Greater COVID-19-related healthcare utilisation with multimorbidity versus single or immunocompromised conditions
Bibliographic record
Abstract
Introduction: The impact of multimorbidity (≥2 chronic conditions) on risk of severe COVID-19 outcomes and healthcare resource utilisation (HCRU) is unknown. We compared COVID-19-related HCRU among individuals with multimorbidity to those with immunocompromising (IC) conditions and those with single high-risk conditions. Methods: We conducted a retrospective study using Optum's de-identified Clinformatics® Data Mart Database and applying US Centers for Disease Control and Prevention criteria for COVID-19 high-risk conditions in 2 time periods: BA/BQ and XBB. The first day of the variant-defined period was the index date. Adults ≥18 years with IC conditions at index were compared to adults with chronic heart disease (CHD), diabetes (DBT), obesity, and hypertension (HTN). COVID-19-related HCRU within 90 days following index included hospitalisation, emergency room or urgent care visits, and outpatient visits. Results: Multimorbidity affected ~3-7 times more individuals than IC and single conditions (Figure). Adults with multimorbidity generally also had greater inpatient and outpatient HCRU than individuals with IC or a single condition, in both age groups, except outpatient visits in younger adults. Conclusions: Adults with multimorbidity had greater COVID-19 HCRU, especially if aged ≥65. Identifying populations with multiple chronic comorbidities can inform COVID-19 vaccination and treatment guidelines. erj;64/suppl_68/PA1522/F1 F1 F1
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".