Determinants of COVID-19 Incidence and Mortality in the US: Spatial Analysis
Bibliographic record
Abstract
ABSTRACT OBJECTIVES The US continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize the spread and success of mitigation efforts. The objective of this study was to assess the ecological determinants (policy, health behaviors, socio-economic, physical environment, and clinical care) of COVID-19 incidence and mortality in the US. METHODS Data from the New York Times’ COVID-19 repository (01/21/2020-10/27/2020), 2020 County Health Rankings, 2016 County Presidential Election Returns, and 2018-2019 Area Health Resource File were used. County-level logged incidence and mortality rate/million were modeled using the Spatial Autoregressive Combined model and spatial lag model. RESULTS Counties with higher proportions of racial minorities (African American β= 0.007, Native Americans β= 0.008, Hispanics β= 0.015), non-English speakers (β= 0.010), population density ([logged] β= 0.028), and air pollution (β= 0.062) were significantly associated with high COVID-19 incidence rates. Subsequently, counties with higher Republican voters (β= 0.017), excessive drinkers (β= 0.107), children in single-parent households (β= 0.018), uninsured adults (β= 0.038), racial minorities (African American β= 0.032, Native Americans β= 0.034, Hispanics β= 0.037), females (β= 0.101), and population density ([logged] β= 0.270), air pollution (β= 0.130), and non-Whites/Whites’ residential segregation (β= 0.014) were significantly associated with high COVID-19 mortality rates. Additionally, longer state-level restrictions were associated with lower COVID-19 incidence and mortality rates. CONCLUSIONS The spatial models identified longer state-level restrictions, population density, air pollution, uninsured rate, and race/ethnicity as important determinants of the geographic disparities in COVID-19 incidence and mortality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".