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Record W4285291721 · doi:10.4236/ojepi.2022.123018

Investigating Trend in Cardiovascular Disease Mortality and Its Association with Obesity in the Gulf Cooperative Council (GCC) Countries from 1990 to 2019

2022· article· en· W4285291721 on OpenAlexaff
Sarah Algahtani, Talal Abozaid, Saad N. Algahtani, Mohamed M. Shoukri, Maha Al-Eid

Bibliographic record

VenueOpen Journal of Epidemiology · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineObesityDemographyDiseaseStroke (engine)Mortality rateCause of deathEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke. Over three quarters of CVD deaths take place in low- and middle-income countries. We have studied the pattern of mortality due to cardiovascular in the six countries of the Arabian Gulf and its association with obesity over the 29 years 1990 to 2019. Methods: We used the linear mixed effect models to investigate the pattern of CVD mortality over the year 1990 to 2019, together with the pattern of change in one of the most important risk factors that is obesity, and its association with CVD mortality over the same period. Conclusions: Although there were fluctuations in the pattern of mortality and the prevalence of obesity over the specified period, there has been a steady decline in the per-100,000 number of deaths and the prevalence of obesity. However, there was a strong association between the two variables. From the fitted models we estimated that a one percent increase in obesity is associated with an average increase in cardiovascular deaths of 2.7 deaths per 100,000.

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)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.123
GPT teacher head0.335
Teacher spread0.212 · 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 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

Citations9
Published2022
Admission routes1
Has abstractyes

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