Prevalence and Risk Factors for Refractory and Resolved Chronic Cough in the Canadian Longitudinal Study of Aging (CLSA)
Bibliographic record
Abstract
Background: Chronic cough (CC) affects 16% of individuals in the Canadian Longitudinal Study of Aging (CLSA), but the prevalence of either refractory chronic cough (RCC) or resolved chronic cough (ResCC), along with medication use, remains unclear. Understanding RCC burden is essential for identifying populations that may benefit from novel therapies. Objectives: To determine the prevalence, demographics, and factors for RCC and ResCC, with and without medication use. Methods: Participants reporting daily cough at baseline and follow-up 3 years later were analyzed. RCC was defined as persistent cough at both time points, while ResCC included those with baseline cough that resolved by follow-up. Medication use for CC was documented. Multivariate models assessed odds ratios (ORs) for RCC with or without medication at baseline. Results: Among 26,606 participants at follow-up, 834 (3%) had RCC with medication, 1,341 (5%) had RCC without treatment, 611 (2%) had ResCC with medication, and 1,273 (5%) had ResCC without medication. Common treatments included anti-acids (62%), inhalers (42%), neuromodulators (14%), nasal sprays (15%), and antihistamines (10%). Factors associated with RCC included persistent smoking, provincial location, wheeze, asthma, COPD, neurological conditions, anxiety, ACE-inhibitor use, and bowel disorders. Smoking cessation was associated with lower RCC risk. Conclusion: Many individuals with RCC remain untreated, while some with ResCC improve without therapy. Identifying key risk factors and the underutilization of treatments highlights the need for better RCC recognition and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".