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 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 | 0.002 | 0.001 |
| 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 | 0.000 | 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".