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Record W2891958410 · doi:10.14288/1.0365642

Population-level studies of the incremental economic burden of systemic autoimmune rheumatic diseases

2020· article· en· W2891958410 on OpenAlexaboutno aff
Natalie McCormick

Bibliographic record

VenuecIRcle (University of British Columbia) · 2020
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationImmunologyAutoimmune diseaseEnvironmental healthAntibody

Abstract

fetched live from OpenAlex

In systemic autoimmune rheumatic diseases (SARDs), immune dysregulation leads to systemic inflammation, organ damage, complications, and disability. I examined the longitudinal, incremental direct medical costs of newly-diagnosed SARDs, incremental productivity costs, and impact of socioeconomic status (SES) on costs, at the general population level. Methods: Nine population-based cohorts, one for each SARD, were identified from the administrative health databases of the province of British Columbia (BC), Canada. Nine non-SARD comparison cohorts were selected from the general population of BC, and matched to each SARD cohort on age, sex, and index-year. Direct Medical Costs: Administrative data captured provincially-funded outpatient encounters and hospitalisations, and all dispensed medications. From these data, I estimated direct medical costs of each SARD and non-SARD cohort for up to five years after diagnosis/index date. I used generalised linear models to determine incremental costs of each SARD overall, and by SES group, controlling for covariates, and incremental costs of systemic lupus erythematosus (SLE, the most common SARD) before diagnosis. Productivity Costs: Random sample of the population-based cohorts completed a survey on absenteeism and presenteeism (working at reduced levels/efficiency) from paid and unpaid work. Survey data were used to determine adjusted, incremental lost productivity costs of three SARDs: SLE, systemic sclerosis (SSc), and Sjogren’s (SjS). Results: Direct Medical Costs: I identified 8,858 incident adult SARD cases for the years 1996-2010 (79.8% female) and 32,727 non-SARDs (79.0% female). Adjusted mean per-person-year incremental costs (over-and-above non-SARDs’) ranged from $7,851 to $54,061 2013 CDN, mainly from hospitalisations. For nearly every SARD, incremental costs of the low-SES exceeded the high-SES, by ~$2,000-$3,000 per-person-year. In each of the five pre-index years, adjusted costs for SLE were significantly greater than non-SLE; male sex and low SES were associated with greater costs among SLE. Productivity Costs: 671 surveys were completed: SLE=167, SSc=42, SjS=90, and non-SARDs=375. Adjusted incremental productivity costs averaged $4,494, $3,582, and $4,357 annually for SLE, SSc, and SjS, respectively. Major contributors were unemployment, presenteeism from paid work, and impairments with unpaid work. Conclusion: These novel findings should inform health resource allocation, and ongoing research to improve outcomes and reduce costs in these chronic diseases.

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)0.005
metaresearch head score (Gemma)0.018
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.241
Teacher spread0.207 · 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
Published2020
Admission routes1
Has abstractyes

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