Meningococcal carriage in high-risk settings: A systematic review
Bibliographic record
Abstract
BACKGROUND: Historically, semi-closed populations have had high rates of meningococcal carriage and have experienced recurrent outbreaks. As such, these high-risk groups are recommended for targeted vaccination in many countries. METHODS: A systematic review of eight databases and Google Scholar forward citations was conducted to characterize serogroup-specific meningococcal carriage in university students, military personnel, and Hajj pilgrims from 2007 to 2016. RESULTS: A total of 7014 records were identified and 22 studies were included. Overall carriage ranged from 0.0% to 27.4% in Hajj pilgrims, from 1.5% to 71.1% in university students, and from 4.2% to 15.2% in military personnel. Among serogroups A, B, C, W, X, and Y, serogroup B was most prevalent in Hajj pilgrims, B and Y in university students, and B, C, and Y in military personnel. 'Other' serogroups were more prevalent in university students than Hajj pilgrims or military personnel. Risk factors for carriage varied by setting. Among Hajj pilgrims, a high endemicity in the country of origin increased the risk of carriage, while smoking, male sex, and frequently attending parties increased the carriage risk for university students. Similarly, smoking increased the carriage risk for professional soldiers. Data gaps remain for many regions. CONCLUSIONS: Preventative vaccination policies for high-risk groups should be based on current disease data in individual countries, supplemented by carriage data. Meningococcal carriage studies and disease surveillance are critical for determining the local epidemiology, populations responsible for disease transmission, and the need for targeted 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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".