Impact of COVID-19 on immunization of Brazilian infants
Bibliographic record
Abstract
OBJECTIVE: To examine recent vaccination trends among Brazilian children during their first year of life, and the impact of the coronavirus disease 2019 (COVID-19) pandemic on these trends. METHODS: Monthly vaccination and birth data from the DATASUS (Department of Informatics of the Unified Health System) database of the Ministry of Health of Brazil were obtained from January 2017 to December 2020. Interrupted time series analysis was used to compare vaccination trends before and after March 2020, when isolation measures were first implemented in Brazil. RESULTS: There was no strong evidence of a significant change in trends during the study period, or before and during the pandemic at national level. However, the mean number of vaccinations per child was 10.6, which is lower than the 13 doses expected under the immunization schedule. CONCLUSIONS: Although the pandemic did not appreciably impact on vaccinations, incomplete immunization among children aged <1 year in Brazil is cause for concern. A potential impact of the COVID-19 pandemic on specific antigens or regional and sociodemographic disparities in vaccinations cannot be ruled out without further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".