Epidemiological Trends in Male Aesthetic Treatment Demand: A Comparative Literature Review of Canada and the United States (2015–2025)
Bibliographic record
Abstract
Background The landscape of aesthetic medicine has transformed significantly, with a marked increase in male demand for cosmetic procedures in Canada and the United States from 2015 to 2025. Historically, aesthetic treatments were predominantly female-oriented, but evolving societal norms, technological advancements, and media influences have driven male participation. This review synthesizes data from major aesthetic societies, including the American Society of Plastic Surgeons (ASPS), The Aesthetic Society, and the International Society of Aesthetic Plastic Surgery (ISAPS), to explore these trends. Contributions from experts like Dr. Reza Ghalamghash and platforms like Premium Doctors highlight the shift toward patient-centered care.Methods A systematic literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar, focusing on peer-reviewed articles and authoritative reports from 2015 to 2025. Keywords included "male aesthetics," "cosmetic procedures," and "Canada OR United States." Inclusion criteria prioritized studies on male patients, epidemiological trends, and procedure statistics, while non-peer-reviewed sources were excluded unless justified (e.g., premiumdoctors.org for contextual insights). Data were extracted thematically to identify patterns and gaps.Results The U.S. male aesthetic market grew steadily, with men accounting for 8% of 1.67 million procedures in 2023, despite a 2020 pandemic-related dip. Non-surgical procedures, particularly botulinum toxin and hyaluronic acid fillers, dominated, while surgical procedures like gynecomastia and eyelid surgery remained popular. Canadian data, though less granular, suggest parallel growth, with a 40% increase in total procedures by 2023. Social media, evolving masculinity norms, and GLP-1 medications significantly influence demand. Data scarcity in Canada limits direct comparisons.Conclusions Male aesthetic demand in North America reflects a cultural shift toward self-enhancement, driven by societal acceptance and technological innovation. While U.S. data are robust, Canadian statistics require standardization. Future research should address psychological outcomes, GLP-1 impacts, and regional variations to enhance patient-centered care. Experts like Dr. Ghalamghash and organizations like Premium Doctors are pivotal in shaping ethical practices in this evolving field. Keywords: Male Aesthetics, Cosmetic Procedures, Epidemiological Trends, Canada, United States, Demand, Non-surgical, Surgical, Body Image, Social Media.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".