Socio-demographic characteristics of the study participants
A total of 505 out of 506 participants who were involved in the study with a response rate of 99.8%, with 79.60% being females. The mean age was 48.05 (± SD 12.36) years, ranging from 19 to 83 years and 96.63% of the participated aged 26 years and above. More than a half had completed either primary (42.97%) or secondary education and above (35.44%). Among the participants who disclosed their religious affiliations, Christians accounted for 51.69%. About two third (62.97%) of the respondents were self-employed. Over 98.0% of the respondents reported being non-smokers; and 54.9% were engaging in regular exercises, among them, 48.3% are exercising three times or more in week (Table 2).
Table 2
Socio-demographic characteristics of participants at ORCI in Dar es Salaam, Tanzania, 2022 (N = 505).
Characteristics | Frequency | Total (N = 505) | Percent (%) |
Female, N = 402 (%) | Male, N = 103 (%) | |
Age in years | | | | |
≤ 25 | 11 (64.71) | 6 (35.29) | 17 | 3.37 |
26–50 | 210 (79.55) | 54 (20.45) | 264 | 52.28 |
More than 50 years | 181 (80.8) | 43 (19.20) | 224 | 44.36 |
Educational status | | | | |
Informal education | 95 (87.16) | 14 (12.84) | 109 | 21.58 |
Primary education level | 175 (80.65) | 42 (19.35) | 217 | 42.97 |
Secondary and above | 132 (73.74) | 47 (26.26) | 179 | 35.44 |
Religion | | | | |
Christians | 207 (79.62) | 53 (20.38) | 260 | 51.69 |
Muslims | 192 (79.34) | 50 (20.66) | 242 | 48.11 |
Traditional | 1 (100.0) | 0 (0.0) | 1 | 0.20 |
Job Status | | | | |
Employed | 34 (72.34) | 13 (27.66) | 47 | 9.31 |
Retired | 8 (66.7) | 4 (33.3) | 12 | 2.38 |
Self-employed | 237 (74.53) | 81 (25.47) | 318 | 62.97 |
Unemployed | 122 (96.06) | 5 (3.94) | 127 | 25.15 |
Student | 1 (100.00) | 0 (0.00) | 1 | 0.20 |
Marital Status | | | | |
Single | 69 (80.23) | 17 (19.77) | 86 | 17.03 |
Married | 255 (75.00) | 85 (25.00) | 340 | 67.33 |
Others** | 78 (98.73) | 1 (1.27) | 79 | 15.64 |
Participation type | | | | |
Quantitative (Questionnaire) | 383 (79.96) | 96 (20.04) | 479 | 94.85 |
Qualitative (In-depth interview) | 19 (73.08) | 7 (26.92) | 26 | 5.15 |
Smoking | | | N = 479 | |
Smokers | 3 (33.3) | 6 (66.7) | 9 | 1.9 |
Non-smokers | 380 (80.9) | 89 (18.9) | 470 | 98.1 |
Regular Exercises | | | N = 479 | |
Yes | 206 (78.3) | 57 (21.7) | 263 | 54.9 |
No | 177 (81.9) | 39 (18.1) | 216 | 45.1 |
Number of exercises per week | | | N = 263 | |
Once | 50 (78.1) | 14 (21.9) | 64 | 24.3 |
Twice | 57 (79.2) | 15 (20.8) | 72 | 27.4 |
Thrice a week | 27 (69.2) | 12 (30.8) | 39 | 14.8 |
More than three-times | 72 (81.8) | 16 (18.2) | 88 | 33.5 |
* The rest type of cancers; ** Divorced, Widowed, and Separated; NA = Not Applicable | |
Clinical characteristics of the study participants
Table 3 shows; the most common primary malignancies were cervical cancer (38.0%) and breast cancer (25.6%). About half (43.4%) of the patients had a malignancy stage II of cancer. Approximately half of study participants were outpatient [53.4% (256/479)], and 98.1% never experienced any COVID-19 symptoms between January 2020 to the time of data collection in September 2022. Only 4.6% of the participants had tested for COVID-19, among them 86.4% had tested negative. Approximately 15.0% of the respondents were already vaccinated against Hepatitis B infection.
