Incremental Costs for Psoriasis and Psoriatic Arthritis in a Population-based Cohort in Southern Sweden: Is It All Psoriasis-attributable Morbidity?
Bibliographic record
Abstract
OBJECTIVE: To estimate incremental costs for patients with psoriasis/psoriatic arthritis (PsO/PsA) compared to population-based referents free from PsO/PsA and estimate costs attributable specifically to PsO/PsA. METHODS: Patients were identified by International Classification of Diseases, 10th ed., codes for PsO/PsA using information from 1998 to 2007 in the Skåne Healthcare Register, covering healthcare use for the population of the Skåne region of Sweden. For each patient, 3 population-based referents were selected. Data were retrieved from Swedish registers on healthcare, drugs, and productivity loss. The human capital method was used to value productivity losses. Mean annual costs for 2008 to 2011 were assessed from a societal perspective. RESULTS: We identified 15,283 patients fulfilling the inclusion criteria for PsO [n = 12,562, 50% women, mean age (SD) 52 (21) yrs] or PsA [n = 2721, 56% women, mean age 54 (16) yrs] and included 45,849 referents. Mean annual cost per patient with PsO/PsA was 55% higher compared to referents: €10,500 vs €6700. The cost was 97% higher for PsA compared to PsO. Costs due to productivity losses represented the largest share of total costs, ranging from 52% for PsO to 60% for PsA. Biological drug costs represented 10% of the costs for PsA and 1.6% for PsO. The proportion of cost identified as attributable to PsO/PsA problems was greatest among the patients with PsA (drug costs 71% and healthcare costs 31%). CONCLUSION: Annual mean incremental societal cost per patient was highest for PsA, mainly because of productivity losses and biological treatment. A minor fraction of the costs were identified as attributable to PsO/PsA specifically, indicating an increased morbidity in these patients that needs to be further investigated.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".