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Record W4399887165 · doi:10.2147/ptt.s430151

Burden of Herpes Zoster Among Patients with Psoriatic Arthritis in the United States

2024· article· en· W4399887165 on OpenAlexaff
David Singer, Philippe Thompson‐Leduc, Siyu Ma, Deepshekhar Gupta, Wendy Y. Cheng, Selvam R Sendhil, Manasvi Sundar, Ella Hagopian, Nikita Stempniewicz, Mei Sheng Duh, Sara Poston

Bibliographic record

VenuePsoriasis Targets and Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsGroup for Research in Decision Analysis
FundersGlaxoSmithKline
KeywordsDermatologyPsoriatic arthritisMedicineArthritisPsoriasisVirologyImmunology

Abstract

fetched live from OpenAlex

Purpose: Patients with psoriasis (PsO) and psoriatic arthritis (PsA) are at increased risk of herpes zoster (HZ), but healthcare resource use (HRU) and costs relating to HZ in adults with PsA are unknown. We aimed to estimate the incidence of HZ among adults with PsA vs without psoriatic disease and the additional HRU and costs among patients with PsA with vs without HZ. Patients and Methods: This retrospective, longitudinal, cohort study estimated HZ incidence in PsA+ vs PsO–/PsA– cohorts and HRU and medical/pharmacy costs among PsA+/HZ+ vs PsA+/HZ– cohorts comprised of adults from Optum’s de-identified Clinformatics Data Mart Database during 2015– 2020. For the HRU/cost analyses, index was the date of first HZ diagnosis (PsA+/HZ+ cohort) or was randomly assigned (PsA+/HZ– cohort). Generalized linear models were used for adjusted comparisons between cohorts. Results: HZ incidence was higher in the PsA+ (n = 57,126) vs PsO–/PsA– (n = 23,837,237) cohort (14.85 vs 7.67 per 1000 person-years; adjusted incidence rate ratio [aIRR]: 1.23; 95% confidence interval [CI]: 1.16– 1.30). Numbers of outpatient visits, emergency department visits, and inpatient admissions were significantly higher in the PsA+/HZ+ (n = 1045) vs PsA+/HZ– (n = 36,091) cohorts during the first month after HZ diagnosis (outpatient: aIRR: 1.74; 95% CI: 1.63– 1.86; emergency department: 3.14; 95% CI: 2.46– 4.02; inpatient: aIRR: 2.61; 95% CI: 1.89– 3.61). Mean all-cause per-patient costs were significantly higher in the PsA+/HZ+ vs PsA+/HZ– cohorts during the first month after index ($6493 vs $4521; adjusted cost difference: $2012; 95% CI: $1204–$3007). HRU and costs were numerically higher in the PsA+/HZ+ cohort during the first 3 and 12 months. Conclusion: These findings, which provide evidence on the increased incidence and HRU and economic burden associated with HZ among adults with PsA, could be used to inform clinical practice and decision-making. Plain Language Summary: Why was the study done? Psoriatic arthritis affects the joints of around 20% of patients with the skin condition, psoriasis.Patients with psoriatic arthritis are at increased risk of shingles, which can cause a painful skin rash and complications.This study aimed to provide information on how many patients with psoriatic arthritis get shingles and the healthcare use and costs of caring for patients with psoriatic arthritis and shingles. What did the researchers do and find? Using data from a large US health plan database, we estimated that for every 1000 patients with psoriatic arthritis observed for 1 year, 15 will develop shingles.Patients with psoriatic arthritis were 23% more likely to develop shingles than people without psoriatic disease.Patients with psoriatic arthritis and shingles had 2– 3 times as many healthcare visits in the month after a shingles diagnosis as patients with psoriatic arthritis but no shingles.This resulted in an average additional cost of approximately $2000 per patient. What do these results mean? Psoriatic arthritis increases the risk of shingles.The costs associated with shingles in patients with psoriatic arthritis are substantial.Measures to prevent shingles in this population could be beneficial. Keywords: claims database, costs, healthcare resource use, incidence, psoriatic arthritis, United States

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.246
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

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