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Record W3006202694 · doi:10.1186/s12889-020-8322-8

Socioeconomic - related inequalities in overweight and obesity: findings from the PERSIAN cohort study

2020· article· en· W3006202694 on OpenAlexaff
Farid Najafi, Shahin Soltani, Behzad Karami Matin, Ali Kazemi Karyani, Satar Rezaei, Moslem Soofi, Yahya Salimi, Mehdi Moradinazar, Mohammad Hajizadeh, Loghman Barzegar, Yahya Pasdar, Behrooz Hamzeh, Ali Akbar Haghdoost, Reza Malekzadeh, Hossein Poustchi, Sareh Eghtesad, Azim Nejatizadeh, Mahmood Moosazadeh, Mohammad Javad Zare Sakhvidi, Farahnaz Joukar, Seyed Mohammad Hashemi‐Shahri, Alireza Vakilian, Ramin Niknam, Elnaz Faramarzi, Ghodrat Akhavan Akbari, Fershteh Ghorat, Arsalan Khaledifar, Davoud Vahabzadeh, Reza Homayounfar, Ali Reza Safarpour, Seyed Vahid Hosseini, Reza Rezvani, Seyed Ahmad Hosseini

Bibliographic record

VenueBMC Public Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsDalhousie University
FundersKermanshah University of Medical SciencesMinistry of Health and Medical Education
KeywordsOverweightSocioeconomic statusMedicineObesityBiostatisticsBody mass indexUnderweightDemographyResidenceConfidence intervalPublic healthEpidemiologyEnvironmental healthGerontologyPopulationInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Overweight and obesity are major health concerns worldwide, with adverse health consequences during the life span. This study measured socioeconomic inequality in overweight and obesity among Iranian adults. Methods Data were extracted from 129,257 Iranian adults (aged 35 years and older) participated in the Prospective Epidemiologic Research Studies in IrAN (PERSIAN) in 14 provinces of Iran in 2014. Socioeconomic-related inequality in overweight and obesity was estimated using the Concentration Index (C n ). The C n further decomposed to find factors explaining the variability within the Socioeconomic related inequality in overweight and obesity. Results Of the total number of participants, 1.98, 26.82, 40.76 and 30.43% had underweight, normal weight, overweight and obesity respectively. The age-and sex standardized prevalence of obesity was higher in females than males (39.85% vs 18.79%). People with high socioeconomic status (SES) had a 39 and 15% higher chance of being overweight and obese than low SES people, respectively. The positive value of C n suggested a higher concentration of overweight (0.081, 95% confidence interval [CI]; 0.074–0.087) and obesity (0.027, 95% CI; 0.021–0.034) among groups with high SES. There was a wide variation in socioeconomic-related inequality in overweight and obesity rate across 14 provinces. The decomposition results suggested that SES factor itself explained 66.77 and 89.07% of the observed socioeconomic inequalities in overweight and obesity among Iranian adults respectively. Following SES, province of residence, physical activity, using hookah and smoking were the major contributors to the concentration of overweight and obesity among the rich. Conclusions Overall, we found that overweight and obesity is concentrated among high SES people in the study population. . Accordingly, it seems that intersectional actions should be taken to control and prevent overweight and obesity among higher socioeconomic groups.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.336
Teacher spread0.268 · 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".

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Citations46
Published2020
Admission routes1
Has abstractyes

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