Impact of the COVID-19 on dental consultations in Primary Health Care in Brazil: an ecological study
Bibliographic record
Abstract
The objective of the study was to analyze the impact of the COVID-19 pandemic on dental consultations performed in Primary Health Care (PHC) in Brazil. This was an ecological study, with secondary data collected from the Health Information System for Primary Care of the Ministry of Health. Monthly reports were generated regarding individual care in the 27 federative units in the periods from April to December of: 2018/2019 (before the pandemic) and 2020 (during the pandemic), later divided into quarters. Descriptive data, mean differences, and percentage of variation of dental care in PHC were obtained and compared for each state between periods using the Mann-Whitney U-test (α<0.05), by using SPSS program. A total of 55,687,591 cases were analyzed, 13.1% of which were during the pandemic. The monthly average was 12,456.6 consultations before the pandemic and, during, 3,732.7, with a significant reduction of 70.0% (p≤0.01). The first quarter (Q2) after recognition of the pandemic (April to June) showed the largest reduction (85,7%) in consultations nationwide, with April being the most affected and all states had a significant reduction when compared with 2018/2019. All states, with the exception of Amapá, showed significant reductions in the 3rd quarter (Q4), despite the gradual increase in consultations throughout 2020. The pandemic had a negative impact on the number of dental consultations performed in PHC in the country. There was a positive evolution in the course of 2020, but the numbers remain well below the levels that were reached before the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".