Life Expectancy Loss and Recovery by Age and Sex Following Catastrophic Events in Europe during the 19th and 20th Centuries
Bibliographic record
Abstract
Abstract Following catastrophic events such as pandemics or wars, a systematic loss in life expectancy at birth ( $$\:{e}_{0}$$ ) can be observed. This study aims to estimate the time required for $$\:{e}_{0}$$ to recover after mortality crises and to identify which age groups contribute to the decline and assist in restoring pre-crisis levels. Focusing on major European pandemics and wars of the 19th and 20th centuries, we used data from the Human Mortality Database (HMD). Arriaga’s decomposition was applied to analyze $$\:{e}_{0}$$ values before the sharpest decline and at the recovery point. Events were categorized into pandemics and non-pandemics, and further stratified by sex. Various statistical tests were used to ensure valid comparisons. The analysis is grounded in demographic resilience, understood as the capacity of a population to return to previous $$\:{e}_{0}$$ levels after a mortality shock. This approach enables comparison between events of different types and historical contexts. Our findings show that the largest $$\:{e}_{0}$$ declines occurred during the World Wars. No significant differences were found by event type or sex. Youth and children emerged as the main contributors to the decline and recovery of $$\:{e}_{0}$$ following catastrophic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".