Table 3
Clinical characteristics of participants at ORCI in Dar es Salaam, Tanzania, 2022 (N = 479).
Characteristics | Frequency | Total (N = 479) | Percent (%) |
Female N = 383 (%) | Male N = 96 (%) |
Primary Type of Malignancy | | | | |
Breast | 123 (100.0) | 0 (0.0) | 123 | 25.6 |
Cervical | 182 (100.0) | NA | 182 | 38.0 |
Esophagus | 5 (26.3) | 14 (73.7) | 19 | 4.0 |
Prostate | NA | 45 (100.0) | 45 | 9.4 |
Other Cancers* | 73 (66.4) | 37 (33.6) | 110 | 23.0 |
Malignancy Stage | | | | |
Stage 0 | 65 (78.3) | 18 (21.7) | 83 | 17.3 |
Stage I | 58 (77.3) | 17 (22.7) | 75 | 15.7 |
Stage II | 172 (82.7) | 36 (17.3) | 208 | 43.4 |
Stage III | 75 (78.9) | 20 (21.1) | 95 | 19.9 |
Stage IV | 13 (72.2) | 5 (27.8) | 18 | 3.8 |
Type of Patient Care | | | | |
Inpatient | 177 (79.4) | 46 (20.6) | 223 | 46.6 |
Outpatient | 206 (80.5) | 50 (19.5) | 256 | 53.4 |
Experienced any COVID-19 symptoms | | | | |
Experienced the symptoms | 6 (66.7) | 3 (33.3) | 9 | 1.9 |
Not experienced | 377 (80.2) | 93 (19.8) | 470 | 98.1 |
COVID-19 test | | | | |
Tested | 18 (81.8) | 4 (18.2) | 22 | 4.6 |
Not Tested | 365 (79.9) | 92 (20.1) | 457 | 95.4 |
COVID-19 test results (N = 22) | | | | |
Positive | 3 (100.0) | 0 (0.0) | 3 | 13.6 |
Negative | 15 (78.9) | 4 (21.1) | 19 | 86.4 |
Vaccinated against Hepatitis B | | | | |
Vaccinated | 55 (76.4) | 17 (23.6) | 72 | 15.0 |
Not vaccinated | 328 (80.6) | 79 (19.4) | 407 | 85.0 |
Past medical history and clinical characteristics of participants
Out of 479 participants in Table 4, 51 (10.6%) reported having a chronic disease(s). Among those with chronic diseases, hypertension was the most common condition reported by 22 (43.1%) (Table 4).
Table 4
History of having chronic diseases among study participants (N = 479)
Characteristic | N = 51 | Percent (%) |
Having other chronic disease(s) | | |
Yes | 51 | 10.6% |
No | 428 | 89.6% |
Disease name | | |
Hypertension | 22 | 43.1% |
HIV | 11 | 21.6% |
Diabetes Mellitus | 7 | 13.7% |
Asthma | 5 | 9.8% |
Ulcers | 2 | 3.9% |
Other diseases (Nose Bleeding, Swelling on the back, Typhoid) | 4 | 7.9% |
Number of years since the first diagnosis | | |
Less than 5 Years | 18 | 35.3% |
6–10 Years | 12 | 23.5% |
More than 11 Years | 21 | 41.2% |
Time on treatment (Years) | | |
Less than 5 Years | 16 | 31.4% |
6–10 Years | 9 | 17.6% |
More than 11 Years | 18 | 35.3% |
Non-Response | 8 | 15.7% |
COVID-19 vaccination uptake among cancer patients
The Table 5 below on COVID-19 vaccination uptake among 479 individuals, reveals that 58.0% of the respondents had received the COVID-19 vaccine. Among the vaccinated ones 43.2% had received the first dose, with only 6.5% having completed both doses. About half (50.4%) of the vaccinated participants were uncertain about the vaccine they received if was the first dose or second. Sinopharm and Janssen were mentioned most frequently, accounting for 23.4% and 21.9% of the responses, respectively. However, a significant portion (50.7%) could not recall the vaccine name they had received. Most participants (97.1%) reported taking whatever vaccine was available, indicating a lack of preference in their vaccine selection, and 98.9% vaccination decision did not based on medical advice or personal research.
