Concerns related to the interactions between COVID-19 vaccination and cancer/cancer treatment were barriers to complete primary vaccination series among Chinese cancer patients: A multicentre cross-sectional survey

ABSTRACT

COVID-19 vaccination is effective for cancer patients without safety concerns. However, COVID-19 vaccination hesitancy is common among cancer patients. This study investigated factors affecting primary COVID-19 vaccination series completion rate among cancer patients in China. A multicentre cross-sectional study was conducted in four Chinese cities in different geographic regions between May and June 2022. A total of 893 cancer inpatients provided written informed consent and completed the study. Logistic regression models were fitted. Among the participants, 58.8% completed the primary COVID-19 vaccination series. After adjusting for background characteristics, concerns about interactions between COVID-19 vaccination and cancers/cancer treatment (adjusted odds ratios [AOR]: 0.97, 95%CI: 0.94, 0.99) were associated with lower completion of primary vaccination series. In addition, perceived higher risk of COVID-19 infection comparing to people without cancers (AOR: 0.46, 95%CI: 0.24, 0.88), perceived a high chance of having severe consequences of COVID-19 infection (AOR: 0.68, 95%CI: 0.51, 0.91) were also associated with lower completion rate. Being suggested by significant others (AOR: 1.32, 95%CI: 1.23, 1.41) and perceived higher self-efficacy to receive COVID-19 vaccination (AOR: 1.48, 95%CI: 1.31, 1.67) were positively associated with the dependent variable. Completion rate of primary COVID-19 vaccination series was low among Chinese cancer patients. Given the large population size and their vulnerability, this group urgently needs to increase COVID-19 vaccination coverage. Removing concerns about interactions between COVID-19 vaccination and cancers, using fear appeal approach, involving significant others, and facilitating patients to make a plan to receive COVID-19 vaccination might be useful strategies.

KEYWORDS:

• Cancer patients
• COVID-19 vaccination
• primary vaccination series
• perceptions
• media influence
• China

Introduction

Globally, cancer is the second leading cause of death. In 2020, the number of newly diagnosed cases and deaths of cancer was 18.1 and 10 million worldwide, and China accounted for 24% of these newly diagnosed cases and 30% of cancer-related deaths.^1–3 Retrospective cohort study of adults with Coronavirus disease 2019 (COVID-19) showed that having metastatic solid tumors was associated with higher odds of mortality with COVID-19.^4,5 As compared to people without cancers, cancer patients have been observed to be at higher risk of COVID-19 infection due to the immunosuppressed status caused by the malignancy and cancer treatments (e.g., chemotherapy and surgery).^6–8 Previous studies consistently showed that cancer patients, especially those with lung cancer, hematological cancer, metastatic cancer, or recent cancer treatment, were observed to have worse clinical outcomes after COVID-19 infection comparing to those without cancers.^6,7,9–11 For instance, studies showed that cancer patients with COVID-19 had a higher risk of hospitalization and death comparing to COVID-19 patients without cancers.^7,11 Moreover, some cancer patients have delayed cancer treatments due to health anxieties associated with high risk and severe outcomes of COVID-19 infection and their desire to avoid exposure to COVID-19.¹² Such a phenomenon might negatively affect their cancer prognosis.

A recent systematic review and meta-analysis demonstrated high immunogenicity from COVID-19 vaccination in patients with cancers, with better results for solid tumors than hematological malignancies, and with a good safety profile.¹³ Given their high vulnerability to COVID-19 and the promising vaccine efficacy, cancer patients are considered as a priority group to receive the COVID-19 vaccination.¹⁴ The World Health Organization (WHO and other international health authorities consistently recommend COVID-19 vaccination for all cancer patients.^15–21 However, cancer patients undergoing hematopoietic cell transplantation and/or cellular therapy, and those with hematologic and/or solid tumor malignancies, should postpone vaccination until their treatment is completed.¹⁶ In China, the National Guideline for COVID-19 vaccination (third edition) listed lymphoma and leukemia as contraindications.²² Other people with solid tumors who are not on immunotherapy, radiotherapy, or chemotherapy are recommended to receive the inactivated, subunit, or recombinant COVID-19 vaccination.^22,23 The guideline also highlighted that patients receiving immunotherapy, radiotherapy, or chemotherapy should receive the COVID-19 vaccination after completing these treatments.^22,23

COVID-19 vaccination hesitancy is common among cancer patients. The prevalence of willingness to receive a COVID-19 vaccination among cancer patients ranged from 17.9% in Hong Kong, China,²⁴ 44.2% in Ethiopia,²⁵ 53.7% in France,²⁶ 55% in Lebanon,²⁷ 60.3% in Poland,²⁸ to 61.8% in Korea.²⁹ As compared to studies among the general population in these countries during the same period, cancer patients had a much lower willingness to receive COVID-19 vaccination.^30–33 In Ethiopia, a study reported that only 14.5% of cancer patients had received at least one dose of COVID-19 vaccination.²⁵ However, no study reported the COVID-19 vaccination uptake among cancer patients in mainland China.

