Epidemiology and Comorbidities of Psychodermatologic Conditions
Bibliographic record
Abstract
INTRODUCTION: Psychodermatologic conditions include primary psychodermatologic disorders (PPDs), psychological conditions manifesting with dermatologic symptoms, and psychophysiological disorders, dermatologic conditions influenced by psychological stress. Despite their clinical significance and considerable impact on quality of life, the comprehensive epidemiology and neuropsychiatric comorbidity profiles of these disorders remain limited. OBJECTIVES: To evaluate the prevalence and comorbidity profiles of psychodermatologic conditions in a diverse, population-based cohort. METHODS: A nested, case-control study was conducted using data from the All of us research program. From 287,011 eligible participants, 984 patients with PPDs (trichotillomania, skin picking disorder, dermatitis artefacta, body dysmorphic disorder, delusions of parasitosis) and 40,535 patients with psychophysiological disorders (psoriasis, atopic dermatitis, acne vulgaris, hidradenitis suppurativa, vulvodynia) were identified using International Classification of Diseases, 10th Revision, Clinical Modification codes. Each patient was paired with 4 controls based on age, sex, and race/ethnicity using nearest-neighbor propensity score matching. Multivariable logistic regression calculated adjusted odds ratios (aOR) and 95% confidence intervals to evaluate associations with various neuropsychiatric comorbidities. RESULTS: PPDs showed low point prevalences (range: ≤0.01-0.17%) but demonstrated markedly higher odds of neuropsychiatric comorbidities, including depressive disorders (range: aOR, 5.72-13.94), anxiety disorders (range: aOR, 5.96-8.40), and personality disorders (range: aOR, 8.67-13.56 Psychophysiological disorders had higher prevalence rates (range: 0.14%-5.72%) but showed more moderate associations, including depressive disorders (range: aOR, 2.24-3.13), neurodevelopmental disorders (range: aOR, 1.20-2.36), and sleep-wake disorders (range: aOR, 2.25-3.95). CONCLUSIONS: Findings reveal distinct but overlapping comorbidity profiles between PPDs and psychophysiological disorders, emphasizing the need for tailored interventions that address the psychosocial and biological complexities of these conditions.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".