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Record W4408884439 · doi:10.1007/s43999-025-00061-5

Sex-specific and regional differences in the prevalence of diagnosed autoimmune diseases in Germany, 2022

2025· article· en· W4408884439 on OpenAlexaff
Manas K. Akmatov, Claudia Kohring, Frank Peßler, Jakob Holstiege

Bibliographic record

VenueResearch in Health Services & Regions · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicineInterquartile rangeEpidemiologyVitiligoAutoimmune diseasePopulationRheumatoid arthritisAutoimmune thyroiditisAutoimmune hepatitisType 1 diabetesDiabetes mellitusImmunologyInternal medicineDiseaseEnvironmental healthThyroiditisEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Research on the epidemiology of autoimmune diseases is impeded due to the rarity of most autoimmune diseases. We aimed to assess the prevalence of diagnosed autoimmune diseases in Germany and examine their sex-specific and regional differences. METHODS: A cross-sectional study using the nationwide ambulatory claims data of females and males of any age with statutory health insurance from 2022 was designed (N = 73,241,305). Autoimmune diseases were identified by diagnostic codes of the International Classification of Diseases and Related Health Problems, 10th Revision, German Modification (ICD-10-GM). Regional differences were examined at the level of urban and rural districts (N = 401). To control for demographic differences across districts we applied the direct standardization method to calculate sex- and age-standardized prevalences with the German population in 2022 used as a standard population. Furthermore, we calculated prevalence ratios (PR) and 99% confidence intervals (99% CI) to examine sex differences. RESULTS: Of 73,241,305 insurees (median age, 45; interquartile range, 26-63 years), 6,307,120 had at least one (any) autoimmune disease in 2022, corresponding to a crude prevalence of 8.61% (99% CI: 8.60-8.62%). Of all individuals with autoimmune diseases, 67% were females. The prevalence of single autoimmune diseases varied between 0.008% (pemphigus) and 2.3% (autoimmune thyroiditis). Other autoimmune diseases with a high prevalence were psoriasis (1.9%), rheumatoid arthritis (1.4%), and type 1 diabetes (0.75%). The prevalence was higher in females than males for 25 of the 31 autoimmune diseases with the highest PR observed for autoimmune thyroiditis (PR 5.92; 99% CI: 5.88-5.95), primary biliary cirrhosis (5.60; 5.36-5.84) and systemic lupus erythematosus (5.15; 4.97-5.36). Males were more likely to be diagnosed than females with type 1 diabetes (1.37; 1.36-1.39), ankylosing spondylitis (1.40; 1.39-1.43) and Guillain-Barré syndrome (1.31; 1.27-1.37). The only autoimmune disease without sex difference was myasthenia gravis (1.00; 0.97-1.03). At district level the age- and sex-standardized prevalence of at least one (any) autoimmune disease differed by a factor of nearly 2 between 5.91% and 11.62%. In general, the prevalence was higher in East (former GDR) than West (former FRG) Germany. CONCLUSION: Although most autoimmune diseases were rare, when considered as a whole, autoimmune diseases turned out to be more common than previously assumed, with one out of 12 individuals affected in Germany.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.401
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes1
Has abstractyes

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