Regional variations and prevalence of psoriasis in Germany from 2010 to 2017: a cross-sectional, spatio-epidemiological study on ambulatory claims data
Bibliographic record
Abstract
OBJECTIVES: Global prevalence rates of psoriasis differ significantly, with lowest rates in the equator region and increasing tendencies towards the north but also differences within-country. Information on regional variations in Germany is missing. This study aims to analyse the change of psoriasis prevalence in Germany over time and to detect regional variations. DESIGN: Cross sectional, spatio-epidemiological study on regional psoriasis prevalence in Germany. SETTING: Claims data study based on nationwide outpatient billing data on county level. METHODS: Analyses based on outpatient billing data for 2010-2017 derived from all people insured in statutory health insurances (about 72.8 million). We performed descriptive spatio-temporal analyses of prevalence rates using probability mapping and statistical smoothing methods, identified spatial clusters and examined a north-south gradient using spatial statistics. RESULTS: The prevalence increased from 147.4 per 10 000 in 2010 to 173.5 in 2017. In 2017, counties' prevalence rates ranged between 93.8 and 340.9. Decreased rates occurred mainly in southern counties, increased rates in northern and eastern counties. Clusters of low rates occur in southern and south-western Germany, clusters of high rates in the north and north-east. The correlation between counties' latitudes and their prevalence rates was high with Pearson's r=0.65 (p<0.05). CONCLUSION: Increased prevalence of psoriasis over time and marked regional variations in Germany were observed which need further investigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".