OP0038 Increase in Prevalence and Incidence Rates of Psoriatic Arthritis in Catalonia, Spain: A Population-Based Study
Bibliographic record
Abstract
Background: According to previous studies, the prevalence and incidence rates for Psoriatic Arthritis (PsA) are 133 per 100,000 individuals (95% CI: 107–164 per 100,000) and 83 per 100,000 person-years (95% CI: 41–167 per 100,000 person-years), respectively [1]. However, these studies have a heterogeneous definition of PsA or are not based on population-base data. Only a few studies in UK and Sweden have analyzed the prevalence and incidence of PsA at a population level using diagnostic codes [2, 3]. Objectives: Our objective was to determine the prevalence and incidence rates of PsA in Catalonia (Spain) during the period from January 1, 2006, and December 31, 2021. Methods: We conducted a population-based cohort study of all existing Catalonian cases who received PsA diagnosis from 2006 to 2021 by using ICD-10-CM codes from the Information System for the Development of Research in Primary Care (SIDIAP). SIDIAP is a database of primary care electronic health records that includes data from 328 primary care practices covering 5.8 million people, 75% of the Catalan population. Prevalence rate was defined as the number of affected persons in the population at a specified time divided by the number of persons at that time. The numerator of prevalence rate was the number of persons, within 10-year age-sex groups, who met the definition of PsA between January 1, 2006, and December 31, 2021, and alive and registered with the SIDIAP on Dec 31, 2021. Incidence rates by age and sex were obtained for the 1-year period between January 1, 2011, and December 31, 2021. Persons diagnosed with PsA during the 5-year run-in period from Jan 1, 2006, until December 31, 2010 were not be eligible to become incident cases. Results: We identified 10,162 prevalent cases of PsA, with a mean age of 51.6 years (SD 13.9). Of these patients, 5,236 (51.3%) were male, and 6,799 (66.9%) had Spanish Nationality. We found an overall prevalence rate of 83 per 100,000 (Female 76.9, Men 86.1). We found a significant increase in prevalence rates in our study period from 26.4 per 100,000 at 2006 to 141.7 per 100,000 at 2021 (p for trend, Poisson model <0.0001). Sex-specific and overall prevalence rates by year are depicted in Figure 1A). By age groups, prevalent cases were 625 (6.5%) in the 18-29 yrs group, 1,532 (15.0%) in the 30-39 group, 2,460 (24.2%) in 40-49 group, 2,694 (26.5%) in 50-59 in group, 1,830 (18.0%) in 60-69 group, 779 (7.6%) in 70-79 group and 242 in older than 80 years. Overall prevalence rates by year according to age groups are depicted in Figure 1B). Overall incident cases during the study period were 6,082 cases, 3,175 male patients (51.7%). Overall incident rate was 10.4 per 100,000, 10.0 per 100,000 for females, and 10.7 per 100,000 for males. We found a slight but signficant increase in incidence rates over our study period (9.9 per 100,000 in 2011 and 14.0 in 2021, p for trend, Poisson model <0.0001). Sex-specific and overall incidence rates by year are depicted in Figure 2A and by age-groups in Figure 2B. Conclusion: This is the first study assessing the prevalence and incidence of PsA in Catalonia at the population level. We found an increase trend of the prevalence and incidence of PsA in our study period. Prevalence and incidence rates were similar between females than males and higher among 50-59 and 60-69 years group. REFERENCES: [1] Scotti L et al. Semin Arthritis Rheum. 2018;48:28-34. [2] Jordan KP et al. Ann Rheum Dis 2014;73:212–8. [3] Löfvendahl S et al. PLoS One 2014;9:e98024 Figure 1APrevalence rates over time according sex Figure 1BPrevalence rates over time according age groups Figure 2AIncidente rates over time by sex Figure 2BIncidence rates over time according age groups Acknowledgements: This work was supported by Instituto Carlos III (PI22/00212). J Ramírez, A Azuaga and J. Cañete have received funding from HIPPOCRATES project (No 101007757). Disclosure of Interests: José A Gómez-Puerta Astra Zeneca, Abbvie, GSK, Janssen, Lilly, Pfizer, Otsuka, Frezenius, Sanofi, Julio Ramírez Abbvie, Janssen, Lilly, Pfizer, UCB, Andrés Ponce Abbvie, Ana Azuaga Abbvie, UCB, Maria Grau: None declared, Cristian Tebe: None declared, Juan Sarmiento-Monroy Boehringer Ingelheim, GSK, Lucia Alascio: None declared, Sandra Farietta Varela Abbvie, Claudia Arango Silva: None declared, Cristina Carbonell-Abella: None declared, Daniel Martínez-Laguna: None declared, Rosa Morlà Novell: None declared, Raimon Sanmarti Abbvie, BMS, Galápagos, Lilly, Pfizer, Roche, J. Antonio Aviña-Zubieta: None declared, Juan de Dios Cañete Crespillo: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".