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Record W3109242892 · doi:10.12927/hcpol.2020.26351

The Impact of Prescription Medication Cost Coverage on Oral Medication Use for Hypertension and Type 2 Diabetes Mellitus

2020· article· en· W3109242892 on OpenAlexafffundvenueabout
Razan Amoud, Kelly Grindrod, Martin Cooke, Mhd Wasem Alsabbagh

Bibliographic record

VenueHealthcare policy · 2020
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsMedical prescriptionMedicineDiabetes mellitusMedication adherenceType 2 Diabetes MellitusHealth insuranceType 2 diabetesFamily medicineHealth careInternal medicinePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: No previous study, to the best of our knowledge, has examined both the time trend and impact of not having insurance or prescription medication cost coverage (PMCC) on the usage of type 2 diabetes and hypertension oral medications in Ontario and New Brunswick, Canada. METHODS: We used data from the Canadian Community Health Survey (CCHS) from 2007 to 2014 to examine the time trend and impact of PMCC. A multivariable-adjusted logistic regression model was fitted. RESULTS: The pseudo-cohort included 23,215 individuals representing a population of approximately 8.7 million people. Overall, 20.0% of respondents reported absence of PMCC. This proportion increased slightly from 19.6% (95% confidence interval [CI] 95% CI [17.5, 22.5]) to 20.7% (95% CI [16.9, 23.1]). Adjusted odds ratios (OR) showed that uninsured individuals were 23% less likely to use their medications (OR = 0.77, 95% CI [0.657, 0.911]). CONCLUSION: There was a slight decline in PMCC over time and this decline was associated with reduced use of medications for type 2 diabetes and hypertension.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.823
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.139
GPT teacher head0.401
Teacher spread0.262 · 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

Citations4
Published2020
Admission routes4
Has abstractyes

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