Incidence and prevalence of vaccine preventable infections in adult patients with autoimmune inflammatory rheumatic diseases (AIIRD): a systemic literature review informing the 2019 update of the EULAR recommendations for vaccination in adult patients with AIIRD
Bibliographic record
Abstract
Objectives: The aims of this study were to update the evidence on the incidence and prevalence rates of vaccine preventable infections (VPI) in patients with autoimmune inflammatory rheumatic diseases (AIIRD) and compare the data to the general population when available. Methods: A literature search was performed using Medline, Embase and Cochrane library (October 2009 to August 2018). The primary outcome was the incidence or prevalence of VPI in the adult AIIRD population. Meta-analysis was performed when appropriate. Results: Sixty-three publications out of 3876 identified records met the inclusion criteria: influenza (n=4), pneumococcal disease (n=7), hepatitis B (n=10), herpes zoster (HZ) (n=29), human papillomavirus (HPV) infection (n=13). An increased incidence of influenza and pneumococcal disease was reported in patients with AIIRD. HZ infection-pooled incidence rate ratio (IRR) was 2.9 (95% CI 2.4 to 3.3) in patients with AIIRD versus general population. Among AIIRD, inflammatory myositis conferred the highest incidence rate (IR) of HZ (pooled IRR 5.1, 95% CI 4.3 to 5.9), followed by systemic lupus erythematosus (SLE) (pooled IRR 4.0, 95% CI 2.3 to 5.7) and rheumatoid arthritis (pooled IRR 2.3, 95% CI 2.1 to 2.6). HPV infection-pooled prevalence ratio was 1.6, 95% CI 0.7 to 3.4 versus general population, based on studies mainly conducted in the SLE population in Latin America and Asia. Pooled prevalence of hepatitis B surface antigen and hepatitis B core antibody in patients with AIIRD was similar to the general population, 3%, 95% CI 1% to 5% and 15%, 95% CI 7% to 26%, respectively. Conclusion: Current evidence shows an increased risk of VPI in patients with AIIRD, emphasising that prevention of infections is essential in these patients.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".