COVID-19 Vaccine Hesitancy and Acceptance Among Individuals With Cancer, Autoimmune Diseases, or Other Serious Comorbid Conditions: Cross-sectional, Internet-Based Survey
Bibliographic record
Abstract
BACKGROUND: Individuals with comorbid conditions have been disproportionately affected by COVID-19. Since regulatory trials of COVID-19 vaccines excluded those with immunocompromising conditions, few patients with cancer and autoimmune diseases were enrolled. With limited vaccine safety data available, vulnerable populations may have conflicted vaccine attitudes. OBJECTIVE: We assessed the prevalence and independent predictors of COVID-19 vaccine hesitancy and acceptance among individuals with serious comorbidities and assessed self-reported side effects among those who had been vaccinated. METHODS: We conducted a cross-sectional, 55-item, online survey, fielded January 15, 2021 through February 22, 2021, among a random sample of members of Inspire, an online health community of over 2.2 million individuals with comorbid conditions. Multivariable regression analysis was utilized to determine factors independently associated with vaccine hesitancy and acceptance. RESULTS: Of the 996,500 members of the Inspire health community invited to participate, responses were received from 21,943 individuals (2.2%). Respondents resided in 123 countries (United States: 16,277/21,943, 74.2%), had a median age range of 56-65 years, were highly educated (college or postgraduate degree: 10,198/17,298, 58.9%), and had diverse political leanings. All respondents self-reported at least one comorbidity: cancer, 27.3% (5459/19,980); autoimmune diseases, 23.2% (4946/21,294); chronic lung diseases: 35.4% (7544/21,294). COVID-19 vaccine hesitancy was identified in 18.6% (3960/21,294), with 10.3% (2190/21,294) declaring that they would not, 3.5% (742/21,294) stating that they probably would not, and 4.8% (1028/21,294) not sure whether they would agree to be vaccinated. Hesitancy was expressed by the following patients: cancer, 13.4% (731/5459); autoimmune diseases, 19.4% (962/4947); chronic lung diseases: 17.8% (1344/7544). Positive predictors of vaccine acceptance included routine influenza vaccination (odds ratio [OR] 1.53), trust in responsible vaccine development (OR 14.04), residing in the United States (OR 1.31), and never smoked (OR 1.06). Hesitancy increased with a history of prior COVID-19 (OR 0.86), conservative political leaning (OR 0.93), younger age (OR 0.83), and lower education level (OR 0.90). One-quarter (5501/21,294, 25.8%) had received at least one COVID-19 vaccine injection, and 6.5% (1390/21,294) completed a 2-dose series. Following the first injection, 69.0% (3796/5501) self-reported local reactions, and 40.0% (2200/5501) self-reported systemic reactions, which increased following the second injection to 77.0% (1070/1390) and 67.0% (931/1390), respectively. CONCLUSIONS: In this survey of individuals with serious comorbid conditions, significant vaccine hesitancy remained. Assumptions that the most vulnerable would automatically accept COVID-19 vaccination are erroneous and thus call for health care team members to initiate discussions focusing on the impact of the vaccine on an individual's underlying condition. Early self-reported side effect experiences among those who had already been vaccinated, as expressed by our population, should be reassuring and might be utilized to alleviate vaccine fears. Health care-related social media forums that rapidly disseminate accurate information about the COVID-19 vaccine may play an important role.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".