Correlates of Coronavirus Disease 2019 (COVID-19) Vaccine Hesitancy Among People Who Inject Drugs in the San Diego-Tijuana Border Region
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) are vulnerable to acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We examined correlates of coronavirus disease 2019 (COVID-19) vaccine hesitancy among PWID in the US-Mexico border region, of whom only 7.6% had received ≥ 1 COVID-19 vaccine dose by September 2021. METHODS: Between October 2020 and September 2021, participants aged ≥ 18 years from San Diego, California, USA, and Tijuana, Baja California, Mexico, who injected drugs within the last month completed surveys and SARS-CoV-2, human immunodeficiency virus (HIV), and hepatitis C virus (HCV) serologic testing. Logistic regressions with robust standard error estimation via generalized estimating equations identified factors associated with being unsure or unwilling to receive COVID-19 vaccines. RESULTS: Of 393 participants, 266 (67.7%) were willing to receive COVID-19 vaccines and 127 (32.3%) were hesitant (23.4% unwilling and 8.9% unsure). Older participants, those with greater food insecurity, and those with greater concern about acquiring SARS-CoV-2 were more willing to be vaccinated. Higher numbers of chronic health conditions, having access to a smart phone or computer, and citing social media as one's most important source of COVID-19 information were independently associated with vaccine hesitancy. COVID-19-related disinformation was independently associated with vaccine hesitancy (adjusted odds ratio: 1.51 per additional conspiracy theory endorsed; 95% confidence interval: 1.31-1.74). CONCLUSIONS: Nearly one third of people injecting drugs in the US-Mexico border region were COVID-19 vaccine hesitant, which was significantly associated with exposure to social media, disinformation and co-morbidities and inversely associated with food security and high perceived threat of COVID-19. Interventions that improve accurate knowledge of and trust in COVID-19 vaccines are needed in this vulnerable population.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".