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Record W2979632678 · doi:10.9778/cmajo.20190085

Income-related disparities in private prescription drug coverage in Canada

2019· article· en· W2979632678 on OpenAlexaffvenueabout
Talshyn Bolatova, Michael R. Law

Bibliographic record

VenueCMAJ Open · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMichael Smith Health Research BCUniversity of British Columbia
Fundersnot available
KeywordsPoisson regressionHousehold incomePrescription drugMedical prescriptionBusinessHealth insuranceEnvironmental healthDemographic economicsEconomicsGeographyMedicineHealth careEconomic growthPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Canada does not have universal public coverage for prescription drugs, which leaves an important role for private insurance plans. However, we do not have recent data on the characteristics of Canadians who report holding such coverage, particularly differences based on household income. We performed a study to examine the relation between household income and private drug insurance coverage in Canada. METHODS: We used data from the 2015-2016 cycle of the Canadian Community Health Survey to investigate the relation between household income and holding private drug insurance. We constructed modified multivariate Poisson regression models with robust error variances, including several potential confounders. RESULTS: Overall, 59.4% of respondents reported having private drug insurance. We found a strong dose-response relation between household income level and private drug insurance coverage: 19.8% of those with a household income less than $20 000 reported private coverage, compared to 76.2% of those with a household income of $80 000 or more. INTERPRETATION: Higher-income households are much more likely to hold private drug insurance coverage in Canada. This likely contributes to differential access to medicines and health outcomes by different income groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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 teacher head, 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

Citations16
Published2019
Admission routes3
Has abstractyes

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