Benefits of using vaccines out of the cold chain: Delivering Meningitis A vaccine in a controlled temperature chain during the mass immunization campaign in Benin
Bibliographic record
Abstract
BACKGROUND: In October 2012, the Meningococcal A conjugate vaccine MenAfriVac was granted a label variation to allow for its use in a controlled temperature chain (CTC), at temperatures of up to 40°C for not more than four days. This paper describes the first field use of MenAfriVac in a CTC during a campaign in Benin, December 2012, and assesses the feasibility and acceptability of the practice. METHODS: We implemented CTC in one selected district, Banikoara (target population of 147,207; 1-29 years of age), across 14 health facilities and 150 villages. We monitored the CTC practice using temperature indicators and daily monitoring sheets. At the end of the campaign we conducted a face-to-face survey to assess vaccinators' and supervisors' experience with CTC. FINDINGS: A mix of strategies were implemented in the field to maximize the benefits from CTC practice, depending on the distance from health centre to populations and the availability of a functioning refrigerator in the health centre. Coverage across Banikoara was 105.7%. Over the course of the campaign only nine out of approx. 15,000 vials were discarded due to surpassing the 4 day CTC limit and no vial was discarded because of exposure to a temperature higher than 40°C or due to the Vaccine Vial Monitor (VVM) reaching its endpoint. Overall confidence and perceived usefulness of the CTC approach were very high among vaccinators and supervisors. INTERPRETATION: Vaccinators and supervisors see clear benefits from the CTC approach in low income settings, especially in hard-to-reach areas or where cold chain is weak. Taking advantage of the flexibility offered by CTC opens the door for the implementation of new immunization strategies to ensure all those at risk are protected.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".