Among the unvaccinated individuals (N = 201), 59.2% expressed a greater inclination towards future vaccination. Notably, 21.4% reported that their decision remained unchanged, signifying hesitancy to take the vaccine. Most of the unvaccinated participants (93.0%) did not consult medical personnel, and among those who did, only a small percentage (1.0%) found the medical advice was irrelevant. There was no statistical difference in COVID-19 vaccination rates between females and males [57.7% (221/383) versus 60.0% (57/96); p-value = 0.684].
Table 5
COVID-19 vaccination status among study participants (N = 479)
Characteristic | Frequency | Percent (%) |
COVID-19 vaccination Status | | |
Vaccinated | 278 | 58.0% |
Not vaccinated | 201 | 42.0% |
Vaccinated individuals | N = 278 | |
COVID-19 vaccine Dose | | |
First Dose | 120 | 43.2% |
Second Dose | 18 | 6.5% |
Don’t know | 140 | 50.4% |
COVID-19 vaccine Name | | |
Janssen | 61 | 21.9% |
Sinopharm | 65 | 23.4% |
BioNTech, Pfizer | 6 | 2.2% |
Moderna | 5 | 1.8% |
Couldn’t recall | 141 | 50.7% |
Vaccine Selection | | |
Chose the preferred one | 8 | 2.9% |
Took what was available | 270 | 97.1% |
Choice based on medical Advice or own research | | |
Did not choose | 275 | 98.9% |
Medical Advice | 3 | 1.1%5 |
Unvaccinated Individuals | 201 | |
Views about vaccine uptake | | |
More inclined towards vaccinating | 119 | 59.2% |
Less inclined towards vaccinating | 39 | 19.4% |
No change – still not going to take it | 43 | 21.4% |
Medical decisions for not vaccinating | | |
Did not Consult | 187 | 93.0% |
Consulted medical personnel | 18 | 6.0% |
Medical advice was irrelevant | 8 | 1.0% |
Factors influencing the uptake of COVID-19 vaccination to cancer patients attending oncology services (N = 479)
The Tables 6(a), 6(b) and 6(c) below show the results for both univariate and multivariate logistic models for different variables. These variables socio-demographic factors, history of chronic diseases, health behaviors, vaccine perception and acceptance, and health beliefs and theories of behaviors. The multivariate logistic regression analysis was performed to assess the associations between various characteristics and COVID-19 vaccine uptake.
Socio-demographic factors influencing the uptake of COVID-19 vaccination (N = 479)
The univariate model results in Table 6(a) of socio-demographic factors with vaccination uptake found that people in the age groups 26–35, 36–45 and over 55 had more vaccination uptake (OR 3.77, 95% CI 1.30–12.0, p = 0.018), (OR 3.40, CI 1.27–10.2, p = 0.019) and (OR 3.18, CI 1.17–9.59, p = .028) respectively. Other factors that were associated with vaccination uptake included having a secondary education and above (OR 2.49, CI 1.50–4.18, p < 0.001), being employed (OR 2.84, CI 1.33–6.80, p = 0.002). Religion was found to have a, marginal association the vaccination uptake. Muslims showed 1.44 times higher odds of vaccine uptake compared to Christians with a borderline statistical significance (p = 0.052), suggesting a potential influence of religious beliefs and practices on individuals’ decision to accept the COVID-19 shot. The results correspondingly show that people who were retired, and those with primary education or less, and inpatients had low vaccine uptake.
