Epidemiological Analysis of COVID-19's Impact on Aesthetic Treatment Patterns in Canada and the United States
Bibliographic record
Abstract
Background: The COVID-19 pandemic significantly disrupted the elective aesthetic medicine sector in Canada and the United States, prompting shifts in treatment patterns driven by lockdowns, safety measures, and evolving patient behaviors.This literature review systematically analyzes the epidemiological impact of the pandemic on aesthetic procedures, highlighting market disruptions, recovery trajectories, and emerging trends such as the "Zoom Boom" phenomenon.Methods: A comprehensive search was conducted across PubMed, Scopus, Web of Science, and Embase, using keywords like "COVID-19," "aesthetic procedures," "non-surgical aesthetics," "Canada," and "United States."Peer-reviewed articles published from 2014 to 2025, focusing on the pandemic's impact in these countries, were included.Data on procedure volumes, patient motivations, and regional differences were extracted and synthesized thematically.Results: Early 2020 saw a 60-80% decline in aesthetic services due to practice closures, with U.S. practices reporting 80-90% revenue losses.A rapid rebound occurred in the U.S. by late 2020, surpassing pre-pandemic levels, while Canada experienced a more gradual recovery.Non-surgical procedures, particularly facial injectables, surged, driven by the "Zoom Boom," increased disposable income, and recovery time availability.Regional disparities reflected differences in healthcare systems, with the U.S. market responding more swiftly.Practices adapted through enhanced safety protocols and telemedicine.Conclusions:The aesthetic market demonstrated resilience, with accelerated trends toward non-surgical treatments and digital integration.Regional variations underscore the influence of healthcare models on recovery.Future research should explore long-term patient outcomes and demographic shifts to inform practice strategies in post-pandemic aesthetic medicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".