Burden of Skin and Subcutaneous Diseases in Iran and Neighboring Countries: Results from the Global Burden of Disease Study 2015.
Bibliographic record
Abstract
BACKGROUND: Iran and its neighboring countries represent four world regions with unique cultures and geography. Skin diseases span a wide diversity of etiologies including infectious, inflammatory, autoimmune, vascular, neurogenic, and oncologic. The Global Burden of Disease Study (GBD) 2015 measures the burden from skin diseases in 195 countries. METHODS: Epidemiologic data were collected from literature review, survey data, and hospital inpatient/outpatient claims data. These raw data entered modeling using a Bayesian meta-regression tool, DisMod MR-2.1, which yielded prevalence estimates by age/sex/location/year. Prevalence estimates were combined with disability weights to yield years lived with disability (YLDs). YLDs are combined with years of life lost (YLLs), from mortality estimates, to yield disability-adjusted life years (DALYs). DALYs were obtained for 16 skin conditions and both sexes in Iran and 15 surrounding countries. The sociodemographic index (SDI) for each country was also correlated with skin disease DALY rate using the Pearson coefficient (r) with two-tailed P-value. RESULTS: There was no significant correlation between individual skin diseases and SDI. Acne and dermatitis caused the greatest burden and BCC the lowest burden of skin diseases in Iran and the other 15 countries. SCC and BCC were responsible for the largest discrepancy by sex, with higher burden in males compared to females. CONCLUSION: Skin diseases, particularly dermatitis and acne, cause considerable burden in Iran and surrounding regions. Objective and transparent epidemiologic data such as GBD has the potential to inform and impact many facets of healthcare, research prioritization, public policy, and international partnerships.
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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".