51,65
S5	History of parental vaccine allergy64
S6	History of parental immunodeficiency/immune disease57
S7	History of parental critical/chronic illness31
S8	History of COVID-19 infection in child/children8,65
S9	History of child/children vaccine allergy61,65
S10	History of childhood immunodeficiency/immune disease49,61,65
S11	History of childhood critical/chronic illness28,65
b. Dimension 2. Personal psychology and behaviour.
S12	Perceived the safety for paediatric COVID-19 vaccine8,14,27,28,31,32,36,42,44,47,49,50,53,55,56,61,63	Side-effects of paediatric COVID-19 vaccine; rapid development leading to insufficient safety information and evidence; unclear potential future impact
S13	Perceived the need for paediatric COVID-19 vaccine9,40,50	Vaccinating children against COVID-19 is necessary or not
S14	Perceived the efficacy for paediatric COVID-19 vaccine8,27,28,31,32,36,38,40,42,44,48,50,56	Duration of protection for paediatric COVID-19 vaccine; vaccination can completely protect children from infection or not
S15	Perceived the importance for paediatric COVID-19 vaccine31	Importance and priority of paediatric COVID-19 vaccination
S16	Risk perception of COVID-1931,32,36,48,53,54,62	Paediatric COVID-19 susceptibility; paediatric COVID-19 severity; paediatric COVID-19 transmission risk
S17	Influenza vaccination9,48,55,59,62	History of influenza vaccination; willingness to receive influenza vaccination
S18	Paediatric influenza vaccination38,39,49,62	History of paediatric influenza vaccination; willingness to receive paediatric influenza vaccination
S19	COVID-19 vaccination9,29,32,39,40,42,46, 47, 48,62	History of COVID-19 vaccination; willingness to receive COVID-19 vaccination
S20	Paediatric routine vaccination8,31,33,56,62	Pay attention to vaccination within the childhood immunisation programme; routine vaccination for children is timely and complete
S21	Trust in health authorities/personnel and information issued28,31,34,54,62	Confidence in health authorities (e.g. hospitals)/personnel and information issued
S22	Trust in official agency/organisation and information issued31,45,62	Confidence in official agency/organisation (e.g. health committees) and information issued
S23	Compliance with infection prevention and control measures31,51	Compliance with mask-wearing, maintaining social distance, etc.
S24	Psychological avoidance33	Tend to avoid thoughts, negative emotions, or information about the outbreak
S25	Psychological distress43,51	E.g. mood disorder, depression, anxiety
S26	Coping style2	The methods and strategies adopted by individuals with personal characteristics in order to reduce or avoid stress and adapt to environment
S27	Self-efficacy2,53	A person's subjective judgement of whether he or she is able to successfully perform a behaviour
S28	Psychological flexibility2	Individual consciously adapts to the present and adheres to or changes behaviour guided by personal values
S29	Protection14,44	Protect people around; protect children
c. Dimension 3. Interpersonal network.
S30	Occupation8,9,28,46,59	Occupation category; non-medical-related occupation and medical-related occupation
S31	Revenue8,28,32,35,44,50,56,62	Annual household income (RMB)
S32	Education level14,28,32,40,47,50,51,53,56,59, 60, 61	Education; education Level
S33	Community support32	Vaccine-related support from other parents or family members
S34	Cognition/attitude/suggestion/communication of healthcare providers28,34,37,47,48,54,58	Healthcare providers' perception and attitude towards paediatric COVID-19 vaccine; healthcare providers can provide effective advice; effectively communicate with healthcare providers
d. Dimension 4. Living and working conditions.
S35	Accessible information sources27,40,41,54,63	Multiple sources of information such as media information, network information and official information are accessible
S36	Source of information relied on60	One or more sources of information that relied on
S37	Information content breadth40,55	The information content is extensive and covers content that has attracted much parental attention such as adverse events and vaccine information
S38	Experienced COVID-1938	Experienced the COVID-19 outbreak
S39	Participate in COVID-19 prevention and control66	Have participated in the work related to the prevention and control of COVID-19 epidemic
S40	History of exposure to vaccine adverse events in children29	Heard of adverse events to paediatric vaccines
e. Dimension 5. National and local social, economic, political, health, environmental conditions and related policy factors.
S41	Permanent residence8,30,51	Resident area
S42	Household registration8,30	Consistent with or inconsistent with permanent residence; rural household registration or urban household registration
S43	Compulsory policy/measure60	E.g. School policy for compulsory COVID-19 vaccination of children
S44	Incentive policy/measure34	E.g. obtaining material rewards after vaccination

DEMATEL analysis

Direct influence matrix, normalised direct influence matrix, total influence matrix and causality plots for dimension 1 are shown in Table 3 and Fig. 2 (data results for the remaining dimensions are shown in the Supplementary Material).

