PREVALENCE AND INCIDENCE OF SYSTEMIC LUPUS ERYTHEMATOSUS IN CATALONIA (SPAIN). A POPULATION-BASED STUDY
Bibliographic record
Abstract
PV084 / #684 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose According to recent epidemiological studies using administrative data, the prevalence of systemic lupus erythematosus (SLE) in Northern European countries ranges around 45-75/100.000 inhabitants.[1-3] The information on epidemiological studies in the European Mediterranean region comes from cohorts of academic centers, but population-based data is limited. Our objective was to determine the prevalence and incidence rates of SLE in Catalonia (Spain) during the period from January 1, 2011, and December 31, 2021. Methods We conducted a population-based cohort study of all existing Catalonian cases who received SLE diagnosis from 2006 to 2021 by using ICD-10-CM codes from the Information System for the Development of Primary Care Research (SIDIAP). 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.[4] 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 5-year age-sex groups, who met the definition of SLE 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 SLE 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 10.609 prevalent cases of SLE, with a mean age of 47.5 years (SD 16.3). Of these patients, 8,851 (83.4%) were female, and 6,885 (64.9%) had Spanish Nationality. Other places of origin included Latin American countries in 524 (4.9%), 279 (2.6% %) from other European countries, and 158 from Africa (1.4%). We found an overall prevalence rate of 13.84 per 100,000 (Female 22.7, Men 4.6). By age groups, prevalent cases were 1,568 (14.7%) in the 18-29 yrs group, 2,335 (22.0%) in the 30-39 group, 2,390 (22.5%) in 40-49 group, 2,595 (24.9%) in 50-64 in group and 1,721 (16.2%) in group older than 64 years. Overall incident cases during the study period were 6.166. Overall incident rate was 9.87, per 100,000, 15.69 per 100,000 for females, and 3.85 per 100,000 for males. Sex-specific and overall incidence rates by year are depicted in Figure 1 and by age groups in Figure 2. Detailed information of incident cases from our study period according to age groups and sex is presented in Table 1. Figure 1. Sex-specific and overall incidence rates by year Figure 2. Incidence rates by age-groups Table 1. Incidence of SLE from 2011-2021 Conclusions This is the first study assessing the prevalence and incidence of SLE in Catalonia at the population level. We found that more than a quarter of SLE individuals are not from Spanish origin. These may have implications for assessment of the diseases burden, and resource allocations. We found a lower prevalence of SLE in Catalonia than in Northern European countries.[1-3] Incidence rates were stable in both genders in our study period, with women showing 4-5 fold higher incidence rates than men. Incidence rates were higher among 40-50 and 50-64 age groups. References: [1.] Rees F. Ann Rheum Dis 2016;75(1):136-41. [2.] Alexander T. Ann Rheum Dis 2023 (AB0512). [3.] Arkema EV. ACR Open Rheumatol 2023;5(8) :426-32. [4.] Recalde M. Int J Epidemiol 2022;51(6):e324-33.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".