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Record W2100757467 · doi:10.3109/02770900903427011

Increased Effect of Obesity on Asthma in Adults with Low Household Income

2010· article· en· W2100757467 on OpenAlexaff
Yue Chen, Michelle Bishop, Heidi Liepold

Bibliographic record

VenueJournal of Asthma · 2010
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsPublic Health Agency of CanadaUniversity of Ottawa
Fundersnot available
KeywordsAsthmaMedicineObesityBody mass indexOdds ratioDemographyLogistic regressionConfidence intervalHousehold incomeOddsEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Both obesity and low income are risk factors of asthma and their joint effect on the disease has not been studied previously. OBJECTIVE: To examine the influence of obesity and household income and their joint effect on asthma. METHODS: The authors conducted an analysis based on data from 115,722 subjects 18+ years of age who participated in a national survey in 2005. Logistic regression analysis was used to adjust for covariates. The joint effect of household income and body mass index (BMI) on asthma was assessed on an additive scale. RESULTS: Obesity associated with an increased risk of asthma was significant in all income categories in women but was only significant in the low-income group in men. A stronger association between obesity and asthma was observed in low- than in high-income families. For men and women combined, the adjusted odds ratio for those with a BMI value of 35 kg/m(2) or more versus those with a BMI less than 25 kg/m(2) was 2.01 in the low-household income group compared with only 1.47 in the high-income group. The corresponding adjusted relative excess risk of interaction was 1.50 (95% confidence interval [CI]: 0.88, 1.65) for men and women combined and was 2.73 (95% CI: 1.50, 5.39) for women. CONCLUSION: These data suggested an interactive effect of obesity and low-household income on the prevalence of asthma.

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)0.001
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 score0.012
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.228
Teacher spread0.224 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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