Studies suggested the immune responses and vaccine efficacy in cancer patients was lower among cancer patients than the general population.^13,34 Compared to healthy adults, there is less evidence of vaccine safety among cancer patients.^16,21,35 Therefore, cancer patients may be more concerned about the interactions between COVID-19 vaccination and cancers/cancer treatments, such as worrying about cancers leading to poorer vaccine efficacy, shorter duration of protection, and more severe side effects. However, no study has investigated whether such concerns are barriers of COVID-19 vaccination uptake among cancer patients. Previous studies also suggested that being male, older age, history of seasonal influenza, overweight or obese, receiving cancer treatment, perceived severe consequences of COVID-19, perceived more benefits of COVID-19 vaccination, and receiving suggestions made by physicians were facilitators to receive COVID-19 vaccination among cancer patients,^{24–26,28,29} while concerns about vaccine safety and side effects, depression, and anxiety were barriers to receive such vaccination.^24,26,28,29 These factors were also considered by this study.

In addition, exposure to information related to COVID-19 vaccination influenced people’s decision to take up such vaccines.³⁶ Thoughtful consideration of the veracity of information may mitigate the negative impact of misinformation related to COVID-19 and COVID-19 vaccination.^37,38 To our knowledge, no study investigated the influence of information exposure or thoughtful consideration of the veracity of exposed information on COVID-19 vaccination uptake among cancer patients.

To fill the abovementioned knowledge gaps, this study was to investigate factors affecting completion of primary COVID-19 vaccination series among cancer inpatients in four Chinese cities in different geographic regions. We examined the effects of following factors: sociodemographic factors, perceptions and media exposure related to COVID-19 and COVID-19 vaccination.

Materials and methods

Study design

This is a multicentre cross-sectional survey conducted between May and June 2022. The study sites covered four conveniently selected Chinese cities, including one in the North (Changzhi), one in the Northeast (Hohhot), one in the West (Urumqi), and one in the South (Shenzhen). During the study period, all study sites followed the national guideline.²² The situation of COVID-19 in China during the study period was shown in Figure 1.

Figure 1. (a) The COVID − 19 situation in China during the study period. (b) The COVID − 19 situation in four conveniently selected Chinese cities during the study period.

Participants

The inclusion criteria of the study participants were: 1) cancer patients who were hospitalized in the four participating hospitals during the study period, 2) aged 18 years or above, and 3) willing to provide written informed consent to complete the survey. Exclusion criteria applied to patients: 1) critical illness or intensive care unit admission, 2) with a diagnosis of lymphoma, leukemia, mental illness, or who were taking medication for mental illness, or 3) were diagnosed with dementia or unable to communicate effectively with the investigators. Physicians confirmed the first two inclusion and all exclusion criteria based on their medical records. We excluded patients with lymphoma or leukemia because both types of cancers were listed as contraindications of COVID-19 vaccination during the study period.²²

Recruitment and data collection

Medical staff in the participating hospitals approached all cancer inpatients, screened their eligibility, briefed them about the study, and invited them to complete a face-to-face interview. We approached all 1325 cancer inpatients during the study period, 1018 were eligible for the study, 125 refused to participate, and 893 provided written informed consent and completed the interview. The main reasons for refusal were lack of time and/or other logistic reasons (n = 90) and lack of interest in joining the study (n = 35). The overall response rate was 87.7% (79.0% in Hohhot, 80.1% in Changzhi, 94.3% in Shenzhen, and 97.6% in Urumqi). The recruitment procedures were shown in Figure 2. No incentives were given to the participants.

Figure 2. Flowchart of recruitment procedures.

Sample size planning

The target sample size was 800. We used “tests for two proportions” function in the PASS software. Among the 800 participants, we assumed 50% of them (n = 400) did not have a facilitating condition and the completion rate of primary vaccination series to be 10–40%. We input these parameters into the PASS and the results showed that such sample size could detect a smallest odds ratio of 1.49 between people with and without such facilitating condition (power: 0.80; alpha: 0.05; PASS11.0, NCSS LLC).

Ethical considerations

Prospective participants were informed that the survey was anonymous, their information would be kept strictly confidential, and they had the right to refuse to participate or withdraw from the study at any time. Refusal and withdrawal would not affect their access to any services. Written informed consent was obtained. This study was conducted following the guidelines of the Declaration of Helsinki. Ethics approval was obtained from the Institutional Review Board of Changzhi Medical College (reference: RT2022027, date of approval: May 18, 2022).

