Social Determinants of Health and COVID-19 Among Patients in New York City
Bibliographic record
Abstract
Background: Covid-19 testing and disease outcomes according to demographic and neighborhood characteristics must be understood. Methods: Using aggregate administrative data from a multi-site academic healthcare system in New York from March 1 - May 14, 2020, we examined patient demographic and neighborhood characteristics according to Covid-19 testing and disease outcomes. Results: Among the 23,918 patients, higher proportions of those over 65 years old, male sex, Hispanic ethnicity, Medicare, or Medicaid insurance had positive tests, were hospitalized, or died than those with younger age, non-Hispanic ethnicity, or private insurance. Patients living in census tracts with more non-White individuals, Hispanic individuals, individuals in poverty, or housing crowding had higher proportions of Covid-19 positive tests, hospitalizations, and deaths than counterparts. Discussion: Variation exists in Covid-19 testing and disease outcomes according to patient and neighborhood characteristics. There is a need to monitor Covid-19 testing access and disease outcomes and resolve racist policies and practices.
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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 | 0.005 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| 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".