The epidemiological impact of reduced childhood vaccination in Brazil: a desk research from 2012 to 2022
Bibliographic record
Abstract
Vaccination acts as the main method of preventing potential changes to global health, which have started outbreaks, endemics, epidemics and pandemics. Epidemiological profile’s analysis allows identifying, planning, executing and evaluating possible actions to control pathologies. A preventive control intervention that contributes positively to these epidemiological actions is immunization. This study aims to analyze the epidemiological impact of the reduction in childhood vaccination between 2012 to 2022. This is a documentary and epidemiological research of a retrospective nature that was carried out in April 2023, using free data, available at the Department of Informatics of the Unified Health System. A significant curve was observed relating vaccination between the years of the research, data demonstrates the continued importance of vaccination in preventing vaccine-preventable diseases. These numbers reflect the impact of the reduction in childhood vaccination and highlight the need for awareness raising and vaccination reinforcement actions to prevent the spread of vaccine-preventable diseases.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".