Impact of Risk Stacking on COVID-19–Related Healthcare Utilization: A Real-World Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: This study evaluated the impact of "risk stacking" on COVID-19-related hospitalizations, emergency department/urgent care (ED/UC) visits, and outpatient visits among non-immunocompromised individuals aged 18-49 and 50-64 years compared with immunocompromised individuals and those ≥ 65 years. METHODS: data, adults were assigned to ≥ 1 category based on underlying CDC-categorized high-risk (HR) conditions: HR-Conclusive (from which immunocompromising conditions were separated), HR-Suggestive, Mixed Evidence, No HR Conditions. The impact of multimorbidity quantities and HR categories on COVID-19 healthcare resource utilization (HCRU) was evaluated. RESULTS: Overall (n = 10,631,427), the most prevalent conditions were hypertension (HTN; 47.4%), obesity/overweight (31.9%), chronic heart disease (CHD; 28.1%), and diabetes (DBT; 20.3%). COVID-19 HCRU was higher for CHD with DBT, CHD with obesity, and HTN with obesity than for immunocompromised individuals and highest among those aged ≥ 65 years. Multimorbidity across multiple HR categories resulted in greater adjusted risk for COVID-19 HCRU for all ages. CONCLUSION: Younger adults with multiple non-immunocompromising comorbidities had greater risk of COVID-19-related HCRU than those with immunocompromising conditions or ≥ 65 years without multimorbidity. Stacking HR comorbidities increased the risk of HCRU. Ensuring broad vaccination and treatment recommendations and access is critical to mitigating severe COVID-19 in HR groups of any age.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".