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Record W2795441436 · doi:10.1159/000488328

Decreases of Life Expectancy Despite Decreases in Non-Communicable Disease Mortality: The Role of Substance Use and Socioeconomic Status

2018· article· en· W2795441436 on OpenAlexaff
Jürgen Rehm, Charlotte Probst

Bibliographic record

VenueEuropean Addiction Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsLife expectancySocioeconomic statusEpidemiological transitionDemographyMedicineMortality rateEpidemiologyEnvironmental healthDiseaseGerontologyPopulationSociologyInternal medicine

Abstract

fetched live from OpenAlex

With the epidemiological transition, causes of death shifted from communicable to non-communicable diseases (NCDs) and life expectancy increased, as these NCD deaths occurred later in life. However, in the United States, over the past years, life expectancy has been stagnating or decreasing despite decreasing NCD mortality rates. Analyses of the most important underlying causes of death with increasing premature mortality reveal that psychoactive substance use played a crucial role for these increases. Furthermore, it can be shown, that a high proportion of the increased premature mortality and decreased life expectancies happened in lower socio-economic strata. Substance use policies should thus focus on lowering the gap between substance-attributable mortality in higher versus lower socioeconomic strata.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.091
GPT teacher head0.387
Teacher spread0.296 · 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 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

Citations56
Published2018
Admission routes1
Has abstractyes

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