The multivariate analysis showed that people with secondary education and above had increased odds of being vaccinated compared to those with low education level [OR 2.26, CI (1.20– 4.27) p = 0.011].
Table 6
(a): Analysing socio-demographics and COVID-19 vaccination in cancer patients (N = 479)
| Vaccination Status | Univariate Models | Multivariate Models |
Characteristic (N) | Vaccinated | Unvaccinated | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value |
Age in Years | | | | | | | | |
≤ 25 (15) | 6 (40.0%) | 9 (60.0%) | Ref | — | | Ref | - | |
26–50 (246) | 145 (58.9%) | 101 (41.1%) | 2.09 | 0.73–6.41 | 0.200 | 1.23 | 0.29–4.99 | 0.800 |
More than 50 (218) | 127 (58.3%) | 91 (41.7%) | 2.17 | 0.75–6.69 | 0.200 | 1.91 | 0.46–7.94 | 0.400 |
Religion | | | | | | | | |
Christian (244) | 131 (53.7%) | 113 (46.3%) | Ref | — | | | | |
Muslim (232) | 145 (62.5%) | 87 (37.5%) | 1.44 | 1.00-2.08 | 0.052 | | | |
Traditional (1) | 0 (0.0%) | 1 (100%) | 0.00 | | > 0.900 | | | |
Sex | | | | | | | | |
Female (383) | 221 (57.7%) | 162 (42.3%) | Ref | — | | | | |
Male (96) | 57 (59.4%) | 39 (40.6%) | 1.07 | 0.68–1.70 | 0.800 | | | |
Education level | | | | | | | | |
Informal (107) | 57 (53.3%) | 50 (46.7%) | Ref | — | | Ref | - | |
Primary (203) | 96 (47.3%) | 107 (52.7%) | 0.79 | 0.49–1.26 | 0.300 | 1.41 | 0.78–2.57 | 0.300 |
Secondary education and above (169) | 125 (74.0%) | 44 (26.0%) | 2.49 | 1.50–4.18 | < 0.001 | 2.26 | 1.20–4.27 | 0.011 |
Job status | | | | | | | | |
Self-employed (304) | 180 (59.2%) | 124 (40.8%) | Ref | — | | Ref | - | |
Unemployed (122) | 57 (46.7%) | 65 (53.3%) | 0.60 | 0.39–0.92 | 0.019 | 0.68 | 0.41–1.12 | 0.130 |
Employed (41) | 33 (80.5%) | 8 (19.5%) | 2.84 | 1.33–6.80 | 0.011 | 1.78 | 0.69–5.31 | 0.300 |
Retired (12) | 8 (66.7%) | 4 (33.3%) | 1.38 | 0.42–5.25 | 0.600 | 1.09 | 0.28–5.10 | 0.900 |
Marital status | | | | | | | | |
Single (73) | 40 (54.8%) | 33 (45.2%) | Ref | — | | | | |
Married (333) | 206 (61.9%) | 127 (38.1%) | 1.34 | 0.80–2.23 | 0.300 | | | |
Others* (73) | 32 (43.8%) | 41 (56.2%) | 0.64 | 0.33–1.23 | 0.200 | | | |
Smoking Status | | | | | | | | |
Not smoking (470) | 273 (58.1%) | 197 (41.9%) | Ref | — | | | | |
Smoking (9) | 5 (55.6%) | 4 (44.4%) | 0.90 | 0.24–3.68 | 0.900 | | | |
COVID-19 patient in the family | | | | | | | | |
No (471) | 273 (58.0%) | 198 (42.0%) | Ref | — | | | | |
Yes (8) | 5 (62.5%) | 3 (37.5%) | 1.21 | 0.29–5.95 | 0.800 | | | |
| 1OR = Odds Ratio, CI = Confidence Interval, * Divorced, Widowed, and Separated |
Clinical factors influencing the uptake of COVID-19 vaccination (N = 479)
Findings from the univariate analysis in Table 6(b) revealed that some factors associated with COVID-19 vaccination uptake. Individuals who had been vaccinated against hepatitis B had a significantly higher odds of receiving the COVID-19 vaccine compared to those who had not received the hepatitis vaccine, this indicates a positive association between prior vaccination behavior and COVID-19 vaccine acceptance. Also, being treated as an outpatient [OR 0.62, CI (0.43–0.90), p = 0.012] was statistically significant with low uptake of the vaccine. People with cancer stage II and III were also higher vaccination uptake [OR 2.57, CI (1.53–4.36), p < 0.001] and [OR 2.14, (CI 1.18–3.93), p = 0.013] respectively.