Table 3.

Direct-influence matrix, normalized direct-influence matrix and total-influence matrix of Dimension 1.

	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11
a. Dimension 1. Direct influence matrix.
S1	0	1.0000	1.6667	2.2222	2.2222	2.3333	1.7778	1.8889	2.0000	2.2222	1.8889
S2	1.0000	0	1.8889	2.1111	1.8889	2.1111	2.0000	2.1111	2.0000	2.2222	2.2222
S3	1.5556	1.6667	0	2.5556	2.4444	2.4444	2.3333	2.5556	2.4444	2.5556	2.3333
S4	1.3333	1.6667	2.0000	0	2.1111	2.3333	2.0000	2.5556	2.7778	2.4444	2.1111
S5	1.1111	1.3333	1.7778	2.6667	0	2.4444	2.2222	2.5556	2.8889	2.5556	2.3333
S6	1.1111	1.5556	1.8889	2.5556	2.6667	0	2.4444	2.7778	2.6667	2.5556	2.4444
S7	1.4444	2.0000	1.7778	2.3333	2.2222	2.4444	0	2.3333	2.6667	2.3333	2.3333
S8	1.3333	1.7778	1.8889	2.4444	2.5556	2.2222	2.0000	0	2.4444	2.5556	2.3333
S9	1.3333	1.7778	2.5556	2.5556	2.7778	2.6667	2.3333	2.6667	0	2.4444	2.3333
S10	1.2222	1.5556	2.1111	2.4444	2.6667	2.8889	2.3333	2.4444	2.5556	0	2.3333
S11	1.2222	1.7778	2.4444	2.3333	2.4444	2.6667	2.4444	2.2222	2.3333	2.3333	0
b. Dimension 1. Normalised direct influence matrix.
S1	0	0.0427	0.0711	0.0948	0.0948	0.0995	0.0758	0.0806	0.0853	0.0948	0.0806
S2	0.0427	0	0.0806	0.0900	0.0806	0.0900	0.0853	0.0900	0.0853	0.0948	0.0948
S3	0.0664	0.0711	0	0.1090	0.1043	0.1043	0.0995	0.1090	0.1043	0.1090	0.0995
S4	0.0569	0.0711	0.0853	0	0.0900	0.0995	0.0853	0.1090	0.1185	0.1043	0.0900
S5	0.0474	0.0569	0.0758	0.1137	0	0.1043	0.0948	0.1090	0.1232	0.1090	0.0995
S6	0.0474	0.0664	0.0806	0.1090	0.1137	0	0.1043	0.1185	0.1137	0.1090	0.1043
S7	0.0616	0.0853	0.0758	0.0995	0.0948	0.1043	0	0.0995	0.1137	0.0995	0.0995
S8	0.0569	0.0758	0.0806	0.1043	0.1090	0.0948	0.0853	0	0.1043	0.1090	0.0995
S9	0.0569	0.0758	0.1090	0.1090	0.1185	0.1137	0.0995	0.1137	0	0.1043	0.0995
S10	0.0521	0.0664	0.0900	0.1043	0.1137	0.1232	0.0995	0.1043	0.1090	0	0.0995
S11	0.0521	0.0758	0.1043	0.0995	0.1043	0.1137	0.1043	0.0948	0.0995	0.0995	0
c. Dimension 1. Total influence matrix.
S1	0.6503	0.8667	1.0709	1.2811	1.2751	1.2987	1.1620	1.2715	1.3065	1.2792	1.1989
S2	0.7023	0.8400	1.0963	1.2968	1.2825	1.3106	1.1886	1.2994	1.3266	1.2990	1.2301
S3	0.8264	1.0370	1.1810	1.5024	1.4912	1.5144	1.3736	1.5054	1.5377	1.5003	1.4124
S4	0.7715	0.9780	1.1880	1.3185	1.3946	1.4239	1.2836	1.4198	1.4612	1.4109	1.3240
S5	0.7820	0.9898	1.2091	1.4552	1.3462	1.4628	1.3230	1.4544	1.5010	1.4492	1.3643
S6	0.8047	1.0268	1.2479	1.4930	1.4897	1.4104	1.3690	1.5039	1.5361	1.4908	1.4075
S7	0.7907	1.0096	1.2036	1.4369	1.4261	1.4562	1.2306	1.4398	1.4861	1.4349	1.3584
S8	0.7771	0.9892	1.1929	1.4236	1.4206	1.4309	1.2934	1.3320	1.4608	1.4255	1.3419
S9	0.8356	1.0633	1.3063	1.5347	1.5349	1.5545	1.4032	1.5418	1.4766	1.5287	1.4429
S10	0.8055	1.0224	1.2507	1.4833	1.4840	1.5144	1.3599	1.4865	1.5262	1.3867	1.3981
S11	0.7947	1.0168	1.2456	1.4592	1.4559	1.4863	1.3455	1.4583	1.4975	1.4571	1.2887