Measurements

Development of the questionnaire

We performed in-depth interview to understand cancer patients’ perspective of COVID-19 vaccination. Ten cancer inpatients were purposively recruited in a participating hospital. With written informed consent, interviews were conducted by medical staff and audio recorded. We transcribed interviews and kept a code book to record special data and transform the data into categories to identify main themes. We identified three themes related to the barriers: 1) concerns that COVID-19 vaccination would be less effective, and with shorter protection period and more side effects for cancer patients, 2) concerns that cancer treatment would reduce effectiveness of COVID-19 vaccination, and 3) concerns about COVID-19 vaccination would negatively affect cancer treatment. Three other themes were related to the facilitators. They were: 1) perceived high risk of COVID-19, 2) cancer patients were more likely to have severe consequences following COVID-19 infection, and 3) suggestions made by doctors and family members. Based on the findings and literature review, a panel of epidemiologists, behavioral health experts, health psychologists, and oncologists developed the questionnaire. The questionnaire was tested among 20 cancer inpatients to assess clarity and readability. All participants in the pilot study agreed that the questions were easy to understand and the length was acceptable. These cancer inpatients did not participate in the actual survey. The panel finalized the questionnaire based on their comments.

Background characteristics

Participants reported sociodemographic characteristics, including age, gender, ethnicity, education level, relationship status, and employment status. The medical staff extracted cancer-related characteristics from their medical records, including type and current treatment of cancers, and the presence of metastatic cancers. Presence of other chronic conditions (e.g., diabetes mellitus, hypertension/hyperlipidemia, and other chronic diseases) were also extracted from their medical record.

COVID-19 vaccination uptake

The number of doses of COVID-19 vaccination received by the participants was extracted from their vaccination records. We defined completion of the primary COVID-19 vaccination series as receiving at least two doses of COVID-19 vaccination. We used a checklist to assess local adverse events (pain, redness, itching, swelling, induration, and skin rashes in the arm where the shot was given) and systematic adverse events (abnormal skin and mucosa, diarrhea, anorexia, vomit, nausea, muscle/joint pain, headache, cough, fatigue, fever, and others) within one month after receiving COVID-19 vaccination. The same checklist was used to measure self-reported adverse events of COVID-19 vaccination among general population³⁹ and diabetic patients in China.⁴⁰

Perceptions related to COVID-19 vaccination

We measured perceptions related to COVID-19 vaccination based on the Health Belief Model (HBM).⁴¹ Five items were constructed to measure concerns related to potential interactions between cancer/cancer treatment and COVID-19 vaccination. These concerns were considered as barriers to receive COVID-19 vaccination in our in-depth interview of cancer patients. The Perceived Barrier Scale was formed by summing up individual item scores. The measurements of cue to action and perceived self-efficacy were adapted from scale/item validated in the Chinese population.^37,42 The response categories for these measurements were 1 = disagree, 2 = neutral, and 3 = agree. The Cronbach’s alpha of the Perceived Barrier Scale and the Cue to Action Scale were 0.92 and 0.89, respectively.

In addition, four items measured perceived susceptibility and perceived severity. Two items asked the participants to compare their risk of COVID-19 and chance of having severe consequences if contracting COVID-19 versus people without cancers (response categories: 1= much lower, 2 = somewhat lower, 3 = similar, 4 = somewhat higher, & 5 = much higher). Two other items measured their chance of contracting the Omicron variant of COVID-19 and having severe consequences if contracting COVID-19 (response categories: 1 = low, 2 = moderate, 3 = high, & 4 = uncertain).

Media influences related to COVID-19 and COVID-19 vaccination

Items measuring the frequency of exposure to topics related to COVID-19 and COVID-19 vaccination were adapted from validated measurements in the Chinese population.³⁷ The topics included infectivity of the Omicron variant of COVID-19, the relatively low risk of having severe consequences or death after infecting Omicron variant of COVID-19, COVID-19 pandemic was not under control after COVID-19 vaccination rollout, and people contracting COVID-19 after completing primary vaccination series (response categories: 1 = almost never, 2 = seldom, 3 = sometimes, & 4 = always). A validated item measured the frequency of thoughtful consideration about the veracity of COVID-19-specific information.⁴³

Statistical analysis

The frequency distribution of all study variables were presented. Mean and standard deviation (SD) of the item and/or scale scores representing perceptions and media influence were also presented. Cronbach’s alpha for the scales was calculated using the “reliability analysis” in the SPSS. Cronbach’s alpha is a measure to assess the reliability, or internal consistency, of a set of scale or test item. We performed exploratory factor analysis using principal component analysis with varimax rotation. The dependent variable was the completion of the primary COVID-19 vaccination series. We first assessed the significance between background characteristics and the dependent variable using univariate logistic regression models. Perceptions and media influence were independent variables of interest. We fitted a single logistic regression model to obtain adjusted odds ratios (AOR), which involved one of these independent variables of interest and all significant background variables in univariate analysis. There were no missing values for the participants who completed the survey. SPSS version 26.0 (IBM Corp) was used for data analysis, with P < .05 considered as statistically significant.

Results

Background characteristics

About half of the participants were over 60 years old (45.1%) and female (45.6%). The majority of the participants were the Han majority (83.2%), married (91.4%), did not receive tertiary education (85.7%), and without afull-time work (79.8%). The most common type of cancer among the participants was lung cancer (24.0%), followed by colorectal cancer (16.2%) and gastric cancer (11.6%). At the time of this study, most of the participants were under cancer treatment (93.5%); 6.5% had metastatic cancers. In addition to cancers, 18.6% of the participants had at least one other chronic conditions (Table 1).