After controlling for other variables, the type of patient care (being inpatient or outpatient) did not significantly influence vaccination acceptance. Similarly, being vaccinated against hepatitis B did not show a significant association with COVID-19 vaccine uptake.
Table 6
(b): Univariate and Multivariate Analysis of Binomial Logistic Regression of Clinical factors influencing the uptake of COVID-19 Vaccination to Cancer Patients Attending Oncology Services (N = 479)
| Vaccination Status | Univariate Models | Multivariate Models |
Characteristic (N) | Vaccinated | Unvaccinated | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value |
Patient care | | | | | | | | |
Inpatient (233) | 143 (64.1%) | 80 (35.9%) | Ref | — | | Ref | — | |
Outpatient (256) | 135 (52.7%) | 121 (47.3%) | 0.62 | 0.43–0.90 | 0.012 | 0.80 | 0.51–1.26 | 0.300 |
Malignancy type | | | | | | | | |
Cervical (182) | 104 (57.1%) | 78 (42.9%) | Ref | — | | | | |
Breast (123) | 62 (50.4%) | 61 (49.6%) | 0.76 | 0.48–1.21 | 0.200 | | | |
Prostate (45) | 29 (64.4%) | 16 (35.6%) | 1.36 | 0.70–2.73 | 0.400 | | | |
Esophagus (20) | 16 (80.0%) | 4 (20.0%) | 3.00 | 1.05–10.8 | 0.058 | | | |
Other types of cancer (109) | 67 (61.5%) | 42 (38.5%) | 1.20 | 0.74–1.95 | 0.500 | | | |
Malignancy stage | | | | | | | | |
Stage 0 (83) | 36 (43.4%) | 47 (56.6%) | Ref | — | | Ref | - | |
Stage I (75) | 41 (54.7%) | 34 (45.3%) | 1.57 | 0.84–2.97 | 0.200 | 1.45 | 0.69–3.10 | 0.300 |
Stage II (208) | 138 (66.3%) | 70 (33.7%) | 2.57 | 1.53–4.36 | < 0.001 | 1.29 | 0.68–2.42 | 0.700 |
Stage III (95) | 59 (62.1%) | 36 (37.9%) | 2.14 | 1.18–3.93 | 0.013 | 1.66 | 0.79–3.52 | 0.400 |
Stage IV (18) | 4 (22.2%) | 14 (77.8%) | 0.37 | 0.10–1.14 | 0.110 | 0.36 | 0.08–1.32 | 0.110 |
Smoking Status | | | | | | | | |
Not smoking (470) | 273 (58.1%) | 197 (41.9%) | Ref | — | | | | |
Smoking (9) | 5 (55.6%) | 4 (44.4%) | 0.90 | 0.24–3.68 | 0.900 | | | |
Vaccinated vs Hepatitis B | | | | | | | |
Not Vaccinated (407) | 225 (55.3%) | 182 (44.7%) | Ref | — | | Ref | — | |
Vaccinated (72) | 53 (73.6%) | 19 (26.4%) | 2.26 | 1.31–4.04 | 0.004 | 1.08 | 0.57–2.07 | 0.800 |
COVID-19 patient in a family member | | | | | | | | |
No (471) | 273 (58.0%) | 198 (42.0%) | Ref | — | | | | |
Yes (8) | 5 (62.5%) | 3 (37.5%) | 1.21 | 0.29–5.95 | 0.800 | | | |
| 1OR = Odds Ratio, CI = Confidence Interval |
Factors influencing the COVID-19 vaccination uptake as per Health Belief Model (N = 479)
Several factors in Table 6(c) were associated with COVID-19 vaccination uptake in univariate logistic regression analysis. Individuals who were hesitant to vaccinate against COVID-19 were likely higher [OR 14.8, CI (5.16–62.7), p < 0.001] to refuse the vaccine. Other factors which were statistically significant are perception on COVID-19 [OR 24,4, CI (10.5–70.9), p < 0.001], perceived severity [OR 0.42, CI (0.29–0.61), p < 0.001], perceived objections [OR 0.05, CI (0.01–0.12), p < 0.001], cues to action [OR 17.4, CI (6.09-73.0), p < 0.001], perceived efficacy [OR 0.27, CI (0.17–0.42), p < 0.001] and perspective [OR 0.29, CI (0.16–0.51), p < 0.001].
