Factors Associated with Health Inequalities in Infectious Disease Pandemics Predating COVID-19 in the United States: A Systematic Review
Bibliographic record
Abstract
Objective: Previous pandemics may offer evidence on mediating factors that contributed to disparities in infection and poor outcomes, which could inform the effort to mitigate potential unequal outcomes during the current COVID-19 pandemic. This systematic review sought to examine those factors. Methods: We searched MEDLINE, PsycINFO, and Cochrane to May 2020. We included studies examining health disparities in adult U.S. populations during infectious disease epidemics or pandemics. Two investigators screened abstracts and full text. We assessed study quality using the Newcastle/Ottawa Scale or the Critical Appraisal Skills Programme Checklist for Qualitative Studies. Results: Sixteen articles were included, of which 14 focused on health disparities during the 2009 H1N1 influenza pandemic. Studies showed that disparities during the H1N1 pandemic were more related to differential exposure to the virus than to susceptibility or access to care. Overall, pandemic-related disparities emanate primarily from inequalities in social conditions that place racial and ethnic minorities and low socioeconomic status populations at greater risk of exposure and infection, rather than individual-level factors such as health behaviors and comorbidities. Conclusions: Policy- and systems-level interventions should acknowledge and address these social determinants of heightened risk, and future research should evaluate the effects of such interventions to avoid further exacerbation of health inequities during the current and future pandemics.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".