Psoriasis Prevalence and Severity by Expert Elicitation
Bibliographic record
Abstract
An estimated 2–4% of Western populations are thought to have psoriasis, with a regional incidence ranging from 0.09% to 11.43%. Variance in estimates is a result of differences in study populations, methodology, regional differences, and definitions of disease. Reliable prevalence estimates of plaque psoriasis are challenging to establish. Further, the distribution of psoriasis severity in the population is unknown. This study aims to establish the utility of expert elicitation (EE) as a method for estimating unknown parameters in dermatology by (1) estimating the prevalence of psoriasis in the adult population, and (2) estimating previously unknown disease severity distribution. An expert panel of 11 Canadian dermatologists with demonstrated expertise in psoriasis was formed. A proof-of-concept EE exercise estimated psoriasis prevalence in the general population in Canada, followed by estimation of psoriasis disease severity distribution by body surface area (BSA). Expert estimates were consolidated using Bayesian methods to statistically model the data and represent uncertainty. The median prevalence of psoriasis in the adult population using the Bayesian estimate was 3.0% (95% credibility interval, 2.7–3.3%), compared with the estimated mean prevalence of 3.4% (95% confidence interval, 2.2–4.9%). By EE, the estimated cumulative distribution of disease severity assessed by BSA suggests that approximately 50% of patients have a BSA of < 3% and 78% of patients have a BSA of < 10%, with only 2% having a BSA of > 50%. The EE approach resulted in prevalence estimates that had a narrow distribution and were consistent with published literature, supporting its value in dermatology as a complementary method to help guide decision-making in areas where evidence is scarce or uncertain. Psoriasis is a common skin disease that affects 2–4% of the population. Prevalence estimates vary depending on factors such as study type and population studied. The distribution of disease severity (what proportion of patients have mild, moderate, or severe psoriasis) is not known. In this study, 11 dermatologists with expertise in psoriasis used an approach called expert elicitation to make educated guesses about prevalence and disease severity distribution in the real world. Using a statistical approach called Bayesian estimation, experts can represent the level of certainty in what they know and do not know and make inferences or assumptions about a population. Bayesian estimates are not based on the amount of data; rather, each datum contributes to a statistically meaningful result. The median prevalence of psoriasis in the adult population using the Bayesian estimate was 3.0%, which is in the expected range based on prior literature and supports the use of this expert elicitation method. This study provides the first expert estimate of disease severity distribution in the population assessed by body surface area affected by psoriasis. Approximately 50% of psoriasis patients have mild disease (< 3% body surface area involved) and 78% of patients have mild or moderate disease (< 10% body surface area involved). Only 2% of patients have more than 50% body surface area involved. This expert elicitation approach can be used to help guide decision-making in areas of dermatology where evidence is lacking or uncertain.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".