Key factors influencing paediatric COVID-19 vaccine hesitancy

Based on the method described earlier, the first quadrant, namely, the most important factors, were considered to be the key factors in this study. A total of 18 key factors were identified in this study. Of these, eight, five, five, two, and one factors were found in each of the five dimensions, respectively (see Table 4 for details).

Table 4.

Key factors influencing paediatric COVID-19 vaccine hesitancy.

No.	Factors	R+C	R − C
Personal innate traits and disease biology
S4	History of COVID-19 infection in parents	29.6589	1.7106
S5	History of parental vaccine allergy	29.9378	1.2638
S6	History of parental immunodeficiency/immune disease	30.6427	1.0835
S7	History of parental critical/chronic illness	28.6055	0.0595
S8	History of COVID-19 infection in child/children	29.8009	1.6248
S9	History of child/children vaccine allergy	31.3389	0.8937
S10	History of childhood immunodeficiency/immune disease	30.3802	0.9449
S11	History of childhood critical/chronic illness	29.2729	0.2617
Personal psychology and behaviour
S12	Perceived the safety for paediatric COVID-19 vaccine	29.1420	0.1584
S16	Risk perception of COVID-19	29.7677	0.5556
S22	Trust in official agency/organisation and information issued	29.0222	0.3786
S25	Psychological distress	27.8212	0.0557
S29	Protection	28.7056	0.5612
Interpersonal network
S32	Education level	16.1452	0.5318
S34	Cognition/attitude/suggestion/communication of healthcare providers	16.4356	0.2530
Living and working conditions
S38	Experienced COVID-19	33.4702	0.9357
S39	Participate in COVID-19 prevention and control	34.7912	0.9212
National and local social, economic, political, health, environmental conditions and related policy factors
S44	Incentive policy/measure	10.0354	0.1046

Discussion

A total of 18 key factors influencing paediatric COVID-19 vaccine hesitancy were screened by the DEMATEL method.

Histories of illness of parents and children were found to be key influencing factors, regardless of whether their histories of illness were associated with COVID-19. First, paediatric COVID-19 vaccine hesitancy may be due to the fact that the vaccine itself has vaccination contraindications^{67 , 68} (i.e. children who are in poor physical condition and have had allergic reactions after vaccination may be at risk of becoming more sensitive to drug reactions due to their vulnerability even if they do not meet the contraindications).^{68 , 69} Second, parents with a history of disease may not have sufficient confidence and self-efficacy to take their children to healthcare facilities for vaccination.⁷⁰ From a genetic point of view, the physical condition of parents may also impact their children;^{71 , 72} thus, parents may hold a wait-and-see attitude towards the COVID-19 vaccine in children because of concerns about the physical condition of their children. Parents who have previously been allergic to the vaccine may have concerns and fears about their children experiencing the same uncomfortable reactions, such as fever, nausea and dizziness.⁶⁴ In terms of the impact of parental history of COVID-19 infection on paediatric COVID-19 vaccine hesitancy, one explanation could be that people often experience unrealistic optimism in the face of familiar risks. Therefore, parents believe that the situation is largely under the control and will of the individual⁷³ and that they can protect their children well and do not need vaccines. If the child has been diagnosed with COVID-19, then their parents will think that infection with the virus will make the body produce antibodies and play a protective role, thereby reducing the perception of the necessity of the COVID-19 vaccine in children.³⁰

