Impact of NCD policies on ischaemic heart disease and premature NCD mortality change in Europe
Bibliographic record
Abstract
Introduction This study aimed to analyse the change in Ischaemic Heart Disease (IHD) mortality and premature mortality rates from non-communicable disease (NCD) between 2000 and 2014 in relation to NCD policies in WHO European countries. Methods IHD mortality rates for over 30 age group and premature (age 30-69) mortality from four major NCDs (cardiovascular diseases, diabetes, chronic respiratory disease and cancer) were obtained from the European Detailed Mortality Database. NCD policy score (NCDPS) was developed using the 2010 WHO NCD Country Capacity Survey and Global Tobacco Epidemic Report 2011 results. Countries were dichotomized as high or low performance, using 100 unit change in IHD and 150 unit change in premature mortality as cut-off values. Logistic regression was used to analyse the association between NCDPS and change in mortality rates and the association was adjusted for change in GDPppp and Human Development Index (HDI) to reflect the socioeconomic development in the same period. R2 values indicated the variability explained in change in mortality rates by the NCDPS. Results IHD mortality rates decreased between years 2000 and 2014 in all WHO Europe countries; the decrease was highest in Kazakhstan, Georgia and Ukraine while premature NCD mortality decrease were highest in Estonia, Azerbaijan and Ireland. NCDPS explained the 14% of the variation in IHD mortality change in both groups; %1 in women and 18% in men(p = 0.04, p = 0.64, p = 0.03). The association between the NCDPS and IHD in men disappeared when adjusted by GDPppp (Beta=-0.04, p = 0.15, R2 =20%) and by HDI (Beta=-0.05, p = 0.09, R2=61%) suggesting that socioeconomic development also contributed the decrease in IHD mortality in men. Premature NCD mortality was not significantly associated with NCDPS. Conclusions There is a decrease in IHD mortality and premature NCD mortality in WHO Europe countries. Change in socioeconomic development and NCDPS explained substantial part of the IHD decrease. Key messages: There is a decrease in IHD mortality and premature NCD mortality in WHO Europe countries. The impact of NCD policies needs to be evaluated further after methodologic refinements in NCDPS such as lag time and stratifying the countries according to socioeconomic development levels
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".