Examining the Relationship Between Alopecia Areata and Mental Health: An Investigation of the Global Burden of Disease Study 2021
Bibliographic record
Abstract
BACKGROUND: Alopecia areata (AA), an autoimmune hair loss disorder, can significantly alter a person's appearance and cause emotional distress. This disorder has been linked to anxiety and depression, but most work has been done on either one-population samples or has been conducted using heterogeneous populations, potentially skewing results. AIMS: We aim to obtain a better understanding of the relationship of AA with anxiety and depression in more finely divided populations based on sex, age, and country. METHODS: We have accessed data present on AA, anxiety and depressive disorders within the Global Burden of Disease Study 2021. We downloaded data from China, Japan, India, Brazil and the United States for males and females less than 20, 20 to 54 and 55 years of age and older. We extracted the prevalence and years lived with disability (YLDs) measures as surrogate markers for extent and severity of disease respectively. Pearson's correlation coefficient was calculated for both prevalence and YLDs for AA versus anxiety as well as for AA versus depression. RESULTS: We found significant positive correlations of AA with anxiety and depression in females: primarily in China, Japan, India, and Brazil for anxiety, and China, India, and Brazil for depression. Additionally, significant correlations tended to occur in younger females. CONCLUSIONS: This study demonstrates differences in the correlation of AA disease extent and severity with anxiety and depression between countries, sex, and age. This highlights the need for more finely detailed studies to truly determine the impact of AA on mental health globally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".