Impact of Pre-Existing Disability on Long-Term Health Care Use Following Hospitalization for COVID-19: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Emerging evidence shows the lasting impact of SARS-CoV-2 infection on health care use and needs. Policy-makers require data on population-level service use to understand patient needs and health system impacts following hospitalization for COVID-19. OBJECTIVE: To compare health service use within 12 months following hospitalization for COVID-19 among people with and without pre-existing disabilities, and to determine the extent to which such use is related to disability and other risk factors. DESIGN: Population-based cohort study, Ontario, Canada. PARTICIPANTS: Adults with and without disabilities hospitalized for COVID-19, 01/25/2020-02/28/2022. MAIN MEASURES: We used Poisson regression to model adjusted rate ratios (aRR) of ambulatory care visits, diagnostic testing, emergency department (ED) visits, hospital admissions, and palliative care visits within 1-year post-discharge, comparing patients with and without disabilities. Models were adjusted sequentially for sociodemographic factors, comorbidities, and prior health service use. The importance of each set of covariates in its ability to explain observed associations was determined by calculating relative changes in disability parameter coefficients after each sequential risk-adjustment. KEY RESULTS: The cohort included 25,320 patients with disabilities and 15,953 without. In the year after hospitalization for COVID-19, people with disabilities had higher rates of ambulatory care visits, diagnostic tests, ED visits, hospital admissions, and palliative care visits. A significant proportion of these associations was explained by sociodemographic factors, comorbidities, and prior health service use. However, adjusted relative rates associated with disability remained elevated, even after adjustment, for ambulatory care visits (aRR 1.09, 95% CI 1.08, 1.10), diagnostic tests (aRR 1.14, 95% CI 1.12, 1.16), ED visits (aRR 1.25, 95% CI 1.21, 1.29), and hospital admissions (aRR 1.21, 95% CI 1.16, 1.29). CONCLUSIONS: These findings support the need to develop and evaluate models of care for the post-COVID-19 condition that address the needs of people with disabilities.
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 | 0.003 |
| 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 | 0.001 |
| 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".