Change in infectious mortality in Russia and its capital in the post-Soviet period
Bibliographic record
Abstract
Abstract Based on data of Rosstat for 1989-2018, changes in structure of infectious mortality (IM) in population of Moscow are analyzed in comparison with situation in the country as a whole. Age structure of European population was used to calculate standardized death rates. The rise in IM after change in Russian socio-political structure was less pronounced and not as long in Moscow as in the country. During analyzed period, IM increased by 1.4 times among men and 2.2 times among women in Russia while in the capital male IM decreased by 8.9% and female IM increased by 1.4 times. IM of Muscovites was lower than average Russian IM by a quarter in 1989 and twice in 2018. The highest IM was observed at age of 85 years and older in 1989 and at age of 35-39 years in 2018. Proportion of tuberculosis (TB) has significantly decreased (from 60.8% in the country and 65.5% in the capital to 19.9% and 9.9%). In the country, male TB mortality decreased by 1.8 times and female TB mortality in 2018 approached the 1989 level; in the capital, mortality of men and women decreased by 4.6 and 2.9 times. Proportion of AIDS has increased. The death rates began to exceed TB mortality since 2015 among men and since 2014 among women in Russia and since 2010 and 2007 in Moscow. In 2018, the share of AIDS was 60.2% (58.2% and 62.9%) in country and 55.0% (55.8% and 52.9%) in capital. Proportion of viral hepatitis has increased. In the country, mortality increased by 3.8 times for men and 3.0 times for women, in the capital, mortality increased by 1.6 and 13.0 times respectively. In 2018, the share of viral hepatitis was 6.5% (6.1% and 6.8%) in country and 16.5% (7.1% and 37.1%) in Moscow. Proportion of socially significant diseases in IM increased from 63.9% in country and 69.4% in capital to 90.7% and 86.2%. The peculiarities of IM in Moscow, given the greater availability of antiretroviral therapy, suggest that this IM is caused by a fairly wide consumption of injecting drugs by residents of the capital. Key messages Proportion of socially significant diseases in infectious mortality increased from 63.9% in country and 69.4% in capital to 90.7% and 86.2%. The peculiarities of IM in Moscow, given the greater availability of antiretroviral therapy, suggest that this IM is caused by a fairly wide consumption of injecting drugs by residents of the capital.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".