Serological Surveys for complementing assessments of vaccination coverage in sub-Saharan Africa: A systematic review
Bibliographic record
Abstract
Background: Serosurveys of biomarkers of infection/vaccination are widely used for evaluating vaccine-induced immunity and monitoring the effectiveness of immunisation programmes in developed countries. In sub-Saharan Africa (sSA) where vaccination coverage (VC) estimates are often incomplete, inaccurate and overestimate effective population immunity, the use of serosurveys is limited. Methods: We conducted a review of the use of serosurveys to assess/complement assessments of VC in sSA by searching electronic databases (PubMed, Embase, Web of Science, Popline, Ovid and Africa Wide Information) for English language articles published from 1 st January 1940 to 31 st January 2017. We also searched the references of retrieved articles. SSA was defined as all of Africa excluding the countries in North Africa. We included only articles that measured VC and assessed the quality of these studies using the Newcastle-Ottawa Scale. Results: We found 1056 unique records, reviewed 20 eligible studies of which just 12 met our inclusion criteria. These 12 studies were serosurveys of measles, tetanus, polio and yellow fever. Antibodies induced by natural infection confounded serological test results and there was significant discordance between vaccination history and the presence of antibodies in all except for tetanus vaccine. No study looked at Hepatitis B. Conclusions: Serosurveys for tetanus or tetanus containing vaccines may be directly useful for ascertainment of vaccination exposure or reliably complement current survey methods that measure VC. Given the limited experience in using serosurveys for this purpose in sSA, well-designed serosurveys of tetanus and possibly hepatitis B are required to further validate/evaluate their performance.
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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 | 0.017 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".