Table 1. Background characteristics of the participants (n = 893).

	n	%
Sociodemographic characteristics
Age group, years
18–45	124	13.9
46–60	366	41.0
61–75	343	38.4
>75	60	6.7
Gender
Male	486	54.4
Female	407	45.6
Ethnicity
Han majority	743	83.2
Other ethnic minorities	150	16.8
Education level
Junior high or below	599	67.1
Senior high or equivalent	166	18.6
College and above	128	14.3
Relationship status
Single/divorced/widowed	77	8.6
Married	816	91.4
Employment status
Full-time	180	20.2
Part-time/self-employed/unemployed/retired/students	713	79.8
Cancer related characteristics
Type of cancer
Lung cancer	214	24.0
Gastric cancer	104	11.6
Liver cancer	23	2.6
Breast cancer	85	9.5
Colorectal cancer	145	16.2
Thyroid cancer	3	0.3
Esophageal cancer	41	4.6
Ovarian cancer	74	8.3
Other cancers	204	22.8
Metastatic cancers
No	835	93.5
Yes	58	6.5
Current treatment for cancer
Not yet started treatment	27	3.0
Chemotherapy only	616	69.0
Radiotherapy only	115	12.9
Immunotherapy only	23	2.6
Chemotherapy and radiotherapy	52	5.8
Immunotherapy and chemotherapy/radiotherapy	29	3.2
Treatment completed	31	3.5
Presence of other chronic diseases, yes
Diabetes mellitus	43	4.8
Hypertension and/or hyperlipidemia	108	12.1
Chronic cardiovascular diseases	28	3.1
Chronic respiratory diseases	5	0.6
Chronic liver and/or kidney diseases	11	1.2
Other chronic diseases	21	2.4
Any of above	166	18.6

COVID-19 vaccination uptake

Among the participants, 29.5% received two doses of COVID-19 vaccination, and 29.3% received the third dose. The proportion of completing the primary COVID-19 vaccination series was 58.8% (95%CI, 55.5–62.0%) (Table 2). Among participants who received at least one dose of COVID-19 vaccination (n = 567), 97.7% (n = 554) completed their last dose in the past year (≤6 months: 49.2%; 7–12 months: 48.5%). Among vaccinated participants, 13.8% reported having some side effects. The most common side effects was pain in the injection site (n = 53, 9.3%), followed by fatigue (n = 17, 3.0%), muscle/joint pain (n = 6, 1.1%) and itching in the injection site (n = 6, 1.1%). Most of the reported side effects were mild (Figure 3).

Figure 3. Adverse effects after vaccination.

Table 2. COVID-19 vaccination uptake, perceptions and media influences related to COVID-19 vaccination (n = 893).

	n	%
COVID-19 vaccination uptake
Number of doses received by the participants
0	326	36.5
1	42	4.7
2	263	29.5
3	262	29.3
Completion of primary vaccination series
No	368	41.2
Yes	525	58.8
Perceptions related to COVID-19 vaccination
Perceived susceptibility to COVID-19
As compared to people without cancers, is your risk of COVID-19 higher or lower?
Much lower than people without cancers	12	1.3
Somewhat lower than people without cancers	49	5.5
Similar to people without cancers	435	48.7
Somewhat higher than people without cancers	313	35.1
Much higher than people without cancers	84	9.4
How high is your risk of contracting the Omicron variant of COVID-19
Low	172	19.3
Moderate	257	28.8
High	213	23.9
Uncertain	251	28.1
Perceived severity of COVID-19 infection
As compared to people without cancers, is your chance of having consequences higher or lower if contracting COVID-19 infection?
Much lower than people without cancers	22	2.5
Somewhat lower than people without cancers	36	4.0
Similar to people without cancers	428	47.9
Somewhat higher than people without cancers	68	7.6
Much higher than people without cancers	339	38.0
Your chance of having severe consequence if contracting COVID-19
Low	41	4.6
Moderate	190	21.3
High	407	45.6
Uncertain	255	28.6
Perceived barriers to receive COVID-19 vaccination, agree/strongly agree
The protection offered by the COVID-19 vaccination is weaker among people with cancers	356	39.9
Cancer therapy could decrease the protection of COVID-19 vaccination	309	34.6
The side effects of COVID-19 vaccination are more severe among people with cancers	386	43.2
The duration of protection of COVID-19 vaccination is shorter among people with cancers	308	34.5
COVID-19 vaccination could negatively affect the control of cancers	380	42.6
Perceived Barrier Scale^a, mean (SD)	16.5	(4.6)
Cue to action related to COVID-19 vaccination, agree/strongly agree
Doctors would suggest you to receive COVID-19 vaccination	195	21.8
Your family members would suggest you to receive COVID-19 vaccination	243	27.2
Cue to Action Scale^b, mean (SD)	5.5	(2.5)
Perceived self-efficacy to receive COVID-19 vaccination, agree/strongly agree
You are confident to receive COVID-19 vaccination if you want to	316	35.4
Item score, mean (SD)	3.1	(1.2)
Media influences related to COVID-19 and COVID-19 vaccination
Frequency of exposure to the following contents on TV, radio, newspaper and internet
Infectivity of the Omicron variant of COVID-19
Almost never	84	9.4
Seldom	82	9.2
Sometimes	336	37.6
Always	391	43.8
Item score, mean (SD)	3.2	(0.9)
Risk of having severe consequences or death is relatively low following infection of the Omicron variant of COVID-19
Almost never	104	11.6
Seldom	188	21.1
Sometimes	428	47.9
Always	173	19.4
Item score, mean (SD)	2.7	(0.9)
COVID-19 pandemic is not under control after COVID-19 vaccination rollout
Almost never	100	11.2
Seldom	322	36.1
Sometimes	347	38.9
Always	124	13.9
Item score, mean (SD)	2.6	(0.9)
Some people contract COVID-19 after completion of their primary vaccine series
Almost never	100	11.2
Seldom	238	26.7
Sometimes	375	42.0
Always	180	20.2
Item score, mean (SD)	2.7	(0.9)
Frequency of thoughtful consideration about veracity of COVID-19-specific information
Almost never	133	14.9
Seldom	257	28.8
Sometimes	347	38.9
Always	156	17.5
Item score, mean (SD)	2.6	(0.9)

