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Record W3121939860

Should Public Drug Plans be Based on Age or Income

2014· article· en· W3121939860 on OpenAlexaboutno aff
Colin Busby, Jonathan Pedde

Bibliographic record

VenueC.D. Howe Institute Commentary · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPrescription drugPopulationWelfareMedical prescriptionPopulation ageingPublic economicsEconomic growthEnvironmental healthMedicineEconomics
DOInot available

Abstract

fetched live from OpenAlex

Drugs have become an increasingly critical part of healthcare services in Canada over the last few decades – with nearly $30 billion spent on prescription drugs nationwide in 2013. But it’s not clear that the current design of most provincial drug plans can withstand the financial pressures of an aging population and offer equitable access to public benefits. Owing to budgetary constraints, each province has designed unique, non-universal drug coverage to fill the gaps where private insurance does not exist. Provincial drug plans offer coverage based on an individual’s age, income, availability of private insurance (through one’s employer), or some combination of the three. We look at the most common age-based provincial plans – as well as the trend towards income-based plans. Age-based plans, which usually apply only to seniors, have major drawbacks. These include a cost structure that will be pressured from an aging population and inequities in benefit access: seniors with income and drug needs similar to a working-age family without private drug coverage pay a much smaller share of their drug costs than the family does. Provinces with age-based plans also extend benefits to those on social assistance, making transitioning off welfare difficult for families with drug needs. Further, low-income workers are those most likely to be under- or uninsured in provinces with age-based plans, which include Ontario, Alberta, Prince Edward Island and Nova Scotia. Income-based plans have challenges as well. They must be designed carefully to avoid significantly increasing in public costs and hindering access to prescribed drugs. Plus, provinces must consider how income-tested benefits can have negative incentive effects on work. High marginal tax rates reduce the incentive to work and earn. And when combined with reductions in the plethora of targeted government programs, badly designed income-based plans can create high marginal tax rates. We compare the advantages and pitfalls in moving from an age-based plan to one based on income. Further, we glean lessons from provinces with income-based plans – British Columbia and New Brunswick, which will have a new plan in 2015. On balance, we find that the benefits of an income-based plan make them superior to age-based ones. An income-based plan would apply to all individuals and families without private coverage, including those on social assistance and seniors. Although much of the discussion for reforming Canada’s drug coverage to date has focussed on creating a universal federal drug plan, other options must be explored absent political traction in pursuit of this approach. Age-based plans might have been a cost-friendly option decades ago when the ratio of seniors to workers was low, but the wave of retiring baby boomers will rapidly makes these plans less affordable. Income-based plans are a better alternative for cash-constrained provinces.

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)0.010
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.130
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.065
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.007
Open science0.0020.002
Research integrity0.0120.007
Insufficient payload (model declined to judge)0.0340.014

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 head0.479
GPT teacher head0.425
Teacher spread0.054 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2014
Admission routes1
Has abstractyes

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