Factors associated with incomplete immunisation in children aged 12–23 months at subnational level, Nigeria: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: National immunisation coverage rate masks subnational immunisation coverage gaps at the state and local district levels. The objective of the current study was to determine the sociodemographic factors associated with incomplete immunisation in children at a sub-national level. DESIGN: Cross-sectional study using the WHO sampling method (2018 Reference Manual). SETTING: Fifty randomly selected clusters (wards) in four districts (two urban and two rural) in Enugu state, Nigeria. PARTICIPANTS: 1254 mothers of children aged 12-23 months in July 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: Fully immunised children and not fully immunised children. RESULTS: Full immunisation coverage (FIC) rate in Enugu state was 78.9% (95% CI 76.5% to 81.1%). However, stark difference exists in FIC rate in urban versus rural districts. Only 55.5% of children in rural communities are fully immunised compared with 94.5% in urban communities. Significant factors associated with incomplete immunisation are: children of single mothers (aOR=5.74, 95% CI 1.45 to 22.76), children delivered without skilled birth attendant present (aOR=1.93, 95% CI 1.24 to 2.99), children of mothers who did not receive postnatal care (aOR=6.53, 95% CI 4.17 to 10.22), children of mothers with poor knowledge of routine immunisation (aOR=1.76, 95% CI 1.09 to 2.87), dwelling in rural district (aOR=7.49, 95% CI 4.84 to 11.59), low-income families (aOR=1.56, 95% CI 1.17 to 2.81) and living further than 30 min from the nearest vaccination facility (aOR=2.15, 95% CI 1.31 to 3.52). CONCLUSIONS: Although the proportion of fully immunised children in Enugu state is low, it is significantly lower in rural districts. Study findings suggest the need for innovative solutions to improve geographical accessibility and reinforce the importance of reporting vaccination coverage at local district level to identify districts for more targeted interventions.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".