Pale rider: pandemic inequalities
Bibliographic record
Abstract
I looked, and behold, a pale horse; and he who sat on it had the name Death … to kill with sword and with famine and with pestilence. Book of Revelation 6: 7–8 Introduction In 1931 Edgar Sydenstricker identified inequalities in the 1918 Spanish flu epidemic, reporting a significantly higher incidence among the working classes. This challenged the widely-held popular, political and scientific consensus of the time that held ‘the flu hit the rich and the poor alike’. In the 2020 COVID-19 pandemic, there have been parallel claims made by politicians and the media: that we are ‘all in it together’ and that the COVID-19 virus ‘does not discriminate’. These claims fly in the face of the significant evidence that the pandemic does in fact kill unequally: COVID-19 deaths are twice as high in the most deprived neighbourhoods as in the most affluent; infection rates are higher in more deprived regions, among people with low incomes, and in urban compared to rural areas. There are also even more stark inequalities by ethnicity and race, with the death rates of minority ethnic communities in the UK, Canada and the US being more than twice as high as their majority White counterparts. This chapter outlines these inequalities, drawing on historical and contemporary international evidence of inequalities in previous respiratory pandemics, ranging from the Spanish flu pandemic of 1918 to the H1N1 outbreak of 2009 and current estimates of social, ethnic and geographical inequalities in the COVID-19 pandemic. It also examines the causes of these inequalities in terms of the unequal burden of risk factors (such as diabetes and respiratory diseases) and the relationship to preexisting inequalities in the social determinants of health, arguing that COVID-19 is a syndemic pandemic. It concludes by reflecting on the longer-term implications of these health inequalities. An unequal pandemic In the very first stages of the pandemic (March to June 2020), it quickly became evident, from the experiences of a variety of countries, that there were significant social and ethnic inequalities in COVID-19 infections, symptom severity, hospitalisation and deaths.
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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 | 0.004 |
| 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 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".