Impact of COVID-19 Pandemic on Chronic Obstructive Pulmonary Disease Healthcare Use, Exacerbations, and Mortality: A Population Study
Bibliographic record
Abstract
Abstract Rationale Existing work suggests that patients with chronic obstructive pulmonary disease (pwCOPD) presented less frequently to the emergency department and were less likely to be hospitalized during the coronavirus disease (COVID-19) pandemic, but it is unclear if this was due to improved health and disease management or to increased barriers and/or avoidance of health care. Objectives The objective of this study was to determine the impact of the pandemic on inpatient and outpatient healthcare use, disease incidence, and mortality rates in pwCOPD. Methods A retrospective population-based analysis using linked administrative datasets from Alberta, Canada 18 months before and after March 12, 2020 was conducted to measure hospitalization, emergency department and outpatient visits, and COPD outpatient exacerbations during these time periods. Mortality data were also analyzed before versus after the pandemic, taking confirmed COVID-19 infection within 30 days into account. Subgroup analysis based on COPD exacerbation risk stratification was undertaken to determine if healthcare use differed based on exacerbation risk. Finally, sex-based analysis of healthcare use during the pandemic was also completed. Results Hospitalization or emergency department visits and outpatient treatment for acute exacerbations of COPD dropped, whereas total outpatient COPD visits, including both virtual and in person, increased during the pandemic for pwCOPD. The mortality rate increased even after adjusting for COVID-19–associated deaths. Sex-based subgroup analysis showed a greater drop in acute care use for females, but the rise in mortality was seen for both sexes, with men experiencing a greater rate of mortality than women. Conclusions Overall, pwCOPD accessed acute care resources less during the pandemic, which may have contributed to a rise in non–COVID-19 all-cause mortality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".