The temporal trend of cause-specific mortality: comparing Estonia and Lithuania, 2001 – 2019
Bibliographic record
Abstract
BACKGROUND: Despite being two Baltic countries with similar histories, Estonia and Lithuania have diverged in life expectancy trends in recent years. We investigated this divergence by comparing cause-specific mortality trends. METHODS: We obtained yearly mortality data for individuals 20 + years of age from 2001-2019 (19 years worth of data) through Statistics Lithuania, the Lithuanian Institute for Hygiene, and the National Institute for Health Development (Estonia). Using ICD-10 codes, we analyzed all-cause mortality rates and created eight major disease categories: ischemic heart disease, cerebrovascular disease, all other cardiovascular disease, cancers (neoplasms), digestive diseases, self-harm and interpersonal violence, unintentional injuries and related conditions, and other mortality (deaths per 100,000 population). We used joinpoint regression analysis, and analyzed the proportional contribution of each category to all-cause mortality. RESULTS: There was a steeper decline in all-cause mortality in Estonia (average annual percent change, AAPC = -2.55%, 95% CI: [-2.91%, -2.20%], P < .001) as compared to Lithuania (AAPC = -1.26%, 95% CI: [-2.18%, -0.57%], P = .001). For ischemic heart disease mortality Estonia exhibited a relatively larger decline over the 19-year period (AAPC = -6.61%, 95% CI: [-7.02%, -6.21%], P < .001) as compared to Lithuania (AAPC = -2.23%, 95% CI: [-3.40%, -1.04%], P < .001). CONCLUSION: Estonia and Lithuania showed distinct mortality trends and distributions of major disease categories. Our findings highlight the role of ischemic heart disease mortality. Differences in public health care, management and prevention of ischemic heart disease, alcohol control policies may explain these differences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".