In the multivariate analysis, Perception on COVID-19 vaccine [OR 8.86, CI (2.84–35.2), p < 0.001], and perceived severity [OR 0.56, CI (0.36–0.87), p = 0.010] were statistically significant.
Table 6
(c): Univariate and Multivariate Analysis of Binomial Logistic Regression of Health Belief Model (HBM), Vaccine Perception, Acceptance and Hesitancy factors influencing the uptake of COVID-19 Vaccination to Cancer Patients Attending Oncology Services (N = 479)
| Vaccination Status | Univariate Models | Multivariate Models |
Characteristic (N) | Vaccinated | Unvaccinated | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value |
COVID-19 Vaccine Hesitancy | | | | | | | | |
No (31) | 3 (9.7%) | 28 (90.3%) | Ref | — | | Ref | — | |
Yes (448) | 275 (61.4%) | 173 (38.6%) | 14.8 | 5.16–62.7 | < 0.001 | 8.86 | 2.84–35.2 | 0.400 |
Perception on COVID-19 vaccine | | | | | | | | |
No (67) | 5 (7.5%) | 62 (92.5%) | Ref | — | | Ref | — | |
Yes (412) | 273 (66.3%) | 139 (33.7%) | 24.4 | 10.5–70.9 | < 0.001 | 8.86 | 2.84–35.2 | < 0.001 |
Perceived Severity | | | | | | | | |
Low severity (259) | 175 (67.6%) | 84 (32.4%) | Ref | — | | Ref | — | |
High severity (220) | 103 (46.8%) | 117 (53.2%) | 0.42 | 0.29–0.61 | < 0.001 | 0.56 | 0.36–0.87 | 0.010 |
Perceived Benefits | | | | | | | | |
No benefits (43) | 5 (11.6%) | 38 (88.4%) | Ref | — | | Ref | — | |
High benefits (436) | 273 (62.6%) | 163 (37.4%) | 12.7 | 5.37–37.5 | < 0.001 | 0.80 | 0.18–3.32 | 0.800 |
Perception of objections | | | | | | | | |
No (429) | 274 (63.8%) | 155 (36.1%) | Ref | — | | | | |
Yes (50) | 4 (8.0%) | 46 (92.0%) | 0.05 | 0.01–0.12 | < 0.001 | | | |
Cues to Action | | | | | | | | |
No (35) | 3 (8.6%) | 32 (91.4%) | Ref | — | | | | |
Yes (444) | 275 (61.9%) | 169 (38.1%) | 17.4 | 6.09-73.0 | < 0.001 | | | |
Health Motivation | | | | | | | | |
Not motivated (32) | 16 (50.0%) | 16 (50.0%) | Ref | — | | | | |
Motivated (447) | 262 (58.6%) | 185 (41.4%) | 1.42 | 0.69–2.92 | 0.300 | | | |
Perspective | | | | | | | | |
No (422) | 260 (61.6%) | 162 (38.4%) | Ref | — | | | | |
Yes (57) | 18 (31.6%) | 39 (68.4%) | 0.29 | 0.16–0.51 | < 0.001 | | | |
Perceived Efficacy | | | | | | | | |
Low efficacy (364) | 239 (65.7%) | 125 (34.3%) | Ref | — | | | | |
High efficacy (115) | 39 (33.9%) | 76 (66.1%) | 0.27 | 0.17–0.42 | < 0.001 | | | |
| 1OR = Odds Ratio, CI = Confidence Interval |
Findings from qualitative study: exploring personal underlying beliefs and perspectives influencing COVID-19 vaccination uptake among oncology patients
Five themes emerged during the analysis, perceptions on the COVID-19 vaccine, motivating factors for vaccination among cancer patients, trust in information sources, barriers to and availability of the vaccine and the influence of key figures.
