Indigenous Peoples, concentrated disadvantage, and income inequality in New Mexico: a ZIP code-level investigation of spatially varying associations between socioeconomic disadvantages and confirmed COVID-19 cases
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease pandemic has disproportionately affected poor and racial/ethnic minority individuals and communities, especially Indigenous Peoples. The object of this study is to understand the spatially varying associations between socioeconomic disadvantages and the number of confirmed COVID-19 cases in New Mexico at the ZIP code level. METHODS: We constructed ZIP code-level data (n=372) using the 2014-2018 American Community Survey and COVID-19 data from the New Mexico Department of Health (as of 24 May 2020). The log-linear Poisson and geographically weighted Poisson regression are applied to model the number of confirmed COVID-19 cases (total population as the offset) in a ZIP code. RESULTS: The number of confirmed COVID-19 cases in a ZIP code is positively associated with socioeconomic disadvantages-specifically, the high levels of concentrated disadvantage and income inequality. It is also positively associated with the percentage of American Indian and Alaskan Native populations, net of other potential confounders at the ZIP code level. Importantly, these associations are spatially varying in that some ZIP codes suffer more from concentrated disadvantage than others. CONCLUSIONS: Additional attention for COVID-19 mitigation effort should focus on areas with higher levels of concentrated disadvantage, income inequality, and higher percentage of American Indian and Alaska Native populations as these areas have higher incidence of COVID-19. The findings also highlight the importance of plumbing in all households for access to clean and safe water, and the dissemination of educational materials aimed at COVID-19 prevention in non-English language including Indigenous languages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".