Seroepidemiology of Varicella and the Reliability of a Self-reported History of Varicella Infection in Singapore Military Recruits
Bibliographic record
Abstract
INTRODUCTION: Varicella is an acute disease with significant morbidity. However, there is little knowledge on the seroepidemiology of the disease in Singapore. The objective of this study was to assess the seroprevalence of varicella zoster virus (VZV) antibodies in military recruits in Singapore and to ascertain the predictive value of a self-reported history of varicella. The latter is a possible proxy for seroprevalence, and may be used to provide efficient identification of candidates for vaccination. MATERIALS AND METHODS: From September 2000 to October 2005, 2189 servicemen were selected during their pre-enlistment medical check-up. Blood samples were obtained to determine the varicella IgG levels via enzyme-linked immunosorbent assay (ELISA). Information about the participant's race, history of varicella and vaccination, and other clinical variables were obtained through a questionnaire. RESULTS: The overall prevalence of VZV seropositivity in military recruits was 76.0% (75.8% in the 16 years to 20 years age group). For the reported history, 73.7% of Chinese participants, 73.0% of Malays, and 63.6% of Indians reported having had varicella infection and/or vaccination. Overall, the sensitivity, specificity, positive and negative predictive values of a self-reported history of varicella for serologically confirmed immunity were 87.2%, 83.2%, 94.3% and 67.1% respectively. CONCLUSIONS: The prevalence of VZV antibodies in pre-enlistees to the Singapore Armed Forces (SAF) is high. Incidence of varicella in the SAF is on the wane, indicating an increase in herd immunity against VZV. A recalled history of varicella infection was also a good predictor of serological immunity and may be used for selection for vaccination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".