County-Level Influenza-Attributable Emergency Department Visits and Their Spatial Correlates in the United States: Cross-Sectional Observational Study
Bibliographic record
Abstract
Background The burden of seasonal influenza on emergency department (ED) visits is poorly quantified due to case ascertainment and data availability challenges. This study estimates county-level respiratory ED visits attributable to influenza using time-series models and examines spatial heterogeneity in county-level burden in 3 states. Objective This study aimed to estimate the county-level respiratory ED visits attributable to influenza using time-series models and examine spatial heterogeneity in county-level burden in 3 states. Methods We used daily hospital discharge records to measure community-level influenza activity in California (2005-2018), Georgia (2010-2018), and New York (2005-2018). County-level respiratory ED visit rates attributable to influenza were estimated by quasi-Poisson time-series models, adjusting for temporal trends and environmental factors. Bayesian spatial models were used to assess associations with county-level socioeconomic status, environmental exposures, and chronic health condition prevalence. Results Influenza-attributable respiratory ED visit rates per 100,000 population were 226 (95% CI 206-246) in New York, 232 (95% CI 206-259) in California, and 547 (95% CI 506-589) in Georgia. A 10% increase in county-level poverty and uninsured rates was associated with higher influenza burden, increasing influenza-attributable respiratory ED visit rates by 160 (95% credible interval [CrI] 127-196) and 217 (95% CrI 168-265), respectively. Long-term PM2.5 (fine particulate matter ≤2.5 µm), humidity, and temperature also exhibited positive associations. Chronic conditions also increased ED visit rates by 1476/100,000 (95% CrI 1167-1778), 588/100,000 (95% CrI 400-747), and 488/100,000 (95% CrI 402-574) per 10% increase in stroke, chronic obstructive pulmonary disease, and diabetes prevalence, respectively. These associations weakened after adjusting for socioeconomic status. Conclusions Influenza-attributable respiratory ED visit rates exhibit significant spatial heterogeneity that is associated with county-level socioeconomic factors, environmental exposures, and chronic disease prevalence.
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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 | 0.001 |
| 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 | 0.001 | 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".