Association between depression and mortality in persons with asthma: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The relation between depression and mortality in patients with asthma is not well known. This study aimed to assess the impact of depression on mortality in asthmatic patients in US adults. METHODS: This observational study used data from the 2005 to 2014 National Health and Nutrition Examination Survey (NHANES). Depression was measured using the Patient Health Questionnaire-9 (PHQ-9). We used survey-weight adjusted Cox proportional hazard models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for the association between depression and all-cause mortality. RESULTS: A total of 1865 participants with asthma were included in this study. Among them, 264 (14.16%) had depressive symptoms. During 9970 person-years of follow-up, there were 24 (9.1%) deaths in 264 patients with depression compared with 100 (6.3%) deaths in 1601 patients without depression. For unadjusted analyses, depression was associated with an increased risk of all-cause mortality (HR, 2.22 [95% CI 1.32-3.73]). This association was persistent after adjustment for age, sex, race/ethnicity, and body mass index (HR, 2.71 [95% CI 1.58-4.66]). However, we did not observe a significant association between depression and mortality after controlling for extensive co-morbidities (HR, 1.92 [95% CI 0.82-4.45]). Subgroup analyses further revealed that depression was an independent risk factor for mortality only in the females (HR, 3.78 [95% CI 1.17, 12.26]) but not all asthmatic patients. CONCLUSIONS: The present study suggested that depressive disorder was common in asthmatic patients and depression in asthmatic patients was associated with a higher mortality rate. Depression was an independent risk factor for mortality in female patients.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".