Long-term health care use and diagnosis after hospitalization for COVID-19: a retrospective matched cohort study
Bibliographic record
Abstract
BACKGROUND: Knowledge pertaining to the health and health care utilization of patients after recovery from acute COVID-19 is limited. We sought to assess the frequency of new diagnoses of disease and health care use after hospitalization with COVID-19. METHODS: We included all patients hospitalized with COVID-19 in Alberta between Mar. 5 and Dec. 31, 2020. Additionally, 2 matched controls (SARS-CoV-2 negative) per case were included and followed up until Apr. 30, 2021. New diagnoses and health care use were identified from linked administrative health data. Repeated measures were made for the periods 1-30 days, 31-60 days, 61-90 days, 91-180 days, and 180 and more days from the index date. We used multivariable regression analysis to evaluate the association of COVID-19-related hospitalization with the number of physician visits during follow-up. RESULTS: The study sample included 3397 cases and 6658 controls. Within the first 30 days of follow-up, the case group had 37.12% (95% confidence interval [CI] 35.44% to 38.80%) more patients with physician visits, 11.12% (95% CI 9.77% to 12.46%) more patients with emergency department visits and 2.92% (95% CI 2.08% to 3.76%) more patients with hospital admissions than the control group. New diagnoses involving multiple organ systems were more common in the case group. Regression results indicated that recovering from COVID-19-related hospitalization, admission to an intensive care unit, older age, greater number of comorbidities and more prior health care use were associated with increased physician visits. INTERPRETATION: Patients recovered from the acute phase of COVID-19 continued to have greater health care use up to 6 months after hospital discharge. Research is required to further explore the effect of post-COVID-19 conditions, pre-existing health conditions and health-seeking behaviours on health care use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".