Prevalence of Hidradenitis Suppurativa
Bibliographic record
Abstract
IMPORTANCE: Hidradenitis suppurativa/acne inversa (HS) is a chronic inflammatory skin disease characterized by occlusion of hair follicles as a primary pathogenic factor. There are scarce data regarding the prevalence of HS. OBJECTIVE: To estimate overall HS prevalence. DATA SOURCES: This review and meta-regression analysis was conducted using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline. The academic search included PubMed, Cochrane registry, ClinicalTrials.gov, and evidence by NHS UK and Trip databases from inception through May 2020. To analyze HS prevalence, only cross-sectional studies or baseline assessments of longitudinal cohorts using census-based surveys or probabilistic and nonprobabilistic epidemiologic methods were considered. The search terms were (prevalence OR incidence OR epidemiology) AND (hidradenitis suppurativa OR acne inversa OR Verneuil's disease). No language restriction was applied. STUDY SELECTION: Original investigations that reported HS prevalence were included. After exclusion criteria were applied, 17 studies qualified for qualitative analysis, but only 16 studies were quantitatively assessed. DATA EXTRACTION AND MEASURES: Two reviewers extracted data by age, diagnostic criteria, presence of any comorbidity, sample sizes, continent/location, sex, and other characteristics. Assessment of bias risk used the Joanna Briggs Institute Critical Appraisal Instrument for Studies Reporting Prevalence Data using random-effects models to synthesize available evidence. MAIN OUTCOMES AND MEASURES: Hidradenitis suppurativa prevalence (with 95% CI) among the overall population and among subgroups. Between-study heterogeneity was assessed (Cochran Q statistic) and quantified (I2 statistic). RESULTS: In 16 quantitatively assessed studies included, prevalence estimates were reported only from Western European and Scandinavian countries, the US, and Australia. Meta-analysis with random effects, after adjusting for publication bias in the prevalence estimates, revealed a 0.40% prevalence (95% CI, 0.26%-0.63%) for HS. Studies based on clinical samples revealed a higher pooled prevalence of HS (1.7%) than population-based studies (0.3%). CONCLUSIONS AND RELEVANCE: The findings of this systematic review and meta-regression analysis may help facilitate policy formulation, channeling funding and guiding principles for better disease diagnosis using universal valid tools and management.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".