Life expectancy loss due to the COVID-19 pandemic exemplified by Moscow
Bibliographic record
Abstract
Abstract There is high excess mortality against the background of the pandemic in Moscow (an increase of 36.2% against 18.1% on the national average). According to preliminary data, the loss of life expectancy is 3.1 years for Muscovites (from 78.9 years in 2019 to 76.2 years in 2020) and 1.9 years for the Russian population (from 73.4 to 71.5 years). As in other countries, elderly people suffering from chronic diseases were the most affected. The age-specific mortality rates in 2020 are significantly higher than those of 2019 in the age interval of 45 years and older as well as among children 10-14 years old and young people 25-29 years old. The maximum increase in mortality was recorded in age groups over 80 years old in males and 25-29 years in females. At the same time, infant mortality in Moscow has significantly decreased which is associated with a sharp decrease in the share of births among nonresidents (it exceeded a quarter of all births in the capital in 2019). The reduction in labor migration due to the closure of borders has led to a decrease in births among nonresidents. Using the decomposition method, it was shown that the greatest negative contribution to the loss of life expectancy in both sexes was made by mortality in the age group of 70-74 years which reduced life expectancy by 0.36 years for men and 0.27 years for women. Life expectancy decreased by 0.25 and 0.19 years due to death of men and women aged 75-79 years and by 0.09 and 0.02 years due to death of people aged 25-29 years. Reducing infant mortality yielded a life expectancy gain of 0.15 years for men and 0.11 years for women. These results changed the assessment of the significance of shifts in mortality in age groups. Analysis of changes in age-specific mortality and evaluation of its impact on changes in life expectancy shows different perspectives of the problem, which is important for making adequate decisions in health. Key messages The reduction in labor migration due to the closure of borders has led to a decrease in births among nonresidents. Analysis of changes in age-specific mortality and evaluation of its impact on changes in life expectancy shows different perspectives of the problem.
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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 | 0.034 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".