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Record W2311795118 · doi:10.14288/1.0105195

The health resource utilization and economic burden of systemic autoimmune rheumatic diseases

2012· article· en· W2311795118 on OpenAlexaffabout
Natalie McCormick

Bibliographic record

VenuecIRcle (University of British Columbia) · 2012
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSystemic diseaseAutoimmune diseaseResource (disambiguation)ImmunologyIntensive care medicineImmunopathologyAntibodyComputer science

Abstract

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Background: SARDs (Systemic Autoimmune Rheumatic Diseases) are a group of rare, chronic conditions (systemic vasculitis, systemic lupus erythematosus, scleroderma, Sjogren's disease, and poly/dermatomyositis) associated with high health resource consumption. However, estimates of their healthcare burden are sparse, with most determined at tertiary centres over short periods. Studying them separately has also limited research progress. Here we grouped the SARDs, for the first time ever, to quantify their collective, longitudinal (twelve-year) burden at the population-level. Methods: A population-based cohort of SARDs cases was identified from the administrative database of BC’s single-payer health system (PopDataBC). A detailed algorithm, with time and specialist parameters, was used to enhance diagnostic specificity. From PopDataBC, all provincially-funded health services, and all prescriptions (regardless of funding source), consumed from 1996 -2007 were captured. Costs for outpatient services and prescriptions were summed directly from paid claims; case-mix methodology was used for most hospitalizations. To quantify their net burden, costs were summed for claims attributable (under broad and narrow definitions) to SARDs. Costs are reported in 2007 Canadian dollars. Results: 18,741 SARDs cases were identified, contributing 82,140 patient-years(PY). After inflation adjustments, the annual mean per-PY direct medical costs of SARDs averaged $6,954/PY, with $1,882/PY(27%) from outpatient, $3,551/PY(51%) from hospital, and $1,521/PY(22%) from prescriptions. Over twelve years, annual costs decreased by 32%, from $8,901/PY in 1996 to $6,087/PY in 2007. Outpatient costs and encounters decreased by 26% ($2,205-$1,641/PY) and 19% (34-27/PY), respectively. Mean annual hospital costs decreased by half ($5,579-$2,776/PY), and admissions by 46% (0.89-0.48/PY). Despite these decreases, the annual mean number of dispensed prescriptions increased by 49% (23-34/PY), and their costs by 50% ($1,117-$1,670/PY). The annual net per-PY costs of SARDs, mainly from hospitalizations(18-43% of costs) and prescriptions(48-76%), averaged $2,011-$3,202/PY. Conclusions: SARDs impart a substantial healthcare burden at the population level, and in 2007 were directly responsible for ≥44% of cases’ gross mean annual healthcare costs ($6,087/PY). Most costs have decreased over twelve years; however, medication costs are rising (by 4% annually, on-average), which suggests comorbidity burdens are too. As demand grows for expensive but potentially-better SARDs therapies, research to assess their impact on long-term comorbidity risk is needed.

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)0.007
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 score0.418
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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