Social determinant of health and COVID-19 vaccine uptake among US cancer survivors.
Bibliographic record
Abstract
369 Background: According to the National Comprehensive Cancer Network Guidelines, all individuals with cancer survivors should receive COVID vaccination. However, the extent to which social determinants of health (SDoH) influence the uptake of COVID-19 vaccines in this specific population remains an area of exploration. We aim to examine the potential impact of SDoH on COVID-19 vaccine uptake in cancer survivors. Methods: Cancer survivors with known COVID-19 vaccination status and SDoH information including food insecurity, housing insecurity and transportation barriers were extracted from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). We employed chi-square tests to compare baseline demographics and COVID-19 vaccine uptake by SDoH factors. Logistic regression model was used to calculate the association between SDoH factors and COVID-19 vaccine uptake among cancer survivors, after adjusting for potential confounders. All analyses were weighted. The significance level was set at 2-sided p<0.05. Results: A total of 13025 cancer survivors were included (weighted N=4,620,447) with 85.9% reporting having at least 1 dose of COVID-19 vaccine in the past. After adjusting for confounders, we found that cancer survivors experiencing food insecurity (odds ratio [OR]:0.63, 95% confidence interval [CI]: 0.44-0.90), housing insecurity (OR: 0.63; 95%CI: 0.42-0.96), transportation insecurity (OR: 0.51; 95%CI: 0.31-0.81) and any of the above barriers (OR: 0.58; 95%CI: 0.41-0.82) were significantly less likely to have received at least one dose of the COVID-19 vaccine. Conclusions: 15% cancer survivors reported never having received COVID-19 vaccine. Additionally, we found that having food, housing and transportation insecurity is independently associated with decreased COVID-19 vaccine uptake among US cancer survivors. Prior studies have shown that cancer survivors who with COIVD infections had a higher mortality and hospitalization rate than non-cancer population. These findings emphasize the importance of addressing SDoH in public health efforts to ensure equitable access to vaccines, particularly for a population at increased risk. SDoH Factors COVID-19 Vaccine Uptake (weighted % 95%CI) p-values OR 95%CI* Food insecurity No 88.9 (87.6-90.2) <0.0001 Ref Yes 75.4 (70.4-80.5) 0.63 (0.44-0.90) Housing insecurity No 88.4 (87.1-89.6) <0.0001 Ref Yes 71.2 (63.9-78.5) 0.63 (0.42-0.96) Transportation insecurity No 87.6 (86.3-88.9) <0.01 Ref Yes 71.4 (61.7-81.0) 0.51 (0.31-0.81) Any of above No 89.4 (88.1-90.7) <0.0001 Ref Yes 76.1 (71.8-80.3) 0.58 (0.41-0.82) *Model adjusted for age, sex, race/ethnicity, marital status, education, income, insurance status, heavy drinking, smoking, BMI, number of comorbidities, had routine check-up last year, self-reported health status, US region, flu vaccine status, whether had COVID infection, type of cancer, and current cancer treatment status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".