Risk Factors for Herpes Zoster Infection: A Meta-Analysis
Bibliographic record
Abstract
Abstract Background The burden of herpes zoster (HZ) is significant worldwide, with millions affected and the incidence rising. Current literature has identified some risk factors for this disease; however, there is yet to be a comprehensive study that pools all evidence to provide estimates of risk. Therefore, the purpose of this study is to identify various risk factors, excluding immunosuppressive medication, that may predispose an individual to developing HZ. Methods The literature search was conducted in MEDLINE, EMBASE, and Cochrane Central, yielding case control, cohort, and cross-sectional studies that were pooled from January 1966 to September 2017. Search terms included the following: zoster OR herpe* OR postherpe* OR shingle* AND risk OR immunosupp* OR stress OR trauma OR gender OR ethnicity OR race OR age OR diabetes OR asthma OR chronic obstructive pulmonary disease OR diabetes. Risk ratios (RRs) for key risk factors were calculated via natural logarithms and pooled using random-effects modeling. Results From a total of 4417 identified studies, 88 were included in analysis (N = 3, 768 691 HZ cases). Immunosuppression through human immunodeficiency virus/acquired immune deficiency syndrome (RR = 3.22; 95% confidence interval [CI], 2.40–4.33) or malignancy (RR = 2.17; 95% CI, 1.86–2.53) significantly increased the risk of HZ compared with controls. Family history was also associated with a greater risk (RR = 2.48; 95% CI, 1.70–3.60), followed by physical trauma (RR = 2.01; 95% CI, 1.39–2.91) and older age (RR = 1.65; 95% CI, 1.37–1.97). A slightly smaller risk was seen those with psychological stress, females, and comorbidities such as diabetes, rheumatoid arthritis, cardiovascular diseases, renal disease, systemic lupus erythematosus, and inflammatory bowel disease compared with controls (RR range, 2.08–1.23). We found that black race had lower rates of HZ development (RR = 0.69; 95% CI, 0.56–0.85). Conclusions This study demonstrated a number of risk factors for development of HZ infection. However, many of these characteristics are known well in advance by the patient and clinician and may be used to guide discussions with patients for prevention by vaccination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".