Direct health care costs associated with asthma hospitalizations before and during the COVID-19 pandemic in the United States: a nationwide inpatient sample analysis
Bibliographic record
Abstract
Asthma increases the hospitalization risk in individuals with COVID-19. The impact of the COVID-19 pandemic on asthma-related hospitalizations in the United States remains unknown. We hypothesized this pandemic led to an increase in healthcare-related costs associated with asthma. We analyzed weighted data from the National Inpatient Sample between 1 January 2018 and 31 December 2020. The outcomes were asthma hospitalization rates, length of stay (LOS), in-hospital mortality rates, and hospital admission costs. The hospitalization rate for individuals with a primary diagnosis of asthma was higher in 2018 compared to 2020 (hospitalization rate per 100,000: 2018: 38.6; 2020: 21.4; p < .001). Hospital costs increased in United States Dollars (USD) (2018: median [IQR] 5251 USD [3426 USD, 8278 USD]; 2020: 5881 USD [3920 USD–9216 USD]; p < .001). Additionally, in-patient mortality rates slightly increased in 2020, rising to 0.44%, compared to 0.20% in 2018 (p < .001). The Mid-Atlantic division had the highest asthma hospitalization rates (p < .001), with rates per 100,000 of 63.48 in 2018 and 33.65 in 2020. In contrast, the Pacific division had the highest hospitalization costs during the same period (p < .05), with median costs of 7116 USD [4557, 11,393] in 2018 and 8430 [5436, 13,982] USD in 2020. A secondary analysis of patients admitted for COVID-19 and a secondary diagnosis of asthma demonstrated a decrease in 2020 compared to previous years. Those admitted during the pandemic had higher mortality and significantly increased direct healthcare costs to society. This investigation provides valuable insights to policymakers about shifts in healthcare utilization during the pandemic.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 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".