POS0042 EPIDEMIOLOGICAL ANALYSIS OF SYSTEMIC SCLEROSIS PREVALENCE AND SURVIVAL IN ISRAEL USING THE CLALIT HEALTH SERVICES DATABASE: GEOGRAPHIC DISTRIBUTION, DEMOGRAPHICS, AND MORTALITY TRENDS
Notice bibliographique
Résumé
Background: Systemic sclerosis (SSc) is an autoimmune disease characterized by a triad of immune dysregulation, small vessel vasculopathy, and progressive fibrosis. The prevalence and incidence of SSc varies around different geographic location and is thought to be influenced by multi factorial causes. The reported prevalence of SSc ranges from 31 per million in Japan to 658 per million among Native Americans in Oklahoma [1], USA, with a recent study from Quebec reporting a prevalence of 289 per million [2]. The standardized incidence of SSc was reported to be 4.14 per 100,000 person years. Israel is a very heterogenous country with several populations including Sephardic and Ashkenazi Jews, Arab, Druze, and Ethiopians. Currently there is no data about SSc prevalence in Israel. Understanding the prevalence and the distribution of SSc in Israel may shed light on risk factors and may lead to identification of environmental or genetic susceptibility in the future. Objectives: The primary aim was to determine the prevalence of SSc in Israel using the central database of Clalit Health Services (CHS), which covers 50% of the Israeli population. Secondary objectives included identifying geographic hotspots where higher prevalence may be linked to genetic or environmental factors, as well as examining the demographics and mortality rates of SSc in Israel. Methods: The CHS databases is a 4.8-million-member, state-mandated, nonprofit health provider in Israel, representing one-half of the population of Israel, from which we extracted epidemiological and descriptive data for SSc patients. Inclusion Criterion for defining SSc was based on a combination of: 1. Diagnosis of "systemic sclerosis" or "scleroderma" based on ICD-9 codes in CHS database at least at two separate records 6 months apart, OR a diagnosis recorded at public hospital, OR a diagnosis made by a rheumatologist, AND 2. The patient must have had at least one positive high titer autoantibody: "anti Scl-70 (anti Topoisomerase-2) antibodies" (Scl-70) or "anti-Centromere antibodies" (ACA) or "anti RNA-polymerase 3 antibodies" (RNAPIII) or "Anti-nuclear antibody" (ANA). Data regarding Age and sex, ethnicity, socioeconomic status, geographic living area at disease diagnosis and birth, as well as clinical information (by diagnosis codes and related terms search) for drug use and comorbidities were extracted. Dates of first diagnosis and mortality were extracted as well. Prevalence was calculated by dividing the number of patients fulfilling our definition of SSc (numerator) by the mid-year total number of adult insured persons under CHS regions (denominator), age for individuals at risk was 15 years or older. Mortality rates were calculated as mortality cases per living patients with SSc. Results: Data was extracted from 2004 to 2023. A total of 3,385 patients met the criteria for SSc, of which 817 (24%) were positive for Scl-70, 821 (24%) for ACA, 416 (12%) for RNA POLII, and 1,331 (40%) were only ANA positive. The percentage of female patients in each group was as follows: 84.5% for Scl-70, 93.18% for ACA, 81.5% for RNAPIII, and 84.6% for ANA.Mean ages were 51.2, 57.7, 52.3 and 54 years for Scl-70, ACA, RNAPIII, and ANA groups accordingly. Comorbidities are delineated in Table 1. Prevalence of SSc was 260 per 1 million in 2005 and increased steadily to 633 per 1 million in 2023. The prevalence among females was 6.8 times higher compared to men. The prevalence of SSc in females was 6.8 times higher than in males. The incidence of SSc remained stable throughout the study period, with an average rate of 3.85 per 100,000 person-years. The prevalence of SSc varied across different geographic regions, with rates ranging from 20 per 1 million to over 1000 per 1 million. A specific hotspot of high prevalence, with more than 100 cases per 100,000, was observed in the Druze villages in the northern part of Israel (Figure 1). Five- and 10-years survival were 89.7% and 78.4% respectively for Scl-70, 89.4% and 80.6% for ACA, 85.9% and 70.6% for RNAPIII, and 86.4% and 76.6% for ANA group. Conclusion: We present herein the first nation-wide study that characterize SSc in Israel, based on a large database (CHS) covering half of its population. We have found an extremely high prevalence of SSc across the country of 633 per 1 million comparable to the highest prevalence that was described in Oklahoma population of Choctaws native American tribe. The increase of prevalence overtime in the last 20 years may be related to increasing awareness, growing numbers of rheumatologists, as well as genetic and environmental factors. Specifically, hotspots of high prevalence in Druze villages in the north of Israel in which consanguineous marriages are extremely common suggest a genetic factor and merit further investigation. The steady incidence over the last 15 years with increasing prevalence may reflect better patient care over time. Mortality rates were in accordance with previous publication worldwide. While this study is limited by its nature being based on electronic data extraction retrospectively, we have tried to overcome this by using a strict and validated inclusion criterion. Further studies are needed to explore the specific epidemiological patterns of SSc in Israel. The work was supported by Boehringer Ingelheim. REFERENCES: [1] Arnett FC, Howard RF, et al. Increased prevalence of systemic sclerosis in a Native American tribe in Oklahoma. Association with an Amerindian HLA haplotype. Arthritis Rheum. 1996 Aug;39(8):1362-70. [2] Muntyanu A, Aw K, et al. Epidemiology of systemic sclerosis in Quebec, Canada: a population-based study. Lancet Reg Health Am. 2024 Jun 8;35:100790. Table 1. Demographic and baseline characteristics of patient population Figure 1A heat map depicting the prevalence of SSc across reported villages. Large circles represents a prevalence of > 380 per 1 million Acknowledgements: We wish to acknowledge Boehringer Ingelheim for supporting our study. Disclosure of Interests: Amir Bieber Boehringer Ingelheim, Research support for this work from Boehringer Ingelheim, Shiri Keret: None declared, Shay Brikman: None declared, Snait Ayalon: None declared, Naama Schwatrz: None declared, Mohammad E. Naffaa: None declared, Doron Rimar: 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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».