^aPerceived Barrier Scale, 5 items, Cronbach’s alpha: 0.92, one factor was identified by exploratory factor analysis, explaining 76.7% of total variance.

^bCue to Action Scale, 2 items, Cronbach’s alpha: 0.89, one factor was identified by exploratory factor analysis, explaining 89.9% of total variance.

Perceptions and media influence related to COVID-19 and COVID-19 vaccination

About 40% of the participants were concerned that COVID-19 vaccination would have weaker protection, shorter protection duration and more side-effects among cancer patients. A similar proportion of participants perceived cancer therapy would reduce effectiveness of COVID-19 vaccination, and COVID-19 vaccination would negatively affect cancer control. About half of the participants perceived a higher risk of COVID-19 infection (44.5%) and more severe consequences of COVID-19 infection comparing to people without cancers (45.6%). Item responses and scale scores of other perceptions related to COVID-19 vaccination were shown in Table 2. Regarding media influence, 43.8% of the study participants were always exposed to information about the infectivity of the Omicron variant of COVID-19 on TV, radio, newspaper, and the internet. Relatively few participants were exposed to other topics through these channels. Only 17.5% of the participants always considered the veracity of COVID-19-specific information (Table 2)

Factors associated with completion of primary COVID-19 vaccination series

In univariate analysis, participants who were older and without a full-time work had lower completion of primary vaccination series. Receiving tertiary education and presence of chronic conditions other than cancers were positively associated with the dependent variable. As compared to those with lung cancers, people having gastric cancers, colorectal cancer and other cancers were more likely to complete the primary vaccination series, while those with breast cancers had a lower completion rate (Table 3).

Table 3. Associations between background characteristics and completion of primary COVID − 19 vaccination series among cancer patients (n = 893).

	Completion of primary COVID-19 vaccination series (n/N, %)	OR (95%CI)	P values
Age group, years
18–45	87/124, 70.2%	1.0
46–60	219/366, 59.8%	0.63 (0.41, 0.98)	.04
61–75	194/343, 56.6%	0.55 (0.36, 0.86)	.01
>75	25/60, 41.7%	0.30 (0.16, 0.58)	<.001
Gender
Male	293/486, 60.3%	1.0
Female	232/407, 57.0%	0.87 (0.67, 1.14)	.32
Ethnicity
Han majority	426/743, 57.3%	1.0
Other ethnic minorities	99/150, 66.0%	1.44 (1.00, 2.09)	.05
Education level
Junior high or below	338/599, 56.4%	1.0
Senior high or equivalent	102/166, 61.4%	1.23 (0.87, 1.75)	.25
College and above	85/128, 66.4%	1.53 (1.02, 2.28)	.04
Relationship status
Single/divorced/widowed	39/77, 50.6%	1.0
Married	486/816, 59.6%	1.44 (0.90, 2.29)	.13
Employment status
Full-time	138/180, 76.7%	1.0
Part-time/self-employed/unemployed/retired/students	387/713, 54.3%	0.36 (0.25, 0.53)	<.001
Type of cancer
Lung cancer	109/214, 50.9%	1.0
Gastric cancer	66/104, 63.5%	1.67 (1.04, 2.71)	.04
Liver cancer	15/23, 65.2%	1.81 (0.74, 4.44)	.20
Breast cancer	30/85, 35.3%	0.53 (0.31, 0.88)	.02
Colorectal cancer	97/145. 66.9%	1.95 (1.26, 3.02)	.003
Thyroid cancer	0/3, 0%	N.A.	N.A.
Esophageal cancer	26/41, 63.4%	1.67 (0.84, 3.33)	.15
Ovarian cancer	43/74, 58.1%	1.34 (0.78, 2.28)	.29
Other cancers	139/204, 68.1%	2.06 (1.38, 3.07)	<.001
Metastatic cancers
No	490/835, 58.7%	1.0
Yes	35/58, 60.3%	1.07 (0.62, 1.85)	.80
Current treatment for cancer
Not yet started treatment	17/27, 63.0%	1.0
Chemotherapy only	338/616, 54.9%	0.72 (0.32, 1.59)	.41
Radiotherapy only	89/115, 77.4%	2.01 (0.82, 4.93)	.13
Immunotherapy only	12/23, 52.2%	0.64 (0.21, 1.99)	.44
Chemotherapy and radiotherapy	34/52, 65.4%	1.11 (0.42, 2.92)	.83
Immunotherapy and chemotherapy/radiotherapy	15/29, 51.7%	0.63 (0.22, 1.83)	.40
Treatment completed	20/31, 64.5%	1.07 (0.37, 3.13)	.90
Presence of any other chronic diseases
No	412/727, 56.7%	1.0
Yes	113/166, 68.1%	1.63 (1.14, 2.33)	.01

