The impact of the COVID-19 pandemic on melanoma diagnosis: a systematic review and meta-analysis of global evidence
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic significantly disrupted healthcare systems worldwide. Prioritizing emergency responses resulted in the postponement of routine medical care, including melanoma diagnoses. We performed a systematic review and meta-analysis to quantify the pandemic’s effect on diagnosis rates, Breslow thickness, stage at presentation, ulceration, histologic subtypes, and patient age. Method We performed a systematic review and meta-analysis following PRISMA guidelines. PubMed, Scopus, Web of Science, and Embase were searched up to 10 September 2024 for observational studies comparing melanoma outcomes in the pre-COVID era (before March 2020) with the COVID era (March 2020 onwards). Two reviewers independently screened records, extracted data on diagnostic counts, patient age, Breslow thickness, ulceration, and histopathological subtype, and assessed study quality using the Newcastle–Ottawa Scale (NOS). Random-effects models pooled rate ratios (RRs) or odds ratios (ORs); fixed-effects models pooled mean differences (MDs). Heterogeneity was evaluated with I², and sensitivity analyses were restricted to high-quality studies (NOS ≥ 7). Results Sixty-two studies (38,676 pre-COVID and 46,846 COVID-era melanomas) met inclusion criteria. New melanoma diagnoses fell by 19% during the pandemic (RR = 0.81, 95% CI 0.75–0.86; I² = 98%). Mean age at diagnosis rose by 0.86 years (95% CI 0.58–1.14; I² = 45%). Tumors were thicker (MD = 0.24 mm, 95% CI 0.02–0.47; I² = 92%) and more frequently ulcerated (OR = 1.29, 95% CI 1.15–1.44; I² = 31%). Nodular melanoma, an aggressive subtype, became more common (OR = 1.34, 95% CI 1.08–1.67; I² = 81%), whereas superficial spreading, acral lentiginous, and lentigo-maligna subtypes showed no significant change. All the key findings persisted in good-quality-only analyses. Conclusion COVID-19-related service disruptions were associated with fewer melanoma diagnoses but a shift toward older patients and biologically adverse tumor features, signaling delayed detection at the population level. Strengthening resilient, rapid-access skin cancer pathways and integrating tele-dermatology with triaged in-person assessment are public-health priorities for future crises. Trial registration PROSPERO registration number CRD42022361569.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".