Impact of COVID-19 pandemic on surgical activity in the Brazilian private healthcare system
Bibliographic record
Abstract
INTRODUCTION: Surgical volume was drastically reduced in many countries due to challenges imposed by the COVID-19 pandemic. OBJECTIVES: We sought to estimate the number of cancelled surgical and diagnostic procedures within the Brazilian private healthcare system between 2020 and 2021 over the course of the COVID-19 pandemic, and to project the procedural backlog generated for specific elective and time-sensitive surgeries, and diagnostic procedures. METHODS: Data were systematically extracted from the Brazilian national regulatory agency for the private healthcare system and included (i) quarterly and annual surgical and diagnostic volume, and (ii) the number of private health insurance beneficiaries between January 2016 and June 2021. Based on pre-pandemic data we estimated the expected number of surgical and diagnostic procedures that failed to be performed between 2020 and 2021. RESULTS: The average quarterly surgical and diagnostic procedures declined by 29.5% in 2020 and by 21.5% in 2021 compared to 2019. In 2020, such reduction reflected a lower number of diagnostic procedures under anesthesia (-35.1%), as well as elective (-14.7%), time-sensitive (-18.8%), and urgent (-4.6%) surgeries. In the first half of 2021, though the surgical and diagnostic procedures increased compared to 2020, they remained significantly below their historical average. The estimated backlogs were 134.385,64 for total surgical procedures, 2.634,64 for bariatric surgery and arthroplasty revision (elective surgeries), 2.845,61 for oncologic (time-sensitive) surgeries, and 304.193,99 for diagnostic procedures, requiring 1.7, 15.9, and 6.8 years, respectively, to make up for such backlogs. CONCLUSION: There was a major decline on the number of surgical and diagnostic procedures due to the COVID-19 pandemic. Despite a slight recovery of elective surgeries throughout the pandemic, many time-sensitive surgeries and diagnostic procedures were cancelled, with potential medium- to long-term consequences to patients and the system as a whole.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".