Neighborhood-Level Poverty, Poverty-Associated Factors, and Severe Outcomes among Adults Hospitalized with Influenza—United States, 2012–2015
Bibliographic record
Abstract
Recent studies demonstrated higher influenza-associated hospitalization rates among individuals living in high-poverty neighborhoods. We explored the further impact of neighborhood-level poverty and individual, poverty-associated factors, on severe outcomes among hospitalized patients with influenza. We linked 2012–2015 data on hospitalized adults from the influenza hospitalization surveillance network (FluSurv-NET), by census tract, to the American Community Survey’s federal poverty estimates. High-poverty neighborhoods were defined as census tracts with ≥20% of households in poverty and low-poverty neighborhoods, <5%. We explored univariate associations between neighborhood-level poverty and influenza vaccination, tobacco use, alcohol abuse, and extreme obesity. Using logistic regression and clustering by census tract, we examined the independent association of these factors with intensive care unit (ICU) admission and death, controlling for age, race, sex, comorbid conditions, antiviral treatment, season, and time from symptom onset to hospitalization. Among 26,106 patients, 4,194 (16%) required ICU admission and 669 (3%) died. Those who currently used tobacco, abused alcohol, were extremely obese, or were unvaccinated were more likely to live in high-poverty (38%, 40%, 37%, 33%) compared with low-poverty neighborhoods (12%, 13%, 13%, 16%; P < .01), respectively. Living in a high-poverty neighborhood was not independently associated with ICU admission (OR: 0.97, CI: 0.87–1.10) or death (OR: 0.82, CI: 0.63–1.08). Being unvaccinated (OR: 1.24, CI: 1.15–1.35), tobacco use (OR: 1.31, CI: 1.19–1.45), and alcohol abuse (OR: 1.68, CI: 1.41–2.00) increased odds of ICU admission; extreme obesity increased odds of death (OR: 1.35, CI: 1.02–1.78). Poverty-associated factors, but not neighborhood-level poverty, were independently associated with severe outcomes among patients hospitalized with influenza. Increased vaccination and reductions in tobacco use, alcohol abuse, and extreme obesity could reduce severe influenza-associated outcomes. E. J. Anderson, AbbVie: Consultant, Consulting fee; NovaVax: Research Contractor, Research support; Regeneron: Research Contractor, Research grant; MedImmune: Research Contractor, Research grant and Research support; W. Schaffner, Pfizer: Scientific Advisor, Consulting fee; Merck: Scientific Advisor, Consulting fee; Novavax: Consultant, Consulting fee; Dynavax: Consultant, Consulting fee; Sanofi-pasteur: Consultant, Consulting fee; GSK: Consultant, Consulting fee; Seqirus: Consultant, Consulting fee
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".