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Record W2686176395

Cost Effectiveness Analysis of Riluzole for ALS in Ontario Home Care Setting

2017· dissertation· nl· W2686176395 on OpenAlexaboutno aff
Yongjin Kim

Bibliographic record

VenueUWSpace (University of Waterloo) · 2017
Typedissertation
Languagenl
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsRiluzoleMedicineCost analysisAmyotrophic lateral sclerosisEngineeringOperations researchInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: To identify the factors associated with the prescription of riluzole and assess its cost-effectiveness for patients diagnosed with Amyotrophic Lateral Sclerosis (ALS) in long stay home care in Ontario, Canada. \n \nMethod: A retrospective cohort study was conducted using the Ontario Association of Community Care Access Centres – Home Care (OACCAC-HC) data. Assessment records of ALS patients admitted into home care between April 1st, 2005 and March 31st, 2013, who had information on whether or not they used riluzole, were reviewed. Univariate and multiple logistic regressions analysis were used to identify the predictors influencing the receipt of riluzole. Variables included in the analyses were chosen in correlation to the prognostic factors identified in the literature review. For the cost-effectiveness analysis, cost data were obtained from relevant literatures and published information on Canadian Institute for Health Information Patient Cost Estimator accounting for the cost of administration of riluzole, standard supportive home care services, and cost-savings from delay in hospitalization. Effectiveness was measured using time to discharge from home care due to death, placement into long-term care, and hospitalization, controlling for potential confounding variables using propensity score stratification. The incremental cost-effectiveness ratio was calculated based on time spent in different states and the associated utility scores using the stratified population and expressed as cost per life-year gained and quality-adjusted life-year gained. Sensitivity analyses included one-way deterministic sensitivity analysis to investigate the change in ICER due to variations in specific input parameters. Scenario analyses were developed to depict the ICERs in best and worst case scenarios. \n \nResuts: The total study population comprised of 1,351 patients diagnosed with ALS, of which 1,277 patients had information on the use of riluzole. In the multiple logistic regression analysis, older age, moderate – moderate severe impairment in cognitive functions, not being married and geographical locations across LHINs (Champlain, Erie St. Clair, Hamilton Niagara Haldimand Brant, Mississauga Halton, North East, South East, and South West) decreased the likelihood of riluzole prescription. Primary analysis showed that treatment with riluzole was associated with prolonged survival in home care [HR = 0.86; 95% confidence interval: 0.745 – 0.99; p=0.046]. Survival gain associated with riluzole was 1.5 months, while the incremental cost was approximately $5,000 per patient. Thus, the incremental cost-effectiveness ratio of riluzole versus standard supportive home care services was $41,128.85 per life-year gained or $55,579.53 per quality-adjusted life-year gained. One-way deterministic sensitivity analysis suggested an ICER ranging from $50,000 – 78,000 per QALY, while scenario analyses depicting best and worst case scenarios suggested an ICERs of $29,890.36 per QALY and $106,641.52 per QALY. \n \nConclusion: Patient characteristics such as age, cognitive score, geographical location and marital status markedly influenced drug utilization of riluzole. In addition, the findings of this study indicate that riluzole has a borderline or unfavorable cost-effectiveness for patients diagnosed with ALS in home care setting.

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)0.006
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.149
GPT teacher head0.361
Teacher spread0.212 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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