Theme 1: Perception about the COVID-19 Vaccine
The data collected from the respondents revealed mixed feelings about the COVID-19 vaccine perception, with some expressing positive perceptions while others expressing negative perceptions. When asked about their perception about the COVID-19 vaccine they answered as follows.
“I am advising my fellow patients that we get tested, get those vaccines, and try to adhere to guidelines… Let us pray to God to bless us so that this pandemic does not continue” [KII1]. Another said “My initial perspective was negative, later on, I realized that it was helpful… Because many people getting vaccinated and some seemed confused crazy or overwhelmed… Our neighbour got vaccinated and he was fine but then he got sick then they went to give him more medicine or something to reduce the effects [of the vaccine]. He went to get an injection to reduce the severity of the medicine… Although I haven’t gotten vaccinated due to our illnesses [cancer], I am afraid” [KII2]. “Everyone is required to be vaccinated to protect themselves” [KII5].
The participants were also asked about the perception of their colleagues regarding the COVID-19 vaccine uptake. The following were the responses “Actually, I don’t know about that [Motive drive to get vaccinated], I am still sceptical because I see that there are many diseases, and I have been affected by HIV and now this one [Cancer]. I am afraid that even if I get vaccinated, I may end up with the same problems” [KII10].
Another one was positive regarding the same issue “Well, the way I think is that we should get vaccinated so that our bodies can function better… I haven’t heard much about it [the vaccine], but when I see someone getting vaccinated, I think that even if COVID-19 comes, it may not be strong because you have already received the vaccine.” [KII20]. Another interviewee was positive about the vaccine “We are supposed to protect ourselves more… so that we don’t get these infections again because if we get these infections again, we are just adding more problems” [KI13].
These findings, concur with the quantitative findings on table 4.8c where the multivariate findings on the perception on COVID-19 vaccine were statistically significant. The findings suggest that personal perceptions and experiences play a significant influence on the choice to get vaccinated, and this are influenced by exposure to (mis)information.
Theme 2: Motivating Factors for Cancer Patients to Get COVID-19 Vaccinated
Respondents said that they are vaccinating because of the chronic diseases (including cancer) they have or being pressured. “…I mean, they just feel pressured. For example, if you have cancer, it’s a complicated disease, and if COVID-19 comes along, you don’t know what will happen”. [KII7]. This aligns with the quantitative finding indicating a higher likelihood of vaccination among cancer patients. Another participant stated that reason to vaccinate against COVID-19 is a personal perception and confidence. “It is matter of a person and their fears… lack of confidence, there are people who believe ‘if I do this, it will help me’. But someone else may see it as insignificant. And a person may ignore it because they haven’t experienced it, but once they do, they realize that these are their issues, but it is a matter of understanding” [KII4].
The qualitative interviews echo the quantitative association, providing a deeper understanding of why individuals with chronic diseases, such as cancer, might feel compelled to get vaccinated. The fear of the unknown and the perceived susceptibility expressed in qualitative responses align with the quantitative data showing higher vaccine uptake among cancer patients.
