An evaluation of the cold chain technology in South-East, Nigeria using immunogenicity study on the measles vaccines
Bibliographic record
Abstract
INTRODUCTION: Vaccines are biological products and their efficacy is affected by storage conditions. They are vital in promoting public health. Failures in immunization programmes often times are blamed on disruption in vaccine cold-chain. This study assessed the immunogenicity/potency of the measles vaccines utilized in childhood immunization in South-East, Nigeria and indirectly assessed the effectiveness of the cold-chain technology in the region. METHODS: This was an experimental study carried out between December 2011 and June 2013. Antibody induction method was used to evaluate the immunogenicity/potency of the measles vaccines sourced from the central cold chain facilities in South-east, Nigeria and indirectly, the effectiveness of the cold chain technology in the zone in maintaining vaccine potency. The neutralizing antibodies in a control group (administered with measles vaccines stored at 37°C for 12 months) and in immunized group were determined after 30 days of immunization using ELISA. RESULTS: The mean storage temperature of the vaccines at the states vaccines central cold chain facilities was -2.4°C and before storage at study site, it was 5.8°C but at the study site it was -4.54°C. Mean ±Standard Error in the Mean (SEM) IgG titers for the measles vaccines sourced from "Open Market", Ebonyi, Enugu, Imo, Anambra and Abia States were 0.793±0.051, 1.621±0.015, 1.621±0.015, 1.715±0.081, 1.793±0.051 and 1.683±0.078 respectively while the mean ±Standard Error in the Mean (SEM) IgM titres were 0.857±0.037, 1.400±0.030, 1.391±0.032, 1.339±0.037, 1.405±0.066 and 1.279±0.025 respectively. One way analysis of variance shows that there is statistical difference in the IgG and IgM antibodies titers produced by the control compared to the vaccines (P value < 0.0001). Also, Bartlett's test for equal variances showed that there was statistical difference (P value < 0.0001 for IgG and = 0.036 for IgM). The antibodies elicited by the vaccines from the states were enough to confer protection but the vaccine samples from "Open Market" (control) could not evoke enough antibodies. CONCLUSION: The cold-chain technology in the region was judged to be optimal as at the time of vaccine sampling since all the measles vaccines had good immunogenicity profile. However, efforts are still needed to maintain these facilities in good condition in order to ensure effective immunization program in the region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".