Excess deaths from COVID-19 and other causes by region, neighbourhood deprivation level and place of death during the first 30 weeks of the pandemic in England and Wales: A retrospective registry study
Bibliographic record
Abstract
BACKGROUND: Excess deaths during the COVID-19 pandemic compared with those expected from historical trends have been unequally distributed, both geographically and socioeconomically. Not all excess deaths have been directly related to COVID-19 infection. We investigated geographical and socioeconomic patterns in excess deaths for major groups of underlying causes during the pandemic. METHODS: Weekly mortality data from 27/12/2014 to 2/10/2020 for England and Wales were obtained from the Office of National Statistics. Negative binomial regressions were used to model death counts based on pre-pandemic trends for deaths caused directly by COVID-19 (and other respiratory causes) and those caused indirectly by it (cardiovascular disease or diabetes, cancers, and all other indirect causes) over the first 30 weeks of the pandemic (7/3/2020-2/10/2020). FINDINGS: There were 62,321 (95% CI: 58,849 to 65,793) excess deaths in England and Wales in the first 30 weeks of the pandemic. Of these, 46,221 (95% CI: 45,439 to 47,003) were attributable to respiratory causes, including COVID-19, and 16,100 (95% CI: 13,410 to 18,790) to other causes. Rates of all-cause excess mortality ranged from 78 per 100,000 in the South West of England and in Wales to 130 per 100,000 in the West Midlands; and from 93 per 100,000 in the most affluent fifth of areas to 124 per 100,000 in the most deprived. The most deprived areas had the highest rates of death attributable to COVID-19 and other indirect deaths, but there was no socioeconomic gradient for excess deaths from cardiovascular disease/diabetes and cancer. INTERPRETATION: During the first 30 weeks of the COVID-19 pandemic there was significant geographic and socioeconomic variation in excess deaths for respiratory causes, but not for cardiovascular disease, diabetes and cancer. Pandemic recovery plans, including vaccination programmes, should take account of individual characteristics including health, socioeconomic status and place of residence. FUNDING: None.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".