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Record W3037429296 · doi:10.1186/s12933-020-01050-3

Cardiovascular and diabetes burden attributable to physical inactivity in Mexico

2020· article· en· W3037429296 on OpenAlexaff
Catalina Medina, Pamela G. Coxson, Joanne Penko, Ian Janssen, Sergio Bautista‐Arredondo, Sı́món Barquera, Kirsten Bibbins‐Domingo

Bibliographic record

VenueCardiovascular Diabetology · 2020
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsQueen's University
FundersConsejo Nacional de Ciencia y TecnologíaGeorgetown UniversityFogarty International CenterNational Institutes of HealthUniversity of California Institute for Mexico and the United States
KeywordsMedicineStroke (engine)Diabetes mellitusIncidence (geometry)Type 2 diabetesDiseaseMyocardial infarctionAngiologyDemographyEnvironmental healthInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Physical inactivity (PI) is associated with the development of non-communicable chronic diseases. The purposes of this study were to estimate the extent to which the 31% relative increase in PI among 35-64 years old Mexicans between 2006 and 2012 influenced diabetes (T2D) and cardiovascular disease (CVD) incidence and mortality, and to estimate the impact of the World Health Organization recommended 10% and 15% relative decrease in PI on CVD and T2D incidence and mortality by 2025 and 2030, respectively. METHODS: Estimates were derived using the Cardiovascular Disease Policy Model-Mexico, a computer simulation, Markov model. Model inputs included cross-national data on PI levels from 2006 and 2012 measured using the International Physical Activity Questionnaire and the published literature review on the independent relationship between PI and cardiometabolic risk. RESULTS: The models estimated that the 31% increase in PI resulted in an increase in the number of cases of T2D (27,100), coronary heart disease (10,300), stroke (2200), myocardial infarction (1500), stroke deaths (400) and coronary heart disease deaths (350). A hypothetical 10% lowering of PI by 2025 compared to status quo is projected to prevent 8400 cases of T2D, 4200 cases of CHD, 1000 cases of stroke, 700 cases of MI, and 200 deaths of CHD and stroke, respectively. A 15% reduction resulted in larger decreases. CONCLUSIONS: While the burden of T2D and CVD raised from 2006 to 2012 in association with increased PI, achieving the WHO targets by 2030 could help reverse these trends.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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.

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

Citations25
Published2020
Admission routes1
Has abstractyes

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