OR: crude odds ratios.

CI: confidence interval.

After adjustment for significant background characteristics, participants who perceived a higher risk of COVID-19 infection than people without cancers did (AOR: 0.46, 95%CI:0.24, 0.88), and a high chance of having severe consequences if contracting COVID-19 (AOR: 0.68, 95%CI: 0.51, 0.91) were less likely to complete the primary vaccination series. A significant and negative association was also found between perceived barriers to receive COVID-19 vaccination and the dependent variable (AOR: 0.97, 95%CI: 0.94, 0.99). Being suggested by significant others (AOR: 1.32, 95%CI: 1.23, 1.41) and perceived self-efficacy to receive COVID-19 vaccination (AOR: 1.48, 95%CI: 1.31, 1.67) were positively associated with the dependent variable. Frequency of exposure to topics on different media channels and thoughtful consideration about the veracity of the information were not significantly associated with the dependent variable (Table 4).

Table 4. Factors associated with and completion of primary COVID-19 vaccination series among cancer patients (n = 893).

	Completion of primary COVID-19 vaccination series (n/N, %)	OR (95%CI)	P values	AOR (95%CI)	P values
Perceptions related to COVID-19 vaccination
As compared to people without cancers, is your risk of COVID-19 higher or lower?
Much/somewhat lower than people without cancers	43/61, 70.5%	1.0		1.0
Similar to people without cancers	263/435, 60.5%	0.64 (0.36, 1.15)	.13	0.58 (0.30, 1.09)	.09
Much/somewhat higher than people without cancers	219/397, 55.2%	0.52 (0.29, 0.92)	.03	0.46 (0.24, 0.88)	.02
How high is your risk of contracting the Omicron variant of COVID-19
Low/moderate/uncertain	408/680, 60.0%	1.0		1.0
High	117/213, 54.9%	0.81 (0.60, 1.11)	.19	0.86 (0.61, 1.20)	.36
As compared to people without cancers, is your chance of having consequences higher or lower if contracting COVID-19 infection?
Much/somewhat lower than people without cancers	37/58, 63.8%	1.0		1.0
Similar to people without cancers	262/428, 61.2%	0.90 (0.51, 1.58)	.71	0.73 (0.39, 1.37)	.33
Much/somewhat higher than people without cancers	226/407, 55.5%	0.71 (0.40, 1.25)	.24	0.58 (0.31, 1.08)	.09
Your chance of having severe consequence if contracting COVID-19
Low/moderate/uncertain	309/486, 63.6%	1.0		1.0
High	216/407, 53.1%	0.65 (0.50, 0.85)	.002	0.68 (0.51, 0.91)	.01
Perceived Barrier Scale	N.A.	0.96 (0.93, 0.99)	.01	0.97 (0.94, 0.99)	.04
Cue to Action Scale	N.A.	1.34 (1.25, 1.42)	<.001	1.32 (1.23, 1.41)	<.001
Perceived self-efficacy to receive COVID-19 vaccination	N.A.	1.46 (1.31, 1.64)	<.001	1.48 (1.31, 1.67)	<.001
Media influences related to COVID-19 and COVID-19 vaccination
Frequency of exposure to the following contents on TV, radio, newspaper and internet
Infectivity of the Omicron variant of COVID-19	N.A.	0.81 (0.70, 0.94)	.01	0.86 (0.73, 1.01)	.07
Risk of having severe consequences or death is relatively low following infection of the Omicron variant of COVID-19	N.A.	0.95 (0.81, 1.10)	.45	1.01 (0.85, 1.19)	.92
COVID-19 pandemic is not under control after COVID-19 vaccination rollout	N.A.	1.02 (0.87, 1.19)	.81	1.12 (0.94, 1.32)	.21
Some people contract COVID-19 after completion of their primary vaccine series	N.A.	0.92 (0.79, 1.06)	.25	1.00 (0.85, 1.18)	.98
Frequency of thoughtful consideration about veracity of COVID-19-specific information	N.A.	1.07 (0.93, 1.23)	.36	1.11 (0.95, 1.30)	.20

N.A.: not applicable.

