The financial burden of non-communicable diseases in the European Union: a systematic review
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases (NCDs) impose a significant and growing burden on the health care system and overall economy of developed (and developing) countries. Nevertheless, an up-to-date assessment of this cost for the European Union (EU) is missing from the literature. Such an analysis could however have an important impact by motivating policymakers and by informing effective public health policies. METHODS: Following the PRISMA protocol, we conduct a systematic review of electronic databases (PubMed/Medline, Embase, Web of Science Core Collection) and collect scientific articles that assess the direct (health care-related) and indirect (economic) costs of four major NCDs (cardiovascular disease, cancer, type-2 diabetes mellitus and chronic respiratory disease) in the EU, between 2008 and 2018. Data quality was assessed through the Newcastle-Ottawa Scale. RESULTS: We find 28 studies that match our criteria for further analysis. From our review, we conclude that the four major NCDs in the EU claim a significant share of the total health care budget (at least 25% of health spending) and they impose an important economic loss (almost 2% of gross domestic product). CONCLUSION: The NCD burden forms a public health risk with a high financial impact; it puts significant pressure on current health care and economic systems, as shown by our analysis. We identify a further need for cost analyses of NCDs, in particular on the impact of comorbidities and other complications. Aside from cost estimations, future research should focus on assessing the mix of public health policies that will be most effective in tackling the NCD burden.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".