Yellow fever vaccine usage in the United States and risk of neurotropic and viscerotropic disease: A retrospective cohort study using three healthcare databases
Bibliographic record
Abstract
BACKGROUND: Yellow fever (YF) vaccines are highly effective and have a well-established safety profile despite the risk of rare serious adverse events (SAEs), vaccine-associated neurotropic (YEL-AND) and viscerotropic disease (YEL-AVD). This study aimed to describe US civilian YF vaccine usage, the population characteristics and pre-existing immunosuppressive medical conditions among those vaccinated, and to provide updated risk estimates of neurotropic and viscerotropic disease post-vaccination. METHODS: A retrospective cohort study was conducted using de-identified patient information from Optum Electronic Healthcare Record (EHR) (2007-2019), Optum Clinformatics Data Mart (CDM) (2004-2019) and IBM MarketScan (2007-2019) databases. YF vaccine recipients were identified using relevant vaccination and procedural codes. Demographic characteristics and pre-existing medical conditions were described. Incidence proportions with 95% confidence intervals (CI) of neurotropic and viscerotropic diseases occurring ≤ 30 days post-vaccination, after exclusion of unlikely cases based on current clinical guidelines of YEL-AND and YEL-AVD, were calculated. RESULTS: A total of 92,205, 46,539 and 125,235 YF vaccine recipients were retrieved from Optum EHR, Optum CDM and IBM MarketScan databases, respectively. The majority of vaccine recipients were aged < 60 years (highest proportion aged 18-29 years) with a higher proportion of females overall. Few vaccine recipients (<1%) had conditions predisposing them to immunosuppression. Four non-fatal cases of neurotropic disease and zero cases of viscerotropic disease were identified. The incidence proportion of post-vaccination neurotropic disease was 1.41 (95% CI: 0.15-6.61) and 3.04 (95% CI: 0.86-8.11) per 100,000 vaccine recipients in Optum EHR and IBM MarketScan, respectively, with no events identified in Optum CDM. CONCLUSIONS: This study provides updated insights into current YF vaccine usage in US civilian recipients and supports the safety profile of YF vaccines in US practice. The low frequency of pre-existing immunosuppressive medical conditions among vaccine recipients suggests good adherence to vaccination guidelines by healthcare practitioners. The risk of developing neurotropic and viscerotropic disease post-vaccination remains rare.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".