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The potential for tobacco control to reduce PBS costs for smoking‐related cardiovascular disease

2004· article· en· W82352668 on OpenAlexaff
Susan F. Hurley, Michelle Scollo, Sandra Younie, Dallas R. English, Maurice Swanson

Bibliographic record

VenueThe Medical Journal of Australia · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsInro Consultants (Canada)
FundersCancer Council VictoriaVicHealth
KeywordsSubsidyEnvironmental healthTobacco controlMedicinePsychological interventionPublic healthEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate Pharmaceutical Benefits Scheme (PBS) subsidies for drugs to treat smoking-related cardiovascular disease (CVD) in 2001-02, and over the period of the government's Intergenerational Report (IGR), assuming current smoking prevalence rates and a 5% absolute reduction. DESIGN AND SETTING: An Australian epidemiological study, using prescribing data, aetiological fraction methodology, and IGR trends. MAIN OUTCOME MEASURES: Estimated smoking-related PBS subsidy costs in 2001-02 and predicted cumulative subsidies until 2041-42, under current and reduced smoking prevalence assumptions. RESULTS: The PBS costs of smoking-related CVD in 2001-02 were $126 million, 9.77% of the cost of drugs for CVD and 2.96% of total PBS subsidies. The cumulative difference in these costs over the 40-year period with a 5% drop in smoking prevalence was predicted to be $4.5 billion, a 17% reduction. The saving would be $1.14 billion discounting future costs at 5% per year. CONCLUSIONS: Further investment in tobacco control interventions could curb the increasing cost of the PBS and contribute to government efforts to ensure the viability of Australia's healthcare-financing programs. The net present value of a campaign to reduce smoking prevalence was estimated at $1 billion, with an internal rate of return of 33%.

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)0.033
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.556
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.191
GPT teacher head0.417
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2004
Admission routes1
Has abstractyes

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