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Record W4408613095 · doi:10.1016/j.lana.2025.101044

Incidence, prevalence, and mortality of localized scleroderma in Quebec, Canada: a population-based study

2025· article· en· W4408613095 on OpenAlexafffundabout
Stephanie Ghazal, Anastasiya Muntyanu, Katherine Aw, Mohammed Kaouache, Lauren Khoury, Maryam Piram, Catherine McCuaïg, Gaëlle Chédeville, Elham Rahme, Mohammed Osman, Janie Bertrand, Elena Netchiporouk

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsCégep de l'OutaouaisUniversity of AlbertaUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityUniversity of OttawaUniversity of TorontoMcGill University Health Centre
FundersCanadian Institutes of Health ResearchNational Scleroderma FoundationCanadian Dermatology FoundationScleroderma Foundation
KeywordsIncidence (geometry)MedicineScleroderma (fungus)DemographyPrevalencePopulationEpidemiologyEnvironmental healthInternal medicinePathology

Abstract

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Background: Localized scleroderma is an understudied autoimmune disease characterized by fibrosis of the skin and/or subcutaneous tissue. To date, only 6 articles reported on the incidence and/or prevalence estimates globally, with significant design limitations and risk of bias. None of the studies originated from Canada or investigated mortality/geospatial epidemiology. We aimed to study the incidence, prevalence, mortality and spatiotemporal trends of localized scleroderma in Quebec, Canada, stratified by sex and age. Methods: Quebec populational health administrative databases were used to identify localized scleroderma cases from 1989 to 2019. Crude incidence rate, age-standardized incidence rate, prevalence and mortality analyses were conducted using negative binomial random walk models. Spatial analyses were conducted using a Poisson Besag-York-Mollié regression model. Findings: There were 6063 incident localized scleroderma cases identified over the total period of the study (mean age 53.0, standard deviation [SD] 20.2 years at diagnosis). The overall age and sex-standardized incidence rate was 3.25/100,000 person-years [95% Confidence Interval (CI) 3.17-3.33]. Among 6063 incident cases, 4510 (74.4%) were female and 1553 (25.6%) were male, yielding a female-to-male ratio of approximately 3:1. In females, we noted an initial increase in age-standardized incidence rate followed by a plateau and a decrease after 2013 (average annual percent change -2.0 [95% CI -3.7 to -0.2]%). In males, a steady decrease in age-standardized incidence rate was observed (average annual percent change -3.3 [95% CI -5.0 to -1.8]%). The highest incidence rate was observed in the 60-79 year-old age group for females and the 80+ group for males. Age-standardized incidence rate varied geographically with hotspots identified in the south of Quebec. The average prevalence was 24.5/100,000 [95% CI 24.3-24.8]. The overall standardized mortality ratio was comparable for females (1.04 [95% CI 0.95-1.14]) and males (1.14 [95% CI 0.98-1.33]) and decreased steadily over time for both sexes (from 1.31 [95% CI 1.06-1.58] in 1996 to 0.81 [95% CI 0.66-0.98] in 2019). Standardized mortality ratio analysis revealed excess death only in females aged 40-59 years. Interpretation: From 1989 onward, we report an initial increase in the age and sex-standardized incidence rate of localized scleroderma in Quebec followed by a recent decrease after 2013, as well as a generally increasing prevalence from 1996 to 2019. Standardized mortality ratio analysis confirmed the clinical observation that localized scleroderma is a morbid rather than life-threatening disease. We demonstrate an uneven geographic distribution of localized scleroderma incidence in Quebec. Funding: This project was funded by Canadian Dermatology Foundation, National Scleroderma Foundation and Canadian Institutes of Health Research. Dr. Netchiporouk received FRQS Junior 1 Clinician Scientist Salary Award.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.362
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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