Decomposing the rural–urban gap in factors associated with childhood immunisation in sub-Saharan Africa: evidence from surveys in 23 countries
Bibliographic record
Abstract
BACKGROUND: About 31 million children in sub-Saharan Africa (SSA) suffer from immunisation preventable diseases yearly and more than half a million children die because of lack of access to immunisation. Immunisation coverage has stagnated at 72% in SSA over the past 6 years. Due to evidence that full immunisation of children may be determined by place of residence, this study aimed at investigating the rural-urban differential in full childhood immunisation in SSA. METHODS: The data used for this study consisted of 26 241 children pooled from 23 Demographic and Health Surveys conducted between 2010 and 2018 in SSA. We performed a Poisson regression analysis with robust Standard Errors (SEs) to determine the factors associated with full immunisation status for rural and urban children. Likewise, a multivariate decomposition analysis for non-linear response model was used to examine the contribution of the covariates to the observed rural and urban differential in full childhood immunisation. All analyses were performed using Stata software V.15.0 and associations with a p<0.05 were considered statistically significant. RESULTS: More than half of children in urban settings were fully immunised (52.8%) while 59.3% of rural residents were not fully immunised. In all, 76.5% of rural-urban variation in full immunisation was attributable to differences in child and maternal characteristics. Household wealth was an important component contributing to the rural-urban gap. Specifically, richest wealth status substantially accounted for immunisation disparity (35.7%). First and sixth birth orders contributed 7.3% and 14.9%, respectively, towards the disparity while 7.9% of the disparity was attributable to distance to health facility. CONCLUSION: This study has emphasised the rural-urban disparity in childhood immunisation, with children in the urban settings more likely to complete immunisation. Subregional, national and community-level interventions to obviate this disparity should target children in rural settings, those from poor households and women who have difficulties in accessing healthcare facilities due to distance.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".