Analysis of Prevalence, Socioeconomic and Disease Trends of Non-Melanoma Skin Cancer in New Zealand from 2008 to 2022
Bibliographic record
Abstract
BACKGROUND: Skin cancer shows geographic and ethnic variation. New Zealand-with a predominantly fair-skinned populations, high UV indices and outdoor lifestyles-has high rates of skin cancer. However, population prevalence data is lacking. This study aimed to determine the demographics and socioeconomic disease trends of non-melanoma skin cancer prevalence in New Zealand from a large targeted-screening study. METHODS: A targeted screening programme was conducted among 32,839 individuals, Fitzpatrick Skin Types I to IV in Auckland, New Zealand during the 2008-2022 period. This data was analyzed retrospectively. Linear regression models were used to assess statistical trends of skin cancer prevalence over time, along with associated factors that included demographics, disease trends and overall prevalence. RESULTS: A total of 32,839 individuals were screened and 11,625 skin cancers were detected. 16,784 individuals were females who had 4,378 skin cancers. 16,055 individuals were males who had 5,777 skin cancers. 54 males and 65 females had multiple skin cancers. The article presents detailed descriptions of tumour types and subtypes detected, age groups, demographic and socioeconomic information. regarding the non-melanoma skin cancers detected. CONCLUSION: Overall men have more non-melanoma skin cancer (NMSC) than females; however females develop more BCC on the lips. BCC is three times more common in the 31-50 age group, whereas SCC are significantly more prevalent after age 80. Prevalence of BCC has not changed over the 15-year timeframe of the study but SCC has increased. Older ages and higher incomes are associated with higher rates of NMSC in New Zealand.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".