Theme 3: Trust in the COVID-19 Sources of Information
Respondents expressed diverse perspectives on the trustworthiness of COVID-19 vaccination information sources. Some conveyed scepticism, linking misinformation to potential motives such as blood clotting and infertility. Others, however, expressed trust in those who have already been vaccinated, believing they haven't encountered problems.
“… let me think a bit … you know, everyone has their own understanding. Someone may tell you, ‘Oh do not get vaccinated, it is not necessary, people are just talking so we can get vaccinated,’ and each person has their own reasons. Now, someone else may say, ‘it is all lies, what are you doing? … everyone says their own thing, some say we should not get vaccinated, and they are deceiving us. Now, maybe they want to prevent us from having children early. Others say even those who say have already been vaccinated are lying to us so we can get vaccinated too. There are many challenges … I believed that if you get vaccinated, you protect yourself and use protective gears like masks to avoid getting infected. But there are people who don’t believe and say things like ‘no, do not get vaccinated, if you get vaccinated, your blood will clot.’ And if someone has already been told that their blood might clot, it means they are afraid they might die.” [KI12]. The response contradicted this “… I believe in people who have already been vaccinated and they say are not experiencing any problems” [KII21].
The above narrative resonates with the quantitative association between trust in information sources and vaccination uptake. The diverse opinions captured in qualitative interviews mirror the variations observed in the quantitative data. Individuals who trust information sources are more likely to get vaccinated, aligning with both sets of findings implying that trust in the sources of information plays a vital role in ensuring all people with cancer get vaccinated.
Theme 4: Barriers to and Availability of COVID-19 Vaccines
Participants echoed the sentiment that the vaccines are readily available, they mentioned various accessible locations such as hospitals, cancer testing centres, and neighbourhood facilities. Also, participants highlighted the importance of proper education from medical personnel, emphasizing that misinformation could be a potential barrier to vaccine acceptance. “Vaccines are easily available. They have announced places where you can get vaccinated, like hospitals, places where cancer is tested, and even in our neighbourhood they were registering people who wanted to get vaccinated. You just have to decide and go and say, ‘I have decided to get this vaccine to protect myself...” [KII19]. The respondents said there are no barriers to get the COVID-19 vaccines “…there are obstacles. I think it depends on our doctors telling us how it is because it did not work before until they themselves tell us what happens when you get vaccinated. But if someone is treated like this and not educated properly, they might think it will not cause them any more trouble when in reality they are being saved”. [KII10]
These narratives align with the quantitative findings emphasizing the perceived ease of access to COVID-19 vaccines. Both sources suggest that the availability of vaccines is a facilitating factor, contributing positively to vaccination uptake. The qualitative information adds a shade by underscoring the significance of health providers in overcoming potential barriers related to misinformation.
Theme 5: Role of influential figures in COVID-19 Vaccination
The qualitative findings also showed that religious leaders and healthcare providers have influence in people’s trust and decision to get the COVID-19 vaccine. “… Doctors [Healthcare providers] knows the disease very well, so when they tell us something, it is something they have already understood, they know its effects and benefits. So that is why they educate us, telling us to do this test, protect ourselves, and even the religious leaders, they have also gained understanding, and they tell us. Sometimes you find that people do not generally get such information in hospitals, but when they go to the churches, they get it, so we are grateful for that … the way they preach to us or give us seminars, we get understand …” [KII4]. About the influence of religious leaders “Exactly, because going to the religious leaders, people have faith, they will follow” [KII26]. The above is the same as “Yes, just your faith, for example, if they call for a seminar, we come, they educate us, even with the cancer issues, they have educated us a lot. So, if you do not educate someone, it can cause problems.” [KII24].
The above findings complement the quantitative findings by providing rich context on how healthcare providers and religious leaders contribute to vaccine acceptance. Both data sources suggest that individuals who trust and are influenced by these figures are more likely to embrace COVID-19 vaccination