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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".