Comparing racial health disparities in pandemics a decade apart: H1N1 and COVID-19
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Centers for Disease Control and Prevention has reported disproportionate health disparities with respect to disease for Blacks/African Americans (AAs) compared to Whites in the USA. In this paper, we identify and compare the factors involved in creating these disparities among these populations during the 2009 H1N1 and current COVID-19 pandemics. METHODS: We included studies describing health disparities towards Blacks/AAs in the USA during the H1N1 and COVID-19 pandemics. Only observational empirical studies with free full-text availability in English from PubMed, PubMed Central and Google Scholar were included. RESULTS: A total of 31 papers were included: 19 pertaining to the H1N1 pandemic and 12 to the COVID-19 pandemic. Qualitative analysis for health disparities resulted in 43 different factors, which were subdivided into nine overarching themes. DISCUSSION: The similarities that exist between the two pandemics indicate that there are many neglected issues in American healthcare that need to be addressed. The listed factors have led to disparities in screening and treating for disease resulting in disparities in infection rates, severity of illness and mortality. This calls for a change in healthcare dynamics to improve access to healthcare, remove any form of possible discrimination, and regain the lost trust with the Black/AA communities, repairing historical damage. CONCLUSIONS: Effective utilisation of social media and faith-based centres to educate patients, implementation of new policies improving access to healthcare, and culture-sensitive education for healthcare providers are suggested to decrease health disparities and improve health outcomes across the USA.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".