Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Objectives To provide a sub-national analysis of excess mortality during the COVID-19 pandemic in Italy. Design Population-based on all-cause mortality official data, available as counts by age and sex. Setting The 7,904 municipalities in Italy. Participants All residents in Italy in the years 2016 to 2020. Main outcome measures All-cause mortality weekly rates for each municipality, based on the first four months of 2016 – 2019. Predicted all-cause weekly deaths and mortality rates at municipality level for 2020, based on the modelled spatio-temporal trends. Results There was strong evidence of excess mortality for Northern Italy; Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed higher mortality from the beginning of March, with 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. After discounting for the number of COVID-19-confirmed deaths, Lombardia still registered 10,197 (9,264 to 11,037) excess deaths, while regions in the North-West and North-East had 2,572 (1,772 to 3,297) and 2,047 (1,075 to 3,058) extra deaths, respectively. We observed marked geographical differences at municipality level. The city of Bergamo (Lombardia) showed the largest percent excess 88.9% (81.9% to 95.2%) at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. Conclusions Our study gives a comprehensive picture of the evolution of all-cause mortality in Italy from 2016 to 2020 and describes the spatio-temporal differences in excess mortality during the COVID-19 pandemic. Our model shows heterogeneous impact of COVID-19, and it can be used to help policy- makers target measures to limit the burden on the health-care system as well as reducing social and economic consequences. Our probabilistic methodology is useful for real-time mortality surveillance, continuously monitoring local temporal trends and flagging where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".