Multiple deprivations as drivers of suboptimal basic child vaccination in Latin America and the Caribbean: cross-sectional analysis of household survey data for 18,136 children across 211 regions in 15 countries
Bibliographic record
Abstract
BACKGROUND: Latin America and the Caribbean (LAC) maintained high childhood vaccination coverage for 17 years but faced setbacks, increasing vulnerability to vaccine-preventable diseases. Despite signs of recovery, geographic inequalities and social deprivations persist. This study provides an up-to-date cross-sectional analysis of prevalence, subnational variation, and key determinants of suboptimal basic child vaccination (BCV). METHODS: We produced weighted estimates of suboptimal BCV prevalence at the national and subnational levels via harmonized data from household surveys spanning a 12-year period (2011-2022) in the LAC region. Six BCV-related outcomes were analysed: completely unvaccinated, no BCG, no DTP, no OPV, no MCV and not fully vaccinated. We employed a four-level mixed-effects logistic regression to analyse determinants of suboptimal BCV and to partition the total outcome variation over country, region, primary sample units (PSUs) and child‒mother‒household levels. Choropleth maps were used to illustrate the weighted mean prevalence of subnational regions for each outcome. Additionally, sensitivity analyses were performed to validate the findings and assess robustness. FINDINGS: A total of 18,136 children aged 12-23 months across 211 subnational regions in 15 LAC countries were analysed. The prevalence of suboptimal BCV ranged from 0.99% completely unvaccinated to 66% not fully vaccinated. Significant subnational disparities were observed: while all subnational regions in Cuba and Costa Rica had consistently low rates of completely unvaccinated children (< 3%), subnational regions or states such as Upper Takutu-Upper Essequibo and Mahaica-Berbice (Guyana) reported much higher rates, reaching 30.23% (95% CI: 9.52-50.94) and 26.56% (95% CI: 11.39-41.73), respectively. Maternal deprivation increased the risk of suboptimal BCV. The prevalence of completely unvaccinated children was significantly greater among those whose mothers did not have institutional delivery (3.35%; 95% CI: 3.07-3.63) than among those whose mothers had institutional delivery (0.74%; 95% CI: 0.70-0.79). The likelihood of suboptimal BCV outcomes increased as health services and socioeconomic deprivation intensified and intersected. CONCLUSIONS: In LACs, geographic inequalities and multiple deprivations increase the risk of suboptimal BCV. These countries should prioritize efforts to vaccinate children whose mothers lack access to one or more key health services, especially those from poor families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".