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Record W1989316919 · doi:10.1080/14034940600858557

Smoking, healthcare cost, and loss of productivity in Sweden 2001

2006· article· en· W1989316919 on OpenAlexaboutno aff
Kristian Bolin, Björn Lindgren

Bibliographic record

VenueScandinavian Journal of Public Health · 2006
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Institutes of HealthAmerican Cancer Society
KeywordsProductivityHealth careEnvironmental healthTotal costMedicinePublic healthCost estimateDemographyHealth economicsBusinessEconomicsEconomic growth

Abstract

fetched live from OpenAlex

AIMS: Objectives were (a) to estimate healthcare cost and productivity losses due to smoking in Sweden 2001 and (b) to compare the results with studies for Sweden 1980, Canada 1991, Germany 1996, and the USA 1998. METHODS: Published estimates on relative risks and Swedish smoking patterns were used to calculate attributable risks for smokers and former smokers. These were applied to cost estimates for smoking-related diseases based on data from public Swedish registers. RESULTS: The estimated total cost for Sweden 2001 was US 804 million dollars; COPD and cancer of the lung accounted for 43%. Healthcare cost accounted for 26% of the total cost. The estimated costs per smoker were US 3,200 dollars in the USA 1998; 1,600 in Canada 1991; 1,100 in Germany 1996; 600 in Sweden 2001; and 300 in Sweden 1980 (all in 2001 US dollar prices). CONCLUSIONS: To reduce the prevalence of smoking is an issue worthwhile pursuing in its own right. In order to reduce the cost of smoking, however, policy-makers should also explore and influence the factors that determine the cost per smoker. Sweden seems to have been more successful than comparable countries in pursuing both these objectives.

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)0.003
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 score0.028
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.090
GPT teacher head0.370
Teacher spread0.280 · 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

Citations31
Published2006
Admission routes1
Has abstractyes

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