The Immunization Data Quality Assessment, Sana’a Capital, 2021
Bibliographic record
Abstract
Background The Expanded Program of Immunization (EPI) aims to increase immunization coverage. However, this cannot be achieved without an efficient data management system and without ensuring data quality. Objective We aimed to assess the quality of immunization data at Sana’a capital. Methods The World Health Organization data quality self-assessment tools were used. Three random urban districts and the only rural district (Bani-Al Hairth) at Sana’a capital were selected. From each district, one-third of the public health facilities (HFs) that were providing EPI services were randomly selected. Accuracy ratios (ARs), discrepancy levels (DLs), completeness, and timeliness were calculated from tally sheets and reports for Bacillus Calmette-Guerin (BCG) vaccines, third doses of pentavalent-3 (Penta-3) vaccines, and first doses of measles and rubella (MR-1) vaccines. The quality index was assessed for the five components (ie, recording and reporting, archiving, demographic information, core output/analysis, and using data for action) through a prestructured questionnaire. Results While the overall ARs and DLs for BCG, Penta-3, and MR-1 indicated overreporting at the HF level, there was overreporting for BCG and Penta-3 and underreporting for MR-1 at the district level. With regard to the overall quality index, recording and reporting achieved the highest score (90% and 96%, respectively), while using data for action had the lowest score (61% and 78%, respectively) at the HF and district levels. While completeness and timeliness were scored 100% at all HFs, both were inadequate at the Al-Sabain (93% and 99%, respectively) and Bani-Al Hairth (75% and 83%, respectively) districts. Conclusions The findings showed that the quality of immunization data in Sana’a capital’s HFs and districts was inadequate, with weaknesses in using data for action. Furthermore, completeness and timeliness were found to be unsatisfactory at the rural district and one of the urban districts. Ensuring data quality through strengthening the EPI data management system should be prioritized. Larger-scale and regular assessments of the EPI data management system are recommended.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".