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Record W4229017484 · doi:10.1177/0272989x221097106

Trends in Author-Reported Cost-Effectiveness Thresholds in the United States from 1995 to 2018: Implications for Discount Rates

2022· article· en· W4229017484 on OpenAlexaff
Ankur Pandya, Mike Paulden, Jinyi Zhu, Tara A. Lavelle, James K. Hammitt

Bibliographic record

VenueMedical Decision Making · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsActuarial scienceEconomicsQuality-adjusted life yearEconometricsPsychologyMedicineDemographyCost effectivenessOperations managementSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Decisions based on cost-effectiveness analyses (CEAs) using equal discount rates for health and cost outcomes are consistent with using a constant cost-effectiveness threshold over time. We sought to analyze trends in author-reported cost per quality-adjusted life-year (QALY) thresholds from CEAs published for the US setting over 24 y to retrospectively assess whether the recommended equal discount rates for costs and health were consistent with trends in the CEA literature. METHODS: We used the Tufts CEA Registry to assess whether author-reported cost-effectiveness thresholds changed in CEAs published for the US setting between 1995 and 2018 and back-calculated the implied discount rate for health based on these trends for inflation-adjusted cost-effectiveness thresholds and an annual discount rate for costs of 3%. RESULTS: We found 1995 CEAs published for the US setting and found that average nominal and inflation-adjusted cost-effectiveness thresholds increased over that time period. The discount rate for health would need to equal 2.43% to 2.48% (depending on the subset of CEAs analyzed) to be consistent with the observed trends in inflation-adjusted author-reported cost-effectiveness thresholds. We also found that restricting our analysis to currency years between 1995 and 2014 would result in a back-calculated discount rate for health of 2.99% to 3.28%. CONCLUSIONS: We found that CEA researchers have implicitly assumed that inflation-adjusted cost-effectiveness thresholds in the United States have been increasing over time (1995-2018), which is inconsistent with the recommended and prevailing choice of equal discount rates for health and cost outcomes. Our results are sensitive to the cutoff year used in the analysis. HIGHLIGHTS: We show visually and through equations that the recommended and prevailing practice of using equal discount rates for cost and health outcomes in cost-effectiveness analyses (CEAs) logically implies a constant inflation-adjusted cost-effectiveness threshold over time.Using data from the Tufts CEA Registry, we found that author-reported cost-effectiveness thresholds used in CEAs published for the US setting with currency years between 1995 and 2018 increased over time (both with and without adjustment for inflation).Assuming an annual discount rate for costs equal to 3%, the discount rate for health would need to equal approximately 2.5% to preserve consistency across decisions taken at different dates given the observed trends in inflation-adjusted author-reported cost-effectiveness thresholds.This finding depends on the cutoff year used in the analysis (data from currency years 1995-2014 would support use of equal discount rates, whereas data after 2014 would suggest a sharper trend toward increasing cost-effectiveness thresholds).

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)0.055
metaresearch head score (Gemma)0.321
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.321
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.010
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
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 head0.489
GPT teacher head0.540
Teacher spread0.051 · 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.

Study designObservational
DomainMethods
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
Published2022
Admission routes1
Has abstractyes

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