Impact of COVID-19 pandemic on sex-disparities in asthma healthcare utilization and outcomes: a population study
Bibliographic record
Abstract
Background Sex-disparities in healthcare access were exacerbated during the COVID-19 pandemic. For respiratory conditions like asthma where females have poorer disease outcomes, it is unclear if the pandemic further worsened this sex-disparity. Thus, theobjectiveof this study is to compare the impact of the pandemic on healthcare utilization, exacerbations, and mortality rates in males and females with asthma. Methods A retrospective population-based provincial-level analysis was conducted using linked administrative datasets from Alberta, Canada. We measured hospitalization, emergency department and outpatient visits, and asthma outpatient exacerbations in female and males with asthma 18 months before and after March 12, 2020. Mortality data were compared pre-versuspost-pandemic, taking into account confirmed COVID-19 infection within 30 days. Subgroup analysis was undertaken to determine if healthcare utilization differed in those with severe asthma. Results Acute care and outpatient encounters for patients with asthma declined for both females and males. Those with severe asthma of either sex experienced a reduction in hospitalizations during the pandemic. Total number of outpatient asthma visits, including both virtual and in-person, increased during the pandemic for both sexes but significantly more in females. Mortality rate was unchanged after adjusting for COVID-19-associated deaths pre-versuspost-pandemic. Conclusion All patients with asthma accessed acute care resources less but outpatient visits increased during the pandemic. There was no increase in non-COVID-related mortality, regardless of sex, suggesting that the previously established sex-disparity in asthma outcomes was not seen during the pandemic.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".