Incidence and trends of skin cancer in the United States, 1999-2016.
Bibliographic record
Abstract
10077 Background: Cutaneous skin cancer is among the most common malignancies in US. While Surveillance, Epidemiology and End Results (SEER) data are vital to estimate its incidence, delays and under-reporting remained major limitations. Surveillance is hindered due to exclusion from states’ reportable diseases and possible outpatient diagnoses’ omission from registries. Thus, exact incidence has not been known. This study determined skin cancer incidence and trends from 1999 to 2016 in a nationally representative sample. Methods: New melanoma, non-melanoma and other skin cancer cases among adults aged ≥20 years were identified in the National Health and Nutrition Examination Survey (NHANES), 1999-2016. Crude and age-adjusted incidences and 95% CIs were estimated by survey year cohorts (1999-2008 and 2009-2016) based on the 2000 US standard population. Sex and age-stratified longitudinal trends were examined in age and sex-adjusted regression models. Statistical analyses accounted for complex survey design with examination sample weight and adjusted for nonresponse. Sensitivity analyses included unadjusted, sex- and age- adjusted modeling. Statistical significance was determined by 2-sided p-value of .05. Results: Among 47,172 adults and 21,192 non-Hispanic whites from 1999-2016, the overall age-standardized incidences of skin cancer per 100000 persons were 390.9 (95% CI: 312-469.7) and 519 (95% CI: 413.8-624.3), respectively. The median age at first diagnosis was 72.2 (mean = 69.8, IQR = 57.5-79.5 years). The incidence was higher in men than women (474.7 vs 313.8 per 100000 persons, p< .001) and increased with older age ( p< .001). Between 1999-2008 and 2009-2016, the incidence was significantly higher in those older than 70, 75 and 80 ( p ≤.01). Rising incidence was also observed in overall population, women, and by approximately 90% among those older than 70. Sensitivity analyses showed similar trends. Conclusions: Our incidence rates for skin cancer were high, particularly in the elderly. From 1999 to 2016, the incidence increased in women and those 70 and older, a concerning observation given the aging population. As understanding susceptible groups has public health implications, our study provided an updated depiction of skin cancer incidence and trends in US. [Table: see text]
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".