Chronic obstructive pulmonary disease in primary care: an epidemiologic cohort study from the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is mostly managed within primary care, but there is little Canadian evidence from this setting. This study was undertaken to determine the prevalence of physician-diagnosed COPD in primary care practices, and the degree of comorbidity with other chronic conditions, and to assess patterns of medication prescribing. METHODS: The Canadian Primary Care Sentinel Surveillance Network is a national "network of networks" whose member practices use electronic medical records (EMRs). At the time of the study, it included data from 444 physicians from 10 networks in 8 provinces. We conducted an epidemiologic cohort study of all patients who had EMR data collected by the network at the end of 2012. Validated case-finding algorithms were used to identify cases of COPD. We used descriptive statistics and multivariate modelling analyses to calculate the prevalence of COPD, its association with key demographic factors and comorbidities, and patterns of medication prescribing. RESULTS: The observed prevalence of COPD was 4.0% (10 043/250 346), which represents a population prevalence of 3.4% using age-sex standardization. Comorbidity was common, with prevalence ratios ranging from 1.1 for the presence of a single comorbid condition to 1.9 for 4 or more comorbid conditions. Anticholinergic agents (63%), short- (48%) and long-acting (38%) β-agonists and inhaled corticosteroids (41%) were the most commonly used medications. INTERPRETATION: The prevalence of physician-diagnosed COPD in Canadian primary care practices was similar to that reported in other practice-based studies at about 3%-4%. Most patients had comorbid conditions and were taking multiple medications. EMR data may be useful to assess both the epidemiology and management of COPD in primary care practices.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".