OR: crude odds ratios.

AOR: adjusted odds ratios, odds ratios adjusted for significant background characteristics listed in Table 3.

CI: confidence interval.

Discussion

This study was the first time to find that concerns about the interactions between COVID-19 vaccination and cancer/cancer treatment were barriers to receive COVID-19 vaccination among cancer patients. According to the principle of social marketing, health promotion should tailor to the needs of the target population. This study provided some practical implications to inform health promotion for cancer patients. We found a low COVID-19 vaccination uptake among our participants, as only 58.8% completed the primary vaccination series, and 29.3% received a booster dose. Although these figures were higher than that reported in Ethiopia (14.5%),²⁵ they were much lower than those of the general population in China. By March 2022, 90.47% of people in China had completed the primary COVID-19 vaccination series, and 48.9% received a booster dose.⁴⁴ Given the large population size of cancer patients in China and their vulnerability to the emerging variants of concerns, low COVID-19 vaccination coverage in this group would diminish the nation’s efforts to build a “great wall of immunization.”⁴⁴ There is hence a strong need to promote COVID-19 vaccination among cancer patients.

This study has numerous practical implications for developing vaccination promotion strategies for cancer patients. First, more attention should be given to older cancer patients who did not receive tertiary education, and were without full-time work. In contrast with studies targeting cancer patients in other countries,²⁹ older participants were less likely to complete the primary COVID-19 vaccination series in our study. Previous studies showed that older people in China had lower COVID-19 vaccination uptake compared to their younger counterparts.⁴² China was recently hit by the Omicron variants of COVID-19 and most of the COVID-19 associated death occurred among older adults who had not completed the primary vaccination series.⁴⁵ Previous studies suggested that cancer and older age were two independent risk factors of COVID-19 mortality.^8,46 Therefore, such information should be disseminated to cancer patients to increase their awareness. In this study, participants with a full-time work were more likely to complete the primary COVID-19 vaccine series. One possible explanation is that many local employers required their employees to receive COVID-19 vaccination. Lower education level was a barrier of COVID-19 vaccination uptake among cancer patients in this study. Therefore, health communication messages promoting COVID-19 vaccination for cancer patients should be straightforward and easy to understand by people with low literacy levels. Moreover, future programs promoting COVID-19 vaccination for cancer patients should be aware of the difference in vaccine hesitancy among people with various cancers. Focused attention should be given to patients with breast cancers and lung cancers as participants in this study with diagnosis of these cancers reported lower COVID-19 vaccination uptake. In addition, people with other chronic diseases reported higher COVID-19 vaccination uptake in this study. Participants with other chronic conditions may perceive that they are more vulnerable to COVID-19 and hence have higher motivation to use COVID-19 vaccination to protect themselves.

Second, removing concerns about interactions between COVID-19 vaccination and cancers/cancer treatments is potentially valuable for future programs, as these concerns were barriers to complete COVID-19 vaccination in this study. Recent evidence showed that in patients with past and active neoplasms, the BNT162b2 vaccine has similar efficacy and safety profiles as the general population.⁴⁷ In China, inactivated vaccines are in the most widespread in China (over 85% of all COVID-19 vaccine doses administered). However, there was a lack of evidence about the differences in efficacy and safety of inactivated COVID-19 vaccines between people with and without cancers. Such evidence is important to address cancer patients’ concerns about interactions between COVID-19 vaccination and cancers/cancer treatments and to support COVID-19 vaccination implementation for cancer patients. The mRNA vaccines are not yet available in China. Although the duration of protection might be shorter for cancer patients, recent evidence showed that the BNT162b2 mRNA vaccine had similar vaccine efficacy and safety profiles among people with and without cancers.³⁴ Therefore, the country may consider introducing them for cancer patients. Furthermore, recent studies showed that receiving a COVID-19 vaccine booster dose could significantly increase antibody titers among cancer patients and many countries have been recommending a third and a fourth dose of COVID-19 vaccine to cancer patients.^48–50 Therefore, the country may also consider introducing a COVID-19 vaccine booster dose to cancer patients. Many regions have already recommended a third and fourth dose of COVID-19 vaccines to immunodeficiency people, such as cancer patients, including in Hong Kong, China.⁵¹ In order to remove participants’ concerns about cancer treatment and COVID-19 vaccination. Health communication messages should emphasize that people undergoing active cancer treatments responded well to COVID-19 vaccination,⁵² and no evidence shows that COVID-19 vaccination would affect cancer treatments.

