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Record W2038406287 · doi:10.1191/1479972305cd056oa

Increased risk of depression in COPD patients with higher education and income

2005· article· en· W2038406287 on OpenAlexafffundabout
Mei‐Hsiang Lin, Yue Chen, Ian McDowell

Bibliographic record

VenueChronic Respiratory Disease · 2005
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsMedicineCOPDDepression (economics)Odds ratioConfidence intervalLogistic regressionDemographyPopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

This study examined potential modifying effects of income and education on the relationship between chronic obstructive pulmonary disease (COPD) and depression. The analysis was based on 44,963 Canadians aged 35 years or more who participated in the Canadian National Population Health Survey in 1996-1997. Logistic regression analysis was used to examine the association between prevalence of depression and COPD according to sex, income adequacy or educational level. We used a bootstrap procedure to take sampling weights and design effects into account. People with COPD had twice the prevalence of depression compared to those without COPD. The association tended to be stronger in well-educated men [adjusted odds ratio (OR) = 3.02, 95% confidence interval (CI) = 1.04, 8.75] and women (OR = 2.60, 95% CI = 1.55, 4.38) than those less educated (men: OR= 1.19, 95% CI = 0.47, 3.05; women: OR = 1.93, 95% CI = 0.96, 3.87). An increased prevalence of depression associated with COPD was also found in women with higher household income (adjusted odds ratio = 4.57, 95% CI = 2.27, 9.19) than those with lower income. However, this pattern was not found in men. In conclusion, COPD patients with higher education are more likely to be depressed. The modifying effect of income may vary by gender. Possible reasons for these findings are explored.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.278
Teacher spread0.269 · 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

Citations29
Published2005
Admission routes3
Has abstractyes

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