The safety of COVID-19 vaccines has attracted much attention since their development and use. Due to the rapid spread of COVID-19,¹ many countries invested in various resources to participate in vaccine development. Due to the urgency of the vaccine, there is a lack of long-term clinical trials and clinical evidence;^{5 , 74} therefore, there are many doubts about the side-effects and potential future effects of COVID-19 vaccines.^{27 , 40 , 44} Risk perception, including paediatric COVID-19 susceptibility, paediatric COVID-19 severity and paediatric COVID-19 transmission, can also influence vaccination decisions.³² Since the start of the pandemic, official organisations in various countries, such as the World Health Organisation or the United States Food and Drug Administration, have issued a variety of information on vaccine research, development and vaccination. The level of public trust in official organisations/agencies, as well as in the online media messages they release, may seriously influence the vaccine decision-making process.^{38 , 65} Willingness to vaccinate is stronger when the public trusts official organisations/institutions and when they provide a wealth of information on the development, testing and safety of the COVID-19 vaccine.³⁵ In addition, psychological distress,⁴³ that is, psychological status, has increasingly been shown to affect vaccination decisions, including but not limited to anxiety-depression.⁵¹ In addition, some other psychological factors, such as psychological flexibility² and trauma,⁷⁰ have also been reported to impact vaccine decision-making.

In general, the educational level of parents affects their perception of the paediatric COVID-19 vaccine; however, the impact of this effect is uncertain. Educational attainment is associated with greater participation in protective and preventive behaviours, which may be because higher education may help people engage in safe behaviour, while protecting them from the irrational fear of being infected or dying.⁷⁵ On the other hand, highly educated individuals usually possess high levels of self-efficacy⁷⁶ and are more confident in their ability to protect themselves and their children (i.e. believing in oneself outweighs believing in a vaccine where the risks remain). Unlike official organisations/institutions, healthcare providers are the most accessible professional help to parents. Healthcare providers’ perceptions and attitudes towards paediatric COVID-19 vaccine and communication between parents and healthcare providers about the paediatric COVID-19 vaccine have all been shown to be important.^{34 , 58} In addition, in terms of local practical policies, we found that some incentive schemes can encourage parents, to some extent, to vaccinate their children. It is easy to see from motivation-related theory that a certain degree of reward is an effective way to promote behaviour.⁷⁷

Previous research has divided the Health Ecology Theory framework into upstream, midstream and downstream sections and formed a chain of health behavioural influences, with upstream influencing midstream and midstream influencing downstream.⁷⁸ Dimensions 3, 4 and 5 are upstream factors influencing health behaviour, dimension 2 is a midstream factor and dimension 1 is a downstream factor.⁷⁸ From a public health perspective, policy makers and public health personnel play an important role in upstream influencing. For example, they can work together to develop incentives or benefits to encourage health behaviours, train healthcare providers in health awareness and communication skills, and the government or official institutions can introduce policies to improve the level of education of individuals and increase the transparency of health information. As a result, the substantive and positive role played by policy makers and health professionals can spread from top to bottom.

This study has some limitations. First, the DEMATEL analysis relies on expert scores, which are highly subjective. Each expert has limited experience in dealing with paediatric COVID-19 vaccine hesitancy; further research and a larger study sample size would make the results more robust. Second, paediatric vaccine hesitancy involves multiple disciplines, such as preventive health, public health and health management, and experts from different specialities may have different views on the factors influencing paediatric COVID-19 vaccine hesitancy, which can lead to deviations between the results calculated by DEMATEL and the actual situation. Finally, the literature is constantly being updated, and additional factors influencing paediatric COVID-19 vaccine hesitancy may be discovered in the future.

Conclusions

Overcoming COVID-19 vaccine hesitancy and realising herd immunisation are worldwide common goals at present. This study used a comprehensive theory to screen for key factors influencing paediatric COVID-19 hesitancy. The study findings are in line with the Determinants of Vaccine Hesitancy Matrix reported by the Strategic Advisory Group of Experts working group on vaccine hesitancy. The key factors influencing paediatric COVID-19 hesitancy that have been identified in this study emphasise the importance of policy development, and prevention and control practice.

Author statements

Acknowledgements

The authors thank all the experts who participated in this survey.

Ethical approval

The studies involving human participants were reviewed and approved by the ethics committee of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. As the study used anonymous, pooled and retrospective data, the ethics committee waived the need for participants to provide written informed consent. The study complies with the Declaration of Helsinki. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Funding

None declared.

Competing interests

None declared.

Author contributions

Y.W. contributed to conceptualisation; data curation; investigation; formal analysis; methodology; visualisation; and writing, reviewing and editing the article. X.Z. contributed to conceptualisation; project administration; supervision; validation; and reviewing and editing the article.

Appendix A. Supplementary data

The following is the Supplementary data to this article: