Examining wealth-related inequality in childhood vaccination in Fiji using the UNICEF Multiple Indicator Cluster Survey 2021
Bibliographic record
Abstract
BACKGROUND: Despite a 95% immunisation rate in Fiji, disparities exist in the distribution of immunisation in children from different socioeconomic backgrounds. We used data from the 2021 Fiji Multiple Indicator Cluster Survey (MICS) to determine socioeconomic inequalities contributing to differences in immunisation coverage. METHODS: Data were extracted from the 'Household', 'Fertility/Birth history' and 'Children under 5' modules from the 2021 Fiji MICS to determine wealth quintiles and calculate vaccination rates for children aged 12-23 mo. Logistic regression was performed with factors of interest. Erreygers' corrected concentration index (ECI) was calculated and used to measure socioeconomic inequality. RESULTS: Out of 417 children; 85.6% (357/417) were fully immunised, 12.0% (50/417) partially immunised and 2.4% (10/417) had no immunisations. Factors associated with increased probability of being fully immunised included being in the highest wealth quintile after adjusting for the number of children in the household. Children from larger households were more likely to be partially immunised after adjusting for household wealth. The ECI for fully vaccinated children was positive, whereas the ECI was negative for partially vaccinated children. Logistic regression also indicated a pro-rich inequality in vaccination. CONCLUSIONS: Our results help guides policy decisions on the delivery of immunisation services, enabling more equitable childhood immunisation in Fiji.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".