Third, modifying other perceptions related to COVID-19 vaccination based on the HBM is potentially useful in future health promotion. Both perceived higher susceptibility and severity of COVID-19 were significantly associated with lower completion of primary vaccination series in this study. It was possible that cancer patients who completed the COVID-19 vaccination felt the vaccines were protecting them and hence perceived a lower risk and less severe consequences of COVID-19 infection. Our study was a cross-sectional survey and could not establish causal relationship. In our study, we measured relative (i.e., as compared to people without cancers) and absolute levels of perceived susceptibility and severity. Interestingly, the relative level of susceptibility and absolute level of severity were significantly associated with the COVID-19 vaccination completion, while the associations between the other two measurements and the dependent variable were non-significant. Our findings suggested that relative level of susceptibility and absolute level of severity was significantly associated with the COVID-19 vaccination completion. Therefore, health communication should increase relative level of susceptibility by emphasizing cancer patients have a higher risk of COVID-19 infection than people without cancers, and enhancing absolute level of severity by highlighting high risk of hospitalization and mortality among cancer patients following COVID-19 infection. In line with studies among cancer patients in other countries and among general population in China, being suggested by significant others to take up COVID-19 vaccination was a facilitator of completing primary vaccination series.^{24–26,28,29} Future programs should involve physicians and family members of cancer patients to provide a solid cue to action to complete COVID-19 vaccination series. In addition, it is useful to increase perceived self-efficacy, as it was a facilitator to complete primary COVID-19 vaccination series and there was much room for improvement. Previous studies suggested some useful strategies to increase self-efficacy, such as informing cancer patients about suitable timing to receive COVID-19 vaccination and facilitating them to form a solid plan to receive the vaccination.⁵³

Fourth, in contrast to our hypothesis, exposure to information related to COVID-19 and COVID-19 vaccination through different media channels were not significantly associated with completing the primary vaccination series. Future health promotion should focus on modifying individual-level determinants (e.g., perceptions). Health promotion needs to be evidence-based.

The first strength of this study lies in being a multi-center study, hence the extrapolation of the research results is more representative. Secondly, this is the first application of the HBM used to explain COVID-19 vaccination uptake related factors, the results of this study theoretically point out the direction for carrying out targeted health education and behavioral intervention to improve the COVID-19 vaccination rate of tumor patients through the HBM model.

This study had some limitations. First, our findings are most applicable to the time when zero COVID policy was implemented in China. When China started to relieve its COVID-19 control measures, the number of COVID-19 cases increased dramatically and people might perceive a much higher risk of COVID-19 and hence a stronger need to get vaccinated. Second, adverse events of COVID-19 vaccination were self-reported and recall bias existed. Third, we only targeted inpatients and our findings could not be generalized to all cancer patients. Fourth, the study sites were selected conveniently and the sample could not represent all cancer patients in China. Fifth, we could not obtain the characteristics of cancer patients who refused to join the study. These patients might have different characteristics as compared to study participants and selection bias existed. However, the magnitude of selection bias might be limited as our response rate was quite high. Moreover, we did not include the perceived benefit of COVID-19 vaccination, a facilitator of vaccination uptake among cancer patients in previous studies, due to the limited length of the questionnaire. Furthermore, this was a cross-sectional survey and could not establish causal relationship.

In conclusion, we found a low completion rate of primary COVID-19 vaccination series among cancer patients in China. Given the large population size and their vulnerability to COVID-19, there is an urgent need to increase COVID-19 vaccination coverage among cancer patients in China. The findings of our study indicated that removing concerns about interaction between COVID-19 vaccination and cancers/cancer treatments, using fear appeal approach, involving significant others, and facilitating patients to make a plan to receive COVID-19 vaccination would be useful strategies.

Authors’ contributions

Conceptualization: Z.W., J.X.; methodology: L.Z., R.S., J.Y., Z.W., J.X.; data curation: L.Z., R.S., J.Y., X.D., Y.W.; project administration: L.Z., R.S., J.Y., X.D., Y.W.; writing-original draft preparation: S.C., P.S-f.C., Z.W.; writing-review and editing: S.C., P.S-f.C., Z.W., J.X. All authors have read and agreed to the published version of the manuscript.

Institutional review board statement

This study was conducted following the guidelines of the Declaration of Helsinki. Ethics approval was obtained from the Institutional Review Board of Changzhi Medical College (reference: RT2022027, date of approval: May 18, 2022).

Informed consent statement

Prospective participants were informed that the survey was anonymous, their information will be kept strictly confidential, and they had the right to refuse to participate or withdraw from the study at any time. Refusal and withdrawal would not affect their access to any services. Written informed consent was obtained.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated and/or analyzed during the current study are not publicly available as they contain sensitive personal behaviors but are available from the corresponding author on reasonable request.

Additional information

Funding

This work was supported by the Joint Foundation of Innovative Environment Construction of Autonomous Region of Xinjiang (#2019D01C326), The Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (2022XM45), the National Natural Science Foundation of China (81872674), and the Shenzhen Science and Technology Innovation Committee Projects (No. JCYJ20220818102817038), the Scientific Research Foundation of Peking University Shenzhen Hospital (No. KYQD2022216). The funder had no role in study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.