Why children are not vaccinated against measles: a cross-sectional study in two Nigerian States
Bibliographic record
Abstract
BACKGROUND: Childhood vaccination rates in Nigeria are among the lowest in the world and this affects morbidity and mortality rates. A 2011 mixed methods study in two states in Nigeria examined coverage of measles vaccination and reasons for not vaccinating children. METHODS: A household survey covered a stratified random cluster sample of 180 enumeration areas in Bauchi and Cross River States. Cluster-adjusted bivariate and then multivariate analysis examined associations between measles vaccination and potential determinants among children aged 12-23 months, including household socio-economic status, parental knowledge and attitudes about vaccination, and access to vaccination services. Focus groups of parents in the same sites subsequently discussed the survey findings and gave reasons for non-vaccination. A knowledge to action strategy shared findings with stakeholders, including state government, local governments and communities, to stimulate evidence-based actions to increase vaccination rates. RESULTS: Interviewers collected data on 2,836 children aged 12-23 months in Cross River and 2,421 children in Bauchi. Mothers reported 81.8% of children in Cross River and 42.0% in Bauchi had received measles vaccine. In both states, children were more likely to receive measles vaccine if their mothers thought immunisation worthwhile, if immunisation was discussed in the home, if their mothers had more education, and if they had a birth certificate. In Bauchi, maternal awareness about immunization, mothers' involvement in deciding about immunization, and fathers' education increased the chances of vaccination. In Cross River, children from communities with a government immunisation facility were more likely to have received measles vaccine. Focus groups revealed lack of knowledge and negative attitudes about vaccination, and complaints about having to pay for vaccination. Health planners in both states used the findings to support efforts to increase vaccination rates. CONCLUSION: Measles vaccination remains sub-optimal, particularly in Bauchi. Efforts to counter negative perceptions about vaccination and to ensure vaccinations are actually provided free may help to increase vaccination rates. Parents need to be made aware that vaccination should be free, including for children without a birth certificate, and vaccination could be an opportunity for issuing birth certificates. The study provides pointers for state level planning to increase vaccination rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".