Epidemiology and Pathogen Shift of Tinea Capitis: A Comparative Analysis of Adults and Children in Nanchang, China (2022–2024)
Bibliographic record
Abstract
BACKGROUND: Tinea capitis remains a public health concern, especially in children, with evolving trends. Data from Nanchang, China, regarding comparative analyses between adults and children is limited. OBJECTIVES: This study aimed to analyze the clinical and epidemiological characteristics, pathogen spectrum, risk factors, and household transmission of tinea capitis in Nanchang from 2022 to 2024, comparing features between adult and pediatric patients. METHODS: A single-center retrospective study was conducted involving 239 patients (170 children, 69 adults) diagnosed with confirmed tinea capitis. Demographic, clinical, and risk factor data were collected. Pathogens were identified via microscopy, culture, and molecular methods. RESULTS: Significant increases were observed in adult cases (28.87% vs. historical 9.73%), particularly among postmenopausal women. Anthropophilic pathogens dominated overall (65.42%), but zoophilic species increased significantly (32.71% vs. historical 16.96%). Clinical types differed: black dot tinea predominated in adults (81.16%), while kerion (42.94%) was more common in children. Animal contact was a key risk factor for children (55.29%). Household transmission occurred in 22.90% of surveyed families, with 100% pathogen concordance and frequent asymptomatic adult carriers (50% of affected households). CONCLUSIONS: Notable differences exist between children and adult tinea capitis in Nanchang, with trends showing increasing adult cases and a shift towards zoophilic pathogens. Prevention strategies should be tailored to specific age groups and transmission modes, emphasising household screening and management of human and animal sources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".