The burden of dietary risk factors in the Nordic and Baltic countries: a systematic analysis for the Global Burden of Disease Study 2023
Bibliographic record
Abstract
Background: Detailed knowledge about the disease burden from unhealthy diet in Nordic and Baltic countries is lacking. This study quantifies and compares deaths and disability-adjusted life-years (DALYs) from dietary risks in these countries. Methods: Data from the Global Burden of Disease study 2023 (GBD 2023) was used. Attributable disease burden from 15 dietary risks was analysed using the comparative risk assessment framework. Steps included: (1) estimating dietary intake; (2) assessing relative risks of dietary factors on disease endpoints; (3) determining theoretical minimum risk exposure levels (TMREL); and (4) estimating dietary risk-attributable disease burden as numbers and age-standardised rates (ASR) of deaths and DALYs. Findings: Across the Nordic and Baltic countries (total population = 34,064,020), dietary risks resulted in 38,450 attributed deaths (95% uncertainty interval 10,749-59,386) and 735,284 DALYs (242,417-1,06,638) in 2023. Leading dietary risks included high intake of processed meat and low intake of fruits and whole grains. Dietary risks accounted for 24.9% of cardiovascular disease burden (5.0-37.6), 29.6% of diabetes and kidney disease burden (18.6-40.0), and 7.8% of neoplasm burden (2.9-12.1), with higher burden in the Baltic countries and Greenland than in the Nordic countries. Interpretation: A substantial disease burden can be attributed to dietary risks in the Nordic and Baltic countries. Knowledge about the impact from unhealthy diet can inform targeted public health policies. Funding: Gates Foundation and Norwegian Institute of Public Health.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".