COVID-19 and missed or delayed vaccination in 26 middle- and high-income countries: An observational survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has disrupted vaccination services and raised the risk of a global resurgence of preventable diseases. We assessed the extent of and reasons for missed or delayed vaccinations (hereafter 'missed') in middle- and high-income countries in the early months of the pandemic. METHODS: From May to June 2020, participants completed an online survey on missed vaccination. Analyses separated missed childhood and adult vaccination in middle-and high-income countries. RESULTS: Respondents were 28,429 adults from 26 middle- and high-income countries. Overall, 9% of households had missed a vaccine, and 13% were unsure. More households in middle- than high-income countries reported missed childhood vaccination (7.6% vs. 3.0%) and missed adult vaccination (9.6% vs. 3.4%, both p < .05). Correlates of missed childhood vaccination in middle-income countries included COVID-19 risk factors (respiratory and cardiovascular diseases), younger age, male sex, employment, psychological distress, larger household size, and more children. In high-income countries, correlates of missed childhood vaccination also included immunosuppressive conditions, but did not include sex or household size. Fewer correlates were associated with missed adult vaccination other than COVID-19 risk factors and psychological distress. Common reasons for missed vaccinations were worry about getting COVID-19 at the vaccination clinic (15%) or when leaving the house (11%). Other reasons included no healthcare provider recommendation, clinic closure, and wanting to save services for others. INTERPRETATION: Missed vaccination was common and more prevalent in middle- than high-income countries. Missed vaccination could be mitigated by emphasizing COVID-19 safety measures in vaccination clinics, ensuring free and accessible immunization, and clear healthcare provider recommendations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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, 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".