COVID-19 and Katrina: recalcitrant racial disparities
Bibliographic record
Abstract
In 2005, Hurricane Katrina inflicted a devastating blow on New Orleans, Louisiana, USA. This natural disaster decimated neighbourhoods and led to the deaths of >1500 residents, disproportionately impacting black Americans. Exacting an unprecedented toll on life and property, Katrina exposed the deep socioeconomic divide impacting Blacks and laid bare the vulnerability and fragility in lower socioeconomic communities. Change did emanate from Katrina, but the root causes that fuelled the disparate outcomes remained. Those social determinants of health—poverty, housing density, high crime neighbourhoods, less than ideal schools, and poor access to healthy foods—left Louisiana ill equipped to deal with subsequent crises, including the current COVID-19 pandemic. Now, the COVID-19 pandemic is shedding an even brighter light on the recalcitrant healthcare disparities in the USA, where infection rates in predominantly Black counties are three-fold that of predominantly White counties.1 Mortality rates are disheartening, particularly in the Southern states. In Louisiana, Blacks represent about a third of the population but account for half of the infections and two-thirds of COVID-related deaths.1 Across the USA, the increased risk of death for Blacks with COVID-19 varies from two-fold to six-fold higher than Whites (Figure 1). Once again, a crisis has exposed deep racial disparities. This exquisitely high burden of disease is halting and requires further explanation. COVID-19 deaths per 100 000 people of each group by race and ethnicity in the USA, reported through 11 May 2020. Includes data from Washington, DC, and the 39 states of Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, New Hampshire, New Jersey, New York, North Carolina, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Vermont, Virginia, Washington, and Wisconsin. Data from the American Public Media Research Lab.13 The higher risk of death is not yet fully resolved and must be adjusted for known comorbidities; indeed some of the mortality difference is attributable to well-described cardio-metabolic risk factors in Blacks.2 Relative to non-Hispanic Whites, Blacks have a higher prevalence of hypertension, obesity, diabetes, and cardiovascular disease, all of which are associated with increased COVID-19 mortality.2,3 The Southern states in the USA represent the epicentre for this increased burden of cardiovascular risk, and the COVID-19-related mortality of black Americans has been disproportionately high in these states (Figure 2). Percentage of COVID-19 deaths and population of black Americans, through to 27 April 2020 in the USA. Includes data from Washington, DC and 36 states. Sorted from most under-represented to most over-represented. Data from the American Public Media Research Lab.14 However, the race-based differences in comorbidities alone do not account for the increased mortality risk in Blacks. The totality of the risk of death is inextricably linked with the social determinants of health. Even the baseline differences in cardio-metabolic risk are partly socioeconomic, driven by poverty, neighbourhood deprivation, poor nutrition, suboptimal health literacy, and unequal access to preventive healthcare2 (Figures 3 and 4). These relevant comorbidities are ostensibly preventable and require not only evidence-based medical therapy and lifestyle change, but also higher level interventions that address the social determinants of health. Racial inequities in the social determinants of health, Orleans Parish, Louisiana. Data from The Data Center, New Orleans, LA, USA.15 *Annual living wage for 1 adult 1 child in New Orleans is US$47 611. These data represent households earning
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".